Download the Book (PDF): Introduction Two facts sit oddly together. The first is that the intestine and the brain are physically and chemically connected in ways that are no longer in scientific dispute: nerve fibres run between them, hormones released by gut cells act on brain circuits, immune signals pass in both directions, and the microbial community of the colon produces thousands of small molecules, some of which the nervous system also uses. The second is that almost none of this has yet produced a treatment for a neurological or psychiatric disorder that a specialist would prescribe with confidence. This booklet is about the space between those two facts. It is a serious field with a serious problem: the biology is real, the enthusiasm has outrun the evidence, and the people most exposed to the gap are patients with conditions that current medicine treats imperfectly. What is established and what is not Begin with the anatomy, because it is the least contested part. The vagus nerve carries fibres between the abdomen and the brainstem, and the large majority of them send information upward, from body to brain rather than the other way round. The wall of the gut contains its own nervous system, extensive enough to coordinate digestion with little instruction from above. The intestinal lining is the body's largest immune interface and one of its most productive endocrine tissues, releasing signalling molecules that reach the brain both through the bloodstream and through nerve endings a fraction of a millimetre away. The colon holds a dense microbial population whose collective metabolism generates compounds including short-chain fatty acids, secondary bile acids, amino acid derivatives and precursors of neurotransmitters. None of that is speculative. What remains open is how much of it matters for any particular human disease, in which direction the influence runs, and whether manipulating the microbes can change the course of illness. The answers differ sharply from one disorder to another, and the central argument of this booklet is that those differences, rather than the general excitement about gut-brain communication, are what a reader most needs to grasp. Stated plainly: the gut-brain axis is a genuine, multi-channel signalling system, but its importance to any given neurological or psychiatric condition must be judged case by case, and the decisive question is always which way the causal arrow points. On that test, Parkinson's disease makes the strongest case for a gut contribution, depression a plausible but modest one, and autism the weakest; meanwhile the microbial treatments tested so far have produced effects that are real in places but smaller, narrower and less consistent than their public reputation suggests. Why the direction of causation decides everything Most claims in this area follow a recognisable sequence. Researchers report that people with a condition carry a different mix of gut microbes from people without it. Someone then shows in rodents that transferring those microbes, or administering a microbial molecule, alters behaviour or brain pathology. The two findings are combined and described as evidence that microbes contribute to the human disease. A small human trial follows, often without a control group, and reports improvement. Each step can be individually defensible while the chain as a whole fails. Consider what else differs between the groups being compared. People who go on to develop Parkinson's disease are frequently constipated for years or decades beforehand, and slow intestinal transit by itself reshapes a microbial community. People with depression eat, sleep and move differently from people without it, and most are taking medications with measurable effects on gut bacteria. Many autistic children eat a narrow range of foods, a pattern that predicts lower microbial diversity in anyone. In each case a microbial difference might be a cause, a consequence, or a marker of some third thing. Animal work has the opposite limitation. It can establish that a mechanism is possible, which is genuinely valuable, but a rodent raised entirely without microbes is a physiologically abnormal animal, and the behaviours measured in such experiments — time spent in the open arm of a maze, duration of sniffing a cagemate — stand at a considerable distance from a human clinical symptom. Small uncontrolled trials, for their part, are vulnerable to placebo response, to regression toward the mean in patients recruited when symptoms are at their worst, and to the shared hopes of families and investigators. So the recurring question here is not whether an association exists. It usually does. The question is what evidence would demonstrate that changing the microbes changes the disease in humans, and how close each research programme has come to producing it. How the booklet proceeds The first three chapters assemble the tools. Chapter 1 sets out the physical channels linking intestine to brain: the vagal and spinal nerves, the enteroendocrine system, the immune compartment, and the barriers that regulate what passes. Chapter 2 turns to the chemistry, examining the specific classes of microbial molecule that have been proposed as messengers and what is actually known about each. Chapter 3 is about method, and it is deliberately placed before the disease chapters: germ-free animals, stool transfer, sequencing, population cohorts and genetic inference each answer a different question, and confusing them is the single most common error in this literature. Four chapters then apply the tools. Parkinson's disease takes two, because it presents two separable stories: the anatomical hypothesis that some cases may begin in the nerves of the gut and ascend (Chapter 4), and the microbial findings together with the first generation of controlled transplant trials (Chapter 5). Chapter 6 examines depression and anxiety, where the plausible routes run through stress physiology and low-grade inflammation, and where the largest population studies have now been done. Chapter 7 examines autism, where reverse causation is hardest to exclude and where a sharp public disagreement among scientists broke out in late 2025. Chapter 8 reads the psychobiotic trial literature — probiotics, prebiotics, synbiotics and targeted metabolite-binding drugs — as carefully as the data allow, distinguishing what has been shown in clinically diagnosed patients from what has been shown in healthy volunteers. Chapter 9 asks what would have to change for this field to produce dependable medicine, and what a well-informed reader can reasonably do in the meantime. The conclusion sets out what follows from the whole argument: which parts of the field are likely to deliver, which are likely to disappoint, and how to tell the two apart when the next announcement arrives. Terms, and a standard of evidence A few words recur. The microbiota is the community of microorganisms in a given place, here overwhelmingly the colon; the microbiome strictly denotes their collective genes, though the two are often used interchangeably. Dysbiosis means a microbial community that departs from some reference state. It is convenient shorthand and a slippery concept, because there is no single healthy configuration and the term smuggles in an implication of harm that often has not been demonstrated. Faecal microbiota transplantation (FMT) is the transfer of screened, processed donor stool into a recipient by colonoscope, enteral tube or capsule. Probiotics are live microorganisms administered in amounts intended to confer a benefit; prebiotics are substrates, usually fermentable fibres, that favour particular resident microbes; synbiotics combine the two. Psychobiotic is a coined term for any such preparation intended to act on mood or cognition. Throughout, the aim is to say what kind of evidence supports each claim: whether it comes from cell culture, from rodents, from observational comparisons between people, or from randomised controlled trials in patients with a diagnosis. Where a specific number appears, it comes from a named study, and where a widely repeated figure turns out to rest on nothing checkable, the booklet says so rather than repeating it. That discipline costs a certain amount of narrative momentum. It is the price of writing usefully about a field whose most confident claims are frequently its least supported ones. Chapter 1: The Wiring Between Gut and Brain The phrase "gut-brain axis" suggests a single line of communication. It is better understood as four parallel systems that operate on different timescales, carry different kinds of information, and can be independently disrupted. A nerve impulse from the intestinal wall reaches the brainstem in milliseconds. A hormone released by a cell in the intestinal lining reaches the brain in minutes. An immune signal may take hours. A change in the metabolic output of the microbial community may take days to register and months to reverse. Any claim that gut microbes influence a brain disorder is implicitly a claim about at least one of these routes, and the first discipline worth acquiring is the habit of asking which. This chapter describes the four routes and the barriers that regulate them. It is deliberately anatomical and physiological rather than clinical, because most of the confusion in this field comes from arguments that skip the plumbing. The nerve route: vagal and spinal afferents The vagus nerve is the tenth cranial nerve and the longest of them, running from the medulla down through the neck and thorax to innervate the oesophagus, stomach, small intestine, and the proximal part of the colon. Its name comes from the Latin for "wandering", which is fair. What is consistently underappreciated in popular accounts is its direction of traffic: the great majority of vagal fibres are afferent, meaning they carry information from the body up to the brain rather than commands from the brain down to the organs. Estimates commonly place the afferent proportion at around eighty per cent. The vagus is, among other things, the brain's principal sensory line from the viscera. Vagal afferents terminate in the nucleus tractus solitarius in the brainstem, which projects onward to the parabrachial nucleus, the hypothalamus, the amygdala and eventually the insular cortex — regions involved in arousal, appetite, autonomic control and the felt sense of bodily state. This anatomy is why vagal signalling can plausibly influence mood, nausea, satiety and stress reactivity without any need for a molecule to reach the brain itself. What do these fibres detect? Mechanical stretch, principally, along with local chemistry. Vagal endings in the gut wall do not sit inside the intestinal lumen; they are separated from its contents by the epithelium. They therefore do not sample bacteria directly. Instead they respond to substances released by the cells that do face the lumen, and to physical distension. One discovery deserves particular emphasis because it changed how quickly gut-brain signalling is thought to occur. A subset of enteroendocrine cells, sometimes called neuropod cells, form direct synapse-like contacts with vagal neurons. Work published from Diego Bohórquez's laboratory at Duke University demonstrated that these cells can transmit information to the vagus using glutamate as a neurotransmitter, on a millisecond timescale, rather than by the slower hormonal route. In other words, the gut has a fast sensory channel to the brainstem, structurally comparable to a sensory organ. This does not by itself prove that gut bacteria use that channel, but it establishes the existence of the wire. The vagus is not the only nerve route. Spinal afferents running with sympathetic nerves carry information from the gut into the dorsal horn of the spinal cord and upward; these fibres are more associated with pain and with signals from the distal colon. The enteric nervous system, meanwhile, is a network of neurons embedded in the gut wall itself, organised into two main plexuses and numbering on the order of hundreds of millions of cells. It coordinates peristalsis, secretion and local blood flow largely autonomously — an intestine removed from an animal and kept alive will still produce coordinated propulsive movements. It is sometimes called a second brain, which flatters it; it has no capacity for anything resembling cognition. But it does mean that a signal from a microbe can be processed locally, altering motility or secretion, before any information reaches the head. There is a striking clinical observation attached to the vagus. Between 1970 and 2010, Swedish surgeons performed vagotomies — surgical cutting of the vagus — as a treatment for peptic ulcer disease, in two forms. Truncal vagotomy severs the main trunks, denervating a wide territory. Selective vagotomy cuts only branches serving the acid-secreting part of the stomach. Both were common enough to leave a substantial registry record, and that record has since been used to ask whether losing the vagal connection changes the risk of Parkinson's disease. Chapter 4 takes up what the answer appears to be; for now the point is only that the nerve route is not a hypothetical construct but something surgeons have interrupted in tens of thousands of people. A reward circuit that begins in the intestine If there were any doubt that vagal signals from the gut can drive behaviour rather than merely report on digestion, an experiment published in 2018 by Wenfei Han and colleagues in Ivan de Araujo's group removes it. The team used optogenetics — genetically installing light-sensitive ion channels in specific neurons so that a pulse of light activates them — to stimulate gut-innervating vagal sensory neurons in mice directly. Activating the right vagal sensory ganglion, though not the left, was sufficient to sustain self-stimulation behaviour: the animals worked to trigger the stimulation. It conditioned preferences for both flavours and places paired with it, and it caused dopamine release from the substantia nigra. Tracing the pathway, the investigators found that glutamatergic neurons in the dorsolateral parabrachial region form the obligatory relay between the right vagal ganglion and the dopamine cells, and stimulating that relay directly reproduced the rewarding effect. Three things make this important. It demonstrates that the gut-to-brain vagal pathway is not only a monitoring channel but a component of the reward system. It shows an unexpected asymmetry — left and right vagal ganglia are not equivalent — which complicates any simple account of vagal signalling and has implications for therapeutic vagus nerve stimulation, which is typically applied on one side. And it is a clean causal experiment in which the intervention is the activation of a defined neuronal population rather than an ecological manipulation with a hundred possible consequences. It is still a mouse, and the stimulation is artificial. But it establishes the upper bound of what this wiring can do: signals from the gut, delivered to the brainstem along the vagus, can generate motivation. The hormone route: the gut as an endocrine organ Scattered through the intestinal epithelium, making up perhaps one per cent of its cells, are the enteroendocrine cells. Collectively they constitute the largest endocrine organ in the body, and they secrete more than twenty distinct hormones. Several of these act on the brain. Glucagon-like peptide-1 (GLP-1) is released by L-cells in the distal small intestine and colon in response to nutrients, and it acts both on pancreatic beta cells and, through vagal afferents and receptors in the brainstem and hypothalamus, on appetite and satiety. Peptide YY, from the same cells, reduces food intake. Cholecystokinin, from I-cells in the upper small intestine, signals fullness and slows gastric emptying. Ghrelin, from the stomach, does the opposite. Serotonin, discussed below, is released by enterochromaffin cells and acts locally on motility and on vagal endings. The pharmacological relevance of this system is no longer theoretical. GLP-1 receptor agonists are now among the most widely prescribed drugs in high-income countries, and a substantial part of their effect on eating behaviour is mediated centrally, through circuits that evolved to respond to a gut hormone. That is the clearest existing demonstration that a signal originating in the intestinal lining can powerfully alter behaviour governed by the brain. Microbes enter this picture because several enteroendocrine responses are triggered by bacterial products. Short-chain fatty acids, produced when colonic bacteria ferment dietary fibre, stimulate L-cells to release GLP-1 and peptide YY through free fatty acid receptors on the cell surface. Bile acids, chemically modified by gut bacteria, act on the TGR5 receptor with similar consequences. Here the logic is reasonably tight: a microbial product acts on a receptor on a host cell that releases a hormone with known central effects. It is one of the better-characterised microbial routes to the brain, though it concerns appetite and metabolism rather than the neurological disorders this book examines. The immune route Roughly seventy per cent of the body's immune cells reside in or around the gut, concentrated in the lamina propria beneath the epithelium, in Peyer's patches, and in the mesenteric lymph nodes. This is not an accident of anatomy but a requirement of it: the intestinal surface is the largest area of the body in contact with the outside world, and it is permanently colonised by trillions of microorganisms that must be tolerated rather than attacked, while genuine pathogens must still be recognised. The immune system's role in gut-brain communication runs through cytokines — signalling proteins released by immune cells. When intestinal immune activation raises circulating levels of cytokines such as interleukin-6, interleukin-1 beta and tumour necrosis factor alpha, those molecules can influence the brain by several routes: acting on the vagus, acting at circumventricular organs where the blood-brain barrier is incomplete, being actively transported across the barrier, and signalling to endothelial cells that then release mediators on the brain side. That cytokines affect brain function is well demonstrated in humans, and not only in laboratory settings. Sickness behaviour — the lethargy, social withdrawal, appetite loss and low mood that accompany infection — is an organised, cytokine-driven response, not merely the incidental fatigue of being unwell. Experimental studies in which healthy volunteers receive low doses of bacterial endotoxin reliably produce transient depressed mood and social withdrawal alongside the inflammatory response. Patients treated with interferon-alpha for hepatitis C historically developed clinically significant depression at substantial rates. These observations establish that inflammation can cause depressive symptoms in people; they do not establish that gut microbes are the usual source of that inflammation, which is a separate and much harder claim. A specific microbial contribution is worth naming. Lipopolysaccharide, a component of the outer membrane of Gram-negative bacteria, is one of the most potent immune stimulants known, acting through Toll-like receptor 4. Bacteria in the gut are a large reservoir of it. If the intestinal barrier becomes more permeable, more lipopolysaccharide reaches the circulation, and circulating markers of this translocation have been reported at elevated levels in several conditions including depression and Parkinson's disease. The concept is sound. The measurement is difficult, the assays vary between laboratories, and the resulting literature is noisier than its confident summaries suggest. The metabolic route and the barriers The fourth route is the simplest to state and the hardest to pin down: gut microbes make molecules, some of those molecules enter the bloodstream, and some of those reach the brain. Chapter 2 deals with the specific chemistry. What matters here is the set of barriers a molecule must cross. The first is the intestinal epithelium, a single layer of cells sealed together by tight junctions and covered by mucus. In the colon the mucus is arranged in two layers, an outer one colonised by bacteria and an inner one that is normally kept largely sterile. Certain bacteria, notably Akkermansia muciniphila, feed on mucin and in doing so appear to stimulate its production, an example of how a microbe can strengthen rather than degrade the barrier. Short-chain fatty acids, particularly butyrate, are the preferred fuel of colonocytes and support tight junction integrity. Increased intestinal permeability — "leaky gut" in popular usage — is a real physiological phenomenon, measurable with sugar absorption tests and with blood markers such as zonulin and intestinal fatty acid binding protein, though each measure has limitations and zonulin assays in particular have been criticised for measuring something other than what they claim. Permeability increases in coeliac disease, in inflammatory bowel disease, with heavy alcohol use, with certain drugs. What has not been established is that a modest increase in permeability causes brain disease in otherwise healthy people, which is the form the claim usually takes outside the literature. The second barrier is the blood-brain barrier, formed by tightly joined endothelial cells with pericytes and astrocyte endfeet. It excludes most large and hydrophilic molecules while admitting others through specific transporters. Notably, the barrier itself appears to be influenced by microbes: germ-free mice show increased blood-brain barrier permeability from fetal life, with reduced expression of tight junction proteins, and colonising them with ordinary microbiota or administering short-chain fatty acid-producing bacteria restores it. That finding, from work published by Viorica Braniste and colleagues in 2014, is one of the more striking demonstrations that microbial status can affect brain-adjacent tissue directly. It is also a mouse finding, and mice raised germ-free are abnormal in many ways. Two further intermediaries deserve mention. Microglia, the brain's resident immune cells, show altered morphology and impaired maturation in germ-free mice, and short-chain fatty acid supplementation partially corrects this. Astrocytes, which regulate the neuronal environment, respond to tryptophan metabolites of bacterial origin acting through the aryl hydrocarbon receptor, a pathway characterised in models of neuroinflammation. Both findings point the same way: the resident cells that maintain the brain's internal conditions are not indifferent to what happens in the intestine. Timescales, and why they matter for treatment One underappreciated implication of having four routes is that each imposes a different expectation on how quickly an intervention should work, and therefore on how long a trial should run. A vagal signal operates in milliseconds to seconds. If a microbial product acts by stimulating an enteroendocrine cell that synapses onto a vagal afferent, the consequence is essentially immediate, and repeated stimulation over days could plausibly shift circuit activity within a week or two. A hormonal signal operates over minutes to hours, with effects on appetite and satiety that accumulate over days. An immune signal changes on the scale of days to weeks: cytokine levels can shift quickly, but the cellular composition of the intestinal immune compartment takes longer, and downstream effects on microglial phenotype longer still. A change in the metabolic output of the microbial community depends first on the community changing, which itself takes days to weeks for a dietary intervention and may take months to stabilise after a transplant — if it stabilises at all. This has practical consequences that trials routinely ignore. An eight-week probiotic study is generously long if the mechanism is vagal and plausibly too short if it runs through slow immune recalibration or through neurodegenerative processes measured in years. A trial of a microbial intervention for a neurodegenerative disease is not testing whether the microbiome matters; it is testing whether the microbiome matters on the timescale and at the stage at which the trial was run. Those are very different propositions, and confusing them has produced a great deal of unnecessary disappointment. What the wiring does and does not license Put together, these four routes make the gut-brain axis a well-founded piece of physiology. A reader who took away nothing else should retain this: there is no need to invoke anything exotic to explain how an intestinal event could influence the brain. The channels exist, they are mapped, and in the case of gut hormones they have already yielded drugs that change human behaviour. But the existence of a channel says nothing about the traffic on it. Every one of these routes is also used by signals that have nothing to do with microbes — mechanical distension, dietary nutrients, host-derived hormones, infections, drugs. When a study reports that a probiotic changed a brain measure, the mechanism might run through any of the four routes or through none of them. When a study reports that a disease is associated with a shifted microbial profile, that shift might be acting through one of these routes, or might be an inert marker of something else, such as constipation or a restricted diet. The chapters that follow ask, for each disorder in turn, whether anyone has traced a signal from a specific microbial source, along a specific route, to a specific clinical outcome, in people. The answer is rarely a clean yes. But it is not always no, and the cases where it comes closest are the ones worth understanding in detail. Chapter 2: What the Microbes Actually Make A microbial community of the size found in the human colon is, considered chemically, a large and rather unruly fermentation plant. It receives what the small intestine could not absorb — mostly complex carbohydrates, some protein, host mucus, sloughed cells and bile — and returns a stream of small molecules, some of which are absorbed into the portal circulation and reach the liver and then the rest of the body. Estimates of the number of distinct compounds involved vary enormously depending on the method used, but the important point is not the total. It is that a handful of these compound classes are plausible candidates for gut-brain signalling, and only a handful. This chapter examines them in turn, with a consistent question applied to each: does it reach the brain, does it reach a target that affects the brain, or does neither claim hold up on inspection? Short-chain fatty acids If any microbial product deserves the label of primary messenger, it is the short-chain fatty acids: acetate, propionate and butyrate, produced when anaerobic bacteria ferment dietary fibre and resistant starch in the colon. They are made in substantial quantities — on the order of hundreds of millimoles per day in an adult eating an ordinary diet — and their concentrations in the colonic lumen are high, in the tens of millimoles per litre. Most of what is produced never travels far. Butyrate in particular is the preferred energy substrate of colonocytes, which consume the majority of it locally. What remains passes into the portal vein, and the liver extracts a large share of the propionate. Acetate is the one that appears in peripheral blood at appreciable concentrations, and acetate is also the short-chain fatty acid for which brain uptake has been demonstrated most clearly, through monocarboxylate transporters at the blood-brain barrier. So the picture is a gradient: enormous concentrations in the colon, moderate in the portal circulation, low in peripheral blood, lower still in the brain. This matters because much of the experimental literature involves applying millimolar concentrations of butyrate to cells in culture, where it acts as a histone deacetylase inhibitor and produces striking changes in gene expression. Whether concentrations anywhere near those are ever achieved in human brain tissue is doubtful. The more defensible routes for short-chain fatty acid effects on the brain are indirect. They stimulate enteroendocrine cells to release GLP-1 and peptide YY. They act on vagal afferent endings. They maintain the epithelial barrier, limiting the translocation of inflammatory bacterial components. They influence regulatory T cell populations in the gut, with downstream consequences for systemic inflammation. And, in mice, they affect microglial maturation: germ-free animals have microglia that look and behave immaturely, and supplying short-chain fatty acids partially normalises them. Butyrate-producing bacteria recur throughout the disease literature in this booklet, usually as the organisms reported to be depleted. It is worth noticing that "butyrate producers" is a functional category spanning several genera — Faecalibacterium, Roseburia, Eubacterium, Coprococcus, Anaerostipes among them — and that these organisms tend to be sensitive to the same conditions: low fibre intake, antibiotics, inflammation, rapid or unusually slow transit. Their depletion in a diseased group is therefore one of the least specific findings imaginable. It appears in Parkinson's disease, in depression, in autism, in inflammatory bowel disease, in type 2 diabetes and in simple ageing. A marker that appears everywhere explains nothing in particular. Neurotransmitters and their precursors The most widely repeated claim in popular writing about the gut-brain axis is that the great majority of the body's serotonin is made in the gut, usually given as ninety or ninety-five per cent. The underlying observation is sound: enterochromaffin cells in the intestinal epithelium synthesise serotonin, and they collectively hold far more of it than the brain does. Work published by Jessica Yano and colleagues in 2015 showed that spore-forming bacteria from mouse and human microbiota promote this synthesis, and that germ-free mice have substantially reduced colonic and circulating serotonin, restored when the microbes are returned. Certain bacterial metabolites were sufficient to raise serotonin in chromaffin cell culture and in germ-free animals. That is a real and important finding. But the inference usually drawn from it — that gut microbes therefore regulate mood through serotonin — does not follow, for a simple anatomical reason. Serotonin does not cross the blood-brain barrier. The serotonin made in the gut acts on gut motility, on platelets, on bone metabolism and on vagal afferents; it does not top up the brain's supply. The brain makes its own from tryptophan. Tryptophan, however, does cross, and here the microbial contribution is more interesting. Dietary tryptophan has three main fates. It can be absorbed and used by the host to make serotonin or converted along the kynurenine pathway. Or it can be metabolised by gut bacteria into indole derivatives. These routes compete. The kynurenine pathway, which is upregulated by inflammation, consumes most ingested tryptophan and generates metabolites with their own neuroactive properties: kynurenic acid, which antagonises glutamate receptors, and quinolinic acid, which is an agonist at NMDA receptors and is neurotoxic in excess. Bacterial indole derivatives, meanwhile, include compounds that act on the aryl hydrocarbon receptor, a pathway characterised in astrocytes and implicated in the regulation of central inflammation. Microbes thus shape the brain's tryptophan supply and the balance of its metabolites, which is a more modest but more credible claim than the serotonin story. Similar reasoning applies to GABA and dopamine. Various lactobacilli and bifidobacteria produce GABA in culture, sometimes in quantities that excite researchers. GABA does not meaningfully cross the blood-brain barrier either. Bacterial GABA can act on enteric neurons and on vagal endings, which is a real mechanism, but it is not the same as putting GABA into the brain. Dopamine is produced by several gut organisms and is likewise excluded from the brain; its precursor L-DOPA is not, which brings us to the clearest and most consequential piece of gut-brain pharmacology in the whole field. Levodopa: the case that actually closes Levodopa remains the most effective drug for Parkinson's disease. It is given because dopamine itself cannot enter the brain; levodopa can, and is converted to dopamine inside the central nervous system. To stop it being converted prematurely in the periphery, it is co-administered with a decarboxylase inhibitor such as carbidopa, which does not cross into the brain. Gut bacteria run a competing reaction. In 2019, Vayu Maini Rekdal and colleagues in Emily Balskus's laboratory at Harvard, working with Peter Turnbaugh's group, described an interspecies bacterial pathway that consumes levodopa in the gut. A pyridoxal phosphate-dependent tyrosine decarboxylase from Enterococcus faecalis converts levodopa to dopamine. A molybdenum-dependent dehydroxylase from Eggerthella lenta then converts that dopamine to m-tyramine. Crucially, the human-targeted decarboxylase inhibitor given with the drug does not block the bacterial enzyme, which is structurally different. The investigators identified a compound that does inhibit the bacterial enzyme in microbiota samples from Parkinson's patients and increases levodopa bioavailability in mice. This deserves attention because it is the rare case where every link is specified: a named organism, a named enzyme, a named substrate that is an actual prescribed drug, a measurable clinical variable (how much levodopa reaches the brain), and a candidate intervention. It is also a useful corrective to the usual framing. Here the microbes are not causing the disease. They are interfering with its treatment. That is a more tractable problem, and arguably a more immediately valuable one: patients on levodopa vary widely in how much drug they need and how erratic their response is, and part of that variation may sit in the colon. Bile acids, trimethylamine and other host-microbe hybrids Several important classes of molecule are neither purely host nor purely microbial but the product of both. Primary bile acids are made by the liver from cholesterol and secreted into the small intestine, where they emulsify fats. Most are reabsorbed, but the fraction that reaches the colon is chemically transformed by bacteria — deconjugated by bile salt hydrolases, then dehydroxylated by a small number of specialist organisms — into secondary bile acids such as deoxycholic and lithocholic acid. These act on host receptors including FXR and TGR5, influencing metabolism, inflammation and enteroendocrine secretion. Bile acids have been detected in brain tissue and cerebrospinal fluid, and altered bile acid profiles have been reported in Alzheimer's disease cohorts, though whether this is cause, consequence or dietary artefact remains unresolved. The 2026 Cell Host and Microbe trial of adjunctive faecal transplantation in depression, discussed in Chapter 6, reported elevated bile acids as one of its mechanistic correlates. Trimethylamine is produced by gut bacteria from dietary choline and carnitine, absorbed, and oxidised by the liver to trimethylamine N-oxide (TMAO). Elevated TMAO has been associated with cardiovascular risk in multiple cohorts and, more tentatively, with cognitive decline. The cardiovascular literature is the more developed, and it illustrates a general lesson: this is a genuine microbe-dependent human metabolite whose blood concentration can be measured reliably and which tracks a hard clinical outcome. Nothing in the neurological literature is yet that well established. 4-ethylphenyl sulfate is worth naming because of its unusual trajectory. It is a host-sulfated derivative of 4-ethylphenol, which is produced by gut bacteria from dietary tyrosine. Elevated levels were reported in a mouse model relevant to autism, and subsequent work indicated that administering the compound to mice produced anxiety-like behaviour and changes in oligodendrocyte maturation. That finding was specific enough to support a drug development programme, discussed in Chapters 7 and 8, aimed at binding the precursor in the gut before it can be absorbed. Table 1 summarises the main classes, the reasoning for each, and the honest state of the evidence. Table 1. Candidate microbial messengers and the strength of the case for each reaching or affecting the brain. Compound class Microbial origin Route to nervous system Evidence level Short-chain fatty acids Fibre fermentation Enteroendocrine release, vagal endings, barrier maintenance, microglia; limited direct brain entry (acetate) Strong in rodents; indirect in humans Serotonin Host cells, microbially stimulated Gut motility, platelets, vagal afferents; does not cross into brain Mechanism sound, brain claim unsupported Tryptophan metabolites Bacterial indoles; host kynurenines Tryptophan supply, aryl hydrocarbon receptor on astrocytes Moderate; active research area GABA, dopamine Lactobacilli, bifidobacteria, others Enteric neurons, vagal endings; do not cross into brain Mechanism local only Levodopa metabolism Enterococcus, Eggerthella enzymes Reduces drug reaching the brain Strong; pathway fully characterised Secondary bile acids Bacterial transformation of host bile FXR and TGR5 receptors, enteroendocrine signalling Emerging 4-ethylphenyl sulfate Bacterial tyrosine metabolism, host sulfation Circulating metabolite; effects shown in mice Preliminary; human trials ongoing Source: Compiled from primary studies cited in this chapter, including Yano et al. (Cell, 2015) and Maini Rekdal et al. (Science, 2019). Microbial drug metabolism as a general phenomenon The levodopa case is the most consequential example in neurology, but it is not an isolated curiosity. Gut bacteria metabolise a wide range of pharmaceuticals, and the field of pharmacomicrobiomics has catalogued dozens of such interactions. The classical example predates modern sequencing by decades. Digoxin, a cardiac glycoside with a narrow therapeutic window, is inactivated in the gut by strains of Eggerthella lenta carrying a specific operon; patients harbouring those strains derive less benefit from a given dose, and the effect was identified clinically before the responsible organism was named. Sulfasalazine, used in inflammatory bowel disease, is the reverse case: it is a prodrug, and bacterial azoreductases are required to cleave it into its active form, so the drug does not work without the right microbial activity. Irinotecan, a chemotherapy agent, causes severe diarrhoea partly because bacterial beta-glucuronidases reactivate the toxic metabolite in the gut lumen after the liver has detoxified it, and inhibitors of those bacterial enzymes have been developed specifically to prevent this. Large-scale screens have extended the list considerably. Systematic testing of hundreds of marketed drugs against panels of human gut bacterial strains has found that a substantial fraction are chemically modified by at least one species, and, in the other direction, that many non-antibiotic drugs inhibit the growth of gut bacteria at concentrations reached in the intestine. Among the drugs shown to have such antibacterial activity are several classes of psychiatric medication, including antipsychotics and selective serotonin reuptake inhibitors. That last observation cuts directly across the depression literature examined in Chapter 6. If antidepressants themselves reshape the microbiome, then any comparison between medicated depressed patients and unmedicated controls is measuring the drug as much as the disease — and any trial of a probiotic given alongside an antidepressant is testing the combination of an organism and a compound that suppresses some organisms. The general lesson is that the best-established gut-brain interactions in humans are pharmacological rather than pathogenic. Microbes are not, on current evidence, a common cause of neurological disease. They are a demonstrable and underappreciated variable in how neurological drugs behave. Bacterial structures and amyloids Not everything the microbiota contributes is a small molecule. Two structural contributions matter. The first is lipopolysaccharide, introduced in the previous chapter. It is a component of Gram-negative bacterial membranes and one of the strongest activators of innate immunity known. Its relevance depends entirely on how much crosses the epithelium, which depends on barrier integrity, and on the sensitivity of the host's immune system, which varies genetically. The second is more specific and more intriguing. Many bacteria produce functional amyloid proteins — the best characterised being curli, made by Escherichia coli and related organisms, which helps bacteria adhere and form biofilms. Curli is a genuine amyloid, adopting the same cross-beta structure that characterises the pathological protein aggregates in neurodegenerative disease. The hypothesis, developed experimentally in rodent work from Robert Friedland's group and others, is that exposure to bacterial amyloid in the gut might seed or accelerate the misfolding of host proteins such as alpha-synuclein, through a templating effect or by priming the immune system to respond to amyloid structures generally. Studies in rats and in Caenorhabditis elegans have shown increased alpha-synuclein aggregation following exposure to curli-producing bacteria. This is a mechanistically elegant idea with a specific prediction: people harbouring more curli-producing organisms should show more synuclein pathology. Human evidence remains thin. It is included here because it illustrates how a microbial hypothesis can be biochemically precise and still await the observation that would make it clinically relevant. What the chemistry permits The chemistry supports a narrower set of claims than the popular literature makes, and a broader set than a strict sceptic would allow. It is not reasonable to say that gut bacteria supply the brain with serotonin, GABA or dopamine. Those molecules do not go there. It is reasonable to say that gut bacteria influence the availability of precursors that do cross, shape the metabolic pathways those precursors enter, generate immune stimuli that can affect brain function, and produce compounds that act on host cells whose own signals reach the brain. It is also reasonable — indeed unavoidable — to say that gut bacteria metabolise drugs, including the central drug of Parkinson's disease, in ways that alter how much reaches the brain. That last point sets a useful standard. The levodopa work names the organism, the enzyme, the reaction and the clinical consequence. When a claim in this field cannot be stated at that resolution, the honest description is that a mechanism has been proposed rather than demonstrated. The next chapter examines why so many claims remain at the proposal stage, and what kinds of study would move them along. Chapter 3: How We Know, and How We Get It Wrong A field is only as trustworthy as its methods, and microbiome research grew up fast. Between roughly 2008 and 2015 the cost of sequencing bacterial DNA fell far enough that any laboratory could characterise the microbial community in a stool sample, and thousands did. What did not fall at the same rate was the cost of doing a properly designed human study, or the difficulty of establishing that a difference between two groups of people has anything to do with the disease that distinguishes them. The result is a literature of uneven quality in which the same set of methodological errors recurs. This chapter sets out what each major technique can and cannot establish, because the disease chapters that follow depend on those distinctions. What a stool sample is Nearly all human microbiome data comes from faeces, for the obvious reason. But a stool sample is a peculiar specimen. It represents chiefly the distal colon, and imperfectly even that: the community adhering to the mucus layer differs from the one suspended in the lumen, and the microbiota of the small intestine, where much of the interaction with the immune and nervous systems occurs, is quite different and effectively unsampled. Comparisons between stool and mucosal biopsies routinely find substantial divergence. Stool composition also varies from day to day within the same person, with what they last ate, with how long the material has been in the colon, and with how the sample was collected, stored and processed. Transit time is a particularly serious confounder. Slower transit means more water absorption, higher pH shifts, more protein fermentation, and systematically different community composition. Stool consistency, as graded by the Bristol scale, is one of the strongest single correlates of microbiome composition in population studies. Any disease associated with constipation — Parkinson's disease conspicuously, but also depression through medication effects, and autism through diet and behaviour — will show microbiome differences partly or wholly attributable to transit. Then there is the sequencing itself. Two main approaches dominate. Amplicon sequencing targets a region of the bacterial 16S ribosomal RNA gene; it is cheap, and it identifies organisms roughly to genus level, with species-level resolution unreliable. Shotgun metagenomics sequences everything present, giving species and strain resolution and information about gene content, at several times the cost. The two methods produce different answers from the same sample, and results reported at genus level from 16S data are frequently not comparable across studies that used different primer regions, different reference databases or different bioinformatic pipelines. Finally, the data are compositional. Sequencing reports relative abundances that sum to one hundred per cent. If one organism doubles in absolute terms, every other organism's relative share falls, even if nothing happened to it. Failing to account for this generates spurious findings, and statistical methods for handling compositional data have improved considerably but are not universally applied. Studies using quantitative approaches that estimate absolute microbial load, such as those from the Raes laboratory in Leuven, have shown that some apparent disease associations change character once total load is accounted for. Cross-sectional comparisons The default design is simple: recruit people with a condition, recruit controls, sequence both groups, report the differences. Thousands of such studies exist. Their limitations are now well catalogued. Sample sizes are often small — tens of participants per group — which, given the variability of the microbiome between healthy individuals, yields low statistical power and unstable findings. Control groups are frequently recruited by convenience, matching poorly on diet, medication, body mass index, smoking, living situation and age. Medication is a particularly large confounder: proton pump inhibitors, metformin, antibiotics, laxatives, antidepressants and antipsychotics all alter the microbiome measurably, and patients take more medication than controls by definition. The consequence is a literature in which almost every disorder has been reported to show a distinctive microbial signature, and almost none of those signatures replicate cleanly across cohorts. An influential meta-analysis published in 2017 by Claire Duvallet and colleagues, pooling dozens of case-control microbiome datasets across many diseases, found that most disease-associated shifts were non-specific: the same organisms appeared as depleted or enriched across unrelated conditions, which is consistent with a general response to illness rather than a disease-specific mechanism. None of this means cross-sectional studies are useless. Done at scale with careful covariate adjustment, they generate hypotheses that are worth pursuing, and a few findings have proven robust. The largest Parkinson's disease metagenomic study to date, by Zachary Wallen and colleagues under Haydeh Payami at the University of Alabama at Birmingham, enrolled 490 patients and 234 controls and used uniform methods throughout, requiring significance by two independent statistical approaches before declaring an association. That design is the exception rather than the rule, and it is no coincidence that its findings are among the most cited. How much does anything explain? There is a number that ought to be quoted far more often than it is. When the Flemish Gut Flora Project and the Dutch LifeLines-DEEP cohort were analysed together by Gwen Falony, Jeroen Raes and colleagues — over 2,200 people with detailed phenotyping, reported in Science in 2016 — 69 clinical and questionnaire covariates were found to be associated with microbiome composition, with a 92 per cent replication rate between the two cohorts. Stool consistency showed the largest single effect. Medication explained the largest total variance and interacted with other associations. The crucial finding, though, was how little of the variation all of these together accounted for: a modest fraction, with the great majority of between-person microbiome variation unexplained by any measured factor. The authors drew the appropriate conclusion, which was that proposed disease-marker genera were themselves associated with host covariates, and that study designs must include those covariates or risk attributing to disease what belongs to stool consistency and drugs. Two things follow. First, a disease-associated microbial difference must be large to stand out against this background of unexplained variation, and most reported differences are not. Second, because so much variation has no known cause, adjusting for known confounders provides no guarantee; the unmeasured remainder could be doing the work. This is a general hazard of observational microbiome research and there is no statistical solution to it, only better designs. Germ-free and gnotobiotic animals The germ-free mouse is the field's most powerful experimental tool and its most misleading one. Powerful, because it allows a causal test that is impossible in humans: take an animal with no microbes, give it a defined community, and see what changes. This design has established that microbial colonisation influences immune development, blood-brain barrier integrity, microglial maturation, myelination, stress hormone responses and a range of behaviours in rodents. The results are often dramatic, because the comparison is between having a microbiome and having none at all. Misleading, for exactly that reason. Germ-free animals are not models of dysbiosis; they are models of sterility, a state that does not occur in humans. They have underdeveloped immune systems, abnormal gut anatomy with enlarged caeca, altered metabolism and exaggerated stress responses. Demonstrating that a germ-free mouse behaves oddly and that colonisation fixes it tells us that microbes matter for normal development. It does not tell us that a shift in microbial composition within the normal human range produces a comparable effect. Then there is the faecal transfer experiment that has become the field's signature move: transplant stool from humans with a condition into germ-free or antibiotic-treated mice, and report that the animals develop features of the condition. These experiments are genuinely informative when done well — with multiple independent donors, adequate numbers of recipient animals, pre-registered outcomes and blinded assessment. They are frequently not done well. Common problems include using stool from a single donor per group, so that any difference between recipient groups may reflect one person's idiosyncrasies; treating individual mice as independent observations when they are clustered by donor and by cage (mice are coprophagic and cagemates converge on a shared microbiome); and measuring many behavioural outcomes while reporting the ones that reached significance. There is also a deeper problem of interpretation, which arises most sharply for psychiatric and developmental conditions. The behavioural assays used — time in the open arms of an elevated plus maze, immobility in a forced swim test, duration of sniffing an unfamiliar mouse, number of marbles buried — are validated as screens for drugs that work in humans, not as models of human illness. Describing reduced social sniffing in a colonised mouse as an autism-like phenotype is a translational leap that the assay cannot support, and this criticism has now been made forcefully in print by researchers within neuroscience. Population cohorts and genetic inference Two approaches address the confounding problem more seriously. The first is the large population cohort with deep covariate data. The Flemish Gut Flora Project, the Dutch LifeLines-DEEP and Rotterdam studies, the multi-ethnic HELIUS cohort in Amsterdam and similar efforts enrol thousands of people, collect diet, medication, anthropometry and clinical measures, and can therefore adjust for far more than a small case-control study can. Mireia Valles-Colomer and colleagues, using the Flemish cohort of 1,054 people with validation in an independent set of comparable size, reported that butyrate-producing Faecalibacterium and Coprococcus were consistently associated with higher self-reported quality of life, and that Coprococcus and Dialister were depleted in depression even after accounting for antidepressant use. A later study by Djawad Radjabzadeh and colleagues, combining the Rotterdam Study with HELIUS for a total of around 2,600 participants, identified thirteen microbial taxa associated with depressive symptoms. Cohorts of this kind give better estimates of effect size, and the estimates are sobering: associations that looked substantial in small studies typically shrink to modest ones. They still cannot separate cause from consequence on their own. The second approach can, in principle. Mendelian randomisation exploits the fact that genetic variants are allocated essentially at random at conception. If genetic variants that increase the abundance of a particular bacterium are also associated with a disease, and the variants have no plausible route to the disease other than through that bacterium, this constitutes evidence of a causal path, immune to reverse causation and to most confounding. Radjabzadeh's group applied this to their findings and reported a potential causal link from the genus Eggerthella to major depressive disorder, with a directionality test favouring that orientation over the reverse. The technique's limitation in this field is that human genetic variants explain only a small fraction of microbiome variation, so the instruments are weak and the estimates imprecise. Different microbiome genome-wide association datasets yield different instruments, and published Mendelian randomisation studies on microbiome-disease links have produced inconsistent results. The approach is the right idea; the raw material is not yet strong enough to settle much. Randomised trials The definitive test is to change the microbiome deliberately and see whether the disease changes. Three intervention types dominate: probiotics and prebiotics, faecal transplantation, and targeted small molecules that act on microbial metabolism. Each carries its own design problems. Probiotic trials frequently use idiosyncratic multi-strain preparations, so results from one product say little about another, and the dose-response relationship is rarely characterised. Faecal transplant trials must decide what a placebo is: autologous transplant, where the patient receives their own processed stool, is the most rigorous comparator, but it is not inert, since the bowel preparation and the procedure itself have effects. Saline placebo preserves blinding less well because gastrointestinal side effects differ. Blinding is generally imperfect for anything administered by colonoscopy. And the outcome measures in psychiatric and neurological trials are largely rating scales administered by clinicians or completed by patients and their families. These are the right instruments, but they are sensitive to expectation, and expectation in microbiome trials is high. Placebo response rates in depression trials commonly exceed thirty per cent. In paediatric behavioural trials, where a parent who has travelled to a research centre rates their child's symptoms, the scope for expectation effects is greater still. This is precisely why an open-label trial with striking results should be treated as a reason to run a controlled one, not as a finding. Table 2 sets out what each design can establish. Table 2. What each study design can and cannot demonstrate about gut-brain claims. Design Answers Cannot answer Chief pitfall Cross-sectional case-control Is composition different? Whether difference causes disease Diet, drugs, transit time confounding Large cohort with covariates How large is the association, adjusted? Direction of causation Residual confounding; self-reported diet Germ-free colonisation Can microbes affect this biology at all? Whether normal-range variation matters Sterility is not dysbiosis Human-to-mouse stool transfer Is a donor community sufficient in an animal? Human relevance of the phenotype Few donors; cage effects; weak behavioural proxies Mendelian randomisation Is there a causal genetic signal? Effect of intervening Weak genetic instruments Randomised controlled trial Does intervening change symptoms? Mechanism, without added measures Placebo response; imperfect blinding A worked example of how it goes wrong It helps to trace one hypothetical but entirely typical sequence, because the individual steps each look reasonable. A research group recruits forty patients with a neurological condition from their clinic and forty controls, mostly staff and spouses. They sequence stool with 16S amplicon sequencing and find that a butyrate-producing genus is significantly depleted in patients, p = 0.03. This is reported as a disease-associated microbial signature. What has actually happened is uncertain in at least five ways. The patients are older on average than the staff who volunteered as controls, and age is associated with microbiome composition. Most patients are on two or three medications that the controls are not taking, and medication explains more microbiome variance than almost anything else. More patients are constipated, and stool consistency has the largest single effect of any measured covariate. The patients eat less fibre, because illness has changed their appetite, and fibre is the main determinant of the abundance of the very organisms found depleted. And with several hundred taxa tested, a p value of 0.03 without correction for multiple comparisons is close to meaningless. The group then sends stool from three patients and three controls to a collaborator who colonises antibiotic-treated mice. The recipient mice differ on a behavioural assay. This is reported as evidence that the microbial difference is causal. In fact the experiment compared three individuals against three individuals, the mice were housed by group and so shared microbiota within cages, and the behavioural assay was one of five that were run. A press release follows, describing a link between gut bacteria and the disease. A supplement company cites it. Nothing in this sequence involves misconduct. Every step is common practice. It is the accumulation that produces a literature in which almost everything has been found and almost nothing replicates, and it is why the disease chapters in this book weight a single well-designed study far above a dozen of this kind. The corrective culture The encouraging development of the last few years is that the field has begun to police itself. Registered reports and pre-registration are becoming more common. Journals increasingly require deposition of sequence data. Several high-profile findings have been subjected to independent reanalysis, and some have not survived it. In November 2025, a group of neuroscientists led by Kevin Mitchell at Trinity College Dublin published a critique in Neuron arguing that the evidence linking the gut microbiome to autism is undermined by conceptual and methodological flaws at every level, from underpowered human studies to behavioural assays in mice that do not model the condition. The response from researchers in the field was vigorous, and the exchange is examined in Chapter 7. Public disagreement of this kind is a sign of health, not of crisis. The questions worth carrying into the disease chapters are simple ones. How many people were studied, and were they compared with anyone appropriate? Was the intervention randomised and controlled, and against what? Was the mechanism measured or assumed? And if the finding came from a mouse, what exactly was measured in the mouse, and what does that have to do with a person? Hashtags: #TheGutBrainAxis #MicrobiomeModulation #NeurologicalDisorders #GutMicrobiota #MicrobiotaGutBrainAxis #VagusNerve #EntericNervousSystem #EnteroendocrineSignaling #ImmuneSignaling #MicrobialMetabolites #ShortChainFattyAcids #TryptophanMetabolism #LevodopaMetabolism #Pharmacomicrobiomics #IntestinalBarrier #BloodBrainBarrier #MicroglialMaturation #Dysbiosis #FaecalMicrobiotaTransplantation #Psychobiotics #ParkinsonsDisease #DepressionAndAnxiety #AutismAndMicrobiome #MicrobiomeCausality #FutureOfGutBrainResearch

Download the Book (PDF): Introduction Most music students can tell you exactly how many hours they practised last week. Very few can tell you how many hours their forearms were under load, how many minutes their ears spent above a hazardous sound level, or what their heart rate did in the thirty seconds before their last jury. These are not idle questions. They describe the three ways a musical life most commonly goes wrong: the hand or shoulder that starts to hurt and then will not stop, the ear that begins to ring and then keeps ringing, and the nervous system that treats a recital hall as a place of mortal danger. None of these problems is rare. Surveys of professional orchestral musicians have repeatedly found that a large majority report pain linked to playing at some point in their careers, and a systematic review of the research literature in 2016 found twelve-month prevalence figures for musculoskeletal complaints ranging from 41 to 93 percent. Among college music students, audiologists have found the tell-tale notch of noise-induced hearing damage in a substantial share of people who are barely twenty. Performance anxiety severe enough to interfere with playing is reported by somewhere between a sixth and more than half of professional musicians, depending on how it is measured. If you are studying music, the odds that you will meet at least one of these problems are not small. They are close to even or better. That is the uncomfortable part. The more hopeful part, and the argument of this book, is that these problems are largely problems of dose and habit, and dose and habit are exactly what a student controls. Overuse injuries grow out of sudden jumps in playing time, long unbroken sessions, tension that goes unnoticed, and a culture that treats pain as proof of commitment. Hearing damage accumulates, minute by minute, from sound levels that feel entirely normal inside a small practice room. Disabling performance anxiety is sustained by avoidance, by particular ways of thinking about mistakes, and by preparation that trains playing in private but never trains playing under pressure. Each of these can be changed. The student years are when they are easiest to change, because the habits are still forming and the damage, in most cases, has not yet been done. Why students in particular It is tempting to think of these as professional hazards, problems for the orchestral violinist with thirty years of service or the rock drummer who played without protection through the 1980s. The research points in a less comfortable direction. The years of conservatory and university study are often the most dangerous years of a musician's life. Consider what happens to a typical first-year music major. Practice time jumps, sometimes doubling or tripling within weeks, as a student who managed an hour or two a day in high school tries to meet the expectations of a new studio teacher. The repertoire gets harder and longer. A new teacher may change fundamental aspects of technique, which means the body is doing unfamiliar things at high volume. Ensemble rehearsals add hours of loud sound on top of individual practice. Juries, auditions, competitions and recitals arrive on a schedule the student does not control. Sleep suffers. Money is tight, so the instrument may be a compromise, the practice room may be a concrete box the size of a closet, and the earplugs, if any, are the foam kind handed out free at a concert. All of this happens at an age when young adults are also at peak risk for the onset of anxiety disorders, and in an environment where admitting difficulty can feel like admitting you do not belong. This is not a counsel of fear. Most music students finish their degrees with healthy hands, working ears and a manageable relationship with nerves. But the ones who do so reliably are rarely lucky. They have usually learned, somewhere along the way, to treat their body and nervous system as part of their technique rather than as the equipment that technique runs on. Three problems, one approach The book is organised around three problems that look different but share a structure. Strain. The first four chapters deal with the body. Chapter 1 sets out the idea of the musician as an athlete of small muscles and explains why load management, the central concept of sports medicine, applies directly to practising. Chapter 2 describes what actually goes wrong in overuse injuries, from tendon irritation to nerve compression to the rare but serious condition of focal dystonia, and includes the voice, which is subject to its own form of overuse. Chapter 3 turns this into a practical routine: how to structure practice so that it builds skill without accumulating damage. Chapter 4 covers what to do when something starts to hurt, how to tell ordinary fatigue from a warning sign, and how to find the right kind of help. Sound. Chapters 5 and 6 deal with the ear. Chapter 5 explains how sound damages the inner ear, why the damage is permanent, and why a musician's exposure is so often underestimated. Chapter 6 covers practical protection: how to measure sound, how to arrange practice and rehearsal, which kinds of hearing protection suit which instruments, and why a baseline hearing test is worth having now. Nerves. Chapters 7 and 8 deal with performance anxiety. Chapter 7 explains what the anxious body and mind are doing, and when anxiety stops being useful arousal and becomes a problem. Chapter 8 sets out the approaches with real evidence behind them, from graded exposure and cognitive techniques to the careful use of medication under a clinician's supervision. What unites the three is a single idea. In each case, the damage comes less from a single catastrophic event than from the accumulation of exposures that feel ordinary at the time: one more hour at the piano, one more rehearsal next to the trumpets, one more performance approached with dread and then avoided. And in each case, the remedy lies less in heroic intervention than in deliberate, repeated habits: breaks taken on schedule, earplugs worn every time, performance practised as a skill in its own right. What this book is and is not This is a book of general health education written for performing arts students, above all instrumentalists and singers. Actors and dancers will find much that applies to them, particularly in the chapters on anxiety and on load, but the examples are drawn from music. It is not a substitute for a clinician. Where a symptom warrants medical assessment, the text says so plainly, and the threshold for seeking help that it recommends is deliberately low. Musicians have a long history of waiting too long: of playing through numbness, of assuming tinnitus will go away, of suffering through years of dread before anyone suggests that performance anxiety is treatable. If something in these pages describes what you are experiencing, the right response is to talk to a doctor, audiologist, physiotherapist or counsellor, ideally one who works with performers. Many universities with music programmes have access to such people; the student health service is a reasonable place to start. A final note on evidence. Performing arts medicine is a young field, and much of its research is based on surveys, small samples and studies of particular institutions. Where the evidence is strong, the book says so. Where it is thin, or where practice rests on clinical experience rather than trials, it says that too. What is not in doubt is the basic picture: that musicians get hurt, that their hearing is at risk, that many of them struggle with performance anxiety, and that a great deal of this is preventable by people who know what to look for. Chapter 1: The Athlete of Small Muscles A professional pianist playing a fast passage may execute well over a thousand individual finger movements a minute, each one timed to a few thousandths of a second and graded in force with a precision no machine yet matches. A violinist holds the left arm raised and rotated for hours, while the fingers of that hand press strings against a fingerboard and the right arm draws a bow with constantly varying weight and speed. A trombonist supports the weight of the instrument with the left arm, controls airflow with the muscles of the abdomen and chest, and holds the delicate muscles of the lips in a precise configuration against a metal mouthpiece. A singer coordinates breathing, the tiny muscles of the larynx, the tongue, jaw and soft palate, and does all of this while acting and moving. These are athletic feats. They differ from the feats of a sprinter or a rower mainly in the size of the muscles involved and the degree of precision required. That difference matters, because it has allowed music education to ignore something sports have understood for decades: that the body adapts to what it is asked to do only if the asking is sensible, and that most injuries in trained people are not accidents but the predictable result of load outrunning capacity. What counts as a playing-related problem Researchers who study musicians' injuries use the term playing-related musculoskeletal disorder, usually shortened to PRMD. The most widely used definition, proposed by the Canadian researcher Christine Zaza and colleagues in 1998, describes it as pain, weakness, numbness, tingling or other physical symptoms that interfere with the ability to play the instrument at the level to which the musician is accustomed. The definition is useful because it sets the threshold at function rather than diagnosis. You do not need to have a named condition. If your body is stopping you from playing the way you normally play, you have a problem worth attending to. By that standard, playing-related problems are extraordinarily common. The 2016 systematic review by Laura Kok and colleagues at Leiden University, which pooled studies of professional musicians, found point prevalence figures (the proportion of musicians with a complaint at the moment they were asked) between 9 and 68 percent, and lifetime prevalence between 62 and 93 percent. The range is wide because studies used different definitions and populations, but even the lowest estimates describe a common experience rather than an unusual one. The neck and shoulders were the most frequently affected areas. In ten of the twelve studies that compared the sexes, women reported more complaints than men. Students are not spared. A 2018 study of 357 members of eleven Dutch student orchestras, most of them amateur players rather than music majors, found that 67.8 percent had experienced a playing-related complaint in the previous year. Female sex, younger age, higher body mass index and playing a string instrument were each independently associated with higher risk. Studies of conservatory students in several countries have produced broadly similar pictures. Earlier, a landmark 1988 survey of more than two thousand musicians in the International Conference of Symphony and Opera Musicians found that three quarters reported at least one medical problem that affected their performance. Numbers like these can produce two unhelpful reactions. One is fatalism: if almost everyone gets hurt, perhaps it is simply the price of the art. The other is dismissal: if almost everyone has some pain, perhaps pain is normal and can be ignored. Both are mistaken. Most of the complaints captured in these surveys are mild and temporary, and a substantial proportion of them could have been prevented or caught early. But a minority become chronic, and some end careers. The difference between the two groups often lies in what happened during the first few weeks after symptoms appeared. Load and capacity Sports medicine offers a simple model that fits musicians well. Every tissue in the body, from tendon to muscle to nerve, has a capacity: an amount of work it can handle without being damaged. Every activity imposes a load. When load stays within capacity, and when there is enough rest between bouts of load for the tissue to recover, the tissue adapts and its capacity grows. This is how training works. When load exceeds capacity, or when rest is too short for recovery, small amounts of damage accumulate faster than they can be repaired. This is how overuse injuries work. Several features of this model deserve emphasis. First, capacity is not fixed. A student who has practised ninety minutes a day for three years has tissues adapted to ninety minutes a day. Those tissues can be trained to handle four hours, but not overnight. Tendons in particular adapt slowly, over weeks and months rather than days, because they have a relatively poor blood supply and their cells turn over collagen gradually. Second, the dangerous thing is usually change, not absolute amount. Sports scientists have found that athletes are most vulnerable in the weeks after a sharp rise in training load, relative to what they had been doing before. The precise ratios proposed in that literature have been debated, and there is no validated formula for musicians. But the underlying principle is consistent with everything clinicians who treat musicians report: injuries cluster around sudden increases in playing time. The start of a new academic year, the run-up to juries or auditions, a summer festival with six hours of rehearsal a day, the return to heavy practice after a holiday of doing nothing, the first weeks with a new teacher who demands a different technique: these are the high-risk moments. Third, load is not just time. A slow Bach sarabande and a Liszt étude impose very different loads on a pianist's forearms, even if they occupy the same minutes. So do playing loudly versus softly, repetitive drilling of a single difficult passage versus varied practice, and playing with excess tension versus playing freely. The same hour of practice can be an easy day or a hard one depending on what fills it. Fourth, capacity is affected by everything else in your life. Poor sleep impairs tissue repair. So do inadequate nutrition, illness, and chronic psychological stress, which raises baseline muscle tension. A student who is sleeping five hours a night during exam week has less capacity than the same student in the middle of a relaxed semester, at precisely the moment when load is highest. Why musicians get it wrong If the load-and-capacity model is so straightforward, why do so many musicians get hurt? Part of the answer is structural and part is cultural. The structural part is that the musical year is built around load spikes. Juries, recitals and auditions arrive at fixed times, and preparation for them tends to be concentrated in the final weeks. A student who has fallen behind on repertoire, as most students periodically do, will try to make up ground by practising more, and the extra practice falls on tissues that are not ready for it. Ensemble schedules pile on top of individual practice. Summer festivals and intensive courses can double a player's daily hours with no transition. And instrumental technique itself often demands long periods of sustained, asymmetrical posture: the violinist's raised left arm, the flautist's rotated head and extended right arm, the guitarist's flexed left wrist. The cultural part is harder to change. Classical training in particular has long valued endurance, discipline and the suppression of discomfort. Stories of great players who practised eight or ten hours a day are part of the folklore. Many students absorb the belief that pain is a sign of effort, that stopping is weakness, and that admitting injury will cost them opportunities. Surveys of music students have found that many do not report symptoms to their teachers, and some continue to play through pain for weeks or months before seeking help. Teachers, for their part, often have little training in anatomy or injury prevention; many learned their own technique in an era when these subjects were not discussed. There is also a feature of musical practice that makes overuse particularly insidious. Unlike a runner, whose fatigue announces itself unmistakably in breathing and legs, a musician working the small muscles of the hand or the larynx often gets little warning. The muscles of the forearm that move the fingers can become fatigued without much sensation, and a player absorbed in a difficult passage may not notice rising tension until it has been present for a long time. By the time pain arrives, the load has usually been excessive for a while. The musician's body as instrument It is common to speak of the body as the instrument for singers and dancers. For instrumentalists the phrase sounds like a metaphor, but it is closer to literal truth than it seems. The wooden or metal object is only half the system. The other half is a set of joints, muscles, tendons and nerves that must deliver force, speed and precision on demand, day after day, for decades. Seen this way, a musician's technical training has two jobs. The first is the familiar one: producing a beautiful sound, accurate intonation, clean articulation and expressive phrasing. The second is less often named: producing all of this in a way the body can sustain. Techniques that achieve the first while neglecting the second are, in the long run, bad techniques, however good they sound this semester. A shoulder that is hiked up to stabilise a violin, a wrist that is locked to control a fast passage on the piano, a jaw clenched to support a high note, a thumb squeezing a clarinet body: each may produce acceptable results for a while and then fail. Good teachers have always known this. The best pedagogy in every instrumental tradition emphasises ease, freedom and efficient use of the body, and many of the great pedagogues were ferocious enemies of unnecessary tension. What performing arts medicine adds is a vocabulary and an evidence base for what used to be passed on as intuition, and a set of clinicians who understand what musicians do. Where the load comes from It helps to know where, in a typical student week, the load actually accumulates. Individual practice is the obvious source, and for most students it is the largest one. But it is not the only one. Ensemble rehearsal adds hours of playing that students often forget to count, and it is played under conditions the student does not control: long runs without breaks, conductor-imposed repetition, seating that may not suit the player's body, and the temptation to force the sound to be heard over the section. Chamber music rehearsals can run long when enthusiasm takes over. Teaching, for students who give lessons to earn money, adds demonstration playing. Gigs, church jobs and pit orchestras may involve hours of playing in cramped, poorly lit spaces with bad chairs. Then there is load that has nothing to do with music. Many students spend hours each day on laptops and phones, often in postures that load the same neck, shoulder and forearm structures their instrument depends on. Heavy backpacks and instrument cases carried on one shoulder add asymmetric strain. Part-time jobs involving typing, lifting or repetitive hand work can contribute. A student who is careful about practice but spends four hours a day hunched over a laptop writing papers is not managing total load. This matters because tissues do not care what caused the load. A forearm tendon irritated by a combination of piano practice and gaming does not distinguish between the two. When clinicians take a history from an injured musician, they ask about all of it, and so should you. Different instruments, different loads Every instrument asks something different of the body, and the patterns of injury follow. Knowing the typical demands of your own instrument helps you anticipate where trouble is most likely. Upper strings. Violinists and violists hold the instrument between the jaw and the left shoulder, with the left arm raised, the forearm rotated outwards and the elbow bent. The left hand works in a narrow, stretched position, and the right arm moves the bow through a wide range while controlling weight and speed with great subtlety. The neck, the left shoulder, the left forearm and the jaw are common sites of complaint. Violists, with a larger and heavier instrument and wider stretches, carry more load than violinists for the same playing time. Lower strings. Cellists and bassists sit or stand with the instrument supported by the floor, so they are spared the neck clamp of the upper strings, but the left arm works at or above shoulder height in the upper positions and the left hand exerts considerable force on thick strings. Back complaints are common, particularly when chairs are poor or the endpin is set at the wrong length. Piano. Pianists sit symmetrically, which is some advantage, but the hands and forearms deliver thousands of rapid movements, often with large stretches, octaves and chords that load the small muscles of the hand and the tendons of the wrist. Players with small hands face particular strain in repertoire written for large ones. The shoulders and upper back tire from holding the arms forward for long periods. Woodwinds. The right thumb of clarinettists, oboists and cor anglais players supports much of the instrument's weight, making thumb and wrist problems common. Flautists hold the instrument to the right with the head turned and the arms raised and rotated, a markedly asymmetric posture that can load the neck, shoulders and upper back. Bassoonists and saxophonists carry heavy instruments on straps that can pull the neck forward. Brass. Brass players have lower rates of musculoskeletal complaints than string players in many surveys, but their risks concentrate in the embouchure, the lips, and the muscles of the face, which are small, delicate and easily overloaded. Trombonists and tuba players also carry considerable weight in the left arm. Percussion and drum kit. Percussionists move between instruments with very different demands, from the controlled precision of snare drum and mallet instruments to the large, fast movements of timpani and kit. Repetitive impact loads the wrists and forearms, and kit players may spend hours in one seated posture. Guitar. Classical guitarists typically sit with the left foot raised and the instrument angled, and the left wrist often works in considerable flexion. Electric and bass guitarists carry the instrument on a strap, sometimes low on the body, which can increase wrist bending and shoulder load. Voice. Singers have their instrument inside their body, which brings its own risks, described in the next chapter, but they also use the whole body in performance: standing for long periods, moving on stage, and often carrying the physical tension of acting and dance. These patterns are generalisations, not destinies. Two violinists can have very different loads depending on their setup, technique and habits, and a well-adjusted player on a demanding instrument may be at less risk than a poorly adjusted player on an easier one. But knowing where your instrument concentrates its demands tells you where to pay closest attention. A semester, seen through the lens of load It may help to see how these principles play out over a real stretch of time. Consider an illustrative student, a second-year cellist, and follow her through an autumn semester. She is not a real individual, but her pattern is one that clinicians who treat musicians describe repeatedly. In the summer she played little, perhaps half an hour on most days. In the first week of term, eager to impress a new teacher and faced with a concerto movement for the end-of-year jury, she jumps straight to three hours of individual practice a day, on top of six hours a week of orchestra. The concerto requires extended passages in thumb position, a technique she has used only occasionally before. Her tissues, adapted to half an hour, are now doing six times as much, much of it in an unfamiliar position. For the first ten days, she feels fine, apart from some tiredness in the left forearm. In the third week, she notices an ache along the thumb side of the left wrist after long sessions. She ignores it, reasoning that it will settle as she gets stronger. By mid-semester, the ache is present at the start of practice. She begins to compensate, gripping differently, and a new pain develops in the forearm. The orchestra begins rehearsing a demanding symphony. Essays are due in several courses, and she spends evenings typing on a laptop at a kitchen table. She is sleeping six hours a night. Two weeks before the jury, she doubles her practice time to make up for lost ground. A week later she cannot play a scale without pain, and she withdraws from the jury. Every element of this story is ordinary. None of it involves carelessness in the usual sense; she was working hard and wanted to do well. What went wrong was the shape of the load: a sudden jump in playing time, concentrated on an unfamiliar technique, with rising non-musical load, falling sleep, and a final spike when capacity was already exhausted. An early response, in the third week, would very probably have kept the problem small. The same semester, managed differently, might have looked like this. Practice rising from half an hour to three hours across the first five or six weeks rather than in the first week. Thumb-position work introduced in short daily doses. A laptop stand and an external keyboard. The ache in the third week treated as information: practice reduced for a few days, the technique discussed with the teacher, and the load then rebuilt more gradually. The final two weeks before the jury treated as a time for performance practice rather than extra hours. Most students who manage their load this way arrive at the jury in better musical shape, not worse. Capacity can be built The final feature of the load-and-capacity model is the most encouraging. Capacity is trainable. Tissues that are loaded progressively, with adequate rest, become stronger and more resilient. This is why the aim of injury prevention is not to minimise practice. A student who practises too little will not build the capacity the profession demands, and will be more vulnerable, not less, when a heavy period arrives. The aim instead is to make increases gradual, to distribute load sensibly across the day and week, to vary its character, to recover properly, and to support the playing muscles with general fitness. A student who adds practice time steadily across a semester, who takes regular breaks, who sleeps adequately and who does some form of general strength and conditioning work will usually end the year able to handle far more than at the start, with fewer symptoms along the way. The chapters that follow turn this principle into specifics. First, though, it helps to understand what exactly goes wrong when load does exceed capacity. Chapter 2: What Goes Wrong in an Overused Body Musicians describe their injuries in the language of sensation: a burning in the forearm, an ache behind the shoulder blade, fingers that feel thick or clumsy, a buzzing in the little finger after a long rehearsal. Clinicians translate those sensations into categories, and the categories matter because they point to different causes and different treatments. This chapter sets out the main ones. It is not a guide to self-diagnosis. Several of these conditions share symptoms, some can mimic each other, and a few need prompt medical attention. The aim is to give you enough understanding to notice what your body is telling you and to have a useful conversation with a clinician. Muscle overuse and myofascial pain The most common playing-related complaint is also the least dramatic: pain in the muscles themselves after too much work. Musicians and clinicians have called it by various names over the years, including overuse syndrome, a term popularised in performing arts medicine in the 1980s, and it is sometimes grouped under the broad and imprecise label of repetitive strain injury. Whatever the name, the typical story is a diffuse ache in the forearm, upper arm, shoulder or neck that appears during or after playing, eases with rest, and returns when playing resumes. In the early stages it appears only after long or intense sessions. If load continues unchanged, it arrives sooner, lasts longer and may eventually be present at rest. Many musicians with this kind of pain have tender spots in the affected muscles, sometimes called trigger points, that reproduce the pain when pressed. The physiology of these points is still debated, but the practical picture is consistent. Muscles that are held in sustained contraction, particularly at low levels for long periods, are prone to becoming painful. This fits the demands of many instruments: the upper trapezius holding a shoulder slightly raised for hours, the forearm muscles bracing a wrist, the small muscles of the hand gripping a bow or a stick. The good news is that muscle-based pain caught early generally responds well to reduced load, attention to technique and posture, and a graded return to playing. The bad news is that, left alone, it can become a persistent pain problem in which the nervous system itself becomes more sensitive, amplifying signals from tissues that are no longer especially damaged. This process, called central sensitisation, is one reason chronic pain is so much harder to treat than acute pain, and one reason early action matters. Tendon problems Tendons connect muscles to bone and transmit the force that moves the joints. When a tendon is loaded beyond its capacity repeatedly, its structure changes: the orderly arrangement of collagen fibres becomes disorganised, and the tendon becomes painful and less able to handle load. Clinicians now generally call this tendinopathy, a term that has replaced the older tendinitis because inflammation turns out to be a minor part of the picture in most chronic cases. In musicians, tendon problems commonly appear around the wrist and elbow. Pain on the outside of the elbow, where the tendons of the wrist extensors attach, is familiar to anyone who has heard of tennis elbow, and it can arise from playing that keeps the wrist extended under load. Pain at the base of the thumb on the wrist side, known as De Quervain's tenosynovitis, involves the tendons that move the thumb as they pass through a narrow tunnel and can be provoked by repetitive thumb movements or by supporting an instrument's weight on the thumb, as clarinettists and oboists do. Trigger finger, in which a finger catches or locks as it bends, involves a thickening of a flexor tendon or its sheath. Tendons adapt slowly and heal slowly. A tendinopathy that has built up over months will usually take months to resolve, and the modern approach to treatment emphasises carefully graded loading rather than complete rest, which weakens tendons further. This is work to do with a physiotherapist or hand therapist, not alone. Nerve compression Nerves run from the spinal cord to the fingers through a series of tunnels and passages, and at several points they can be squeezed. Compression produces symptoms different from muscle or tendon pain: numbness, tingling, pins and needles, a feeling that the fingers are asleep, clumsiness, and in more advanced cases weakness and wasting of the small muscles of the hand. These symptoms deserve prompt attention because nerve damage that goes on too long may not fully recover. Three compression syndromes turn up regularly in musicians. Carpal tunnel syndrome affects the median nerve at the wrist and produces tingling and numbness in the thumb, index, middle and part of the ring finger, often worse at night. It is associated with sustained or repeated wrist flexion and with gripping. Cubital tunnel syndrome affects the ulnar nerve at the elbow, where it runs through the groove often called the funny bone. It produces tingling and numbness in the little finger and half of the ring finger. Sustained elbow bending stretches and compresses the nerve, which is why the left arm of violinists and violists, held in a flexed position for long periods, is a common site. Some players notice symptoms when they wake after sleeping with the elbow bent. Thoracic outlet syndrome is a more controversial diagnosis involving compression of the nerves and sometimes blood vessels as they pass from the neck to the arm between the collarbone and first rib. It is sometimes suggested in musicians who hold their arms raised and forward for long periods, especially with a drooped shoulder posture, and can produce vague aching, heaviness and tingling in the arm. Its diagnosis is difficult and should be made by an experienced clinician. Nerve symptoms should never be treated as simply a sign to practise more carefully. If you notice numbness or tingling that persists after playing, that wakes you at night, or that comes with weakness or clumsiness, see a doctor. The voice Singers face a form of overuse that is structurally similar and anatomically distinct. The vocal folds are two small bands of muscle and specialised tissue in the larynx that vibrate hundreds of times per second during singing. Excessive vocal load, whether from too much singing, singing too loudly, singing with inefficient technique, or talking loudly for long periods in noisy environments, can cause swelling of the fold tissue. Over time, repeated trauma can lead to vocal fold nodules (small, usually symmetrical callus-like thickenings), polyps, or other lesions, and occasionally to a haemorrhage, a bleed into the fold itself. The warning signs are hoarseness, a loss of the upper range, a breathy or rough quality that persists, increased effort to produce sound, a reduced ability to sing softly at the top of the range, and vocal fatigue that appears earlier in the day than it used to. A sudden loss of voice during forceful singing, particularly with a sensation of something giving way, is an emergency in vocal terms: stop singing and see a laryngologist quickly. Persistent hoarseness also needs a look. The clinical practice guideline on hoarseness from the American Academy of Otolaryngology–Head and Neck Surgery recommends examination of the larynx when hoarseness has not resolved within four weeks, or sooner when there is a serious concern, and it singles out professional voice users as people for whom earlier assessment is reasonable. For a voice student, a change that persists for a couple of weeks without an obvious cause such as a cold is worth raising with a clinician. Singers' load is not limited to singing. Many voice students also speak for hours in lessons, rehearsals, part-time jobs and social life, and loud talking in bars and parties after a performance is a familiar route to a tired voice. Acid reflux, dehydration, allergies, smoking and vaping, and upper respiratory infections all reduce the voice's capacity to handle load. The principles of Chapter 1 apply with full force: capacity is trainable, increases should be gradual, and rest matters. Focal dystonia One condition stands apart from the others because it is not an overuse injury in the ordinary sense, and because musicians and teachers often mistake it for one. Musician's focal dystonia is a neurological disorder in which the brain's control of a highly trained movement breaks down. It typically appears as a loss of control in a specific task: a pianist's fourth finger that curls involuntarily in fast passages, a guitarist's middle finger that will not extend, a brass player whose embouchure fails in a particular register, a violinist's bow arm that jerks. The movements are usually painless, and the hand typically works normally for other tasks. The condition is rare in absolute terms but strikingly common among musicians compared with the general population. Eckart Altenmüller and Hans-Christian Jabusch, who have studied it extensively in Hannover, estimate that about one percent of professional musicians are affected. Men are affected more often than women. The disorder tends to appear in musicians whose instruments demand maximal fine motor control, and in the more heavily used hand. Research has found evidence of altered inhibition and maladaptive reorganisation in the brain's sensory and motor systems, and a genetic predisposition appears to play a role. Psychological studies have found that musicians with dystonia show more perfectionist tendencies than unaffected musicians, and more recent work has pointed to educational factors, including habitual rumination on mistakes, as possible contributors. Focal dystonia matters here for three reasons. First, it is often misdiagnosed as a technical problem, leading students to practise the failing movement harder, which appears to make things worse. Second, it is not something that load management alone prevents, although excessive repetitive practice, sudden increases in practice and high stress are among the suggested triggers. Third, early assessment by a neurologist with experience of musicians gives the best chance of useful treatment, which may include retraining approaches and, in some cases, botulinum toxin injections. If you notice a loss of control over a specific movement, without pain, that persists and does not improve with ordinary rest, do not simply drill it. Get it assessed. Telling them apart Because these conditions overlap, it helps to know the broad patterns that distinguish them. Table 1 summarises the typical features. It is a guide to noticing and describing, not to diagnosing; many musicians have more than one problem at once, and the only reliable way to sort them out is a proper examination. Table 1. Common playing-related conditions and their typical warning signs. Condition Typical sensation Common sites in musicians When to seek assessment Muscle overuse Diffuse ache, tenderness, fatigue Forearm, neck, upper back, shoulder Pain lasting days after playing, or present at rest Tendinopathy Localised pain on loading a tendon Outer elbow, thumb side of wrist, fingers Pain that persists beyond a week or two of reduced load Nerve compression Numbness, tingling, weakness Fingers, hand, forearm Promptly, especially with night symptoms or weakness Vocal fold injury Hoarseness, lost range, effort Larynx Hoarseness that persists without a cold, or sudden loss of voice Focal dystonia Painless loss of control in one task Specific fingers, embouchure Any persistent, task-specific loss of control Who is more vulnerable Load and capacity explain most overuse problems, but individuals differ in how much load they can tolerate, and some of those differences are worth knowing about. Sex is one. As Chapter 1 noted, most studies find that women musicians report more playing-related complaints than men. The reasons are not fully established. Proposed explanations include differences in average hand size and muscle mass relative to instruments designed around larger bodies, differences in willingness to report symptoms, and hormonal influences on tissues. Whatever the cause, it suggests that women, and anyone with smaller hands, have particular reason to attend to instrument fit and to repertoire that demands large stretches. Joint hypermobility is another. Some people have joints that move further than average, sometimes described as being double-jointed. In a study published in the New England Journal of Medicine in 1993, Lars-Gunnar Larsson and colleagues examined 660 musicians and found that hypermobility could be both an advantage and a disadvantage depending on where it occurred. Musicians with hypermobile wrists and fingers, which move repetitively in playing, reported fewer symptoms in those areas, perhaps because the extra range reduced strain. But hypermobility of the knees and spine, which must provide stable support, was associated with more back and knee complaints. A hypermobile player may need more muscular stability to control the joints, and some players with very mobile finger joints find that their fingers collapse at the joint under pressure, which can generate tension elsewhere. A physiotherapist can assess hypermobility and design strengthening work where it is needed. A history of previous injury raises risk, as it does in sport. So does a previous episode of playing-related pain that was never properly rehabilitated. Other factors that appear in the research literature include higher body mass index, as in the Dutch student orchestra study, and psychological stress, which increases muscle tension and may lower pain thresholds. None of these makes injury inevitable. Each is a reason to be a little more attentive. What an assessment involves Musicians who have never seen a clinician for a playing problem sometimes delay because they do not know what to expect. A good assessment by a clinician experienced with performers usually has several parts. It begins with a history. The clinician will ask where the symptoms are, what they feel like, when they started, what makes them better or worse, and how they have changed. They will ask about playing in detail: the instrument, the number of hours, recent changes in practice, repertoire, technique, teacher or instrument, and the schedule of performances. They will ask about activities outside music, about general health, sleep and stress, and about previous injuries. Then comes a physical examination. The clinician will look at posture, test the range of motion of the neck, shoulders, elbows, wrists and fingers, test strength, press on tender structures, and perform specific tests that provoke symptoms from particular tendons or nerves. For suspected nerve problems, they will test sensation and the strength of small hand muscles. Crucially, an experienced clinician will usually ask you to play. The problem is often visible only in the act of playing: a shoulder that rises in a particular passage, a wrist that collapses, a thumb that squeezes. Some clinicians video the playing to review it in slow motion. It is worth bringing your instrument to any appointment, even if you have to ask whether it is welcome. Tests are not always necessary. For most muscle and tendon problems, the diagnosis is clinical, based on history and examination. For suspected nerve compression, nerve conduction studies and electromyography, which measure how well nerves carry signals, may be used. Ultrasound or magnetic resonance imaging may help in some tendon and joint problems. For voice problems, a laryngologist will examine the vocal folds directly, often using a camera passed through the nose or mouth, sometimes with a strobe light that allows the vibration of the folds to be seen in slow motion. The result should be a working diagnosis, an explanation of likely contributing factors, and a plan. If you leave an appointment without understanding what is wrong, why, and what to do next, ask. Beyond the arms and larynx Playing-related problems are not confined to hands and voices. Brass and wind players can develop problems with the muscles of the embouchure, including a rare but serious tear of the orbicularis oris muscle around the lips, sometimes associated with a sudden loss of endurance or range after forceful playing. Jaw pain and temporomandibular joint problems are reported by violinists, violists and wind players. Lower back pain is common among cellists, double bassists, guitarists and percussionists, and among any musician who sits for long hours on poor chairs. Dental problems, skin problems where the instrument contacts the body (the so-called fiddler's neck), and contact allergies to nickel or rosin also appear in the clinical literature. The common thread in all of these is the one set out in the previous chapter: the body adapts to reasonable loads and breaks down under unreasonable ones. The next chapter turns to what a reasonable load looks like in a real practice routine. Chapter 3: Practice That Builds Skill Without Building Damage There is a persistent myth in music that the amount of practice is what matters most. The musicians who practise the most, the story goes, become the best. The research on skill acquisition tells a more interesting story. What distinguishes experts is not raw hours but the quality of those hours: focused, goal-directed work on specific weaknesses, with feedback, and with attention fully engaged. The psychologist K. Anders Ericsson, whose studies of violin students in Berlin in the early 1990s launched the modern research on deliberate practice, noted that the best students tended to practise in limited sessions, often in the morning, and to nap and sleep more than their less accomplished peers. Deliberate practice is effortful enough that few people can sustain it for more than a few hours a day. This is good news for your body. The practice habits that build skill most efficiently turn out to overlap substantially with the habits that protect tissues from overload. Short, focused sessions with real breaks are better for learning and better for tendons. Varied practice is better for retention and distributes load across different muscles. Mental practice, which costs the body nothing, contributes to learning. And sleep, which consolidates motor learning, is also when tissues repair. Plan the week, not just the day Most students think about practice in daily terms: how many hours today? A better unit is the week, and better still the semester. Planning at this scale lets you see load spikes coming and smooth them out. Start with the known fixed points: juries, recitals, auditions, ensemble concerts, festival dates. Work backwards from each to see when intensive preparation will be needed. If a jury is eight weeks away, the repertoire should be learned and practice time built up steadily over the preceding weeks, rather than compressed into the final fortnight. This is ordinary good planning for musical reasons, and it is also the single most effective thing a student can do to avoid the overuse injuries that cluster before assessments. When you need to increase practice time, do it gradually. There is no validated formula for musicians, but clinicians who treat them commonly advise modest weekly increases rather than jumps, and a longer ramp after time off. A student returning from a three-week winter break with little playing should not resume at full volume on the first day back. Treat the first week as a rebuilding week, at perhaps half the usual time, with more slow practice and less repertoire at tempo. The same applies after illness or injury. Count all of your playing, not just individual practice. Ensemble rehearsals, chamber music, lessons, gigs and teaching all load the same tissues. If you know that the orchestra is rehearsing four hours a day during production week, reduce individual practice that week rather than trying to maintain it on top. Finally, build in lighter days. Athletes rarely train at maximum intensity every day, and musicians need not either. A week that includes one or two lighter days, with more mental practice, score study, listening and slow work, will usually produce better results than seven identical days, and it will give tissues a chance to catch up. Structure the session Within a single session, a few principles make a large difference. Warm up gradually. Starting practice by launching straight into the hardest passage at full tempo is the musical equivalent of sprinting without warming up. Begin with easy, slow playing that moves through the full range of the instrument, gradually increasing speed, dynamic range and technical demand over ten minutes or so. Some general movement before playing, such as walking, gentle arm swings and shoulder rolls, raises body temperature and circulation. The evidence that warming up specifically prevents injury in musicians is limited, but it is consistent with what is known about tissues, and it also improves the quality of practice. Take regular breaks. Clinicians who work with musicians commonly suggest a break of around five minutes for every twenty-five to thirty minutes of playing, and a longer break after an hour or so. The exact figures matter less than the habit. During the break, put the instrument down, stand up, move, look at something far away, and let the playing muscles do something different. A timer helps, because absorption in practice makes time vanish. Breaks are also good for learning: short rests during motor practice appear to help the brain consolidate what has just been practised. Vary the load. Drilling a single difficult passage for forty minutes subjects the same muscles to the same movements hundreds of times. Alternate demanding technical work with less strenuous material: slow lyrical playing, sight-reading, work on tone or intonation, score study. Research on motor learning also suggests that varying practice, rather than repeating the same thing in a block, leads to better long-term retention, even though it feels less productive in the moment. Use slow practice deliberately. Slow practice is one of the most effective tools for learning difficult passages, and it is also one of the gentlest on the body, provided you are genuinely relaxed rather than playing slowly with the same tension you would use at speed. Use slow practice to find the most economical way to execute a passage, and to notice and release unnecessary effort. Stop at the first sign of trouble. Fatigue, rising tension, discomfort or a loss of precision are signs to change what you are doing, take a break or stop for the day. Pushing through them trains nothing useful and risks damage. It helps to decide in advance what your stopping signals are, because in the moment the temptation to finish the page is strong. Mental practice One of the most underused tools in music education is mental practice: rehearsing music in the mind without physically playing. This includes silently reading through the score while imagining the sound, visualising the physical movements, and imagining the sensation of playing. Research in music and in sport has found that mental practice improves performance, although usually less than physical practice, and that combinations of physical and mental practice can approach the results of physical practice alone. For a student concerned about load, the value is obvious. Mental practice allows learning to continue while the body rests. It is an excellent way to use the break between physical practice sessions, to prepare while travelling, and to keep repertoire moving during recovery from an injury. It also tends to reveal gaps in knowledge: passages that your fingers know but your mind does not, which are exactly the passages likely to fall apart under pressure. The pianist Walter Gieseking was famous for learning scores away from the keyboard, and many professionals report learning a great deal of repertoire this way. Tension and technique Among the most important determinants of load is how much effort you use to play. Two students playing the same passage may impose very different loads on their forearms, depending on whether they grip, press and brace or play with the minimum force needed. Excess tension is widespread among musicians, often unnoticed, and it can be the difference between a sustainable technique and an injurious one. Common sites of excess tension include: • the shoulders, which rise towards the ears, particularly under pressure or in difficult passages; • the jaw and tongue, clenched in concentration; • the thumb, squeezing against the instrument or the bow; • the wrist, locked to provide stability; • the neck, clamping a violin or viola rather than letting it rest; • the breath, held during difficult passages by players of every instrument, not just winds and singers. Learning to notice and release tension is a skill, and like other skills it improves with practice. Some students find it helpful to do brief body scans during practice breaks, checking each area in turn. Others use a mirror or record themselves on video, which can be revealing. Somatic methods such as the Alexander Technique and the Feldenkrais Method are widely taught in music schools and are valued by many musicians for developing awareness of habitual tension. The research evidence for their effects on musicians is modest and mostly based on small studies, but they are low in risk and many students find them useful. Technique changes should be made with your teacher. If you suspect that some aspect of your technique is causing strain, raise it in a lesson. A good teacher will welcome the question. If your teacher dismisses physical concerns or insists that pain is part of learning, that is important information too, and it may be worth seeking a second opinion from another teacher or from a clinician experienced with musicians. Setup and ergonomics The physical fit between player and instrument deserves more attention than it usually gets. An instrument that is too large, too heavy or poorly adjusted for your body increases load with every note. For string players, the fit of the chin rest and shoulder rest can make an enormous difference to neck and shoulder strain; there is no universal right answer, and experimenting with different models and heights, ideally with your teacher's guidance, is worthwhile. Players with smaller hands may benefit from a smaller-bodied viola or a cello with a shorter string length. For wind players, thumb rests, neck straps, harnesses and supports can transfer weight away from the right thumb. Guitarists can adjust footstools, supports and strap heights. Pianists should make sure the bench height lets the forearms be roughly level with the keys, and should be aware that playing on heavy actions or poorly regulated instruments increases load. Chairs matter for everyone who sits to play. Many practice rooms and rehearsal spaces have chairs that tilt backwards, encouraging slouching. A chair with a flat or slightly forward-tilting seat at an appropriate height, allowing the feet to rest flat on the floor, supports a more balanced posture. Some players use a wedge cushion. Beyond the instrument, look at the rest of your day. A laptop used for hours on a table at the wrong height loads the neck and forearms. Raising the screen and using a separate keyboard can help. Carrying a heavy instrument case on one shoulder, day after day, adds asymmetric load; backpack-style straps distribute weight better, and case wheels help for large instruments. Fitness and recovery Musicians have historically neglected general fitness, sometimes on the theory that physical training might stiffen the hands or interfere with technique. There is no good evidence for that fear, and there are good reasons to think the opposite. Strength in the shoulders, upper back and core supports the arms during long periods of playing. Aerobic fitness improves circulation and recovery and is associated with better sleep, mood and stress tolerance. Some researchers and clinicians in performing arts medicine have argued that targeted strength and conditioning should be a standard part of music training, as it is for athletes. A sensible programme for most students includes regular aerobic exercise, such as walking, cycling, swimming or running, and two or three sessions a week of strength training that emphasises the upper back, shoulders and trunk. Students who are unfamiliar with strength training should start with light loads under guidance and progress gradually. The same load-and-capacity principles apply to the gym as to the practice room. Recovery completes the picture. Sleep is when tissues repair and when the brain consolidates motor learning. The research on sleep and motor skill consolidation, some of it using musical tasks such as finger-tapping sequences, suggests that learning continues offline, during sleep, after practice ends. A student who cuts sleep to fit in more practice may be trading away some of the benefit of the practice itself. Regular meals, adequate hydration and sensible limits on alcohol also support recovery. Keeping a practice log A practice log is the simplest tool for managing load, and one of the most revealing. Most musicians have only a vague idea of how much they actually play, and memory tends to smooth out the spikes that matter most. A useful log need not be elaborate. For each day, record the minutes of individual practice, the minutes of ensemble rehearsal, lessons and any other playing, and a rough note of the content: technical work, new repertoire, run-throughs, sight-reading. Add a line for how the body felt, perhaps a simple rating from zero to ten for any discomfort, and note the hours of sleep. A notebook in the instrument case or a note on your phone is enough. After a few weeks, patterns become visible. You may see that your practice time jumps sharply on weekends, or collapses during exam weeks and then rebounds. You may notice that discomfort appears two days after long orchestra rehearsals, or after days of intensive work on a particular study. You may see that the weeks when you slept least were the weeks your forearm complained. This information is valuable for planning and for any conversation with a teacher or clinician. The log also serves musical purposes. Recording what you practised, and what worked, helps you plan the next session and see progress that is otherwise invisible from day to day. Many teachers recommend practice journals for exactly this reason. Planning towards a performance The run-up to a jury, recital or audition is the time when load management most often breaks down, and it repays careful planning. Suppose a student has a jury in eight weeks, with a programme of three contrasting works. A sensible plan might divide the period into phases. In the first two or three weeks, the priority is learning. The student works through all of the repertoire, identifying the hardest passages and giving them regular, short, focused attention every day rather than long drilling sessions. Practice time rises steadily, if it needs to rise at all. Slow practice, mental practice and score study carry much of the load. In the middle weeks, the priority shifts to consolidation and integration. Tempos come up gradually. The student begins playing longer sections and whole movements, and starts regular run-throughs of individual pieces. Practice time reaches its planned peak and stays there rather than continuing to climb. In the final two or three weeks, the priority becomes performance. The student plays the complete programme through regularly, performs for friends and in studio class, records run-throughs and listens back, and practises in the jury room if possible. Total practice time holds steady or even falls a little, because the goal is no longer to acquire new material but to make what is already learned reliable under pressure. The last few days before the jury are lighter still, much as athletes taper before a competition, so that the body and mind arrive fresh. The most common departure from this pattern is the late surge: a student who fell behind in the early weeks and tries to catch up in the final fortnight with long days of intensive practice. It is the pattern most likely to produce injury, and it also tends to produce poor performances, because the repertoire never has time to settle. The best protection against it is starting early, which means being honest in the first weeks about how much work the programme really requires. A realistic day Putting this together, a sustainable practice day for a music major during a normal part of the semester might look something like this. A warm-up of ten minutes, starting with easy material. Two or three focused sessions of forty-five to sixty minutes, each broken into shorter blocks with brief breaks, spread across the day rather than concatenated, with a mix of technical work, repertoire and slower, lighter material. Mental practice or score study in between. Ensemble rehearsals counted as part of the total, with individual practice reduced on heavy rehearsal days. Some physical activity. Reasonable sleep. The precise numbers will vary with the instrument, the student's stage of training and the time of year. What matters is the structure: planned rather than improvised, gradual rather than spiky, distributed rather than crammed, and responsive to the signals the body sends. The next chapter deals with what to do when those signals indicate that something has already started to go wrong. Hashtags: #TheHealthyMusician #PerformingArtsHealth #PlayingRelatedMusculoskeletalDisorders #LoadAndCapacity #OveruseInjuries #PracticeLoadManagement #MusicianInjuryPrevention #MusculoskeletalHealth #Tendinopathy #NerveCompression #VocalHealth #FocalDystonia #PerformanceAnxiety #HearingConservation #NoiseInducedHearingLoss #PracticeRoomSafety #DeliberatePractice #MentalPractice #PracticeBreaks #MusicalErgonomics #TechniqueAndTension #StrengthAndConditioning #RecoveryAndSleep #PerformancePreparation #FutureOfMusicianWellness

Business ecosystems and digital platforms now organise a large share of economic activity, from mobile software and cloud computing to music, health care and smart cities. At the centre of many of these ecosystems sits an anchor firm that does not own most of the value being created, yet decides who may join, which tools partners receive, how quality is policed and how the money is shared. This article examines how such anchor firms govern open and closed ecosystems, and how the same governance choices that encourage partner innovation can also raise barriers for new competitors. Using an integrative review of recent management, information systems and competition policy research published mainly between 2021 and 2024, supported by well documented illustrations from the public record, the article builds a conceptual framework that links four governance levers (access rules, boundary resources, rules and quality control, and value sharing) to two outcomes: ecosystem innovation and barriers to entry. It proposes a typology of four ecosystem designs, explains how anchor firms manage partners in tiers, and traces a common life cycle in which governance tends to drift from openness toward control. The analysis shows that openness and closedness are not opposites but adjustable settings, that the innovation loop of indirect network effects is also the main source of entry barriers, and that regulation is increasingly part of the governance environment. The article closes with lessons for students, managers and policy makers, and with the limits of a conceptual study. Keywords: business ecosystems, platform governance, ecosystem orchestration, openness, complementors, barriers to entry, network effects, competition policy 1. Introduction Think about the last time you installed an app on your phone. You probably did not think about governance at all. You searched, tapped a button, and the app arrived. Yet behind that simple moment sits a carefully designed system of rules. Someone decided which developers are allowed to publish, which software tools they can use, what content is banned, how apps are ranked in search results, and what share of each payment goes to the store owner. That someone is the anchor firm of a #business_ecosystem, and the set of choices it makes is what scholars now call #platform_governance. Over the last two decades, many of the most valuable companies in the world have built their position not mainly by making better products alone, but by organising networks of other firms around a shared technology. Gawer (2022) describes platforms and ecosystems as the dominant organisational forms of the digital age, sitting alongside the older forms of markets and hierarchies. Instead of owning every piece of value, the anchor firm builds a core, such as an operating system, a marketplace or a cloud service, and invites outside firms to add complements. These outside firms are usually called #complementors. Their apps, services, devices and content make the core more attractive to users, and more users make the core more attractive to complementors. This self-reinforcing pattern is known as #indirect_network_effects, and it lies behind both the remarkable growth of platform ecosystems and the growing concern about their power. This creates a puzzle that sits at the heart of this article. An anchor firm needs partners to be creative, to invest their own money and to take risks. To attract them, it must offer a degree of openness: access to its users, its tools and its technology. But the anchor firm also wants to keep enough control to protect quality, to capture a fair share of value and to defend its position against rivals. Too much openness and the firm may lose control of its own ecosystem, or see rivals free ride on what it has built. Too much control and partners may leave, slow down, or turn to a competing ecosystem. The way a firm resolves this tension shapes not only its own success but also how much innovation happens in the ecosystem and how hard it is for a new competitor to enter the market. The research focus of this article is therefore twofold. First, it evaluates strategies for orchestrating closed and open business ecosystems. Second, it examines how anchor firms manage multi-partner networks in ways that drive innovation while, at the same time, creating barriers to entry for new competitors. The second point is important because these two outcomes are often treated separately. Management research has tended to celebrate the innovation side, while competition law and economics have focused on the market power side. As Jacobides and Lianos (2021) argue, the two conversations need to meet, because the same structures that make ecosystems productive can also make them hard to challenge. Three research questions guide the discussion. The first question asks how anchor firms combine governance levers to create more open or more closed ecosystem designs, and what each design offers and costs. The second question asks how anchor firms coordinate large networks of very different partners, ranging from global corporations to individual developers, without the formal authority of a manager over employees. The third question asks through which mechanisms governance that supports innovation can also make it harder for new competitors to enter. Answering these questions requires combining ideas from several fields, because no single discipline holds all the pieces. Strategy research explains the choices of the anchor firm, information systems research explains the technical tools through which those choices are put into effect, and economics and law explain the wider effects on competition and on society. The article makes three contributions for student readers. First, it brings together a scattered and fast growing body of recent research into one clear framework that links governance levers to outcomes. Second, it offers a simple typology that helps readers compare different ecosystem designs rather than treating "open" and "closed" as a single on and off switch. Third, it explains in plain terms how innovation and entry barriers are linked through the same mechanism, which helps students reason about current debates on regulating large technology firms. The rest of the article is organised as follows. Section 2 reviews the literature on business ecosystems, platforms and governance. Section 3 explains the approach used. Section 4 presents the conceptual framework. Section 5 develops the analysis in several themed parts, covering ecosystem designs, tools of orchestration, the management of partners in tiers, the link between innovation and entry barriers, the life cycle of governance, and the role of regulation. Section 6 discusses implications for practice and policy, and Section 7 concludes with the limits of the study and ideas for future research. 2. Literature Review 2.1 From value chains to ecosystems For most of the twentieth century, business strategy was explained through the idea of the #value_chain. A firm bought inputs from suppliers, transformed them and sold outputs to customers. Competition happened between firms, or between chains of firms linked by contracts. This picture still describes many industries, but it struggles to explain situations in which value depends on many #interdependent firms whose products only work well together, without one firm being able to order the others around. The concept of the business ecosystem was developed to capture these situations. In simple terms, an ecosystem is a group of firms and other actors whose offerings are interdependent and must be aligned for a joint #value_proposition to reach the customer. Adner (2021) argues that the key strategic question in such settings is not only how to beat a rival but how to win the right game, which means building and aligning the set of partners needed to deliver a new value proposition, and choosing the right moment and sequence to do so. In his view, many promising innovations fail not because the product is weak but because the surrounding partners are not ready or not willing to play their roles. Jacobides (2022) adds that ecosystems become especially important as industries #digitise and begin to collide. When a car becomes a computer on wheels, or a bank becomes a mobile app, firms from previously separate industries suddenly compete and cooperate in the same space. In these conditions, firms need a way to think about which ecosystem they want to build or join, and what role they want to play inside it. Holgersson et al. (2022) similarly describe a set of forces that push ecosystems to form, grow and change over time, reminding readers that ecosystems are not fixed structures but evolving arrangements. 2.2 Platforms as the core of many ecosystems Not every ecosystem has a #digital_platform at its centre, but many of the most influential ones do. A platform, in this literature, is a foundation of shared technology and rules on which other actors can build complements and through which they can reach users. Cennamo (2021) explains that competing in digital markets requires a platform based perspective, because firms increasingly compete through the systems of complements they can mobilise rather than through standalone products. Rietveld and Schilling (2021), in a broad review of #platform_competition research across economics, strategy and information systems, show that outcomes in these markets depend heavily on network effects, on the quality and variety of #complements, and on the strategic choices platform owners make about pricing, openness and timing. Gawer (2021) adds an important idea about the boundaries of platforms. She argues that a platform's boundary is not one line but three related dimensions: the scope of the firm itself (what it owns and does in house), the sides of the platform (which groups of users and partners it brings together), and its digital interfaces (the technical connections through which outsiders plug in). Changing any one of these boundaries changes the platform's strategy. For example, a platform that starts producing its own complements expands its firm scope, and this may change how its complementors feel about investing in it. 2.3 Governance as a meta-organisation problem Once a platform has outside partners, someone has to make rules for them. This is where governance enters the picture. Traditional ideas of governance came from two places: the hierarchy inside a firm, where managers can give orders, and the market, where prices and contracts coordinate exchange. Ecosystems fit neither model well. The anchor firm cannot simply command thousands of independent developers, but it also does far more than sign individual contracts with each one. Kretschmer et al. (2022) propose that platform ecosystems are best understood as #meta_organisations, meaning organisations whose members are themselves autonomous organisations. In a meta-organisation, the leading firm shapes behaviour through membership rules, shared standards and decision rights, rather than through employment relationships. Chen et al. (2022), in a detailed review of platform governance and design, take the same view and organise the field around questions of who decides, how control is exercised, and how value is shared. These reviews provide the foundation for the governance levers used in the framework of this article. Empirical studies show that governance choices have real consequences. Zhang et al. (2022) examine platform gatekeeping, meaning the rules that decide which complements are allowed in, often called #gatekeeping, and show that gatekeeping decisions change how far complementors are willing to share knowledge with one another. Adam et al. (2023) study #input_control, the screening of who and what may enter a platform, and find that such control sends signals that influence whether complementors want to join at all. Panico and Cennamo (2022) show that the best strategy for a platform also depends on what users value, for example whether they care more about variety or about quality, which shapes the interaction between the platform and its partners. 2.4 Orchestration and ecosystem leadership A related stream of work focuses on #ecosystem_orchestration, the active work of building, coordinating and steering an ecosystem. Autio (2022) presents a multi-layered framework in which orchestration operates across several layers at once, including the technical architecture, the economic incentives and the shared understandings that hold an ecosystem together. Foss et al. (2023) treat ecosystem leadership as a #dynamic_capability, a set of skills that allow a firm to sense opportunities, design the ecosystem and reconfigure it as conditions change. Linde et al. (2021) develop a similar capability based view for smart city initiatives, where public bodies, technology providers and service firms must be coordinated without any single owner. Not every ecosystem is orchestrated by a single firm. Lingens et al. (2022) show that firms can act as "loners" that keep all orchestration tasks to themselves, or as "team players" that share these tasks with other ecosystem actors. Khanagha et al. (2022), studying Cisco's attempt to create a new platform for fog computing, show that new platforms can emerge through #mutualism, where potential rivals cooperate because each needs the others for the platform to take off. Thomas and Ritala (2022) add that young ecosystems need #legitimacy, which is built through collective action among many actors and not only through the efforts of the leading firm. 2.5 Power, dependence and competition The final stream deals with the darker side of ecosystem leadership. Cutolo and Kenney (2021) describe the position of #platform_dependent_entrepreneurs, small firms whose entire business runs through one platform. They show that these firms face deep power asymmetries, since the platform can change rules, fees or ranking algorithms at any time, often with little warning. Jacobides et al. (2024) argue that ecosystems are built to manage externalities and complementarities between actors, but that the same structures can generate failures from within, for example when the anchor firm captures too much value or when partners stop investing. On the policy side, Jacobides and Lianos (2021) explain why traditional #competition_law, which focuses on single markets, struggles to deal with ecosystem power that spreads across many connected markets. Parker et al. (2021) analyse platform mergers and show how #acquisitions of small firms can strengthen an ecosystem leader's position in ways that older merger rules may not capture. Cusumano et al. (2021) ask whether platforms can regulate themselves, and conclude that self-regulation can help but tends to work best when the threat of government action is credible. Ozalp et al. (2022) document how large technology platforms enter highly regulated sectors such as health care and education, a process they describe as "digital colonization", while Geurts and Cepa (2023) show how platformisation in the music industry drove #ecosystem_envelopment, in which a platform extends its reach to absorb neighbouring parts of an industry. 2.6 The gap this article addresses Taken together, these studies offer rich insight but are spread across different fields that do not always talk to each other. Strategy research explains how to orchestrate. Information systems research explains how technical interfaces and controls work. Competition policy research explains why ecosystem power worries regulators. Few accessible works bring these strands into a single picture that a student can use to reason about a real case. In particular, the link between governance for innovation and governance as an entry barrier is usually mentioned in passing rather than explained step by step. This article aims to fill that gap. 3. Approach and Method This article is a conceptual study based on an integrative literature review. An integrative review differs from a systematic review in that its aim is not to count every published paper on a topic but to bring together insights from different fields to build a new framework. This approach suits the topic, because ecosystem governance sits at the meeting point of strategy, information systems, economics and law. The review focused on peer reviewed journal articles and scholarly books published mainly between 2021 and 2024, with one book from 2024 on design and modularity. Sources were chosen from leading journals in management and related fields, including Strategic Management Journal, Journal of Management, Research Policy, Long Range Planning, California Management Review, Industrial and Corporate Change, and Information Systems Journal. Priority was given to studies that either reviewed a whole sub-field or offered a framework or empirical finding directly related to openness, governance, orchestration, or competition. The insights from these sources were grouped into themes: ecosystem structure, governance levers, orchestration capabilities, partner management, innovation outcomes, and competitive and regulatory effects. The framework presented in Section 4 was built by linking these themes. To make the ideas concrete, the analysis also uses a small number of well known illustrations from the public record, such as the design of mobile app stores and open source software communities. These illustrations are used to explain the concepts, not to test them, and the article does not claim to provide new empirical data. Readers should keep in mind two limits of this approach. First, a conceptual framework simplifies reality, and real ecosystems often mix features from several categories. Second, the literature itself is still developing quickly, and some findings come from particular settings, such as one platform or one industry, that may not apply everywhere. 4. Conceptual Framework The framework at the centre of this article links what anchor firms do (their governance levers) to what ecosystems produce (innovation and entry barriers). Figure 1 summarises it. Figure 1. Conceptual framework linking governance levers, orchestration and ecosystem outcomes. 4.1 The four governance levers The first element of the framework is a set of four levers that anchor firms can adjust. These levers draw on the reviews by Chen et al. (2022) and Kretschmer et al. (2022), simplified for clarity. The first lever is #access_rules. These decide who may join the ecosystem and on what terms. Access can be wide, as when any developer can sign up for a small fee, or narrow, as when only invited partners are allowed. Access rules also include the screening that happens before a partner or product is admitted, which Adam et al. (2023) call input control. The second lever is #boundary_resources. These are the tools, #interfaces and documents that let outsiders build on the platform, such as application programming interfaces (#APIs), software development kits, technical standards and design guidelines. Boundary resources decide what complementors can actually do. A platform may be formally open to everyone but share only limited tools, which in practice keeps partners on a short leash. Gawer (2021) places these digital interfaces at the heart of how platform boundaries are drawn. The third lever is rules and quality control. This covers the ongoing policing of behaviour inside the ecosystem: review processes, content policies, ranking and search algorithms, and rules about how partners may use customer data. Zhang et al. (2022) show that such gatekeeping shapes relationships not only between the platform and each partner but also among the partners themselves. The fourth lever is #value_sharing. This covers how money and other benefits are divided: commissions, revenue splits, fees for access, subsidies for early partners, and rules about whether partners may steer customers to pay outside the platform. Value sharing determines whether partners believe their investment is worthwhile. 4.2 Orchestration as a design choice The second element of the framework is orchestration itself. The anchor firm combines the four levers into an overall design. The key design choice is the degree of openness. Importantly, openness is not one setting but a combination. A firm may be very open on access but closed on boundary resources, or generous on value sharing but strict on quality control. This is why the framework treats openness as the result of several decisions rather than as a single on and off switch. Orchestration also includes the capabilities discussed by Foss et al. (2023) and Autio (2022): the ability to sense what partners need, to design incentives, and to adjust the levers as the ecosystem grows. These capabilities explain why two firms using similar rules may still achieve very different results. 4.3 The two outcomes and the feedback between them The third element is the pair of outcomes. The first outcome is #ecosystem_innovation: the variety, speed and quality of new complements and the willingness of partners to invest. The second outcome is #barriers_to_entry: the difficulty a new competitor faces in building a rival ecosystem or replacing the anchor firm. The dashed arrow in Figure 1 shows that these outcomes are connected. Successful innovation attracts users and partners, which strengthens network effects and in turn raises the barrier for newcomers. This feedback is the central idea of the article and is developed in Section 5.4. Finally, the framework recognises that orchestration happens in a context. Regulation, rival ecosystems and technological change all influence what governance choices are possible and which ones pay off. As later sections show, regulation in particular has become an active force in the governance of large ecosystems. 5. Analysis and Discussion 5.1 Four designs of ecosystem governance To compare ecosystems in a useful way, it helps to separate two questions that are often blended together. The first question is how open access is: can many partners join freely, or only a selected few? The second question is who controls the core: does the anchor firm hold the key technology and the rules, or are these shared among members? Placing these two questions on two axes produces four ecosystem designs, shown in Figure 2. Figure 2. A typology of ecosystem governance designs based on openness of access and control of the core. The first design is the #walled_garden. Access is limited to partners the anchor firm chooses, and the anchor firm controls the core technology and the interfaces. This design offers strong control over quality and user experience, and it allows the anchor to capture a large share of value. The cost is lower variety, since only a small number of partners contribute, and a risk that partners feel they are working for the anchor firm rather than with it. Some closed device systems, in which only approved accessory makers or service providers are allowed to connect, fit this pattern. The second design is the #curated_platform. Access is open to large numbers of complementors, often anyone who accepts the terms, but the anchor firm keeps tight control of the core and of the rules. Mobile app stores are a familiar example. Almost any developer can apply, yet the store owner sets review rules, decides on ranking, controls payment systems and can change the terms. This design combines the variety of openness with the control of a closed system. It is arguably the most commercially successful design of the digital age, and it is also the one that has attracted the most regulatory attention. The third design is the #open_commons. Access is open and control of the core is shared, often through a community, a foundation or open licences. #Open_source software projects are the classic example. Here the anchor firm, if there is one, must lead through influence rather than ownership. The advantage is very high participation and trust. The disadvantage is that the leading firm finds it harder to capture value directly and must usually profit from related products or services instead. The fourth design is the club or #consortium. Access is limited to selected members, but control is shared through joint standards and collective decision making. Industry alliances that develop common technical standards often work this way. Khanagha et al. (2022) show how a large firm such as Cisco worked with other firms, including potential competitors, to establish a new platform space for fog computing. In such settings, firms cooperate because none of them can create the market alone. Three lessons follow from this typology. First, "open versus closed" is too simple. A curated platform is open in one sense and closed in another, and much of the debate about platforms is really about this mixed design. Second, firms can move between designs. A firm may start as a consortium member to build legitimacy, then try to become the orchestrator of a curated platform once the market takes off. Third, each design creates a different kind of entry barrier. Walled gardens rely on exclusive control and integration, curated platforms on scale and network effects, open commons on community loyalty and shared standards, and consortia on collective agreements that outsiders may find hard to join. 5.2 The orchestration toolkit: how levers are used in practice The framework identifies four levers. This section looks more closely at how anchor firms use each one, and what trade-offs arise. Access rules. An anchor firm that wants rapid growth often lowers access barriers in the early stages. Fees are low or zero, sign-up is easy, and screening is light. As the ecosystem matures, access rules usually become stricter. The reason is simple. When there are few complements, every new one adds value. When there are millions, low quality entries start to clutter the system, and users find it harder to discover good products. Adam et al. (2023) show that input control can actually attract complementors, because strict screening signals that the platform is serious about quality and that being admitted is worth something. This finding is useful because it shows that openness is not always what partners want. Many partners prefer a well run ecosystem to a free for all. Boundary resources. The tools a platform shares decide what kinds of innovation are possible. A platform that offers rich, stable and well documented interfaces lets partners build ambitious products. A platform that holds back key features for its own use keeps an advantage for itself. Baldwin (2024) explains, in her work on design rules and modularity, that the way a technology is divided into modules shapes the way organisations around it are structured. The decision about where to draw the line between the core, which the anchor keeps, and the modules, which partners build, is therefore one of the most strategic choices in ecosystem design. If the line is drawn so that the most valuable functions stay in the core, partners can innovate only at the edges. Rules and quality control. Once partners are inside, the anchor firm must keep the ecosystem healthy. Review processes remove harmful or low quality products. #Ranking_algorithms decide which complements users see first. Data rules decide what partners may learn about users and what the anchor itself may learn about partners. Each of these rules has a double edge. Ranking can reward quality, but it can also lead to #self_preferencing, favouring the anchor's own products. Data rules can protect privacy, but they can also prevent partners from building direct relationships with their customers. Zhang et al. (2022) remind us that governance affects the social life of the ecosystem as well, because it changes how willing partners are to help each other. Value sharing. Money is the clearest signal of how an anchor firm sees its partners. A widely discussed example is the standard commission charged by major mobile app stores, long set at 30 percent of the price of many digital purchases. At the start of 2021, Apple began applying a reduced rate of 15 percent for smaller developers through a small business programme, and similar reductions appeared elsewhere. Such changes show that value sharing is not fixed. It responds to partner pressure, public criticism and regulation. Uzunca et al. (2022) show that #governance_rigidity, the tendency to keep rules unchanged even as an industry evolves, can damage a platform's ability to capture value over time. In other words, a firm that refuses to adjust its terms may eventually lose more than it gains. The broader lesson is that the four #governance_levers must be used together. A firm that is generous on access but stingy on value sharing may attract many partners who then do little. A firm that shares value generously but provides poor tools may find partners unable to build anything worthwhile. Good orchestration means aligning the levers so that they send a consistent message about what kind of partnership the ecosystem offers. 5.3 Managing multi-partner networks in tiers Large ecosystems contain very different kinds of partners. Some are global firms with their own strong brands. Others are small studios or single developers. Some build products that are essential to the ecosystem's appeal, while others serve small niches. It would be inefficient and unfair to treat them all the same way, and in practice anchor firms rarely do. Instead, they manage partners in tiers, as shown in Figure 3. Figure 3. Tiered management of a multi-partner network around an anchor firm. The inner tier is made up of strategic partners. These #strategic_partners are firms whose contributions are central to the ecosystem's value proposition, such as major hardware makers, large content owners or key technology suppliers. Relationships with this tier are managed through tailored contracts, shared technology roadmaps, joint investment and regular executive contact. The anchor firm may give these partners early access to new tools or special commercial terms. In return, it expects commitment and alignment with its own plans. The middle tier consists of #complementors who work under standard rules. They have access to the same tools and terms as everyone else at their level, but they are large enough or important enough to receive some direct support, such as partner programmes, technical help or marketing opportunities. The relationship is more formal and less personal than in the inner tier. The outer tier includes the #long_tail of small complementors and, in many platforms, the users themselves. These partners are managed almost entirely through automated systems: self-service sign-up, online documentation, standard contracts and algorithmic review. The anchor firm has little personal contact with them. Cutolo and Kenney (2021) show that this distance is a source of risk for small firms, since they may depend completely on a platform that does not know them and can change its terms without consultation. Tiering helps the anchor firm in three ways. First, it makes coordination manageable, since only a small number of relationships require personal attention. Second, it allows the firm to focus its limited resources on the partners who matter most for the ecosystem's success. Third, it creates a ladder that partners can climb, which motivates investment. A small developer that grows may move into a higher tier with better support. Tiering also has strategic effects that relate to entry barriers. Strategic partners in the inner tier often have commitments that make it costly to support a rival ecosystem at the same time. Long term contracts, joint investments and early access arrangements all raise the cost of switching. Meanwhile, the outer tier is held in place by the sheer scale of users that only the leading ecosystem can offer. A new competitor must therefore persuade both the strategic partners, who are bound by commitments, and the long tail, who follow the users. This combination makes the network as a whole hard to pull away. The management of tiers also raises questions about who does the orchestrating. Lingens et al. (2022) show that orchestration tasks can be shared. In some ecosystems, a strategic partner takes on part of the coordination, for example by managing a sub-group of smaller suppliers. In others, industry bodies or foundations handle standards while the anchor firm manages commercial relationships. Sharing tasks can reduce the anchor's burden and increase partner trust, but it also reduces the anchor's control. The choice between acting as a loner or a team player is therefore a governance choice in its own right. 5.4 How innovation and entry barriers grow from the same root This section develops the central argument of the article: the mechanism that drives innovation in ecosystems is also the mechanism that creates entry barriers. Figure 4 shows this loop. Figure 4. The innovation loop of indirect network effects and how it becomes an entry barrier. The loop works like this. When more complementors join and invest, the ecosystem offers more variety and more new products. This attracts more users and generates more usage data. A larger user base and richer data make the ecosystem more attractive to new complementors, who join and invest, and the cycle repeats. Rietveld and Schilling (2021) show that this kind of dynamic is a central driver of platform competition across many industries. It explains why successful ecosystems can grow very quickly and why they often become more innovative as they grow. Now consider the same loop from the point of view of a new competitor. To offer users a real alternative, the newcomer must match not just one product but the whole system of complements. Users will not switch to an ecosystem that lacks the apps, services or content they already rely on. Complementors will not build for an ecosystem that lacks users. The newcomer faces what is often called a #chicken_and_egg_problem, and the leading ecosystem's past success makes this problem harder to solve. In this sense, #network_effects are both the engine of innovation and the moat around the castle. Governance turns this natural advantage into a more deliberate barrier in at least four ways. The first way is through #switching_costs. When partners build products using a platform's specific tools, those products often cannot be moved easily to another platform. When users store their data, purchases and social connections inside an ecosystem, leaving means losing them. Governance choices about #interoperability and #data_portability decide how high these costs are. An anchor firm that makes it difficult to export data or to use cross-platform tools raises the cost of switching for everyone. The second way is through #data_advantages. The leading ecosystem collects information about what users want, which products succeed and how partners behave. If governance rules let the anchor firm use this data while limiting what partners and rivals can see, the anchor gains insight that newcomers cannot match. Jacobides et al. (2024) note that the management of such externalities is part of what ecosystems are designed to do, but it can also concentrate advantages in the hands of the orchestrator. The third way is through control of interfaces. If the anchor firm controls key interfaces, such as the operating system features that apps need, the payment system or the default settings on a device, it can decide how easily rival services reach users. Gawer (2021) explains that changes in digital interfaces redraw a platform's boundaries, and they can be used to include or exclude others. The fourth way is through #envelopment. A strong ecosystem can extend into neighbouring markets by bundling new services with its existing core. Geurts and Cepa (2023) show how platformisation in the music industry allowed platforms to absorb functions that were once performed by separate firms. Ozalp et al. (2022) document similar moves by large technology firms into health care and education. Envelopment means that a newcomer in one market may suddenly face competition from an ecosystem leader that can offer the same service as part of a larger package, often at little or no extra cost to users. It is important to note that none of these mechanisms is automatically harmful. Switching costs can reflect real investment in quality. Data can improve products. Control of interfaces can protect security. Bundling can make life easier for users. The problem arises when these mechanisms are used mainly to block rivals rather than to serve users and partners. Jacobides and Lianos (2021) argue that telling the difference requires looking at the ecosystem as a whole, rather than at individual products in isolation. This is precisely what makes ecosystem cases difficult for competition authorities. There is also a cost to the anchor firm itself when barriers become too high. Jacobides et al. (2024) describe how ecosystems can fail from within when the orchestrator captures too much of the value or when partners lose faith in fair treatment. If complementors believe that any successful product will be copied or squeezed by the platform owner, they may stop investing. Innovation then slows, and the ecosystem may become vulnerable to a rival that offers better terms. In other words, an anchor firm that uses governance only to build barriers may weaken the very innovation loop that made it strong. 5.5 Open strategies that still create barriers It might seem that open ecosystems create weaker barriers than closed ones. In practice, this is not always true. Openness can be a powerful way to build a position that rivals find hard to challenge. Consider the strategy of releasing core technology under an open licence while keeping control of key complementary assets. The widely used mobile operating system Android is built on an open source project that any manufacturer can use. Yet the most popular services that run on top of it, including the main app store and other widely used applications, have been licensed by Google under separate agreements. In 2018, the European Commission fined Google for illegal practices related to these Android agreements, a decision that showed how openness at one level can coexist with tight #control at another. The lesson for students is that openness and control are often layered. A firm may open the base layer to win adoption and keep the layer above it closed to capture value. A second open strategy is #standard_setting. When a firm helps to create an open industry standard, it can shape that standard in ways that suit its own technology and capabilities. Other firms adopt the standard because it is open, but the leading firm often keeps an advantage because it understands the standard best and was first to build products around it. Thomas and Ritala (2022) show that building legitimacy for a new ecosystem is a collective effort. Firms that lead this effort can gain influence that lasts long after the standard is established. A third open strategy is the creation of large communities of developers, users or contributors. A firm that hosts a community can benefit from the loyalty, knowledge and contributions of its members, even when the products they create are freely available. A rival who wants to compete must rebuild not only the technology but also the community, which is much harder. These examples show that the choice between open and closed governance is not a choice between building barriers and not building them. Both approaches can create barriers. They simply build them in different ways. Closed designs rely more on ownership and exclusion, while open designs rely more on adoption, standards and community. 5.6 The life cycle of ecosystem governance Governance choices change over time. Many ecosystems follow a recognisable path, shown in Figure 5, although not every ecosystem passes through every stage. Figure 5. A typical life cycle of ecosystem governance. In the launch stage, the anchor firm's main problem is to attract the first partners and users. It typically opens the doors wide, offers subsidies or favourable terms, and accepts lower quality in exchange for growth. Adner (2021) emphasises that the order in which partners are brought in matters greatly at this stage. A firm that starts by securing a few critical partners may build a stronger base than one that tries to attract everyone at once. Thomas and Ritala (2022) add that the ecosystem must win legitimacy among potential partners and users, which often requires collective effort. In the growth stage, the main problem shifts to managing quality and coordination. The anchor firm adds tools, standards and review processes. It builds partner programmes and begins to separate partners into tiers. Linde et al. (2021) show that this stage requires dynamic capabilities for orchestration, such as the ability to integrate new partners and to adapt structures as the ecosystem expands. In the maturity stage, the ecosystem is large and the anchor firm's position is strong. The temptation now is to tighten rules, raise fees and extend the platform into new areas, which is the point of greatest #value_capture. This is where governance most clearly becomes a tool for capturing value and for raising barriers. It is also where the risk of partner frustration grows. Cutolo and Kenney (2021) describe how dependent firms may feel trapped, unable to leave yet unhappy with their treatment. In the contest stage, the anchor firm faces pushback. This may come from rival ecosystems offering better terms, from large partners who threaten to leave or to build their own alternatives, or from regulators who decide that the ecosystem's power needs to be limited. Uzunca et al. (2022) suggest that firms whose governance is too rigid may struggle in this stage, because they cannot adjust fast enough to new conditions. The arrow at the bottom of Figure 5 captures a common pattern: governance tends to drift from openness toward control as an ecosystem matures. This drift is not inevitable. Competition or regulation can push it back, and some firms deliberately reopen parts of their ecosystems to renew innovation. But students should expect that the generous terms offered by a young ecosystem may not last, and partners should plan accordingly. 5.7 Evaluating the strategies side by side Having described the designs, the tools, the tiers and the life cycle, it is now possible to evaluate the main strategies against each other. A useful evaluation looks at five criteria: the amount of innovation a design tends to generate, the share of value the anchor firm can capture, the strength of the barrier it builds against new competitors, the level of risk it places on partners, and its exposure to regulation. No design scores well on every criterion, which is exactly why governance is a strategic choice rather than a technical detail. The walled garden usually generates moderate innovation, because the small number of chosen partners can work closely with the anchor and produce well integrated offerings, but the total variety remains limited. Value capture is high, because the anchor owns the core and negotiates with each partner individually. The entry barrier is strong but narrow. It rests on #vertical_integration and exclusive relationships rather than on a huge network, so a rival with a clearly better product can sometimes break through. Partner risk is moderate, since chosen partners usually have negotiated contracts that give them some protection. Regulatory exposure is generally lower than for curated platforms, unless the anchor firm already dominates its market. The curated platform generates high innovation, because it combines very wide access with tools and rules that make building on the platform attractive. Value capture is high, because the anchor controls the payment system, the ranking and the interfaces. The entry barrier is the strongest of all four designs, because it rests on the full innovation loop described in Section 5.4, with millions of users and complements locked into one system. Partner risk is high, especially for the long tail of small firms, who depend on rules they cannot influence. Regulatory exposure is also the highest, which explains why most new platform rules are aimed at firms using this design. The open commons generates high innovation, often of a different kind, since contributors can modify the core itself and not only the edges. Value capture by any single firm is low or indirect. Firms that lead open commons usually earn money from support, cloud hosting, hardware or premium features built on top of the shared core. The entry barrier is real but subtle. It rests on #community_loyalty, the depth of shared knowledge and the position of the leading firm as the most trusted contributor. Partner risk is low, because no single firm can easily change the rules for everyone. Regulatory exposure is low as well, although the commercial layers built on top of an open core may still attract attention, as the Android case shows. The consortium or club generates moderate innovation, usually focused on shared standards and infrastructure that no member could build alone. Value capture is spread across members according to their contributions and their bargaining power. The entry barrier depends on membership rules. A consortium that admits newcomers on fair terms creates only a weak barrier, while one that keeps outsiders away from essential standards can create a strong one. Partner risk is moderate, since members share decision rights but must accept collective compromises. Regulatory exposure arises mainly where cooperation among competitors risks turning into collusion, which is why consortia usually operate under careful legal guidance. Two broader points follow from this evaluation. First, the strategy that is best for the anchor firm in the short run, usually the curated platform at maturity, is not necessarily the one that is best for the ecosystem or for society in the long run. Second, firms are increasingly using #hybrid_governance, mixing elements of several designs in different layers of the same ecosystem. A firm might run an open commons at the technical base, a curated platform at the application layer and a closed set of strategic partnerships in hardware. Understanding these layers is essential for any accurate evaluation of a real ecosystem. 5.8 Regulation as part of the governance environment For most of the history of digital platforms, governance was something anchor firms decided for themselves. This is changing. In many jurisdictions, governments now treat the rules of large ecosystems as a matter of public interest. The most prominent example is the European Union's #Digital_Markets_Act, adopted in 2022. It designates very large platforms as #gatekeepers and imposes obligations on them, including rules on interoperability, self-preferencing, data use and the ability of business users to reach customers outside the platform. In 2023, the European Commission designated its first set of gatekeepers, including several of the world's largest technology firms, and their obligations began to apply in 2024. Other jurisdictions have introduced or debated similar rules. From the perspective of the framework in this article, regulation acts directly on the governance levers. Rules on interoperability affect boundary resources and switching costs. Rules on self-preferencing affect ranking and quality control. Rules on steering and payment affect value sharing. In effect, regulators are setting minimum standards of openness for the most powerful ecosystems. Cusumano et al. (2021) argue that platforms can and sometimes do practise #self_regulation, for example by removing harmful content or adjusting fees. However, they conclude that self-regulation tends to be most effective when firms believe that government action will follow if they fail to act. Parker et al. (2021) add that competition authorities need better tools for assessing platform mergers, because acquisitions of small firms can strengthen ecosystem power in ways that traditional rules may miss. For anchor firms, the lesson is that governance can no longer be designed in private. Choices about access, interfaces, data and fees are increasingly subject to public scrutiny. Firms that anticipate regulatory expectations and design fair governance from the start may face fewer conflicts later. For new competitors, regulation may lower some barriers, for example by forcing interoperability or data portability, though whether it will be enough to change market outcomes remains an open question. 5.9 Lessons from different settings Most of the discussion so far draws on examples from consumer technology, but the same governance questions appear in many other settings. In industrial and business to business markets, ecosystems often form around equipment makers, software providers and service firms. Here, closed or club designs are common, because partners need deep technical integration and long term commitments. Khanagha et al. (2022) show that in emerging technical fields, cooperation between potential rivals can be necessary to get a new platform off the ground. In public and civic settings, such as #smart_cities, there may be no single commercial owner. Linde et al. (2021) show that orchestration in these settings depends on capabilities for coordinating public bodies and private firms, and that legitimacy and trust are as important as technology. In developing economies, digital platforms can create new opportunities for #small_firms and workers, but also new dependencies. Bonina et al. (2021) highlight the role of platforms in development and call for attention to how governance choices affect inclusion and local value creation. In highly regulated industries such as health care and education, Ozalp et al. (2022) show that large technology platforms face particular challenges and opportunities as they enter. Existing rules and institutions can slow their progress but cannot always stop it. These different settings confirm that the four governance levers are widely applicable, but the best combination depends strongly on context. What works for a consumer app store may not work for an industrial alliance or a city partnership. 5.10 New frontiers: artificial intelligence and data ecosystems The governance questions discussed in this article are now appearing in a new field: ecosystems built around #artificial_intelligence models and large data resources. Firms that develop general purpose AI models increasingly offer them to outside developers through interfaces, so that other firms can build applications on top of them. In the language of this article, the model becomes the core, the interfaces become boundary resources, and the developers become complementors. The same choices therefore return. Some firms release #model_weights openly, allowing anyone to run and modify them, which resembles the open commons design. Others keep models closed and offer access only through paid interfaces with usage policies, which resembles the curated platform. Many combine the two, opening smaller or older models while keeping their most capable systems closed. Each choice has consequences for innovation, for safety and for competition. Open release can spread innovation widely and make it harder for one firm to dominate, but it reduces the provider's ability to control misuse. Closed access allows stronger rules on use and a clearer business model, but it concentrates power and creates dependence among the firms that build on top. The link between innovation and entry barriers also appears in this field. Developers who build on a particular model invest in learning its strengths, adjusting their products to it and connecting it to their data. These investments create switching costs. Usage of a model can generate feedback that helps improve it, which resembles the data advantage discussed in Section 5.4. At the same time, the very large cost of building leading models is itself a barrier to entry that sits outside platform governance altogether. Because this field is changing rapidly, research on it is still at an early stage, and students should treat confident claims about its future structure with caution. However, the framework offered here provides a useful starting point. Asking who may access the model, what tools they receive, what rules apply and how value is shared will help readers understand AI ecosystems in the same way as older platform ecosystems. 6. Implications 6.1 Implications for managers of anchor firms For managers who lead ecosystems, the analysis offers several practical lessons. First, treat openness as a set of separate dials rather than one switch. Decide deliberately how open to be on access, tools, rules and value sharing, and make sure the settings support each other. Second, invest in orchestration capabilities, since the same rules can produce very different results depending on how well they are applied and adjusted. Third, manage partners in tiers, but provide paths for partners to move up, so that the ecosystem remains attractive to new talent. Fourth, remember that barriers built through governance can weaken the innovation loop if partners lose trust. #Fair_treatment is not only an ethical issue but a strategic one. Fifth, anticipate regulation and design governance that can withstand public scrutiny. 6.2 Implications for complementors and new entrants For complementors, the analysis suggests the importance of understanding the governance of any ecosystem they join. Before investing heavily, they should ask how stable the rules are likely to be, how much control the anchor firm keeps over interfaces and data, and what would happen if the terms changed. Where possible, reducing dependence on a single platform, for example by building direct customer relationships or supporting several ecosystems, can limit risk. For new entrants who want to compete with an established ecosystem, the analysis suggests that matching a single product is rarely enough. Successful entry usually requires either a different value proposition that the incumbent cannot easily copy, a group of partners willing to commit early, or a change in the environment, such as new technology or new regulation, that lowers switching costs. Adner (2021) emphasises that changing the game, rather than playing the incumbent's game better, is often the most realistic path. 6.3 Implications for policy makers For policy makers, the analysis supports the view that #ecosystem_power cannot be understood by looking at individual markets alone. Jacobides and Lianos (2021) argue for approaches that consider the whole ecosystem, including the relationships between the anchor firm and its partners. The framework in this article suggests that regulation can usefully focus on the specific levers that turn innovation advantages into entry barriers, such as interoperability, data access and self-preferencing, while leaving room for the governance choices that genuinely support quality and innovation. 6.4 Implications for students For students, the main lesson is a way of seeing. When you look at any platform or ecosystem, ask four questions. Who can join? What tools do they get? What rules govern them? How is the value shared? The answers will tell you a great deal about how innovative the ecosystem is likely to be, how much power the anchor firm holds, and how hard it would be for someone new to compete. These questions apply equally to app stores, cloud services, online marketplaces, automotive software, industrial equipment and city partnerships. 6.5 Ethical considerations Ecosystem governance is not only a matter of strategy and competition. It also raises ethical questions that students should keep in view. When an anchor firm writes the rules for millions of partners and users, it exercises a kind of private power that affects livelihoods, access to information and the fairness of markets. Cutolo and Kenney (2021) show that small firms can lose most of their income overnight when a platform changes a ranking rule or suspends an account, often without a clear explanation or a way to appeal. Several principles can guide fairer governance. The first is #transparency, meaning that rules, ranking criteria and fee changes should be explained clearly and announced in advance. The second is #due_process, meaning that partners who are penalised should have a fair chance to understand the decision and to challenge it. The third is consistency, meaning that the anchor firm should apply the same rules to its own products as to those of its partners. The fourth is proportionality, meaning that controls on partners should be justified by real needs, such as security or quality, rather than by the wish to block competition. These principles are partly reflected in new regulations, but they can also be adopted voluntarily, and doing so may strengthen trust and the long term health of the ecosystem. 7. Conclusion This article has examined how anchor firms govern open and closed business ecosystems, and how the same governance choices that encourage partner innovation can also raise barriers for new competitors. Drawing on recent research from strategy, information systems and competition policy, it has offered a framework that links four governance levers, namely access rules, boundary resources, rules and quality control, and value sharing, to two outcomes, ecosystem innovation and barriers to entry. Four main findings emerge. First, openness and closedness are not opposite poles but combinations of settings across several levers. The typology of walled gardens, curated platforms, open commons and consortia captures the main designs, and the curated platform, open in access but closed in control, is both the most successful and the most contested. Second, anchor firms manage multi-partner networks in tiers, with tailored relationships at the centre and automated governance at the edge. This structure makes coordination manageable but also binds partners in ways that hinder rivals. Third, the innovation loop of indirect network effects is the common root of both innovation and entry barriers, and governance choices about switching costs, data, interfaces and envelopment determine how far this natural advantage becomes a deliberate barrier. Fourth, governance changes over a life cycle, often drifting from openness toward control, and regulation is increasingly part of the environment that shapes these choices. The study has limits. As a conceptual review, it does not offer new empirical tests, and its illustrations are drawn from public information rather than original data. The framework simplifies complex realities, and many ecosystems combine features from more than one design. The literature on which it draws is young and still developing, and some findings come from specific platforms or industries. Future research could test the framework empirically, for example by measuring how changes in specific governance levers affect complementor investment and new entry. It would also be valuable to study how new regulations such as the Digital Markets Act change governance in practice, and whether lower barriers actually lead to more competition. Finally, as artificial intelligence services develop their own platform ecosystems, the questions explored here about openness, control, innovation and power are likely to become even more important. For students and future managers, the key message is simple. In an economy organised around ecosystems, the most important #strategic_decisions are often not about products but about rules. Those who understand how those rules are made, and whose interests they serve, will be better equipped to compete, to cooperate and to shape the markets of the future. References Adam, M., Croitor, E., Werner, D., Benlian, A., and Wiener, M. (2023). 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Strategic Management Journal, 43(3), 599-626. https://doi.org/10.1002/smj.3191 #BusinessEcosystems #platform_strategy #ecosystem_governance #anchor_firms #platform_economy #open_vs_closed_ecosystems #complementor_networks #entry_barriers #orchestration_strategy #DigitalPlatforms #innovation_ecosystems #StrategicManagement

Traditional sectors such as banking, manufacturing, law, healthcare and public administration face strong pressure to innovate, yet their cultures were built for reliability, control and careful repetition. This article examines how senior executives, often called the C-suite, try to move such organisations toward a culture of continuous innovation, and how they deal with the internal resistance that this kind of change almost always creates. The study uses an integrative review design. It brings together recent peer-reviewed research, published mostly between 2021 and 2026, on strategic leadership, organisational culture, digital transformation, organisational ambidexterity, sensemaking and resistance to change. From this body of work the article builds a conceptual framework with three layers: leadership levers, change mechanisms and outcomes. Five levers stand out: strategic sensemaking and vision, top team knowledge and integration, deliberate culture design, ambidextrous structures, and participation with genuine voice. These levers work through three mechanisms, namely shared meaning across levels, psychological safety and learning, and the reallocation of decision rights and resources. The review also shows that resistance is not a single problem. It takes cognitive, emotional, behavioural and identity-based forms, and each form calls for a different response. The article proposes a five-phase pivot process, a decision matrix for placing innovation activity, and a resistance navigation map. It closes with practical lessons for students and future managers, along with the limits of the evidence. Keywords: strategic leadership, organisational culture, continuous innovation, institutional change, resistance to change, C-suite executives, organisational ambidexterity, traditional sectors 1. Introduction Most people picture innovation as something that happens in young technology firms, in garages and start-up offices full of whiteboards. The harder and arguably more important story takes place somewhere else. It happens inside old banks, insurance companies, steel producers, law firms, hospitals, universities and government departments. These organisations carry long histories, deep routines and strong professional identities. They were designed to do the same things well, again and again, with little error. Now they are being asked to do something very different: to question their own routines, to experiment, to accept some failure, and to keep renewing themselves. This shift is what this article calls #innovation_driven_change at the institutional level. The people who sit at the top of these organisations, the chief executive officer, the chief financial officer, the chief operating officer and, more recently, the chief digital or chief innovation officer, carry special responsibility for this shift. Together they are usually called the #C_suite. They set strategy, control major resources, design structures and send the strongest signals about what really matters. When they talk about innovation but reward only short-term efficiency, employees notice the gap quickly. When they protect a risky experiment that fails and still praise the learning, employees notice that too. In this sense, #strategic_leadership is not only about choosing a direction. It is about shaping the conditions under which thousands of people decide, every day, whether to try something new or to stay with what is safe. The difficulty is that the very strengths of traditional organisations can work against change. Standard procedures reduce mistakes but also reduce curiosity. Clear hierarchies speed up routine decisions but slow down new ideas that do not fit existing categories. Professional pride protects quality but can turn into a quiet belief that outsiders and new tools have little to teach. Recent work on law firms, for instance, shows that established firms often left digital exploration to new legal technology companies and stayed focused on improving their existing services, partly because of their professional culture (Kronblad et al., 2023). Studies of large incumbents in other industries show similar patterns of structural and cultural barriers (Bastone et al., 2025; Hoessler and Carbon, 2024a). This creates a central problem. Innovation cannot simply be ordered from the top, because it depends on many people choosing to share ideas, admit doubts and take reasonable risks. Yet without strong direction from the top, innovation in traditional sectors tends to remain scattered, small and easily stopped by the weight of existing routines. Senior leaders therefore face a balancing act. They must be directive enough to make change real and open enough to make it shared. The second part of the problem is #resistance_to_change. Almost every serious transformation meets resistance. Some people doubt the logic of the change. Some fear losing their jobs, their status or their expertise. Some quietly continue old practices while appearing to accept the new ones. Recent systematic reviews describe resistance as the result of interacting individual, organisational and cultural factors, and they link successful change to #transparent_communication, #inclusive_leadership, psychological safety and cultural alignment (Alshaikh et al., 2026). Resistance, in other words, is not just an obstacle to be removed. It is information about how people experience the change. This article asks two connected questions. First, what methods do senior executives use to move organisational culture toward continuous innovation in traditional sectors? Second, how can they successfully navigate internal resistance to such strategic transformation? To answer these questions, the article reviews and synthesises recent research and builds a framework that students can use to understand, analyse and later practise this kind of leadership. The contribution is threefold. First, the article organises a scattered literature into a clear conceptual framework that links leadership levers, change mechanisms and outcomes. Second, it offers a process view of the cultural pivot, describing five phases that leaders move through repeatedly rather than once. Third, it treats resistance as several distinct forms, each with its own logic and its own fitting response. Throughout, the aim is to keep the discussion readable for students while keeping the structure and care of a research article. The rest of the article is organised as follows. Section 2 explains the review approach. Section 3 reviews the relevant literature. Section 4 presents the conceptual framework. Section 5 discusses the methods executives use to pivot culture, and Section 6 discusses how they navigate resistance. Section 7 draws practical and theoretical implications, and Section 8 concludes with limits and directions for future research. 2. Research Approach This article is a conceptual study based on an integrative literature review. An integrative review is useful when a topic sits across several research streams that rarely speak to each other. Strategic leadership, organisational culture, digital transformation, ambidexterity and resistance to change are usually studied separately, by different groups of scholars, in different journals. Bringing them together allows a fuller picture of what senior leaders actually do when they try to change a traditional organisation. The review focused on peer-reviewed journal articles and scholarly books, mainly published between 2021 and 2026, so that the discussion reflects recent conditions such as accelerated digitalisation, the experience of the pandemic and the rise of artificial intelligence. Sources were located through scholarly databases using combinations of terms such as strategic leadership, top management team, organisational culture, digital transformation, incumbent firms, ambidexterity, sensemaking, middle managers, psychological safety and resistance to change. Studies were selected when they dealt directly with leadership in established organisations, with cultural change toward innovation, or with employee responses to strategic change. Every reference was checked against its publisher record. The analysis followed three steps. First, the main claims and findings of each study were extracted. Second, these findings were grouped into recurring themes, such as the role of the top team, the design of culture, the structuring of exploration and the forms of resistance. Third, the themes were connected into a framework and a set of process models. The figures in this article are original syntheses drawn from that analysis, and where a figure adapts a specific study, the source is named. Because this is a conceptual review, it does not report new statistical data. Its value lies in organising existing evidence, showing how separate findings fit together and offering tools for thinking. This also sets its limits, which are discussed in Section 8. 3. Literature Review 3.1 Strategic leadership and the role of the top team Strategic leadership refers to the ability of #senior_leaders to anticipate change, set direction, build commitment and align the organisation so that it can adapt over time. It differs from day-to-day supervision because it deals with the whole organisation and with its long-term position. #Upper_echelons_theory, a long-standing idea in strategy research, argues that organisations become reflections of their top managers, because the knowledge, values and experiences of executives shape what they notice and how they decide. Recent research has applied this idea directly to innovation in traditional industries. Firk et al. (2022) studied industrial firms in the United States over several years and found that the digital knowledge of the #top_management_team was positively related to digital innovation. They also found that the chief executive and the chief digital officer could play integrating roles that helped this knowledge spread across the top team. However, a strongly hierarchical top team weakened the integrating role of the #chief_digital_officer. The lesson is important: appointing a new innovation executive is not enough if the rest of the top team keeps a rigid pecking order. Similar findings appear outside the industrial sector. In a study of financial institutions in emerging markets, Islam, Lucas and Paton (2026) found that the chief executive was seen as the main initiator of digital strategy adoption, acting as idea generator, advocate and final decision-maker. Other actors could either support or restrict that role. The characteristics that mattered included technology knowledge, specialist and overseas education, digital training and a particular mindset. Together, these studies suggest that the knowledge and integration of the top team are key starting points for innovation-driven change. 3.2 Digital leadership as a new layer of strategic leadership Much of today's innovation pressure comes through digital technology, which has given rise to the idea of #digital_leadership. A systematic review by Lin (2025) identified five key characteristics of digital leadership: vision and motivation, digital empowerment, innovation and entrepreneurship, cross-boundary collaboration and dynamic adaptation. The same review reported that digital leadership has a positive role in organisational change and innovation, team effectiveness and employee creativity. An earlier review by Khaw et al. (2022) examined two decades of work on digital leadership and sustainable performance and found that the field was still developing its measures and concepts. For students, the key point is that digital leadership is not a separate job for technical specialists. It is a layer added to strategic leadership. Senior leaders do not need to write code, but they do need enough understanding of digital possibilities to ask good questions, judge proposals and avoid both naive enthusiasm and fearful rejection. Broader reviews of #digital_transformation research reach a similar conclusion, describing transformation as a business and management challenge rather than only a technical one (Kraus et al., 2022). 3.3 Organisational culture and innovation #Organisational_culture is often described as the shared assumptions, values and visible practices that guide how people in an organisation think and act. A useful distinction separates three levels: visible artefacts such as offices, rituals and stories; espoused values that people say they hold; and deeper basic assumptions that people take for granted. Culture change is difficult because the deepest level is rarely discussed openly. Recent work shows that culture is not just a background condition for innovation but something leaders can deliberately design. In a multi-case study of three well-known industrial organisations undergoing digital transformation, Butt et al. (2024) identified the cultural artefacts, values in action and assumptions that these firms needed to refresh. They found that the leading firms approached culture in a strategically proactive and thoughtful way, and that their leaders used culture as a social control system for the adoption of digital technology. The authors proposed a framework for the strategic design of culture covering purpose, governance, ecosystem and organisation. Quantitative evidence points in a similar direction. Cao, Duan and Edwards (2025) examined four cultural types and found that an #adhocracy_culture, which values flexibility, creativity and experimentation, was the strongest positive influence on what they called digital transforming capability. Clan culture came next, followed by market and hierarchy cultures. This capability was in turn positively related to product innovation. For traditional sectors, which often lean toward hierarchy, this suggests a clear direction of travel: not the abolition of control, but a gradual strengthening of flexible, collaborative and experimental values alongside it. 3.4 Ambidexterity: running today while building tomorrow A central challenge for traditional organisations is that they must keep their existing business running efficiently while exploring new opportunities. Scholars call this #organisational_ambidexterity, the capacity to combine #exploitation, which means refining what already exists, with #exploration, which means searching for what is new. O'Reilly and Tushman (2021) argue that established firms often fail not because they lack ideas, but because their structures, incentives and cultures are built for exploitation and quietly starve exploration. Recent studies of #incumbent_firms add useful detail. Hoessler and Carbon (2024b) interviewed 33 experts on digital transformation in incumbent companies and found that leaders need to understand the difference between exploration and exploitation, and how both relate to digital change. In their findings, exploration in digital transformation was about using digital technologies to rethink #business_models, while exploitation was about using them to optimise existing processes, products and infrastructure. In a related study, the same authors developed a decision tree for choosing whether to separate or integrate digital activities, based on how close those activities are to the core business and whether they are mainly exploratory or exploitative (Hoessler and Carbon, 2024a). Other studies show how this works in practice. Sund, Bogers and Sahramaa (2021) examined how European #banks used #innovation_labs to explore new business models while protecting their core operations. Pu, Jeong and Choi (2026) analysed Michelin, a firm in the long-established tyre industry, and found that it combined inbound, outbound and coupled #open_innovation with structural and domain separation, allowing it to absorb and institutionalise exploratory initiatives while keeping its efficiency advantages. These cases show that ambidexterity is not a slogan. It is a set of concrete structural choices that senior leaders must make and keep adjusting. 3.5 Sensemaking and the role of middle managers Strategy does not travel from the boardroom to the shop floor unchanged. It is interpreted, translated and sometimes reshaped as it moves through the organisation. #Sensemaking is the process through which people make sense of unclear or surprising situations, and #sensegiving is the effort to shape how others understand them. Both are central to strategic change. Research shows that senior leaders' own sensemaking shapes the direction of change. Li and Liu (2026) followed a privately owned Chinese firm over time and showed how top managers drew on multiple institutional logics to interpret shifting industrial policy and craft #strategic_responses. At the same time, much of the real work of meaning-making happens in the middle. A diary study of middle managers engaged in strategic change led Kieran, MacMahon and MacCurtain (2022) to propose four simple rules: plan creative opportunities for discussion, ensure parity between leaders and middle managers, keep re-planning and re-prioritising together, and make sure leadership effort continues beyond the launch of change. Blakcori and Psychogios (2021) similarly found that middle managers in #public_organisations play an intricate role in interpreting and managing change, often under real limits on their formal power. More recently, Wowerath (2026) showed how #objectives_and_key_results, a goal-setting tool, can support #middle_managers in their sensemaking and sensegiving by reinforcing goal orientation, reflection, feedback, empowerment and transparency. The common message is that senior executives cannot pivot culture alone. They need middle managers who understand the change well enough to explain it in local terms. 3.6 Psychological safety and innovative behaviour Innovation requires people to speak up, share half-formed ideas, question assumptions and admit mistakes. All of these behaviours carry interpersonal risk. #Psychological_safety, the shared belief that a team is safe for this kind of risk-taking, is therefore a key condition for innovation. Edmondson (2023) argues that organisations need to distinguish between different kinds of failure, so that thoughtful experiments that do not work are treated as useful learning rather than as reasons for blame, while preventable errors in routine work are still taken seriously. Leadership behaviour strongly shapes psychological safety. A meta-analysis by Lee et al. (2023) on #paradoxical_leadership, a style in which leaders hold apparently opposite demands together, such as control and autonomy, found that it was consistently and positively associated with follower creativity, voice and innovation. The study also found that these effects worked partly through psychological safety, role clarity and the quality of the leader-follower relationship. For traditional sectors, this is particularly relevant, because leaders there must constantly balance reliability with experimentation. 3.7 Resistance to change The study of resistance to change has moved away from seeing resistance as simple stubbornness. A systematic review by Khaw et al. (2023) mapped the wide range of reactions employees can have toward organisational change, from support to rejection. Alshaikh et al. (2026), reviewing research on technology-driven transformation, concluded that resistance is shaped by interrelated individual, organisational and cultural factors, and that successful change is consistently associated with transparent communication, inclusive leadership, psychological safety and cultural alignment. #Professional_identity is an important source of resistance in traditional sectors. In a qualitative study of physicians in a large Brazilian hospital facing changes to its payment model, Bahia Filho et al. (2024) found that doctors perceived threats to their clinical and financial autonomy. Some accepted the need for change but wanted a more active role in designing it. This finding matters well beyond #healthcare. In law, engineering, banking and education, expert professionals often resist change not because they dislike progress, but because they feel their judgement and standing are being ignored. 3.8 The research gap Taken together, the literature offers rich but fragmented insight. Studies of the top team explain who drives innovation but say less about culture. Culture studies describe what must change but less about resistance. Ambidexterity research explains structures but less about meaning and emotion. Resistance research explains employee reactions but often treats leadership in general terms. Bastone et al. (2025), in their review of research on incumbents' digital transformation, likewise describe the field as fragmented. This article responds by integrating these streams into a single framework focused on the specific methods senior executives use and the specific ways they can navigate resistance. 4. Conceptual Framework The framework developed here links what senior leaders do, the mechanisms through which their actions work, and the outcomes they seek. It is shown in Figure 1. Figure 1. Conceptual framework linking strategic leadership levers, change mechanisms and outcomes in traditional sectors. Source: author synthesis of the reviewed literature. 4.1 Leadership levers The left side of the framework shows five #leadership_levers that senior executives can pull. The first is strategic sensemaking and vision. Leaders read their environment, decide what it means for the organisation and turn that interpretation into a direction others can understand. This draws on research showing that top managers' interpretations shape strategic responses (Li and Liu, 2026). The second is top team digital knowledge and integration. The knowledge held by the top team, and how well that knowledge is shared across its members, influences the organisation's capacity for innovation (Firk et al., 2022; Islam et al., 2026). The third is deliberate culture design. Leaders can identify and refresh the artefacts, values and assumptions that block or support innovation (Butt et al., 2024; Cao et al., 2025). The fourth is ambidextrous structures. Leaders decide where exploration happens, how it is protected and how it is connected back to the core (Hoessler and Carbon, 2024a; O'Reilly and Tushman, 2021). The fifth is participation and voice. Leaders open real channels for employees, professionals and middle managers to shape the change, not only to receive it (Alshaikh et al., 2026; Bahia Filho et al., 2024). 4.2 Change mechanisms The levers do not produce outcomes directly. They work through three mechanisms, shown in the centre of Figure 1. The first mechanism is shared meaning across levels. People act on change when they understand why it matters and what it means for their own work. Sensemaking and sensegiving by senior and middle managers create this shared meaning (Kieran et al., 2022; Wowerath, 2026). The second mechanism is psychological safety and learning. When people feel safe to speak up and experiment, ideas surface, problems are reported early and learning becomes routine (Edmondson, 2023; Lee et al., 2023). The third mechanism is the reallocation of decision rights and resources. Culture changes only when the rules of the game change. Gomes et al. (2024) showed that in large incumbent firms, success in digital transformation depended on adapting what they call entrepreneurial judgment governance, meaning who has the right to judge and allocate resources for new opportunities. They describe three phases: recognising, distributing and orchestrating this judgment. 4.3 Outcomes and context The right side of Figure 1 shows two related outcomes. The first is a #continuous_innovation culture, in which experimentation and renewal become normal rather than exceptional. The second is reduced and redirected resistance. The arrow between them indicates that the two outcomes reinforce each other. As innovation becomes normal, resistance weakens. As resistance is handled well, innovation spreads more easily. The bottom of the figure shows that all of this takes place within a specific context. Traditional sectors carry #legacy_routines, heavy #regulation and strong professional identities. These features do not make change impossible, but they shape which levers are most urgent and which forms of resistance are most likely. 5. Discussion: How Executives Pivot Culture Toward Continuous Innovation This section discusses the main methods senior executives use to move traditional organisations toward continuous innovation. It begins with a process view and then examines each method in turn. 5.1 A five-phase pivot process Cultural change is not a single event. It moves through phases, and successful organisations tend to repeat these phases rather than treating transformation as a one-time project. Drawing on the reviewed research and on the practice-oriented work of Kotter, Akhtar and Gupta (2021), who emphasise mobilising people around opportunity in uncertain conditions, this article proposes a five-phase #cultural_pivot process, shown in Figure 2. Figure 2. A five-phase process for pivoting organisational culture toward continuous innovation, with a feedback loop from renewal to sensing. Source: author synthesis. In the sense phase, leaders read their environment and honestly name the gap between where the organisation is and where it needs to be. In the frame phase, they build a shared story of why change matters for this particular organisation. In the seed phase, they launch protected experiments and look for early wins that make the change visible. In the embed phase, they change the formal rules, rewards, roles and decision rights so that new behaviour becomes normal. In the renew phase, they review what has been learned and adjust. The feedback loop shows the key idea: continuous innovation means the cycle never fully ends. Lessons from renewal feed the next round of sensing. Each phase is linked to one or more of the methods discussed below. 5.2 Sensing honestly and naming the gap The first method is honest diagnosis. Traditional organisations often have strong reasons to underestimate threats. Past success creates confidence, and existing performance measures tend to track the old business, not the emerging one. Senior leaders therefore need deliberate ways to see what their own systems may hide. Research on incumbent firms during crises shows how powerful this can be. Browder, Dwyer and Koch (2024) studied incumbent firms classed as essential businesses during the COVID-19 pandemic. They analysed 72 adaptations these firms made and found that digital capabilities acquired earlier, but not fully used, helped them adapt in ways that their crisis plans had not anticipated. These adaptations accelerated digital transformation initiatives that years of strategic planning had failed to achieve. The authors also emphasise that strategic leaders played a key role in combining new and legacy technologies. The lesson for students is that crises can reveal the real gap more clearly than planning documents. A wise executive does not wait for a crisis, but uses external benchmarks, customer feedback, conversations with front-line staff and partnerships with new entrants to see the gap early. Naming the gap honestly, without blaming individuals, is the first step in building #trust for the change that follows. 5.3 Framing a shared story of change The second method is framing. Once the gap is clear, leaders must explain why change matters in terms that make sense to people inside the organisation. In traditional sectors, this often means connecting innovation to existing values rather than presenting it as a rejection of the past. A bank can frame innovation as a better way to protect customers. A hospital can frame it as a better way to deliver safe care. A law firm can frame it as a way to give clients better access to justice. Good framing respects the organisation's history while being honest about the need for change. Li and Liu (2026) showed how top managers drew on multiple institutional logics to make sense of shifting conditions. This suggests that effective framing does not rely on a single message but connects change to several values that different groups already hold, such as efficiency, quality, service and responsibility. Framing is also not a one-off speech. Kieran, MacMahon and MacCurtain (2022) stressed that leaders must keep investing effort after the initial launch, and that middle managers need real parity in discussions about change. A shared story of change is built through many conversations, not announced once. This is why #change_communication works best as a two-way process. 5.4 Building digital knowledge and integration at the top The third method concerns the top team itself. If the top team does not understand the opportunities and risks of new technologies and business models, it cannot lead the change credibly. Firk et al. (2022) showed that top team digital knowledge was positively related to digital innovation, and that chief executives and chief digital officers could play integrating roles. Three practical steps follow from this research. First, senior leaders can deliberately build their own knowledge through learning programmes, #reverse_mentoring by younger or more technical staff, and visits to organisations that have already changed. Second, they can bring people with new kinds of expertise into the top team, as Islam et al. (2026) found in the financial sector. Third, and perhaps most important, they can reduce rigid hierarchy within the top team so that new knowledge actually influences decisions. The finding that a strongly hierarchical top team hampers the integrating role of the chief digital officer is a warning against symbolic appointments (Firk et al., 2022). 5.5 Designing culture deliberately The fourth method is deliberate #culture_design. Butt et al. (2024) found that the most advanced industrial organisations approached culture proactively, examining and refreshing their artefacts, values in action and underlying assumptions. In practice, this involves several concrete actions. At the level of artefacts, leaders can change visible symbols. They can tell new stories about successful experiments, celebrate teams that learn from failure, redesign meetings so that junior staff present ideas, and create physical or virtual spaces for collaboration. At the level of values in action, leaders must make sure that what they reward matches what they say. If a company says it values innovation but promotes only those who never take risks, employees will believe the promotions, not the speeches. At the level of assumptions, leaders need to bring hidden beliefs into the open. A common hidden assumption in traditional sectors is that the expert at the top always knows best. Challenging this belief is uncomfortable but necessary if ideas are to flow upward. The evidence from Cao, Duan and Edwards (2025) adds a useful compass. Since adhocracy culture had the strongest positive relationship with digital transforming capability, leaders in hierarchical organisations can aim to add adhocracy features, such as flexibility, #experimentation and entrepreneurial energy, without abandoning the reliability that their customers and regulators expect. The goal is a blend, not a replacement. 5.6 Structuring for ambidexterity The fifth method is structural. Senior leaders must decide where and how innovation happens. Figure 3 offers a simple decision matrix, adapted from the decision tree developed by Hoessler and Carbon (2024a), which considers how close an activity is to the core business and whether it is mainly about exploitation or exploration. Figure 3. A decision matrix for placing innovation activity, based on distance from the core business and type of activity. Adapted from Hoessler and Carbon (2024a). When an activity is close to the core and mainly about improvement, it can be integrated into existing teams, who use new tools to optimise current work, a form of #incremental_innovation. When it is close to the core but exploratory, the core business can own it while working with a dedicated innovation partner who helps translate new ideas. When it is further from the core but still about improvement, small teams can pilot inside the organisation and then scale with clear sponsors. When it is far from the core and highly exploratory, a separate and protected unit with its own rules and budget is often needed for #radical_innovation, because the core organisation's routines would otherwise smother it. The value of this matrix is that it prevents two common mistakes. The first is pushing every new idea into a separate innovation lab, which can create an isolated island of creativity that never changes the main business. The second is forcing radical ideas through normal approval processes, where they are judged by the standards of the old business and quickly rejected. Sund, Bogers and Sahramaa (2021) showed the importance of how banks manage innovation labs, and Pu, Jeong and Choi (2026) showed how Michelin combined separation with mechanisms for bringing exploratory work back into core operations. Hoessler and Carbon (2024a) also highlight specific leadership activities that make structural ambidexterity work, including clear responsibilities, collaborative decision-making, careful selection of people and close collaboration with information technology functions. Structures do not run themselves. They need ongoing attention from senior leaders. 5.7 Redistributing decision rights and resources The sixth method goes to the heart of power in organisations. Culture follows the allocation of decision rights and money. If every new idea must pass through the same approval committee that guards the existing business, exploration will rarely survive. Gomes et al. (2024) found that firms which succeeded in digital transformation adapted their governance of entrepreneurial judgment through three phases: recognising the need for new forms of judgment, distributing the right to judge to new actors, and orchestrating these distributed judgments so they fit together. For senior executives, this often means letting go of some control. It may involve creating #innovation_funds managed by mixed teams, giving business units the right to run small experiments without central approval, or using #stage_gate processes with lower early hurdles and stricter later reviews. The key idea is that #decision_rights must match the kind of uncertainty involved. Routine investments can follow normal rules. Highly uncertain opportunities need people who are closer to the problem and willing to learn as they go. 5.8 Building psychological safety and learning routines The seventh method is creating conditions in which people feel safe to experiment and speak. Without psychological safety, cultural change remains on the surface. Employees learn new vocabulary but keep their real doubts and ideas to themselves. Edmondson (2023) offers a practical distinction that helps leaders here. Basic failures in well-understood routine work should be prevented, but #intelligent_failures, which occur in new territory, are small, are based on thoughtful hypotheses and produce learning, should be welcomed. In traditional sectors where errors can be costly, this distinction is particularly helpful. It allows leaders to maintain high standards in core operations while encouraging experimentation in new areas. Leaders can build #learning_culture routines such as #after_action_reviews, regular discussions of what did not work and why, and public recognition of learning as well as results. The meta-analytic evidence from Lee et al. (2023) suggests that leaders who can hold opposite demands together, being both demanding and supportive, both controlling and enabling, are particularly effective at fostering innovation, partly through psychological safety. 5.9 Using middle managers as translators The eighth method is working through the middle of the organisation. Middle managers translate strategy into daily practice. If they are confused or unconvinced, change stalls. Blakcori and Psychogios (2021) showed how middle managers in public organisations make sense of change under real constraints, while Wowerath (2026) found that tools such as objectives and key results can help them translate strategic goals into concrete steps. Senior executives can support middle managers by involving them early in shaping the change, giving them enough information to answer their teams' questions honestly, and allowing them to adapt the change to local conditions. They should also protect middle managers' time, because change work is often added to full workloads without any reduction elsewhere. Treating middle managers as #change_agents, rather than as simple message carriers, is one of the clearest differences between transformations that spread and those that stay on slides. 5.10 Leading by example at the top The ninth method is the simplest to describe and one of the hardest to sustain: senior executives must behave in the way they ask others to behave. Employees in traditional organisations have usually seen several change programmes come and go. Many have learned to wait quietly until the latest initiative fades. What convinces them that this time is different is rarely a new slogan. It is the visible conduct of the people at the top. #Role_modelling at the executive level takes several forms. Leaders can openly share their own experiments, including those that did not work, and explain what they learned. They can ask questions in meetings before giving their own view, so that junior staff are not simply echoing the most senior person in the room. They can attend training on new tools alongside their teams rather than sending others to learn on their behalf. They can also give up some symbols of status, for example by joining cross-functional project teams as ordinary members for a time. These behaviours connect directly to the mechanisms in Figure 1. When executives admit uncertainty, they make it safer for others to do the same, which strengthens psychological safety. When they ask before they tell, they create shared meaning rather than one-way instruction. When they willingly hand over decisions to teams closer to a problem, they make the reallocation of decision rights visible and believable. In this way, personal conduct turns abstract strategy into everyday evidence. There is also a warning here. Inconsistent behaviour does more damage than no behaviour at all. An executive who praises experimentation in a town hall and then publicly criticises a team for a failed pilot teaches the whole organisation that the official message cannot be trusted. Because cultural signals travel fast, a single visible contradiction can undo months of careful work. Senior leaders therefore need to watch their own reactions closely, especially in moments of disappointment or pressure, which are exactly the moments employees remember. 5.11 Building capabilities, not only enthusiasm The tenth method concerns skills. Culture change often fails not because people refuse to innovate but because they do not know how. Employees in traditional sectors may have deep expertise in existing processes but little experience with new methods such as rapid prototyping, data analysis, customer research or agile project work. Asking such employees to innovate without giving them new skills is likely to produce frustration and, eventually, resistance. Senior leaders can address this by investing in #capability_building as a core part of the transformation rather than an afterthought. This includes structured training, but also learning on the job through mixed teams in which experienced staff work alongside people with new skills. Such teams allow knowledge to flow in both directions. Newcomers learn the realities of the business, its customers and its regulatory limits, while long-serving employees learn new tools and methods. This two-way learning also reduces the risk that experienced staff feel replaced, because their knowledge is visibly needed. Hoessler and Carbon (2024b) found that leaders themselves need a working understanding of exploration, exploitation and digital transformation, including digital literacy. The same logic applies throughout the organisation. When people understand both the old and the new ways of working, they are better able to judge where each fits, which is the practical core of ambidexterity. 6. Discussion: Navigating Internal Resistance The previous section described methods for pivoting culture. This section turns to the second research question: how senior leaders can navigate the resistance that such change produces. 6.1 Rethinking resistance A traditional view treats resistance as a problem located in difficult employees. A more useful view treats it as a signal about how the change is experienced. Alshaikh et al. (2026) found that resistance is shaped by individual, organisational and cultural factors working together. This means that leaders who blame individuals for resistance often miss its real causes, such as unclear purpose, poor timing, unfair processes or threats to identity. This shift in view has a practical consequence for #change_management. Instead of asking how to overcome resistance, senior leaders can ask what resistance is telling them. Sometimes it reveals real flaws in the plan. Sometimes it reveals fears that need attention. Sometimes it reveals practical barriers, such as missing skills or conflicting targets. Listening to resistance does not mean giving up on change, but it often makes the change better. 6.2 Four forms of resistance and fitting responses Change researchers commonly distinguish between thinking, feeling and acting dimensions of how people respond to change. This article adds a fourth form that is especially strong in traditional sectors: identity-based resistance. Figure 4 links each form to a fitting leadership response. Figure 4. A resistance navigation map linking four forms of resistance to fitting leadership responses. Source: author synthesis of the reviewed literature. Cognitive resistance occurs when people doubt the logic of the change. They may think it will not work, that it addresses the wrong problem or that its costs outweigh its benefits. The fitting response is evidence and dialogue. Leaders can share data, invite challenge and, most powerfully, co-design #pilot_projects with the sceptics so that they can test the ideas themselves. A sceptic who helps run a pilot often becomes its most credible advocate. Affective resistance concerns emotions such as fear, anxiety and a sense of loss. People may worry about their jobs, about being unable to learn new skills or about losing familiar relationships and routines. The fitting response is support. Leaders need to listen, acknowledge the losses involved, offer honest timelines and provide training and practical help. Psychological safety is essential here, because people who are afraid to admit their fears will express them indirectly. Behavioural resistance appears as delay, quiet workarounds and continued use of old practices. Often it is not deliberate opposition but the result of conflicting pressures. If employees are told to innovate but are still measured only on old targets, they will follow the targets. The fitting response is to align roles, processes and #incentive_alignment so that the new behaviour is the easiest one to perform. #Fair_process also matters: people accept difficult outcomes more readily when they believe decisions were made in a fair and transparent way. Identity-based resistance arises when change seems to threaten people's professional identity, expertise or status. The physicians studied by Bahia Filho et al. (2024) illustrate this clearly, as they perceived threats to their clinical and financial autonomy and wanted a voice in designing the new model. Similarly, the professional culture of law firms helped explain their reluctance to explore digital alternatives (Kronblad et al., 2023). The fitting response is to re-craft roles in ways that honour expertise, to give professionals real ownership of parts of the change and to show how their knowledge remains essential in the new model. 6.3 Participation as the central response Across all four forms, one response appears again and again: genuine participation. When people help shape a change, they understand it better, worry less about hidden agendas, adapt their behaviour more easily and feel their identity is respected. This is why #employee_voice sits among the leadership levers in Figure 1. Participation, however, must be real. Consultations that ask for views after all decisions have been made usually increase cynicism rather than reduce it. Real participation means giving people influence over parts of the change, such as how it is implemented, in what order and with what support. Senior leaders keep responsibility for the overall direction, but they share the design of the path. 6.4 Timing, pacing and the value of early wins Resistance is also shaped by pace. Change that moves too slowly loses energy and credibility. Change that moves too quickly overwhelms people and feeds fear. Senior leaders need to judge pace carefully, often by moving quickly in a few visible areas while giving more time elsewhere. #Early_wins play a special role. When a protected experiment produces a visible success, it gives sceptics evidence, gives anxious people hope and gives supporters something to point to. Kotter, Akhtar and Gupta (2021) emphasise the energy that comes from mobilising people around real opportunities. Early wins also make it easier to move into the embed phase of Figure 2, because they create a case for changing formal rules and rewards. 6.5 When resistance should change the plan Not all resistance should be overcome. Sometimes those who resist are right. Experienced staff in traditional sectors often understand risks, customer needs and regulatory constraints better than senior leaders or consultants. A mature leader treats resistance as a possible source of correction. This attitude requires #humility, which is itself a cultural signal. When employees see senior executives change a plan because of good arguments from below, they learn that speaking up is valued. This is perhaps the most powerful way to build a culture of continuous innovation, because it shows that ideas are judged by their quality rather than by the rank of the person who offers them. 6.6 Common mistakes in managing resistance The reviewed research also points, directly or indirectly, to several common mistakes that senior leaders make when facing resistance. Naming them clearly can help students and future managers recognise them early. The first mistake is treating all resistance as the same. Leaders who respond to every objection with more communication, for example, may help people with cognitive doubts but do little for those who feel anxious or whose professional identity is threatened. As Figure 4 suggests, the response must fit the form. The second mistake is relying on announcements instead of dialogue. Large meetings, newsletters and polished presentations can share information, but they rarely change minds on their own. People need chances to ask hard questions and to receive honest answers, often in small groups led by managers they already know. The third mistake is ignoring incentives. If performance measures, bonuses and promotion criteria continue to reward only the old behaviour, no amount of inspiration will change daily practice for long. Behavioural resistance, in particular, often disappears once the formal system stops punishing the new behaviour. The fourth mistake is excluding experienced professionals from the design of change. This mistake is especially costly in traditional sectors, where expertise is deeply tied to identity. The physicians in the study by Bahia Filho et al. (2024) did not reject change outright, but they wanted a meaningful role in shaping it. Excluding such groups turns potential allies into opponents. The fifth mistake is declaring victory too early. When the first pilots succeed, leaders may move their attention elsewhere, leaving the new practices unprotected. Kieran, MacMahon and MacCurtain (2022) stressed that leadership effort must continue beyond the launch of change. Without that continued attention, old habits tend to return, often quietly and gradually. 7. Implications 7.1 Implications for theory This review contributes to theory in three ways. First, it connects research on the top management team with research on organisational culture. The upper echelons tradition explains how executive characteristics shape strategy, while culture research explains how shared assumptions shape behaviour. The framework in Figure 1 shows how these two streams meet: executive knowledge and integration become cultural change through mechanisms of shared meaning, psychological safety and redistributed decision rights. Second, the review extends ambidexterity research by placing structural choices inside a broader cultural and emotional process. Structures such as separate innovation units do not work by themselves. They succeed when supported by shared meaning and psychological safety, and when connected to the core through deliberate leadership attention. Third, the review supports a more differentiated view of resistance. Treating resistance as four forms, each with a fitting response, offers a more useful theoretical lens than treating it as a single negative force. In particular, the emphasis on identity-based resistance highlights a feature of traditional sectors that general change models often underplay. 7.2 Implications for practice For senior executives and future managers, the review suggests several practical lessons. First, start with the top team. Build its knowledge, bring in new expertise and reduce rigid hierarchy within it, so that new perspectives actually influence decisions. Second, treat culture as something to design. Examine artefacts, values in action and hidden assumptions, and change the ones that block innovation. Make sure that rewards and promotions send the same message as speeches. Third, place innovation deliberately. Use the logic of Figure 3 to decide what to integrate, what to link, what to pilot and what to separate. Revisit these choices as activities mature. Fourth, share decision rights and resources. Give people closer to new opportunities the authority and funding to experiment, and build mechanisms to coordinate their efforts. Fifth, build psychological safety and learning routines. Distinguish between preventable errors and intelligent failures, and celebrate learning as well as success. Sixth, work through middle managers. Involve them early, inform them well and give them room to adapt the change locally. Seventh, diagnose resistance before responding. Ask whether resistance is cognitive, affective, behavioural or identity-based, and respond accordingly. Above all, invite genuine participation and be ready to change the plan when resistance reveals real flaws. 7.3 Implications for students For students of business, public administration and management, this topic offers a useful lens for case analysis. When studying any organisational transformation, students can ask a set of guiding questions based on this article. Did the top team have the knowledge and integration needed? Was culture designed deliberately or left to chance? Where was innovation placed, and was that choice appropriate? Who held decision rights? Did people feel safe to speak up? How did middle managers interpret the change? What forms did resistance take, and how did leaders respond? These questions turn a general story of success or failure into a structured analysis, which is exactly what good academic work requires. Students preparing for management careers can also take a personal lesson from this review. Leadership for #organisational_change is not reserved for the C-suite. Every team leader makes small decisions about whether to welcome ideas, how to respond to mistakes and how to explain change. The habits formed early in a career, such as listening to doubts, framing change honestly and treating failure as learning, are the same habits that senior executives need later on a larger scale. 7.4 A short self-check for leaders To make these lessons easier to use, the framework can be turned into a short set of questions that a leadership team might ask itself during a transformation. Does our top team share enough knowledge about the opportunities in front of us, and does that knowledge actually shape our decisions? Can every manager explain, in their own words, why this change matters for their team? Do our rewards, promotions and budgets support the behaviour we say we want? Have we decided clearly where each innovation activity belongs, and do we review those choices? Do people feel safe telling us that something is not working? Have we listened carefully to those who resist, and have we changed anything as a result? A team that can answer these questions honestly, even when the answers are uncomfortable, is far better placed to sustain #continuous_improvement and genuine innovation than one that relies on confident announcements alone. 8. Conclusion This article set out to explore how senior executives pivot organisational culture toward continuous innovation in traditional sectors, and how they can navigate the internal resistance that such transformation creates. Drawing on recent research, it has argued that this pivot depends on five leadership levers: strategic sensemaking and vision, top team knowledge and integration, deliberate culture design, ambidextrous structures, and participation with genuine voice. These levers work through three mechanisms, namely shared meaning, psychological safety and learning, and the reallocation of decision rights and resources. Together they support two connected outcomes: a culture of continuous innovation and reduced, redirected resistance. The article has also argued that the pivot is a repeating process rather than a single project, moving through sensing, framing, seeding, embedding and renewing. It has offered a matrix for placing innovation activity, and it has shown that resistance takes at least four forms, each requiring a different response. Across all of these, genuine participation and a willingness to learn from resistance emerge as central. 8.1 Limitations This study has several limits. First, it is a conceptual review and does not present new empirical data. Its framework and models should be treated as structured propositions to be tested, not as proven laws. Second, the reviewed research comes from a range of countries, sectors and research designs, and findings from one context may not transfer directly to another. Third, much of the recent literature focuses on digital transformation, so the framework may fit technology-related innovation better than other forms, such as innovation in services, social practices or sustainability. Finally, the review relied on published, peer-reviewed sources, which may underrepresent failed transformations that rarely appear in print. 8.2 Directions for future research Future research could test the framework empirically, for example by surveying employees and executives in traditional organisations to measure the relationships between leadership levers, change mechanisms and outcomes. Longitudinal case studies could follow the five-phase pivot process over several years to see how organisations move through and repeat the cycle. Comparative studies could examine how the forms of resistance differ across professions, cultures and sectors. Research on identity-based resistance in particular could help explain why some expert professions adapt more easily than others. Finally, the growing use of #artificial_intelligence in traditional sectors offers a timely setting in which to study how senior leaders balance enthusiasm, caution and the concerns of employees. In the end, the central lesson is a simple one, even if putting it into practice is hard. Traditional organisations do not become innovative because their leaders announce a new strategy. They become innovative when leaders change what people understand, what people feel safe to do and what the organisation actually rewards, and when those leaders are willing to listen and learn from the people who doubt them. References Alshaikh, R., Al-Khafaf, I., Ahmed, V., and Bahroun, Z. (2026). Navigating organizational change: A systematic review of resistance and technology-driven transformation. 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J., Veiga, P., Kailer, N., and Weinmann, A. (2022). Digital transformation in business and management research: An overview of the current status quo. International Journal of Information Management, 63, 102466. https://doi.org/10.1016/j.ijinfomgt.2021.102466 Kronblad, C., Pregmark, J. E., and Berggren, R. (2023). Difficulties to digitalize: Ambidexterity challenges in law firms. Journal of Service Theory and Practice, 33(2), 217-236. https://doi.org/10.1108/JSTP-05-2022-0120 Lee, A., Lyubovnikova, J., Zheng, Y., and Li, Z. F. (2023). Paradoxical leadership: A meta-analytical review. Frontiers in Organizational Psychology, 1, 1229543. https://doi.org/10.3389/forgp.2023.1229543 Li, Y., and Liu, J. (2026). Sensemaking with institutional logics: How top managers drive strategic change in response to evolving and diverse industrial policies. Management and Organization Review, 22(3), 613-638. https://doi.org/10.1017/mor.2025.10104 Lin, Q. (2025). Digital leadership: A systematic literature review and future research agenda. European Journal of Innovation Management, 28(6), 2469-2488. https://doi.org/10.1108/EJIM-07-2023-0522 O'Reilly, C. A., and Tushman, M. L. (2021). Lead and disrupt: How to solve the innovator's dilemma (2nd ed.). Stanford Business Books. Pu, J., Jeong, H., and Choi, S. (2026). Organizational ambidexterity and open innovation: A case analysis of Michelin. Administrative Sciences, 16(3), 110. https://doi.org/10.3390/admsci16030110 Sund, K. J., Bogers, M. L. A. M., and Sahramaa, M. (2021). Managing business model exploration in incumbent firms: A case study of innovation labs in European banks. Journal of Business Research, 128, 11-19. https://doi.org/10.1016/j.jbusres.2021.01.059 Wowerath, C. (2026). Objectives and key results as a tool for middle managers to enhance the process of strategy implementation. Journal of Business Research, 202, 115774. https://doi.org/10.1016/j.jbusres.2025.115774 #StrategicLeadership #strategic_leadership_in_practice #InnovationCulture #culture_of_innovation #change_management #ChangeLeadership #digital_transformation #DigitalTransformation #leading_transformation #executive_leadership #incumbent_firms #traditional_industries #innovation_management #corporate_renewal #StudentResearch

Organisations in higher education, healthcare, and finance work under dense and changing government rules, yet they are also expected to adapt quickly to new technology, new competitors, and new public expectations. This article asks how such organisations can build #strategic_agility without weakening the compliance that protects students, patients, and customers. Using an integrative review of recent peer-reviewed studies and books, mostly published between 2021 and 2026, together with a comparative reading of the three sectors, the article develops a framework of regulated strategic agility. The framework explains agility as three linked mechanisms: regulatory sensing, bounded seizing, and compliant reconfiguring, supported by enabling conditions such as leadership unity, a both/and mindset, digital and RegTech capability, and trust-based relationships with regulators. The review finds that regulation is not only a brake on change. In several recent studies, regulatory pressure is associated with higher agility, and tools such as regulatory sandboxes, agile project teams, and shorter strategy cycles allow organisations to test new ideas under controlled conditions. At the same time, layered rules, long approval cycles, and compliance-first cultures can slow innovation. The article offers a typology of four strategic postures, a six-stage cycle for bounded experimentation, and practical lessons for students, managers, and policymakers. It closes by noting the limits of the evidence and suggesting directions for future research. Keywords: strategic agility, regulated industries, compliance, strategic innovation, higher education, healthcare, financial regulation, regulatory sandboxes 1. Introduction Most textbooks on strategy describe a world in which firms are free to choose. A company spots a gap in the market, designs a product, and launches it. If the product fails, the company changes course. In this picture, speed is almost always good, and the main limits on action come from money, talent, and competition. For a large share of the economy, however, this picture is incomplete. A university cannot open a new degree simply because students want it; the programme usually needs internal approval and, in many countries, approval or review by a national agency. A hospital cannot adopt a new diagnostic tool overnight; the tool must meet safety rules, and the hospital must protect patient data. A bank cannot launch a new lending product without checking it against conduct rules, capital requirements, and anti-money laundering duties. In these #regulated_industries, the freedom to choose is real but bounded. This article is about what happens to strategy inside those boundaries. Its central idea is #strategic_agility, which recent research describes as the ability of an organisation to notice important changes early, make timely strategic choices, and move resources quickly to act on those choices (Clauss et al., 2021; Girod et al., 2023). Agility has become a popular word in management, and it is easy to assume that it simply means being fast. In a regulated setting, that assumption is risky. Moving fast in the wrong way can cost an institution its licence, its accreditation, or the trust of the public. The more useful question is therefore not "How fast can we move?" but "How can we change direction well while staying inside the rules that protect people?" The question matters for at least three reasons. First, regulated sectors are very large. Education, health, and financial services together employ millions of people and touch almost every citizen. Second, these sectors face strong pressure to change. Digital technology, artificial intelligence, new kinds of competitors, and shocks such as the COVID-19 pandemic have all exposed slow and rigid ways of working. Third, the rules themselves are changing. Regulators have started to experiment with new tools, such as #regulatory_sandboxes in finance and lifecycle approaches to software in medicine, that change what is possible for organisations willing to work with them (Cornelli et al., 2024; Zhang et al., 2025). 1.1 The problem The problem this article addresses can be stated simply. Strategy research tells us that agile organisations tend to innovate more and perform better (Clauss et al., 2021; Asghar et al., 2025). Regulation research tells us that rules exist to protect important public values such as safety, fairness, and stability. Yet there is still limited work that explains, in practical terms, how organisations combine the two. Much agility research comes from relatively free markets, while much regulation research looks at rules from the point of view of the regulator rather than the organisation. Students and managers are left with two separate stories: one that praises speed and flexibility, and one that praises control and caution. Recent evidence suggests that these stories can be joined. Studies of hospitals in Indonesia find that regulatory pressure is positively related to organisational agility (Suyitno et al., 2026). A study of the telecommunications sector in Ghana finds that industry pressures, including government regulation, strengthen the link between agility and quality management (Koomson, 2025). Research on European universities shows that agile practices can be used in strategic planning even when rules and many stakeholders make full agility difficult (Begicevic Redep et al., 2026). These findings suggest that regulation can shape the form agility takes rather than simply blocking it. 1.2 Aim, questions, and contribution The aim of this article is to explain the mechanisms through which organisations in highly regulated sectors form and carry out strategy, and to show how #strategic_innovation can be achieved within strict compliance boundaries. Three questions guide the discussion: 1. How do regulation and compliance shape the way strategy is formulated and executed in higher education, healthcare, and finance? 2. What organisational mechanisms allow these organisations to be agile without breaking the rules that govern them? 3. What practical lessons follow for managers, regulators, and students preparing to work in these sectors? The article makes three contributions. First, it brings together recent research on strategic agility, regulation, and sector-specific innovation into one readable account. Second, it proposes a framework of regulated strategic agility, built around three mechanisms and a set of enabling conditions. Third, it offers two practical tools: a typology of strategic postures toward regulation, and a six-stage cycle for bounded experimentation. The article is written for students, so it explains key ideas in plain language, but it follows the structure of a research article so that readers can see how an argument is built from evidence. 1.3 Structure of the article Section 2 explains the approach and the sources used. Section 3 reviews the literature on #strategic_agility, on regulation and innovation, and on each of the three sectors. Section 4 sets out the conceptual framework. Section 5 analyses the mechanisms of strategy formulation and execution, compares the three sectors, and discusses the tools and postures that make compliant innovation possible. Section 6 discusses the implications, and Section 7 concludes with findings, limits, and directions for future research. 2. Approach and Sources This article uses an integrative review approach. An integrative review brings together studies that use different methods, such as surveys, case studies, comparative policy analysis, and conceptual papers, in order to build a new understanding of a topic. It differs from a statistical meta-analysis, which combines numerical results from similar studies. An integrative review is suitable here because the topic crosses several fields: strategic management, public administration, health services research, higher education studies, and financial regulation. Sources were identified through searches of large scholarly databases using combinations of terms such as strategic agility, organisational agility, regulated industries, regulatory sandbox, RegTech, quality assurance, accreditation, healthcare agility, and agile governance. Priority was given to peer-reviewed journal articles and academic books published within the last five years, so that the review reflects current conditions, including the experience of the pandemic and the rapid spread of digital tools. A small number of slightly older but widely cited works from 2021 were kept where they define core concepts. Every source in the reference list was checked against its publisher record. The reading of the sources followed three steps. First, each source was read for its main claim, its setting, and its method. Second, the claims were grouped into themes: what agility is, how regulation affects it, which tools enable compliant innovation, and which conditions help or hinder. Third, the themes were compared across the three sectors to see what is shared and what is specific. The framework presented in Section 4 is the result of this comparison. It is a conceptual framework, meaning it organises existing evidence into a model that can be tested in future studies; it is not itself a tested statistical model. Two limits should be stated early. The review relies on published studies, and published studies tend to report successes more often than failures. Also, the three sectors differ greatly between countries, so examples from one country may not transfer directly to another. These limits are discussed again in Section 7. 3. Literature Review 3.1 What strategic agility means The idea of agility first became popular in manufacturing and software development, where it described flexible production and short development cycles. In strategic management, the term was extended to the level of the whole organisation and its long-term direction. A widely used view describes strategic agility through three parts: strategic sensitivity, which is the ability to notice and make sense of change; leadership unity, which is the ability of top managers to make bold decisions together; and resource fluidity, which is the ability to move people, money, and capabilities to where they are needed. Recent empirical studies still use this three-part view. For example, Aponte-Lopez et al. (2026) measured strategic agility in a Colombian university through exactly these three dimensions. Recent reviews show that the field has grown quickly but remains somewhat unclear in its definitions. Asghar et al. (2025) reviewed 110 peer-reviewed articles and concluded that organisational agility is best understood as a bundle of related capabilities rather than one single trait. They distinguish macro-agility, which includes strategic and partnering agility, from micro-agility, which includes operational, innovation, and workforce agility. They also group the drivers of agility into contextual factors, fundamental attributes, vital enablers, and dynamic capabilities. Girod et al. (2023) similarly describe business agility as a broad set of themes rather than a single recipe, and they call for more attention to context. Context is exactly where regulated industries differ. Prange (2021) offers an important warning for these settings: agility is not the same as speed. She argues that agile organisations also need the capacity to slow down, reflect, and choose the right moment to act. In a sector where a mistake can harm a patient or destabilise a bank, the ability to pause and check is part of agility, not its opposite. Research on the microfoundations of agility, meaning the individual-level and team-level behaviours that add up to organisational agility, is also useful. Ferraris et al. (2022) found that the tenure, experience, and cognitive skills of subsidiary chief executives in emerging markets were positively linked to the #strategic_agility of multinational firms, and that the subsidiary's embeddedness in the local context shaped these links. Hutton et al. (2024) studied a firm that used open innovation in new product development and identified six mechanisms that together supported three practices: knowledge-based agility at the boundary between the firm and its environment, behavioural agility in decision making, and organisational agility in internal processes. These studies suggest that agility is built from many small routines and decisions, not only from top-level plans. Finally, research links agility to innovation and performance. Clauss et al. (2021) showed that strategic agility supports business model innovation, which in turn supports firm performance. Troise et al. (2022) found that agility helps small and medium-sized firms cope with volatile, uncertain, complex, and ambiguous environments during #digital_transformation. These links help explain why regulated organisations are interested in agility: they too need to renew their business models in a fast-changing world. 3.2 Strategic agility as a dynamic capability Many authors place strategic agility within the theory of #dynamic_capabilities. In simple terms, ordinary capabilities let an organisation do its current work well, while dynamic capabilities let it change what it does. #Dynamic_capabilities are often described as three activities: sensing opportunities and threats, seizing opportunities by committing resources, and transforming or reconfiguring the organisation so that it fits the new situation. Several of the studies in this review explicitly use this view. Koomson (2025) draws on dynamic capability theory to explain how agility improves quality management and innovation performance. Aponte-Lopez et al. (2026) use the dynamic capabilities view to explain how human capabilities and digital technologies build agility in a university. The dynamic capabilities view is helpful for regulated industries because it separates noticing, deciding, and changing. In a regulated setting, each of these activities has a compliance dimension. Sensing must include sensing changes in rules. Seizing must include checking whether a new idea is allowed. Reconfiguring must include rebuilding processes so that they remain compliant. The framework in Section 4 builds on this observation. 3.3 Regulation, compliance, and innovation A long-standing debate asks whether regulation helps or harms innovation. One view sees regulation as a cost: it slows approvals, raises the expense of entry, and discourages experiments. Another view argues that well-designed regulation can push organisations to innovate, for example by setting clear standards that reward better products. Recent research in regulated sectors offers evidence for both views, and shows that the effect often depends on how rules are designed and how organisations respond. On the positive side, several recent studies find that regulatory pressure can act as a catalyst for agility. Suyitno et al. (2026), in a survey of 173 hospital directors in Indonesia, found that regulatory pressure had a significant positive effect on organisational agility, and that agility in turn improved hospital performance. Koomson (2025) found that industry pressures, which included government regulation, strengthened the relationship between agility and total quality management in telecommunications firms. Koomson also recommended that regulators set quality standards while allowing firms flexibility in how they meet them, a point that is central to this article. On the negative side, layered and slow rules can make change difficult. Kohoutek et al. (2025), in a comparative study of quality assurance policies in Poland, the Czech Republic, Hungary, and Slovakia, found that national agencies used combinations of different tools that prioritised regulation, but that the effectiveness of these combinations was limited by the problematic layering of individual instruments. Alghamdi et al. (2026), studying quality assurance reforms in Saudi #higher_education, found that bureaucratic compliance cultures and resistance to change constrained innovation, even though quality assurance was widely prioritised in strategic plans. A third line of research looks at how the relationship between regulators and regulated organisations is changing. Mergel et al. (2021) describe agile methods as a new way of governing, in which public agencies adopt iterative and user-centred ways of working. Mergel (2024) explores the social side of agile governance, showing that agility in public organisations depends on how people interact and collaborate. When regulators themselves become more agile, the space for agile strategy in regulated organisations also changes. This literature supports a balanced position. #compliance is not simply an obstacle to be minimised. It protects values that organisations in these sectors depend on, including public trust. But compliance can be done in ways that either support or suppress innovation. The task is to understand which ways do which. 3.4 Paradox and both/and thinking Paradox theory offers a useful lens for this balance. A paradox, in management research, is a set of demands that seem to contradict each other but that both need to be met over time. Smith and Lewis (2022) argue that leaders facing such tensions often fall into either/or thinking, choosing one side and neglecting the other. They recommend #paradox_thinking, a both/and approach that treats tensions as permanent features to be managed rather than problems to be solved once. In #regulated_industries, the tension between innovation and compliance is a classic paradox. An organisation must change to stay relevant, and it must stay stable to stay trusted. Choosing only innovation risks sanctions and harm; choosing only compliance risks decline. Paradox thinking suggests that the most effective organisations build structures and habits that allow them to pursue both at once, for example by creating protected spaces for experiments while keeping core operations stable. As later sections show, many of the tools used in regulated sectors, such as sandboxes and pilot programmes, can be read as practical ways of living with this paradox. 3.5 Agility in higher education #higher_education is regulated in different ways in different countries, but most systems share some features. Governments fund or license institutions, set rules for degree titles, and require external quality review through agencies or #accreditation bodies. These rules protect students, employers, and the value of qualifications. They also create long cycles: designing and approving a new programme can take a year or more. Recent studies show both the need for and the limits of agility in universities. Begicevic Redep et al. (2026) interviewed 21 experts from eight European universities about agile practices in strategic planning and decision making. They found that universities face complex bureaucratic structures and long planning cycles, and that institutional regulations and the need to involve many stakeholders make it hard to adopt agility fully. Even so, they identified areas where agile practices work, including collaboration, decision-making style, change management, lean approaches, and continuous improvement. They also suggested that shorter strategy cycles, such as annual reviews, would fit better with today's rapid changes. Aponte-Lopez et al. (2026) surveyed 157 academic leaders across several campuses of a Colombian university that operates in face-to-face, distance, and virtual modes. They found that human capabilities and digital technologies both strengthened strategic agility, and that #strategic_agility had a significant positive effect on innovation performance. Interestingly, adaptability did not significantly moderate this relationship, which the authors interpret as a sign that adaptability may be a baseline condition rather than a point of difference in very dynamic educational settings. Studies of quality assurance add an important layer. Kohoutek et al. (2025) show that quality assurance systems in Central Europe combine diverse tools, with control as a baseline function. Alghamdi et al. (2026) show that in Saudi Arabia, leadership, technological integration, and faculty development emerged as key enablers of reform, while bureaucratic #compliance constrained innovation. Together these studies suggest that universities can become more agile, but that the design of quality assurance and the culture around it strongly shape how far they can go. 3.6 Agility in healthcare #healthcare is perhaps the most sensitive of the three sectors because errors can cost lives. Rules cover the licensing of professionals and facilities, the approval of medicines and devices, the protection of health data, and the reporting of outcomes. These rules exist for strong reasons, but they can also slow the spread of useful innovations. Recent research shows that agile methods can work in healthcare when they are carefully adapted. Desai et al. (2024) describe an accelerator that brought together 18 healthcare organisations to prepare the tools needed to launch home hospital programmes, in which patients receive hospital-level care at home. Using the Scrum framework, the teams produced the planned 20 knowledge products, including workflows, inclusion criteria, and protocols, in 32 working weeks instead of the planned 40. Participants reported that teams worked well and produced high-quality products, although some found the framework hard to learn and struggled to balance the work with their normal duties. At the level of whole systems, Dube et al. (2024) studied how organisations in the United Kingdom medicine supply system responded first to Brexit, an event whose effects could be anticipated, and then to the COVID-19 pandemic, whose effects could not. They found that learning moved from surface knowledge to deeper understanding and, at the highest level, to what they call transcendent learning, which looks beyond the present and beyond the national system. They developed a theory of system-level strategic agility in which adaptation depends on reallocating resources and continuously reconfiguring processes, protocols, regulations, and structures. This study is important because it shows that, in #healthcare, rules themselves can be part of what is reconfigured during agile responses. The regulation of digital health shows how rules can evolve. Zhang et al. (2025) reviewed a decade of regulation of artificial intelligence medical devices across the United States, the European Union, China, Japan, and South Korea. They found that regulators have introduced various policies to deal with the special risks of these devices, aiming to balance innovation with #patient_safety, but that gaps remain, especially in harmonising standards across regions. Schmidt et al. (2024) mapped 141 binding policies relevant to artificial intelligence in health in the European Union and ten European countries, and concluded that rules specifically about artificial intelligence were still at an early stage, while a mix of data, technology, innovation, and human rights policies already formed a baseline framework. 3.7 Agility in finance #finance is regulated mainly to protect financial stability, consumers, and the integrity of markets. Banks and other financial firms face rules on capital, liquidity, conduct, reporting, and the prevention of money laundering. After the financial crisis of 2007 to 2009, these rules became more detailed. At the same time, digital technology and new financial technology firms have increased pressure on traditional institutions to change. Two developments are especially relevant to agility. The first is the regulatory sandbox, a framework in which a regulator allows firms to test new products with real customers under close supervision and with agreed limits. Cornelli et al. (2024) studied the sandbox run by the United Kingdom Financial Conduct Authority and found that firms entering the sandbox went on to raise significantly more capital, which suggests that the sandbox reduced uncertainty for investors. Kalman (2025) compared sandboxes in the United Kingdom, Singapore, and Hungary, and found that they improved access to funding, sped up product development, and encouraged collaboration between regulators and firms, with more mature ecosystems benefiting most. Raudla et al. (2024) examined sandboxes and innovation hubs in the Baltic states, showing that smaller jurisdictions also adopt these tools as ways of engaging with innovators. Not all researchers are enthusiastic. Just et al. (2024) argue that the spread of sandboxes reflects a shift in which regulators become promoters of innovation, and they call for a more critical view of the assumption that regulators should be "open for business." The second development is #RegTech, meaning the use of technology to manage regulation and compliance. Grassi and Lanfranchi (2022) reviewed the RegTech literature and described a framework in which regulation, technology, and data interact. They noted benefits such as greater efficiency, accuracy, transparency, and lower compliance costs, but also risks, including cyber risk, algorithmic bias, and the reduced role of human judgement. #RegTech matters for agility because it can turn compliance from a slow, manual check at the end of a process into an automated control built in from the start. 3.8 Gaps in the literature The review reveals three gaps. First, research on strategic agility rarely treats regulation as a central feature of context; it is usually one variable among many. Second, sector studies of universities, hospitals, and banks often describe agile practices without linking them to a general theory of strategy. Third, there is little work that compares the three sectors to identify shared mechanisms. This article responds to these gaps by proposing a framework that places regulation at the centre of strategic agility and by using it to compare the sectors. 4. Conceptual Framework: Regulated Strategic Agility The framework proposed here starts from a simple observation. In regulated industries, the regulatory environment does not sit outside the organisation's strategy; it runs through every stage of it. Strategy formulation must take account of what rules allow and how they may change. Strategy execution must stay within those rules at every step. Innovation must either fit existing rules, use special routes such as sandboxes, or help to change the rules over time. Figure 1 shows the framework. Figure 1. The framework of regulated strategic agility: three mechanisms, enabling conditions, outcomes, and a feedback loop to the regulatory environment. 4.1 Mechanism one: regulatory sensing The first mechanism is regulatory sensing. All agile organisations scan their environment, but regulated organisations must scan two environments at once: the market and the rulebook. Regulatory sensing includes following new laws and guidance, reading consultation papers, tracking enforcement decisions, and understanding what regulators are worried about. This activity is often called #regulatory_intelligence. It turns rule changes from surprises into signals that can be prepared for. Regulatory sensing also includes sensing what the rules do not yet cover. When Schmidt et al. (2024) found that rules specific to artificial intelligence in European health systems were still at an early stage, they described a space where organisations face uncertainty but also have room to propose responsible practices. Organisations with good regulatory sensing notice such spaces early. Dube et al. (2024) show the value of sensing in the medicine supply system: learning moved from knowing what was happening to understanding why and how, and finally to anticipating effects beyond the present situation. 4.2 Mechanism two: bounded seizing The second mechanism is bounded seizing. In free markets, seizing an opportunity often means committing resources quickly and at scale. In regulated markets, seizing usually needs to be bounded: limited in time, in the number of users, in the volume of activity, or in the type of risk. Bounded seizing allows an organisation to act on an opportunity without exposing itself or the public to unlimited risk. Regulatory sandboxes are the clearest example. A sandbox allows a firm to test a product with real customers under agreed limits and close supervision (Cornelli et al., 2024; Kalman, 2025). Pilot programmes in hospitals and small-scale programme trials in universities play a similar role. The accelerator described by Desai et al. (2024) is another form of bounded seizing: it created a temporary, structured space in which many organisations could develop new care models together before launching them. Bounded seizing links to Prange's (2021) argument that agility includes knowing when to slow down: the bounds are a deliberate way of controlling pace. 4.3 Mechanism three: compliant reconfiguring The third mechanism is compliant reconfiguring. Once an experiment has shown value, the organisation must change its structures, processes, and resource allocations to use the new idea at scale. In regulated sectors, this change must keep the organisation compliant throughout. The practical name for this is #compliance_by_design: building rule requirements into new processes from the beginning rather than checking them at the end. RegTech supports compliant reconfiguring by automating checks, reporting, and monitoring (Grassi and Lanfranchi, 2022). Resource fluidity, one of the three classic dimensions of #strategic_agility, is also central here. Aponte-Lopez et al. (2026) show that digital technologies and human capabilities strengthen this kind of agility in a university. Dube et al. (2024) show that in healthcare, reconfiguring can extend to protocols and even regulations, when crises require rapid change and regulators respond. 4.4 Enabling conditions The three mechanisms do not work on their own. They rest on enabling conditions, shown in the middle layer of Figure 1. Five conditions stand out in the reviewed literature. The first is leadership unity. Top managers must agree on direction and be willing to make decisions together, especially when an idea touches both innovation and compliance. The second is a both/and mindset, the paradox thinking described by Smith and Lewis (2022), which allows leaders to hold innovation and compliance together rather than trading one against the other. The third is digital and RegTech capability, which allows faster sensing and cheaper, more reliable compliance. The fourth is relationships of trust with regulators, built through transparency and early dialogue; sandbox studies show that regulator-firm collaboration is one of the main benefits of these schemes (Kalman, 2025). The fifth is people skills and a learning culture. Studies of universities and hospitals repeatedly show that leadership, faculty development, teamwork, and readiness for change are key enablers (Alghamdi et al., 2026; Desai et al., 2024). 4.5 Outcomes and the feedback loop The outcome of regulated strategic agility is strategic innovation inside compliance boundaries: new services, programmes, and business models that create value while keeping the organisation's licence to operate and the public's trust. Recent hospital research suggests that agility is the mechanism through which capabilities and institutional demands turn into performance (Suyitno et al., 2026). Figure 1 also shows a feedback loop from outcomes back to the regulatory environment. This loop is easy to miss but important. When organisations run bounded experiments and share evidence, regulators learn. Over time, that learning can lead to new guidance or new rules. Sandboxes are partly designed for this purpose: they help regulators understand innovations before deciding how to regulate them. In this sense, agile organisations in regulated sectors do not only adapt to rules; they also help shape them. This must be done openly and responsibly, a point discussed in Section 5.5. 5. Analysis and Discussion This section uses the framework to analyse how strategy is formulated and executed in the three sectors. It begins with formulation, moves to execution, compares the sectors, presents a typology of strategic postures, and then examines practical tools and risks. 5.1 Strategy formulation under regulation Strategy formulation is the process of deciding where an organisation wants to go and how it plans to get there. In regulated industries, three features shape this process. The first is that the set of possible options is defined partly by others. A university's choice of new programmes is shaped by national qualification frameworks and quality assurance standards. A hospital's choice of services depends on licensing and reimbursement rules. A bank's choice of products depends on what it is authorised to do and how much capital each activity requires. This means that strategy formulation in regulated industries always begins with a question that free-market firms rarely ask so directly: what are we allowed to do, and what might we be allowed to do in future? The second feature is that time horizons are longer and more fixed. Approval cycles, #accreditation reviews, and reporting periods create a rhythm that organisations cannot easily change. Begicevic Redep et al. (2026) found that long planning cycles were one of the main obstacles to agility in European universities. Their recommendation, shorter strategy cycles such as annual reviews, is an example of how organisations can adjust their own planning rhythm even when external rhythms stay fixed. A university might keep a five-year strategic plan for accreditation purposes while reviewing priorities every year and adjusting resource allocations accordingly. The third feature is that more stakeholders have a formal voice. Universities answer to governments, #accreditation agencies, academic senates, students, and employers. Hospitals answer to health ministries, professional bodies, insurers, and patients. Banks answer to several supervisors, shareholders, and customers. This makes formulation slower but can also make it more robust, because more perspectives are considered. The agile response is not to bypass stakeholders but to involve them earlier and in more structured ways, for example through regular consultation forums or joint working groups with regulators. Within these features, regulatory sensing plays a central role in formulation. Organisations that track regulatory developments can build strategies that anticipate change. A bank that understood early how open banking rules would develop, for example, could prepare data-sharing capabilities before competitors. A university that tracks policy discussions on micro-credentials can design short courses that fit emerging frameworks. A hospital that follows the development of lifecycle approaches to software medical devices can plan digital tools that will be easier to update once approved. In each case, the regulatory environment becomes a source of strategic insight, not only a list of restrictions. 5.2 Strategy execution within compliance boundaries Execution is often where strategy fails. In #regulated_industries, execution has an extra layer of difficulty because every step must stay compliant. Two common patterns of failure appear in the literature and in practice. The first pattern is late #compliance. A project team designs a new product or service, then sends it to the compliance or legal department for approval near the end. The compliance team finds problems, the project is delayed or redesigned, and frustration builds on both sides. This pattern treats compliance as a gate at the end rather than a partner from the beginning. It wastes time and often damages relationships between innovators and compliance staff. The second pattern is compliance paralysis. Here, fear of breaking rules leads staff to avoid any change that is not explicitly approved. Alghamdi et al. (2026) describe how bureaucratic compliance cultures in Saudi higher education constrained innovation even when leaders promoted reform. In such cultures, rules are interpreted in the most restrictive way possible, and people learn that doing nothing new is safest. Regulated strategic agility addresses both patterns through bounded seizing and compliant reconfiguring. Compliance specialists are brought into projects early, often as members of the team rather than reviewers outside it. Rule requirements are translated into design features. Experiments are bounded so that risk stays within acceptable limits. #RegTech tools monitor activity continuously so that problems are detected early. The healthcare accelerator studied by Desai et al. (2024) illustrates part of this approach: by using short cycles and cross-functional teams, it produced protocols and workflows, the very documents that make new care models safe and compliant, faster than usual. Execution also depends on resource fluidity. In many regulated organisations, budgets are allocated through annual cycles and are hard to move. Staff roles are defined by professional rules that limit who can do what. Agile execution requires mechanisms that allow resources to move within these constraints, for example innovation funds that can be allocated during the year, or flexible teams drawn from several departments. Aponte-Lopez et al. (2026) show that digital technologies support this kind of fluidity in a university by making it easier to deliver programmes in different modes. 5.3 Comparing the three sectors Although the three sectors share the basic challenge of innovating within rules, they differ in what regulation protects, how it constrains, and which agility levers are most available. Figure 2 summarises these differences. Figure 2. A comparison of regulators, protected values, typical constraints, and main agility levers in higher education, healthcare, and finance. In higher education, regulation mainly protects academic standards and the fair recognition of qualifications. The typical constraint is the cycle of programme approval and quality review, often made heavier by the layering of several instruments (Kohoutek et al., 2025). The main agility levers are internal: shorter strategy cycles, modular programme designs that allow parts of a curriculum to change without full re-approval, digital delivery, and the development of staff capabilities (Begicevic Redep et al., 2026; Aponte-Lopez et al., 2026). Universities have relatively few formal sandbox-type schemes, so much of their agility depends on how they interpret and organise their own internal governance. In healthcare, regulation protects #patient_safety and clinical quality. The typical constraints are the approval of medicines and devices, the protection of health data, and the need for clinical evidence. The main agility levers include agile project methods for developing new care models (Desai et al., 2024), system-level learning and reconfiguration during crises (Dube et al., 2024), and newer regulatory approaches that treat software as something that evolves over its lifecycle rather than a fixed product approved once (Zhang et al., 2025). Healthcare also shows that regulatory pressure can encourage agility: in Indonesian hospitals, regulatory pressure was positively linked to agility (Suyitno et al., 2026). In finance, regulation protects financial stability, consumers, and market integrity. Typical constraints include licensing, capital and liquidity rules, and anti-money laundering and reporting duties. #Finance has the most developed set of formal agility levers: #regulatory_sandboxes, innovation hubs, and a large RegTech industry (Cornelli et al., 2024; Kalman, 2025; Raudla et al., 2024; Grassi and Lanfranchi, 2022). This may be because financial regulators faced strong pressure from new technology firms and saw innovation as linked to competition and growth, a link that Just et al. (2024) examine critically. The comparison suggests an interesting pattern. Where regulators have created formal routes for experimentation, as in finance, organisations can rely more on external bounded seizing. Where such routes are fewer, as in #higher_education, organisations must rely more on internal flexibility. #Healthcare sits in between, with growing regulatory innovation for digital tools but a strong and justified emphasis on evidence before scale. Students should notice that agility therefore looks different in each sector even when the underlying mechanisms are the same. 5.4 Four strategic postures toward regulation Organisations differ in how they relate to regulation. Two questions capture the main differences. First, is the organisation's innovation posture defensive or proactive? Second, does the organisation simply take rules as given, or does it try to shape them? Combining these questions gives four postures, shown in Figure 3. Figure 3. A typology of four strategic postures toward regulation, based on innovation posture and regulatory stance. The cautious guardian follows rules closely and changes slowly. This posture is safe in the short term, and it may be appropriate for activities where risk is very high. But over time the cautious guardian may fall behind more adaptive competitors, and its staff may lose the skills needed to change. Universities with strong compliance cultures and weak strategic leadership often drift into this posture (Alghamdi et al., 2026). The quiet optimiser innovates within current rules. It looks for improvements that do not require rule changes, uses RegTech to make compliance cheaper and faster, and reads rules carefully to find legitimate flexibility. Much valuable innovation happens here: better processes, better digital services, and better use of data within existing permissions. Koomson's (2025) finding that agile firms improve their quality management practices fits this posture. The protective gatekeeper is active in shaping rules, but mainly to protect its existing position. It may lobby for rules that raise barriers to new entrants or delay change. This posture can bring short-term benefits to the organisation, but it creates little new value and can damage public trust when it becomes visible. The co-creating innovator is both proactive and engaged with regulators. It uses sandboxes and pilots, shares evidence openly, and works with regulators to develop rules that allow responsible innovation. Firms that use sandbox schemes well, as described by Cornelli et al. (2024) and Kalman (2025), are examples. This posture combines the three mechanisms of the framework most fully, and it is the one most consistent with the feedback loop in Figure 1. A fifth path sits outside the grid: deliberately moving ahead of the rules and dealing with regulators later. This path can bring fast growth, but in the sectors discussed here it carries serious risks for the public and for the organisation. The framework in this article does not recommend it, because #strategic_innovation that harms students, patients, or customers undermines the very trust these sectors depend on. The typology is not a fixed classification. An organisation may adopt different postures for different activities, for example acting as a cautious guardian in core clinical care while acting as a co-creating innovator in digital services. Students can use the typology to analyse real organisations and to ask whether their posture fits their goals and risks. 5.5 The bounded experimentation cycle How can an organisation turn the ideas above into practice? Drawing on the sandbox literature, the healthcare accelerator studied by Desai et al. (2024), and the recommendations for universities by Begicevic Redep et al. (2026), this article proposes a six-stage cycle for bounded experimentation. Figure 4 shows the cycle. Figure 4. The six-stage bounded experimentation cycle, with every stage kept inside the compliance boundary. Stage one is to frame the idea against the rulebook. Before investing heavily, the team maps the duties that apply: which laws, standards, and guidance cover the activity, and which permissions are needed. This is not a search for reasons to say no; it is a map that shows where the idea can go. Stage two is early dialogue with compliance staff and, where appropriate, the regulator. Many regulators now offer innovation offices, hubs, or informal guidance for this purpose, especially in finance (Raudla et al., 2024). In universities, the equivalent may be early contact with the quality assurance office or the external agency. Early dialogue reduces uncertainty and builds trust. Stage three is to design safeguards into the idea. Safeguards may include limits on who can take part, clear consent processes, data protection measures, human review of automated decisions, and an audit trail that records what was done and why. This is compliance by design in practice. Stage four is to run a small, bounded pilot. The pilot is limited in time, in the number of users, and in the volume of activity. In finance, this may happen inside a formal sandbox. In healthcare, it may be a pilot on one ward or with one group of patients. In higher education, it may be a short course or a single module delivered in a new way. Stage five is to collect evidence and report transparently. The team gathers data on outcomes, risks, and unexpected effects, and shares results with internal decision makers and, where relevant, with the regulator. Transparent reporting is what turns a private experiment into shared learning. Stage six is to decide whether to scale, adjust, or stop, and to share lessons. Stopping is a legitimate and valuable result. An organisation that can stop failed experiments quickly is more agile than one that keeps them running to avoid admitting failure. The dashed arrow in Figure 4 shows the learning loop: lessons from each cycle shape how the next idea is framed. The central box in Figure 4 is a reminder that every stage remains inside legal and ethical limits. Bounded experimentation is not a way around the rules; it is a way of exploring what is possible within them, and of producing the evidence needed to improve them. 5.6 The role of digital technology and RegTech Digital technology plays two roles in regulated strategic agility. It is often the source of the innovation itself, as with digital learning platforms, artificial intelligence diagnostic tools, or mobile banking services. It is also a tool for making compliance faster and more reliable. The second role deserves more attention than it usually receives. Traditional compliance relies heavily on manual checks, paper records, and periodic reporting. These methods are slow and costly, and they tend to push #compliance to the end of processes. #RegTech changes this by automating checks, monitoring transactions continuously, and producing reports from live data (Grassi and Lanfranchi, 2022). When compliance can be checked in real time, organisations can run more experiments with less risk, because problems are seen quickly. #digital_transformation also supports agility in higher education and healthcare. Aponte-Lopez et al. (2026) found that digital technologies strengthened strategic agility in a university and partly carried the effect of human capabilities on agility. In healthcare, digital tools allow new care models such as home hospitals, which Desai et al. (2024) helped to spread through an agile accelerator. Technology also brings new risks. Grassi and Lanfranchi (2022) warn of cyber risk, algorithmic bias, and the loss of human judgement. In healthcare, Zhang et al. (2025) note that the adaptive and opaque nature of artificial intelligence creates challenges for traditional regulation. This means that digital tools must themselves be governed carefully. An organisation that adopts artificial intelligence to speed up decisions without proper oversight may become faster but less compliant, which is the opposite of regulated #strategic_agility. 5.7 People, leadership, and culture Frameworks and tools matter, but in the end agility is carried out by people. The reviewed studies consistently highlight leadership and culture as enabling conditions. At the top, leadership unity allows organisations to make decisions that cross the boundary between innovation and compliance. If the head of strategy and the head of compliance pursue different goals, every new idea becomes a negotiation. If they share a view of the organisation's posture toward regulation, decisions become faster and more consistent. Ferraris et al. (2022) show that the experience and cognitive skills of senior leaders matter for strategic agility, and this is likely to be especially true when leaders must interpret complex rules. At the middle levels, managers translate strategy into action. In regulated organisations, middle managers often feel squeezed between pressure to innovate and fear of breaking rules. Training in both the content of rules and the methods of agile work can help them handle this tension. The both/and approach described by Smith and Lewis (2022) is useful here, because it encourages managers to look for solutions that meet both demands rather than choosing one. At the front line, staff need psychological safety to raise concerns and suggest improvements. A culture where people fear blame for any mistake will produce compliance paralysis. A culture where people feel free to ignore rules will produce compliance failures. The goal is a culture where staff take rules seriously because they understand the values behind them, and feel able to propose new ideas because they trust that good ideas will be tested responsibly. Studies of quality assurance reform show that faculty engagement and development are key to this kind of culture (Alghamdi et al., 2026). 5.8 Risks and critical perspectives A balanced discussion must also recognise the risks of promoting agility in regulated industries. The first risk is that agility becomes a slogan that hides weaker protection. If agility is understood only as speed, organisations may cut corners on safety, fairness, or stability. This is why the framework stresses bounds, safeguards, and transparency, and why Prange's (2021) reminder that agility includes slowness is so important. The second risk concerns the relationship between regulators and industry. When regulators take on a role in promoting innovation, they may become too close to the firms they supervise. Just et al. (2024) argue that the spread of regulatory sandboxes reflects a political choice to treat regulators as promoters of competition and growth, and that this choice deserves critical attention. Students should keep this concern in mind: co-creation with regulators is valuable only if regulators remain independent and focused on the public interest. The third risk is unequal access. Large organisations can afford compliance teams, RegTech systems, and the time needed to engage with regulators. Smaller organisations may find these costs heavy. Kalman (2025) found that sandbox benefits were greater in mature ecosystems, which suggests that the tools of regulated agility may favour those already well positioned. Policymakers need to consider how small universities, rural hospitals, and small financial firms can also benefit. The fourth risk is measurement. Many of the studies reviewed rely on surveys of managers' perceptions. Perceived agility is not always the same as actual agility, and survey studies often cannot prove cause and effect. This does not make the findings worthless, but it does mean that conclusions should be held with appropriate caution. 6. Implications 6.1 Implications for managers For managers in regulated organisations, the framework suggests several practical steps. First, build regulatory sensing into strategic planning. Assign responsibility for tracking regulatory developments and discuss them at the same table where market trends are discussed. Second, shorten internal strategy cycles where external cycles cannot change. Annual or even quarterly reviews of priorities can make an organisation more responsive without breaking accreditation or reporting rhythms (Begicevic Redep et al., 2026). Third, bring compliance staff into innovation projects from the start, and treat them as designers of safeguards rather than gatekeepers at the end. Fourth, use bounded experiments, internal or external, to test ideas before committing at scale. Fifth, invest in digital and RegTech capabilities, while governing them carefully. Sixth, choose a deliberate posture toward regulation, using Figure 3 as a guide, and communicate it clearly across the organisation. 6.2 Implications for regulators and policymakers For regulators, the review suggests that the design of rules shapes how organisations innovate. Rules that set clear outcomes while allowing flexibility in how they are achieved, as Koomson (2025) recommends, give organisations space to be agile. Layered instruments that overlap and conflict, as Kohoutek et al. (2025) describe in Central European quality assurance, make agility harder. Formal routes for experimentation, such as sandboxes and innovation hubs, can reduce uncertainty and attract investment (Cornelli et al., 2024), but they should be designed with clear public interest goals and evaluated independently. In higher education, regulators might consider risk-based approaches in which institutions with strong records receive lighter review for low-risk changes, freeing resources for closer attention where risks are higher. In healthcare, lifecycle approaches to evolving software show how rules can follow the nature of the technology rather than forcing it into older categories (Zhang et al., 2025). 6.3 Implications for students For students, the main lesson is that strategy in the real world is rarely a free choice. Many graduates will work in regulated sectors, either directly or as suppliers, advisers, or partners. Understanding how rules shape strategy is therefore a practical skill. Students preparing for careers in these sectors can build three habits. The first is to read regulation as a strategic document, asking what values it protects and where it leaves room for choice. The second is to practise both/and thinking, looking for solutions that meet innovation and compliance goals together. The third is to value evidence: in regulated sectors, good ideas spread when they are supported by data from careful pilots. 6.4 An illustrative scenario To bring these ideas together, consider a simple illustrative scenario. This is not a case study of a real organisation; it is an example built to show how the framework can be applied. A mid-sized university wants to offer short, stackable courses in data analysis for working adults. Through regulatory sensing, its planning office notices that national policy discussions increasingly mention micro-credentials, but that formal rules for recognising them are not yet settled. Leadership agrees that the opportunity is strategic and that the university should act as a co-creating innovator rather than waiting. The university frames the idea against existing rules and finds that it can offer non-credit courses without external approval, while credit-bearing courses would need review. It opens early dialogue with its internal quality office and with the national agency, explaining its plans and asking how future recognition might work. It designs safeguards: clear information to learners about what the courses do and do not lead to, assessment standards aligned with existing modules, and records that would allow later credit recognition if rules permit. It runs a bounded pilot with two courses and a limited number of learners. It collects evidence on learning outcomes, completion, and learner satisfaction, and shares a summary with the agency. Based on the results, it adjusts the courses and prepares a proposal for credit recognition that draws on its own evidence. In this scenario, the university has not broken any rules or waited for perfect clarity. It has used the three mechanisms of the framework, relied on enabling conditions such as leadership unity and trust with the regulator, and contributed evidence that may help shape future policy. The same logic could be applied to a hospital testing a remote monitoring service or a bank testing a new digital savings product. 7. Conclusion 7.1 Main findings This article set out to explain how organisations in highly regulated sectors form and carry out strategy, and how strategic innovation can be achieved within strict compliance boundaries. Four main findings emerge from the review. First, strategic agility in regulated industries is best understood not as raw speed but as the ability to sense, seize, and reconfigure within rules. The framework of regulated strategic agility captures this through three mechanisms: regulatory sensing, bounded seizing, and compliant reconfiguring. Second, regulation does not only constrain agility; it can also encourage it. Recent studies find positive links between regulatory pressure and agility in hospitals and between industry pressures and agile quality management in telecommunications (Suyitno et al., 2026; Koomson, 2025). At the same time, layered rules and compliance-first cultures can suppress innovation (Kohoutek et al., 2025; Alghamdi et al., 2026). The effect depends on rule design and on organisational response. Third, the three sectors share mechanisms but differ in their main levers. Finance has the most developed external tools, especially sandboxes and RegTech. Healthcare combines agile project methods with evolving rules for digital tools. Higher education relies more on internal levers such as shorter strategy cycles and modular programme design. Fourth, enabling conditions matter as much as tools. Leadership unity, a both/and mindset, digital capability, trust-based relationships with regulators, and a learning culture appear repeatedly as foundations of agility in regulated settings. 7.2 Limitations The article has several limitations. It is based on an integrative review rather than new empirical data, so the framework is a proposal to be tested rather than a proven model. Many of the studies reviewed are single-country studies, and regulatory systems differ widely across countries. Several rely on cross-sectional surveys of managers, which cannot establish cause and effect. Published research may also over-represent successful innovations. Finally, the article focuses on three sectors; other regulated industries, such as energy, telecommunications, aviation, and pharmaceuticals, may show different patterns. 7.3 Directions for future research Future research could test the framework with longitudinal studies that follow organisations over several years, measuring sensing, seizing, and reconfiguring alongside regulatory change. Comparative studies across countries could show how different regulatory designs affect agility. More work is needed on small organisations and on how they can access the tools of regulated agility. Research on the regulator side could examine how sandboxes and similar schemes change regulators' own learning and independence. Finally, as artificial intelligence spreads in all three sectors, studies could examine how organisations govern these tools so that they support, rather than undermine, compliant innovation. For students, the broader message is encouraging. Rules do not make strategy impossible; they make it more thoughtful. The organisations that thrive in regulated sectors are those that learn to treat compliance and innovation not as enemies, but as two sides of the same responsibility to serve people well. References Alghamdi, M., Ahmed, S. N., Rehman, S. U., and Jack, K. (2026). Quality assurance in Saudi higher education. Journal of International Students, 16(14), 21-44. https://doi.org/10.32674/czxbqm38 Aponte-Lopez, A. C., Trujillo-Gallego, M., and Rincon-Guio, C. (2026). From local capabilities to global innovation: The role of strategic agility in higher education. Research in Globalization, 12, 100352. https://doi.org/10.1016/j.resglo.2026.100352 Asghar, J., Kanbach, D. K., and Kraus, S. 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Regulatory sandboxes and innovation hubs for FinTech: Experiences of the Baltic states. European Journal of Law and Economics, 61(1-2), 55-75. https://doi.org/10.1007/s10657-024-09830-y Schmidt, J., Schutte, N. M., Buttigieg, S., Novillo-Ortiz, D., Sutherland, E., Anderson, M., de Witte, B., Peolsson, M., Unim, B., Pavlova, M., Stern, A. D., Mossialos, E., and van Kessel, R. (2024). Mapping the regulatory landscape for artificial intelligence in health within the European Union. npj Digital Medicine, 7(1), 229. https://doi.org/10.1038/s41746-024-01221-6 Smith, W. K., and Lewis, M. W. (2022). Both/and thinking: Embracing creative tensions to solve your toughest problems. Harvard Business Review Press. Suyitno, E., Syah, T. Y. R., Ediansyah, E., and Ariwibowo, A. F. (2026). How organizational agility translates internal capabilities into hospital performance: Evidence from 173 hospital directors in Indonesia. F1000Research, 15, 1078. https://doi.org/10.12688/f1000research.184815.1 Troise, C., Corvello, V., Ghobadian, A., and O'Regan, N. (2022). How can SMEs successfully navigate VUCA environment: The role of agility in the digital transformation era. Technological Forecasting and Social Change, 174, 121227. https://doi.org/10.1016/j.techfore.2021.121227 Zhang, S., Li, Y., Liu, W., Chu, Q., Wang, S., Li, J., and Chen, Y. (2025). A decade of review in global regulation and research of artificial intelligence medical devices (2015-2025). Frontiers in Medicine, 12, 1630408. https://doi.org/10.3389/fmed.2025.1630408 #strategic_agility #regulated_industries #compliance_by_design #strategic_innovation #regulatory_sandboxes #RegTech #higher_education #healthcare #finance #agility_within_rules #innovation_under_regulation #compliant_innovation #regulatory_strategy #strategic_management #STULIB

Sudden technological disruption has become a normal condition of business life. The pandemic years forced firms to move sales, work and service online in weeks, and the arrival of generative artificial intelligence has since opened a second wave of change that touches almost every knowledge task. This article asks how organizations cultivate the capabilities that allow them to adapt quickly, reconfigure their resources and open their innovation processes so that they survive such shocks and, in some cases, grow stronger. Using the dynamic capabilities framework of sensing, seizing and reconfiguring as its lens, the article carries out an integrative review of recent peer-reviewed research published mainly between 2022 and 2025, covering qualitative case studies, survey-based studies, systematic reviews and conceptual work in strategy, information systems and innovation management. The analysis shows that capability building rests on concrete routines rather than on general agility; that resource reconfiguration works through four repeatable moves, namely releasing, redeploying, recombining and acquiring resources; that open innovation acts as a borrowed capability which extends what a single firm can sense and seize; and that managerial skills, learning culture and social ties form the microfoundations that make these processes work. The article also identifies limits of the framework in very hostile environments, where improvisation matters more than planned routines. It closes with an integrated model, a practical roadmap and an agenda for future research written for students of management and business. Keywords: dynamic capabilities, digital transformation, technological disruption, resource reconfiguration, open innovation, organizational agility, generative artificial intelligence, microfoundations 1. Introduction 1.1 Background: when change stops being gradual For most of the twentieth century, managers could treat technological change as something that arrived slowly enough to plan for. New machines, new software and new channels appeared, but there was usually time to study them, test them and fold them into existing routines. That assumption has weakened sharply. In the space of a few years, organizations have faced a pandemic that closed physical channels overnight, a rapid shift to remote and hybrid work, and the public release of generative artificial intelligence tools that can write, summarize, code and design. Each of these shocks arrived faster than normal planning cycles allow. Together they have turned #digital_transformation from a long-term project into a test of how fast an organization can learn and change. Students who enter the workforce today will spend their careers in this environment. They will be asked to help organizations respond to technologies that did not exist when their degree started. Understanding why some organizations cope well with such shocks while others stall is therefore not only an academic question. It is a practical skill. The most widely used theory for answering that question in strategic management is the theory of #dynamic_capabilities, which explains how firms sense new opportunities and threats, seize them through decisions and investment, and reconfigure their resources so that they stay fit for a changing environment (Teece, 2023, 2025). Dynamic capabilities are different from ordinary capabilities. An ordinary capability is the ability to do something well today, such as run a factory efficiently or process insurance claims accurately. A dynamic capability is the ability to change what the organization does and how it does it. When technology moves slowly, ordinary capabilities can carry a firm for many years. When technology moves suddenly, the ability to change becomes the main source of survival. This is why recent research has linked dynamic capabilities so closely to digital transformation (Ellstrom et al., 2022; Ghosh et al., 2022; Kowalski et al., 2025). 1.2 Research problem and questions Although the link between dynamic capabilities and digital transformation is now well established, students and managers still face a practical gap. Much of the literature describes dynamic capabilities at a high level of abstraction. It tells us that firms must sense, seize and reconfigure, but it says less about what these activities look like on a Monday morning inside a real organization, how they are built, and how they change when disruption arrives suddenly rather than gradually. There is also a tendency to treat the single firm as the only unit that matters, when in practice many organizations survived recent shocks by working with partners, users, start-ups and public bodies, a pattern usually described as #open_innovation (Jabeen et al., 2023; Liu et al., 2022). This article therefore addresses three connected research questions. First, through which concrete routines and practices do organizations build the capability for rapid adaptation when technology changes suddenly? Second, how do organizations reconfigure their resources, including people, technology, data, partners and business models, during and after a disruption? Third, what role does open innovation play in extending the dynamic capabilities of a single organization, and where are its limits? 1.3 Contribution and structure The article makes three contributions. It brings together recent empirical and conceptual work, most of it published since 2022, into one integrated model that connects sensing, seizing and reconfiguring with three outcomes that matter under disruption: rapid adaptation, resource reconfiguration and open innovation. It translates the abstract language of the theory into concrete routines and moves that students can recognize and apply. And it highlights where the theory struggles, especially for small firms in very hostile environments, so that readers do not treat it as a universal recipe. The rest of the article is organized as follows. Section 2 reviews the literature on dynamic capabilities, digital transformation and open innovation. Section 3 sets out the conceptual framework and presents the integrated model. Section 4 explains the review method. Section 5 develops the analysis in a series of themed subsections. Section 6 discusses implications for theory, practice and students. Section 7 concludes and outlines limitations and directions for future research. 1.4 Key terms used in this article Because the article is written for students, it helps to define a few terms at the start. A #capability is the ability of an organization to perform a coordinated set of tasks using its resources, for example to launch a new product or to serve customers through a mobile app. A dynamic capability is a higher-order ability to change those ordinary capabilities. A #routine is a repeated pattern of activity that people in an organization carry out, often without thinking about it, such as a monthly review of customer data or a standard way of approving a pilot project. #Disruption, in this article, refers to a sudden change in technology or in the wider environment that makes existing ways of creating value less effective, whether it comes from a new technology, a crisis or a change in customer behaviour. Two further terms are important. Resource reconfiguration means changing the mix, use and arrangement of the resources an organization controls or can access, including people, technology, data, partners and its business model. Open innovation means using purposeful flows of knowledge across the boundaries of the organization, both inward and outward, to innovate faster and better than the organization could alone. Finally, #resilience refers to the capacity of an organization to absorb a shock, keep functioning and recover, ideally emerging stronger. Throughout the article, these terms are used in their plain sense, and each is linked to the evidence where it appears. 2. Literature Review 2.1 From the resource-based view to dynamic capabilities The roots of dynamic capabilities lie in the #resource_based_view of the firm. This view argues that firms differ in performance because they control different resources, and that resources which are valuable, rare and hard to copy can support lasting #competitive_advantage. The resource-based view was powerful, but it was mainly a theory of stable advantage. It explained why a firm was strong at a given moment better than it explained how a firm stayed strong when the environment changed. A recent effort to renew the resource-based view makes this point clearly, arguing that the theory must address new contexts such as digital platforms and ecosystems, new concepts such as resource orchestration, and new methods for studying how resources are combined over time (Helfat et al., 2023). Dynamic capabilities theory grew out of that limitation. In his recent reflections on the evolution of the framework, Teece (2023) describes it as an attempt to explain how managers build, integrate and renew organizational and technological capabilities so that the firm stays aligned with its environment. He also notes that digital transformation, corporate entrepreneurship and organizational behaviour have become important sources of theoretical development for the framework. In a later book-length treatment, Teece (2025) stresses that firms with strong dynamic capabilities do not only respond to their environment but can also shape it, and he presents a capabilities-based view of the firm as an alternative to theories that treat the firm as a simple bundle of contracts. The framework is usually summarized through three clusters of activity. #Sensing refers to scanning, interpreting and learning about changes in technology, customers and competitors. #Seizing refers to deciding how to respond, designing business models, committing investment and mobilizing people. #Reconfiguring, sometimes called transforming, refers to recombining and renewing assets, structures and routines so that the organization can deliver on the choices it has made. These three clusters give the article its basic structure. 2.2 Digital transformation as a strategic process Digital transformation is more than the adoption of digital tools. It is a process in which digital technologies change how an organization creates and captures value, how it is structured and how it relates to customers and partners. A widely cited review of business and management research shows how quickly this field expanded and how many disciplines now study it, from information systems and marketing to operations and human resource management (Kraus et al., 2022). A more recent editorial in information systems research reflects on where the field is heading and argues that scholars need clearer concepts to separate genuine transformation from routine digitalization (Wessel et al., 2025). Several studies apply the dynamic capabilities lens directly to digital transformation. Ellstrom et al. (2022), working with a firm undergoing digital transformation and a consultancy that advises such firms, identified six routines that are specifically relevant for digital transformation. These span sensing, seizing and reconfiguring and are discussed in detail in Section 5. Ghosh et al. (2022) examined industrial businesses and argued that the digital transformation of such firms is best understood as a dynamic capability challenge rather than a technology purchasing decision. Froehlich et al. (2025), studying an enterprise through interviews with managers and internal documents, found that sensing, seizing and reconfiguring supported digital transformation through the reformulation of strategy, new routines, investment in technological and human resources, and deliberate efforts to change culture. A further step is the study of #microfoundations, meaning the individual skills, processes and structures that make up a dynamic capability. Kowalski et al. (2025), studying eight companies, proposed new microfoundations for digital transformation, including analytics for the customer experience journey and the management of digital analytics innovation. They also identified internal barriers, such as leadership without digital skills and a lack of strategic human resource management for transformation, and internal enablers, such as a defined strategy for transformation and a data-driven culture. Their work also suggested links between dynamic capabilities and #digital_maturity. 2.3 The acceleration: crisis shocks and generative AI The idea of accelerated digital transformation captures the observation that recent shocks compressed years of change into months. During the COVID-19 crisis, many firms moved sales online, digitized internal workflows and redesigned services at a pace that would have seemed impossible before. Research on small and medium-sized enterprises showed that some firms responded with temporary business model innovation, rapidly using their core competencies and networks to create new business models that were sometimes very different from their old ones, and that this improved their #strategic_flexibility (Clauss et al., 2022). A large integrative review of research on innovation during the pandemic analysed 218 studies and concluded that collaboration, communication and open innovation among government, research institutions and the private sector were critical to how well economies coped (Sharma et al., 2022). The second accelerator is #generative_AI, a branch of #artificial_intelligence. Unlike earlier waves of automation, generative tools affect knowledge work directly, from writing and analysis to design and software development. Reviews of the field show that artificial intelligence can support each stage of the innovation process, from idea generation to development and commercialization (Truong and Papagiannidis, 2022), and that research on artificial intelligence and corporate innovation has grown rapidly across several streams (Bahoo et al., 2023). A Delphi study with leading innovation scholars identified ten research themes at the intersection of generative AI and innovation management, including the effect of generative AI on organizational design and boundaries, on new product development and on the risks of misuse (Mariani and Dwivedi, 2024). For dynamic capabilities theory, generative AI is both a new disruption that firms must sense and seize, and a new tool that can strengthen sensing and seizing themselves. 2.4 Open innovation in turbulent times Open innovation describes the purposeful use of knowledge flows into and out of the organization to speed up innovation and to find new markets for internal ideas. During the pandemic, many of the most visible innovation successes happened across organizational boundaries. In the healthcare sector, the fast design and manufacture of protective equipment, medical devices, testing, treatment and vaccine technologies relied heavily on collaboration beyond single organizations, and researchers used a co-evolution view of business ecosystems to explain these patterns (Liu et al., 2022). In smaller firms, a multiple case study of six SMEs in traditional sectors found that open innovation management played a key role in driving #business_model_innovation during the pandemic, sometimes together with digital transformation (Jabeen et al., 2023). Digital technologies also change the cost and reach of collaboration. A panel study of Spanish manufacturing firms over the period 2007 to 2017 found that higher levels of digitalization were associated with collaboration with a wider range of technology partners, and, contrary to the authors' expectations, that this effect was stronger in family firms (Nieto et al., 2023). This suggests that digital capability and open innovation reinforce each other: digital tools make it easier to work with partners, and partners help firms make sense of digital opportunities. 2.5 Gaps in the literature Three gaps stand out. First, the literature on dynamic capabilities for digital transformation is rich in case studies but still fragmented, with different studies naming different routines and microfoundations. Second, research on rapid adaptation, resource reconfiguration and open innovation has often developed in separate streams, even though in practice they happen together during a disruption. Third, there is limited discussion of when the dynamic capabilities lens itself falls short, for example when environments are so hostile that firms improvise rather than follow routines (Penarroya-Farell and Miralles, 2022). The framework developed in the next section is designed to bring these streams together while keeping their limits in view. 3. Conceptual Framework 3.1 Three capability clusters under sudden disruption The framework adopted in this article keeps the classic three clusters of sensing, seizing and reconfiguring, but reads them through the conditions of sudden disruption. Under gradual change, sensing can rely on annual planning cycles and market research. Under sudden disruption, #sensing must work continuously and must draw on signals from outside the firm's own industry. Under gradual change, seizing can involve large, carefully planned investments. Under sudden disruption, #seizing tends to involve rapid, smaller and reversible commitments such as pilots and temporary business models. Under gradual change, reconfiguring can proceed through scheduled restructuring. Under sudden disruption, #reconfiguring must happen while the organization is still running, much like repairing a ship at sea. The framework then links these clusters to three outcomes that the research focus of this article highlights. The first is #rapid_adaptation, meaning the ability to adjust operations, offers and ways of working quickly, often described as #organizational_agility. The second is #resource_reconfiguration, meaning the ability to release, redeploy, recombine and acquire resources. The third is open innovation, meaning the ability to use external partners and ecosystems to sense and seize faster than the firm could alone. These outcomes, in turn, support survival and renewal, which may include greater resilience and the creation of new value. 3.2 Microfoundations and enablers Capabilities do not float above an organization. They rest on the skills of managers and employees, on processes and structures, and on culture and relationships. The framework therefore includes four enablers that recent research identifies as especially important. The first is managerial capability, including the entrepreneurial and leadership skills of managers, their social ties and their way of thinking (Heubeck, 2023). The second is #organizational_learning and the leadership styles that support it (Bornay-Barrachina et al., 2025). The third is data and artificial intelligence capability, including a data-driven culture (Kowalski et al., 2025). The fourth is social capital, the networks of trust and cooperation that support collaborative innovation in a crisis (Al-Omoush et al., 2022). 3.3 The integrated model Figure 1 brings these elements together. Disruption, whether driven by a crisis shock or by a new technology such as generative AI, triggers all three capability clusters. Sensing feeds seizing, and seizing feeds reconfiguring, though in practice the three often overlap and run in parallel. Each cluster is most closely linked to one outcome: sensing to rapid adaptation, seizing to decisions about which resources to change, and reconfiguring to the recombination of resources inside and outside the firm, including through open innovation. All three outcomes contribute to survival and renewal. A feedback loop returns from renewal to the starting point, because an organization that has come through one disruption well is usually better prepared to sense the next one. The enablers sit beneath the whole model, because without them the routines do not work. Figure 1. Integrated model of dynamic capabilities under sudden technological disruption. Developed by the author from the literature reviewed in this article. The model is deliberately simple. It is not intended as a set of hypotheses to be tested in one study, but as a map that helps students organize what the recent literature says and see where individual studies fit. The arrows show the main direction of influence, not a strict sequence. A study of blockchain-driven capabilities for circular supply chains in the Ghanaian mining industry, for example, found that sensing, seizing and reconfiguring are all crucial but do not strictly follow a sequence and often overlap (Quayson et al., 2023), which supports reading the model as a set of interacting processes rather than a pipeline. 4. Methodology This article uses an integrative literature review design. An integrative review brings together different kinds of evidence, including qualitative case studies, quantitative surveys, systematic reviews and conceptual papers, in order to build a new framework rather than to estimate a single effect size. This design suits the research questions because the relevant knowledge is spread across strategy, information systems, innovation management, operations and human resource management, and because the aim is to explain how capabilities are built rather than to measure one relationship. Sources were identified through searches of major scholarly databases using combinations of terms such as dynamic capabilities, digital transformation, sensing, seizing, reconfiguring, organizational agility, open innovation, crisis and generative artificial intelligence. Priority was given to peer-reviewed journal articles, books and book chapters published within the last five years, with most sources published between 2022 and 2025. Each source was checked against its publisher record to confirm authorship, year, journal and pages. Older classic works were not cited directly; instead, the article relies on recent reflections and reviews that summarize the classic foundations. The selected works were read and coded according to which capability cluster they addressed, which outcome they examined, which enablers or barriers they identified, and what context they studied, such as industry, firm size and country. Patterns across studies were then compared to build the themes presented in Section 5. Because the review is integrative rather than systematic, it does not claim to cover every relevant publication. Its strength lies in connecting findings across streams and translating them into a form that is useful for students. Its limitations are discussed in Section 7. 5. Analysis and Discussion 5.1 Sensing in a noisy digital environment The first finding of the review is that sensing under accelerated digital change is not a general alertness but a set of specific routines. The clearest example comes from Ellstrom et al. (2022), whose six routines are shown in Figure 2. Two of these routines concern sensing. The first is cross-industrial digital sensing, which means looking for digital developments outside the firm's own industry. This matters because the most dangerous disruptions often start elsewhere. A retailer may learn more about the future of its customer interface from gaming platforms or banking apps than from other retailers. The second is inside-out digital infrastructure sensing, which means examining the firm's own systems, data and technical infrastructure to see what new opportunities they make possible and what weaknesses they hide. Figure 2. Six routines of dynamic capabilities for digital transformation, grouped by capability cluster. Adapted from Ellstrom et al. (2022). These two routines point outward and inward at the same time. Outward sensing looks for threats and opportunities in the wider environment, while inward sensing looks for hidden potential and hidden fragility in the firm's own assets. Students should note that inward sensing is easy to neglect. Many firms discover during a crisis that their data is scattered across incompatible systems, or that a key process depends on one outdated application. Those discoveries are costly when they come late. Recent research adds a data-driven dimension to sensing. Kowalski et al. (2025) proposed analytics for the customer experience journey as a new microfoundation, meaning the systematic use of data to understand how customers move through their interactions with the firm. In a fast-changing market, customer behaviour is often the earliest signal that something has shifted. A firm that tracks it closely can see a change in demand before it shows up in quarterly sales figures. This is where #data_analytics becomes part of strategic capability rather than a back-office function. Generative AI changes sensing in two ways. On one hand, it is a new object of sensing, since managers must understand what the technology can and cannot do in their own setting. On the other hand, it is a new instrument of sensing. A capability-based framework for generative AI in supply chain and operations management identifies learning, perception, prediction, interaction, adaptation and reasoning as key AI capabilities and maps them onto thirteen decision areas, including demand forecasting and risk management (Jackson et al., 2024). Several of these areas are essentially sensing tasks. When used well, AI tools can widen and speed up the flow of signals into the organization. When used badly, they can flood managers with noise or give a false sense of certainty. 5.2 Seizing: deciding fast without betting the whole firm Sensing produces information, but information alone does not change an organization. Seizing is the moment when managers decide what to do, commit resources and design the business model that will carry the response. The review suggests that seizing under sudden disruption has three distinctive features. The first feature is the need for a clear #digital_strategy. Ellstrom et al. (2022) identify digital strategy development as a core seizing routine, and Kowalski et al. (2025) list a defined strategy for transformation as a key internal enabler. A strategy matters because without it, sensing leads to a scattered set of experiments that compete for attention and money. A strategy does not need to be a long document. It can be a short statement of which customer problems the firm will solve digitally, which capabilities it will build itself, and which it will source from others. The second feature is the decision about enterprise boundaries, which Ellstrom et al. (2022) also identify as a seizing routine. In a digital environment, firms must decide what to own, what to rent, what to build with partners and what to leave to others. These boundary choices have become more frequent and more consequential, because cloud services, application interfaces and platform ecosystems make it possible to assemble capabilities from outside the firm much faster than before. A boundary decision is therefore also a decision about open innovation, which is discussed further in Section 5.5. The third feature is the preference for small, fast and reversible commitments. The research on #temporary_business_models during the pandemic illustrates this. Clauss et al. (2022) studied five SMEs in Austria, Germany and Liechtenstein that, within a short period, used their core competencies and networks to launch new business models, some of them very different from their usual ones. These temporary models created new revenue, increased strategic flexibility, and could be kept for the long term if they proved worthwhile. The lesson for seizing is that under uncertainty, it is often better to place several small bets that can be expanded or abandoned than one large bet that cannot be reversed. Seizing also depends heavily on managers. Heubeck (2023), studying German Industry 4.0 firms, found that the entrepreneurial skills of managers facilitated digital business model transformation that was important for firm performance, while leadership skills did not show a direct or indirect effect on performance in that setting. Higher levels of managerial social capital and lower levels of managerial cognition strengthened the positive effect of entrepreneurial skills. This finding is useful because it warns against assuming that any form of strong leadership will drive transformation. What seems to matter most in seizing digital opportunities is the ability to recognize and pursue new business possibilities, supported by a network of contacts. 5.3 Rapid adaptation, agility and improvisation The research focus of this article stresses rapid adaptation. In the literature, this is usually studied as organizational agility, meaning the capacity to detect change and respond quickly and effectively. Agility and dynamic capabilities are closely related but not identical. Agility describes how fast and smoothly an organization moves. Dynamic capabilities describe the underlying routines and skills that make such movement possible. Recent evidence helps clarify what supports agility. A study of 502 hospitality firms in Greece during the COVID-19 period found that financial and technological capital had a positive influence on agility-related dynamic capabilities, specifically the ability to capitalize on market opportunities and the ability to adjust operations, while social capital had a negative effect on one and no effect on the other (Chasapi et al., 2024). This is a striking result for students, because it suggests that in a sector hit hard by a crisis, the slack provided by money and technology mattered more for agility than relationships did. It also shows that the effect of a resource depends on context, since other studies find social capital to be highly valuable in crisis innovation. Supply chain research adds another layer. Dubey et al. (2023), combining thirteen interviews with a survey of 203 respondents, examined digital adaptability and digital agility as dynamic capabilities and found that digital adaptability is an important driver of digital agility. They also found that government effectiveness, including support such as tax credits, interest deferral, digital investment and soft loans, strengthened supply chain resilience by enhancing these digital capabilities. This finding is important because it reminds students that dynamic capabilities are not built in isolation. Public policy and the wider institutional environment can make it easier or harder for firms to adapt. Rapid adaptation also has a human side. When change is sudden, organizations often have to act before they have a plan. Research on cultural and creative firms in Spain argues that in very hostile environments such as the pandemic, the dynamic capabilities approach alone does not fully explain how owners made strategic decisions, and that emergency management and #improvisation offer a better explanation of business model adaptation (Penarroya-Farell and Miralles, 2022). Improvisation, in this sense, is the ability to combine available resources in new ways under time pressure without a prepared routine. It does not replace dynamic capabilities, but it fills the gap when routines have not yet been developed or when the situation is too new for them to apply. Taken together, these studies suggest that rapid adaptation rests on three layers. The first is slack, in the form of financial and technological resources that give the organization room to move. The second is routine, in the form of practiced sensing, seizing and reconfiguring processes. The third is improvisation, in the form of people who are trusted and able to act creatively when routines run out. An organization that has only one of these layers is likely to struggle in a sudden disruption. 5.4 Resource reconfiguration: four repeatable moves Reconfiguring is often described in very general terms, such as renewing assets or transforming the organization. The review suggests that it can be broken down into four repeatable moves that apply across different types of resources. These moves are shown in Figure 3, applied to four resource types: people and skills, technology and data, partners and networks, and the business model itself. Figure 3. Four moves of resource reconfiguration applied to four resource types. Developed by the author from the literature reviewed in this article. The first move is releasing resources. This means letting go of resources that no longer fit, such as legacy systems, outdated roles or failing product lines. Releasing is often the hardest move, because existing resources usually have defenders inside the organization. Yet without releasing, there is rarely enough money, attention or talent to build anything new. The second move is redeploying resources, meaning moving existing resources to new uses. During the pandemic, many firms redeployed staff from closed physical channels into online sales or customer support, and reused existing data to design new services. The temporary business models studied by Clauss et al. (2022) are a clear example of redeployment, since the firms used existing competencies and networks to serve new needs. The third move is recombining resources, meaning putting existing resources together in new ways. This is the heart of innovation. In digital transformation, recombination often means bringing domain experts together with data specialists, or combining existing platforms with new AI tools. Ellstrom et al. (2022) describe the creation of a unified #digital_infrastructure as a reconfiguring routine. A unified infrastructure is valuable precisely because it makes recombination easier: when data and systems connect, new combinations can be assembled quickly. The fourth move is acquiring or borrowing resources from outside. This includes hiring and upskilling, licensing technology, using cloud services and joining partnerships and ecosystems. Borrowing is especially important under sudden disruption, because building new capabilities internally takes time that the firm may not have. This move links reconfiguration directly to open innovation. A further reconfiguring routine identified by Ellstrom et al. (2022) is the decomposition of digital transformation into specified projects. This routine helps explain why some organizations manage to reconfigure while others stall. Transformation that is described only as a grand vision tends to remain abstract. Transformation that is broken into specific projects, each with clear goals, owners and timelines, can be managed, monitored and adjusted. For students, this is a practical lesson that connects strategy to project management. Human resources deserve special attention in reconfiguration. Kowalski et al. (2025) identify the lack of strategic human resource management for transformation as an internal barrier, and Froehlich et al. (2025) emphasize investment in human resources and culture change as part of the reconfiguring process. Technology can be bought quickly, but the skills and habits needed to use it well take longer to build. This is why #upskilling and talent management are increasingly treated as strategic tasks rather than administrative ones. 5.5 Open innovation as a borrowed capability The third research question concerns the role of open innovation. The review suggests that open innovation is best understood as a borrowed capability: it allows an organization to extend its sensing, seizing and reconfiguring beyond what its own resources would allow. Figure 4 summarizes the three main modes of open innovation and their role under disruption. Figure 4. Modes of open innovation and their benefits and risks under sudden disruption. Developed by the author from the literature reviewed in this article. Inbound open innovation brings external knowledge into the organization from universities, start-ups, suppliers and users. Under disruption, inbound flows strengthen sensing, because partners often see changes earlier or from a different angle. Outbound open innovation takes internal ideas and technologies to external markets through licensing, spin-offs or sharing. Under disruption, outbound flows can turn unused assets into income or into contributions to a wider response. Coupled open innovation combines both directions through co-creation in consortia, platforms and ecosystems. Under disruption, coupled forms allow organizations to share risk and pool scarce capabilities. The pandemic offers rich evidence of these modes in action. In healthcare, many innovation projects for protective equipment, devices, testing, treatment and vaccines happened beyond organizational boundaries, and the response of the sector can be understood as the co-evolution of a business ecosystem (Liu et al., 2022). For SMEs in traditional sectors, open innovation management was central to business model innovation during the crisis (Jabeen et al., 2023). Across the wider literature, effective collaboration among government, research institutions and the private sector was identified as a key factor in how well economies responded (Sharma et al., 2022). Open innovation depends on relationships, which brings #social_capital back into the picture. Al-Omoush et al. (2022), surveying 289 managers in leading manufacturing firms in Jordan, found that social capital significantly influenced collaborative innovation, collective intelligence and organizational sustainability during the COVID-19 crisis, and that collective intelligence itself affected collaborative innovation and sustainability. The authors present these as dynamic capabilities that help organizations stay sustainable and recover from crises. This finding sits in interesting tension with the hospitality study discussed earlier, in which social capital did not support agility. One possible explanation is that social capital matters most when the response requires collaboration and knowledge sharing, and less when it requires fast internal operational change. Digital capability makes open innovation cheaper and wider. The finding that digitalization is associated with collaboration with a broader set of technology partners (Nieto et al., 2023) suggests a reinforcing loop: digital tools reduce the cost of finding and working with partners, and those partners in turn help the firm make better use of digital technologies. This loop is one reason why firms that had invested in digital infrastructure before the pandemic were often better able to collaborate during it. Open innovation is not without risk. Working with partners can lead to knowledge leakage, dependence on others and high coordination costs. Firms that rely heavily on external partners may find that they have outsourced capabilities they later need to control. The boundary decisions discussed under seizing are therefore central to good open innovation. Organizations need to decide which capabilities are core and must be protected or built internally, and which can safely be borrowed. The concept of #ecosystems is helpful here, because it shifts attention from single partnerships to the wider network of actors whose combined capabilities determine what is possible. 5.6 People, leadership and learning as microfoundations Every capability discussed so far rests on people. The review identifies three human microfoundations that consistently matter: managerial skills, leadership styles that support learning, and the wider skills base of the workforce. On managerial skills, the evidence from Heubeck (2023) suggests that entrepreneurial skills matter more than general leadership skills for driving digital business model transformation and performance. Kowalski et al. (2025) add that leadership without digital skills acts as a barrier. Together, these findings suggest that #digital_leadership is not simply a matter of enthusiasm for technology. It requires enough understanding of digital possibilities to recognize real opportunities, and enough entrepreneurial drive to pursue them. On learning, Bornay-Barrachina et al. (2025), surveying 106 firms, examined how exploitative and exploratory learning interact with transformational and transactional leadership in developing sensing, seizing and reconfiguring. They found that organizational learning influences dynamic capabilities either directly or indirectly through these leadership styles, and that the pattern depends on the type of department. This is a useful reminder that a single leadership style will not suit every part of an organization. A research and development team may need a different combination of learning and leadership than an operations team. On the workforce, the arrival of generative AI has made skills an urgent strategic topic. Chowdhury et al. (2024) propose a strategic human resource management framework for integrating generative AI that includes alignment with business objectives, seizing opportunities, assessing and orchestrating resources, re-institutionalizing practices, realignment and a culture of continuous learning and adaptation. The language of this framework closely mirrors the dynamic capabilities framework, which suggests that human resource management is becoming one of the main places where dynamic capabilities are built, making #human_resource_management a strategic partner in transformation. #Knowledge_management offers a further angle. Alavi et al. (2024) examine generative AI through the processes of knowledge creation, storage, transfer and application. They note that generative AI can support individual and organizational learning, but also that it may reduce human socialization and could marginalize junior knowledge workers if used carelessly. For dynamic capabilities, this is an important warning. Sensing and seizing depend on people who understand the business deeply. If junior staff lose the opportunity to build that understanding because routine tasks are automated, the organization may weaken the very capabilities it needs for future disruptions. 5.7 Generative AI as the newest test of dynamic capabilities Generative AI provides a live test of the ideas in this article, because it is a sudden technological disruption that is still unfolding. Several patterns from the review apply directly. First, sensing generative AI requires both outward and inward routines. Outward sensing means following how the technology is being used in other industries, not only in the firm's own sector. Inward sensing means understanding which internal tasks, data assets and workflows are most affected. The capability-based framework of Jackson et al. (2024) is useful here, because it encourages managers to look at specific decision areas rather than at AI in general. Second, seizing generative AI benefits from small and reversible commitments. Because the technology changes quickly and its risks are still being understood, pilots and limited deployments allow learning without excessive exposure. Mariani and Dwivedi (2024) highlight risks such as misuse and biased innovation, along with open questions about intellectual property and regulation. These uncertainties strengthen the case for staged commitments. Third, reconfiguring for generative AI is mainly about people and processes. The technology itself can be accessed relatively easily through external services, which is a form of borrowing. The harder work is redesigning roles, building skills and changing routines, as the human resource framework of Chowdhury et al. (2024) makes clear. In the language of Figure 3, generative AI requires all four moves: releasing some tasks, redeploying people, recombining human expertise with AI tools, and acquiring new skills and services. Fourth, generative AI is also an open innovation challenge. Many organizations will rely on external model providers, consultants and ecosystems. Boundary decisions about which AI capabilities to build, which to buy and which data to share will shape competitive position for years. Organizations that treat generative AI only as a tool purchase, rather than as a test of their dynamic capabilities, risk repeating the mistakes that some firms made in earlier waves of digitalization. 5.8 Small firms, hostile environments and the limits of the framework A balanced account must also consider where the framework struggles. Two limits stand out from the review. The first limit concerns #SMEs. Small firms often lack the slack resources, specialist staff and formal routines that the dynamic capabilities literature tends to assume. Yet the evidence also shows that small firms can be remarkably adaptive. The temporary business model innovation studied by Clauss et al. (2022) and the open innovation responses studied by Jabeen et al. (2023) both involved SMEs that moved quickly by drawing on core competencies, networks and partnerships. For small firms, dynamic capabilities may rest less on formal routines and more on the entrepreneurial skills of owners and managers and on their external relationships. The second limit concerns very hostile environments. As noted earlier, research on cultural and creative firms argues that the dynamic capabilities lens alone does not fully explain strategic decisions in conditions such as the pandemic, and proposes emergency management and improvisational capability as additional lenses (Penarroya-Farell and Miralles, 2022). The same study highlights organizational proximity in the diffusion of innovations under the open innovation paradigm. The implication is that dynamic capabilities theory works best when there is enough time for routines to operate, even if that time is short. When an environment changes so fast that no routine applies, improvisation and proximity to partners may matter more. A third, more general limit is that the framework can sound like a recipe when it is not. Knowing that a firm should sense, seize and reconfigure does not tell managers which signals to trust, which opportunities to pursue or which resources to release. Those judgments remain difficult and depend on context. The value of the framework lies in giving structure to these judgments, not in removing the need for them. 5.9 Cross-sector patterns in the evidence Reading the studies side by side shows that the same capability clusters take different forms in different sectors. This matters for students, because a recommendation that works well in one industry may need adjusting in another. In #healthcare, the dominant pattern during the pandemic was ecosystem-level open innovation. The urgency of the crisis and the complexity of the required solutions, from protective equipment to vaccines, meant that no single organization could respond alone. Sensing, seizing and reconfiguring happened largely at the level of the ecosystem, with firms, research institutions and public bodies co-evolving their roles (Liu et al., 2022). For students, this shows that dynamic capabilities can sometimes be a property of a network rather than of a single firm. In #manufacturing, the evidence points to the importance of digital infrastructure, managerial skills and partnerships. Industrial businesses face particular challenges in digital transformation because of their heavy physical assets and long product cycles (Ghosh et al., 2022). Studies of German #Industry4_0 firms highlight the role of entrepreneurial managers (Heubeck, 2023), while research on manufacturers in Jordan highlights social capital and #collective_intelligence in crisis innovation (Al-Omoush et al., 2022), and research on Spanish manufacturers shows that digitalization widens the range of technology partners (Nieto et al., 2023). The common thread is that manufacturing firms need both internal digital strength and external connections. In #hospitality and other service sectors hit directly by closures, the evidence emphasizes slack resources. The finding that financial and technological capital supported agility in Greek hospitality firms while social capital did not (Chasapi et al., 2024) suggests that, when a whole sector is shut down, the ability to fund and execute rapid operational change matters more than relationships, at least in the short term. In #supply_chains, the evidence highlights digital adaptability and agility, together with the role of government. Dubey et al. (2023) show that public support can strengthen the digital capabilities that underpin resilience, while Jackson et al. (2024) map how generative AI may affect decision areas from forecasting to risk management. Supply chains are an especially clear case where one firm's dynamic capabilities depend on the capabilities of its partners and on the policy environment. In creative industries and among small firms more generally, the evidence points to improvisation, proximity and entrepreneurial flexibility (Penarroya-Farell and Miralles, 2022; Clauss et al., 2022). These firms often lack formal routines but can move quickly because decision-making is concentrated and relationships are close. These patterns do not contradict the integrated model presented in Figure 1. Instead, they show that the relative weight of each element changes with context. In some sectors, open innovation carries most of the load. In others, slack resources or managerial skills are decisive. A useful exercise for students is to take the model and ask which parts carry the most weight in the industry they know best. 5.10 A worked illustration for students To show how the framework can be applied, consider a simple illustration. The company described here is hypothetical and does not refer to any real firm. It is used only to demonstrate how the concepts fit together. Imagine a medium-sized regional logistics company that delivers goods for local retailers. Its business has been stable for years, built on reliable trucks, experienced drivers and long relationships with clients. A disruption arrives in two forms. First, a sudden shift in consumer behaviour pushes its retail clients toward online sales with home delivery, changing order patterns overnight. Second, new AI-based route planning and demand forecasting tools become widely available, allowing competitors to offer faster and cheaper service. Applying the framework, the company would first examine its sensing. Does it look beyond logistics to see how online retail and AI tools are changing customer expectations? Does it understand its own data, such as delivery times and order patterns, well enough to see what new services it could offer? If not, building these outward and inward sensing routines would be the first priority. Next, the company would consider seizing. Instead of committing at once to a large new technology system, it might run a small #pilot_project of AI-based route planning with one client, set clear criteria for success and decide quickly whether to expand. It would also make boundary decisions: should it build its own forecasting tools, license them, or partner with a technology start-up? Then it would reconfigure. Using the four moves from Figure 3, it might release an outdated manual scheduling process, redeploy some office staff to customer data analysis, recombine driver experience with AI route suggestions, and acquire new skills through training and a partnership. It would break the transformation into specified projects with clear owners. It would also consider open innovation. Partnering with a technology start-up could bring in capabilities the company lacks, while working closely with its retail clients could help it design new delivery services together. At the same time, it would need to manage the risk of becoming dependent on a single external provider. Finally, it would attend to the enablers. Are its managers entrepreneurial and digitally aware? Does it have a culture that supports learning? Has it invested in the skills of its staff? Without these, the routines and moves described above are unlikely to work. Even in this simple illustration, the value of the framework is clear: it turns a vague sense of threat into a structured set of questions and actions. 5.11 A practical roadmap for building dynamic capabilities Drawing the analysis together, Figure 5 presents a six-step roadmap that organizations can use to build dynamic capabilities for accelerated digital transformation. The roadmap is not a strict sequence. In practice, steps overlap and repeat. But it gives students and managers a structured way to think about where an organization stands and what it should develop next. Figure 5. A six-step roadmap for building dynamic capabilities for accelerated digital transformation. Developed by the author from the literature reviewed in this article. The first step is to build sensing routines that look both outward across industries and inward at the firm's own infrastructure and data. The second step is to decide quickly but in small, reversible steps, guided by a clear digital strategy and explicit boundary choices. The third step is to reconfigure through the four moves of releasing, redeploying, recombining and acquiring resources, and to break transformation into specified projects. The fourth step is to open up to partners, ecosystems, start-ups and users, while protecting the capabilities that are truly core. The fifth step is to embed change through a learning culture, skills development, governance and measurement. The sixth step is to review and repeat, because dynamic capabilities, like any capability, weaken if they are not used and renewed. Each step connects to evidence discussed above. Outward and inward sensing come from Ellstrom et al. (2022). The emphasis on strategy and data culture comes from Kowalski et al. (2025). The value of small and temporary bets comes from Clauss et al. (2022). The importance of partners and social ties comes from Jabeen et al. (2023) and Al-Omoush et al. (2022). The focus on skills and learning comes from Bornay-Barrachina et al. (2025) and Chowdhury et al. (2024). The roadmap is therefore an attempt to make the scattered findings of recent research usable in one place. 6. Implications 6.1 Implications for theory For theory, the review supports three points. First, the dynamic capabilities framework remains a strong lens for understanding digital transformation, but it gains much of its explanatory power from studies that specify concrete routines and microfoundations. Future theoretical work should continue to move from abstract clusters toward identifiable practices. Second, rapid adaptation, resource reconfiguration and open innovation should be studied together rather than in separate streams, because under sudden disruption they operate as connected parts of one response. Third, the framework needs to be combined with other lenses, such as improvisation and emergency management, when environments become extremely hostile (Penarroya-Farell and Miralles, 2022). This fits the broader call to renew resource-based thinking with new contexts, concepts and methods (Helfat et al., 2023). 6.2 Implications for practice For managers, the main implication is that dynamic capabilities must be built before they are needed. A firm cannot create sensing routines, a unified digital infrastructure, a network of trusted partners and a skilled workforce in the middle of a crisis. These must be developed in calmer periods, so that they are ready when disruption arrives. A second implication is that #change_management and capability building are closely linked. Culture change, human resource strategy and project discipline are not side issues but core parts of reconfiguration (Froehlich et al., 2025; Kowalski et al., 2025). A third implication is that managers should pay attention to the wider environment, including public policy, because government support can strengthen digital capabilities and resilience (Dubey et al., 2023). A fourth implication concerns the selection and development of managers. If entrepreneurial skills and digital understanding matter more than general leadership skills for driving transformation (Heubeck, 2023; Kowalski et al., 2025), then organizations should consider these qualities explicitly when appointing and training managers responsible for digital change. 6.3 Implications for students For students, the article offers a way to think about their own role in organizations. Many of the routines discussed here are carried out by ordinary employees, not only by senior managers. Scanning for new tools, sharing observations about customer behaviour, proposing small experiments, helping colleagues learn new systems and building relationships with external partners are all contributions to an organization's dynamic capabilities. Students who develop these habits will be valuable in any workplace that faces rapid technological change. Students can also use the integrated model and roadmap in their coursework. When analysing a case company, they can ask a set of structured questions. How does the company sense change, both outside and inside? How does it decide, and how quickly? What resources has it released, redeployed, recombined or acquired? Which partners does it rely on, and what risks does that create? What skills and learning practices support these processes? These questions turn an abstract theory into a practical tool for analysis. 7. Conclusion 7.1 Summary of findings This article set out to explain how organizations cultivate capabilities for rapid adaptation, resource reconfiguration and open innovation in the face of sudden technological disruption. Drawing on recent research and using the dynamic capabilities framework as its lens, it reached four main conclusions. First, rapid adaptation rests on concrete routines rather than general agility. Specific routines such as cross-industrial digital sensing, inside-out infrastructure sensing, digital strategy development, boundary decisions, decomposition of transformation into projects and the creation of a unified digital infrastructure give shape to the abstract clusters of sensing, seizing and reconfiguring (Ellstrom et al., 2022). These routines are supported by slack resources and, when routines run out, by improvisation. Second, resource reconfiguration can be understood through four repeatable moves: releasing, redeploying, recombining and acquiring resources. These moves apply to people, technology, data, partners and business models. Temporary business models are a powerful form of redeployment under uncertainty (Clauss et al., 2022). Third, open innovation acts as a borrowed capability that extends what a single organization can sense and seize. It was central to many successful responses during the pandemic (Liu et al., 2022; Jabeen et al., 2023; Sharma et al., 2022) and is strengthened by digital capability (Nieto et al., 2023) and social capital (Al-Omoush et al., 2022), though it carries risks of leakage and dependence. Fourth, the microfoundations of all these capabilities are human. Entrepreneurial managerial skills, digital understanding among leaders, learning-oriented leadership, a data-driven culture and strategic human resource management consistently appear as enablers, while their absence appears as a barrier (Heubeck, 2023; Kowalski et al., 2025; Bornay-Barrachina et al., 2025; Chowdhury et al., 2024). Generative AI makes these human foundations more important, not less. 7.2 Limitations The article has several limitations. As an integrative review, it does not claim to cover all relevant research, and the selection of sources reflects the author's judgment about relevance and quality. Many of the studies reviewed are qualitative case studies or cross-sectional surveys in specific countries and industries, which limits how far their findings can be generalized. The evidence on generative AI is still at an early stage and much of it is conceptual. Finally, the integrated model and roadmap are proposed as organizing tools, not as tested causal models. 7.3 Directions for future research Future research could take several directions. Longitudinal studies that follow organizations through successive disruptions would help show how dynamic capabilities are built, used and renewed over time, and whether experience with one shock improves the response to the next. Comparative studies across firm sizes, industries and countries would clarify when the framework holds and when other lenses such as improvisation are needed. Research on generative AI should move from conceptual frameworks to empirical studies of how organizations actually sense, seize and reconfigure around the technology, including its effects on learning and on junior employees. Finally, more work is needed on the boundary between dynamic capabilities and open innovation, especially on how firms decide which capabilities to build and which to borrow from ecosystems. 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Springer. https://doi.org/10.1007/978-3-031-11371-0_6 Teece, D. J. (2025). Dynamic capabilities. Cambridge University Press. https://doi.org/10.1017/9781009562713 Truong, Y., and Papagiannidis, S. (2022). Artificial intelligence as an enabler for innovation: A review and future research agenda. Technological Forecasting and Social Change, 183, 121852. https://doi.org/10.1016/j.techfore.2022.121852 Wessel, L., Mosconi, E., Indulska, M., and Baiyere, A. (2025). Digital transformation: Quo vadit? Information Systems Journal, 35(4), 1294-1308. https://doi.org/10.1111/isj.12578 #DynamicCapabilities #digital_transformation #DigitalTransformation2026 #technological_disruption #open_innovation #OpenInnovationStrategy #resource_reconfiguration #organizational_agility #business_resilience #GenerativeAI #innovation_management #strategic_management #future_of_work #STULIB #management_students

Ten years after the United Nations adopted the 2030 Agenda, the world is far from meeting the Sustainable Development Goals, and business is widely seen as both part of the problem and part of the solution. Many companies now publish SDG icons in their reports, yet the evidence suggests that a large share of this activity is symbolic, selective, or disconnected from core operations and supply chains. This article asks how corporations can embed the SDGs into strategy, operations and supplier networks without weakening profitability or global competitiveness. Using an integrative review of recent peer-reviewed research and books on corporate SDG engagement, sustainable supply chain management, sustainability reporting and the financial performance of sustainability, the article develops an Integrated SDG Alignment Framework with five connected layers: purpose and governance, materiality and prioritisation, core operations and business model, supply chain integration, and measurement, disclosure and learning. The analysis shows that profitability and SDG contribution are most likely to move together when firms focus on the goals most connected to their value chain, link targets to capital allocation and incentives, collaborate with suppliers instead of only auditing them, and report impacts honestly, including trade-offs. The main barriers are cherry-picking, SDG-washing, short-term financial pressure, weak supplier capability, fragmented data and uneven regulation across countries. The article closes with practical guidance for managers, policy makers and students, and with directions for future research. Keywords: Sustainable Development Goals, corporate strategy, sustainable supply chain management, SDG-washing, shared value, sustainability reporting, competitiveness, ESG performance 1. Introduction In September 2015, the member states of the United Nations adopted the 2030 Agenda for Sustainable Development and its seventeen #Sustainable_Development_Goals. The goals cover poverty, hunger, health, education, gender equality, water, energy, decent work, industry and innovation, inequality, cities, responsible consumption, climate, oceans, land, peace and partnerships. Unlike the earlier Millennium Development Goals, which were mainly addressed to governments of developing countries, the SDGs were written for every country and openly invited the private sector to take part. Companies were no longer seen only as taxpayers and employers. They were described as partners whose investment, technology and global reach would be needed if the goals were to be met. A decade later, the picture is sobering. The Sustainable Development Report 2025 found that, at the global level, none of the seventeen goals is on course to be achieved by 2030, and only about 17 percent of the targets are on track (Sachs et al., 2025). The same report notes that progress has been strongest in areas such as access to basic services and infrastructure, while other areas, including several environmental and governance targets, have stalled or moved backwards. These results do not mean that business has done nothing. They do mean that the scale and depth of corporate contribution are not yet close to what is needed. At the same time, corporate interest in the SDGs has grown fast. Large companies regularly map their activities to the goals, place the colourful SDG icons in annual and sustainability reports, and link products or projects to specific targets. Studies of reporting show that SDG disclosure rose steadily after 2016 across many countries (Bose and Khan, 2022). However, a growing body of research warns that much of this activity is selective or decorative. Firms often pick the goals that are easiest to claim and ignore the ones where their negative impacts are greatest, a pattern described in the literature as cherry-picking and #SDG_washing (Heras-Saizarbitoria et al., 2022; del Rio et al., 2024). In other words, there is a gap between talking about the goals and changing how the business actually works. This gap matters because the largest corporate impacts on the SDGs, positive and negative, usually sit inside core operations and #supply_chains, not in philanthropy or side projects. A clothing brand affects decent work (SDG 8) mainly through the factories that sew its garments. A food company affects hunger, water, land and climate (SDGs 2, 6, 13 and 15) mainly through how its raw materials are grown. A technology company affects responsible consumption (SDG 12) through the minerals in its devices and what happens to those devices at the end of their life. If the SDGs are to be more than a reporting label, they must reach these places. Many managers, however, still fear that deep SDG integration will cost money, slow decisions, and make the company less competitive against rivals who do not carry the same burden. This fear is not irrational. Some sustainability investments do have high upfront costs, uncertain payback periods, or benefits that are hard to capture inside the firm. Suppliers in lower-income countries may lack the capital or skills to meet new standards. Regulations differ from one market to another, so a firm that leads in one country may face higher costs than local competitors in another. The question that drives this article is therefore practical as well as academic: under what conditions, and through which strategic frameworks, can corporations embed the SDGs into their core operations and supply chain management without sacrificing #profitability or #global_competitiveness? To answer this question, the article brings together recent research from strategy, international business, operations and supply chain management, accounting and finance. It makes three contributions. First, it summarises in plain language what recent studies say about the business case for the SDGs and about the main reasons why corporate engagement often stays shallow. Second, it proposes an Integrated SDG Alignment Framework that links purpose and governance, materiality, operations, supply chains and measurement into one system, and it explains how each layer can protect or even improve financial performance. Third, it discusses the key challenges and opportunities that students, future managers and policy makers should understand, including the tensions that cannot be fully resolved. The rest of the article is organised as follows. Section 2 reviews the literature on business and the SDGs. Section 3 explains the review approach. Section 4 presents the conceptual framework. Section 5 analyses how the framework works in practice, moving from the business case to prioritisation, operations, supply chains, governance and finance, measurement and competitiveness. Section 6 discusses the main challenges and opportunities. Section 7 sets out implications for practice and policy, and Section 8 concludes with limitations and directions for future research. 2. Literature Review 2.1 From corporate social responsibility to the SDGs Before the SDGs, the main language for business responsibility was #corporate_social_responsibility, often abbreviated as CSR. CSR covered a wide range of activities, from charitable giving to environmental management systems, but it lacked a shared set of targets. Each company decided for itself what responsibility meant. The SDGs changed this by offering a common, globally agreed list of 17 goals and 169 targets. Fallah Shayan et al. (2022) argue that the SDGs can serve as an overarching framework for CSR, with traditional CSR categories placed under the relevant goals. In their view, this creates a better roadmap with more measurable outcomes and allows companies to speak the same language as governments, investors and civil society. Early reviews of management research confirmed a rapid rise in scholarly attention. Pizzi et al. (2020) examined 266 articles published in leading journals between 2012 and 2019 and found that work on business and the SDGs was growing quickly but remained scattered, with many studies focused on reporting and governance. Mio et al. (2020), in a systematic review published in Business Strategy and the Environment, concluded that much of the literature dealt with aspects of strategy execution, while several areas closely tied to strategy, such as how goals shape business models or competitive position, remained underexplored. More recently, Minutiello et al. (2024) reviewed the latest developments and found great variety in the field, with emerging themes such as the #circular_economy, artificial intelligence and Industry 4.0, and recurring discussion of stakeholders and of the financial and non-financial benefits of SDG strategies. They also stressed that integrating the goals into business models is a complex task requiring many skills and resources. 2.2 Multinational enterprises and the SDGs International business scholars have paid special attention to #multinational_enterprises because of their size and their position at the centre of global value chains. Montiel et al. (2021), in the Journal of International Business Studies, offered one of the most useful frameworks for this article. Building on the economic idea of externalities, they grouped the 17 goals into six categories. Three categories involve increasing positive externalities: knowledge, wealth and health. Three involve reducing negative externalities: overuse of natural resources, harm to social cohesion, and overconsumption. They then placed these categories along an extended value chain, from inputs to end of life, so that managers can see where in their operations each kind of contribution or harm is created. Crucially, they argued that multinationals can contribute to the SDGs through their ordinary investments. When a firm invests in a host-country subsidiary to make it more competitive, for example by training workers or improving local infrastructure, it can also create positive effects for the surrounding community. When it invests in the community, it can in turn strengthen its own subsidiary. Cuervo-Cazurra et al. (2022) later summarised the pros and cons of the SDGs for managers of multinationals. On the positive side, the goals provide a shared reference point, help managers explain their impact to many audiences, and encourage partnerships. On the negative side, the framework is broad and complex, many targets were designed for governments rather than firms, and the goals can easily be used for superficial branding. They advised managers to focus on genuine impact, avoid choosing only easy goals, and work with partners. Lashitew (2021) asked whether corporate uptake of the SDGs is mere greenwashing or the beginning of real institutional change. His answer was cautiously balanced. He found evidence of both symbolic adoption and early signs of change in how firms think about their role, suggesting that the SDGs could gradually shift norms even if current practice is uneven. This idea that symbolic use can sometimes lead to substantive change over time is important for this article, because it suggests that the right frameworks and pressures can turn talk into action. 2.3 Reporting, disclosure and SDG-washing A large part of the literature examines how companies report on the SDGs. Bose and Khan (2022) studied 6,942 company-year observations in 30 countries between 2016 and 2019. They found that SDG reporting was low in 2016 but rose steadily, and that it was higher in countries with sustainability regulation and better national SDG performance. Interestingly, companies in developing countries reported more on the SDGs than those in developed countries, and firms in shareholder-oriented countries reported more than those in stakeholder-oriented countries. Subramaniam et al. (2023) also found that institutional and organisational factors can have contrasting effects on SDG reporting, showing that disclosure depends on both the national context and the characteristics of the firm. Whittingham et al. (2023) used computer-aided text analysis on the sustainability reports of 164 large corporations to compare language before and after 2015. They found that alignment with the language of some SDGs increased after the goals were launched, while alignment with others did not change much, and that patterns differed across industries and institutional contexts. This points to selective attention: firms engage more with some goals than with others. The most critical stream deals with #greenwashing and SDG-washing. Heras-Saizarbitoria et al. (2022) examined how organisations engage with the goals and found that much of this engagement was shallow, with a tendency to cherry-pick goals and use them for communication rather than for changing practice. Del Rio et al. (2024) compared ex ante measurement tools such as the SDG Compass with ex post compliance indicators and showed that inconsistencies between these tools can make both greenwashing and SDG-washing easier. Their work highlights that the problem is not only corporate intention but also the design of measurement systems. On the regulatory side, Christensen et al. (2021) reviewed the economics of mandatory CSR and #sustainability_reporting. They explained that disclosure mandates can improve comparability and investor information, but that reporting alone does not guarantee better real-world outcomes, and that costs and benefits depend on how rules are designed and enforced. This insight is directly relevant to the SDGs, because many governments and stock exchanges are now moving from voluntary to mandatory sustainability disclosure. 2.4 Sustainable supply chain management and the SDGs Supply chain research has grown into one of the most active areas connected to the SDGs. Leal Filho et al. (2025) reviewed 97 publications on #sustainable_supply_chain_management and the SDGs. They concluded that sustainable supply chain practices have strong potential to reduce environmental, social and economic pressures and contribute to sustainable development, but that the literature had not fully explained how supply chains influence or interact with the goals. Raman et al. (2023) used SDG mapping algorithms on 7,009 publications on green supply chain management from 2013 to 2022 and found that the goals most often linked to this research were SDG 9 on industry, innovation and infrastructure, SDG 12 on responsible consumption and production, and SDG 17 on partnerships. Their results show that supply chain research naturally sits at the meeting point of operations, innovation and collaboration. Collaboration is a central theme. Sudusinghe and Seuring (2022) reviewed 82 journal articles on circular supply chains and found that collaboration practices, both inside the firm and with external partners, are closely tied to better sustainability performance. Cross-functional coordination inside the firm and collaboration with government agencies outside it were the most frequently discussed practices. Seuring et al. (2022) reflected on theory development in this field and argued that sustainable supply chain research needs stronger theoretical foundations to explain why some firms succeed in changing their supply networks while others do not. Van Holt et al. (2021) provide one of the clearest links between supply chain sustainability and financial value. They studied agrifood supply networks, modelling the coconut supply network in the Philippines and comparing it with Brazilian beef. They observed that multinational food companies struggle to bring the SDGs into their supply chains partly because key intermediaries do not see sustainability as economically feasible. Using a return on sustainability investment approach, they identified benefits such as a more stable supply chain, long-term contracts, sustainable products, brand value and innovation, and lower corporate risk. Their study is important because it shows that the business case for supply chain sustainability often depends on making benefits visible to every actor in the chain, not only to the brand at the top. 2.5 The business case and the profitability debate The final stream concerns the relationship between sustainability and financial performance. Atz et al. (2023) surveyed 1,141 peer-reviewed papers and 27 meta-reviews published between 2015 and 2020. They found that studies focused on companies generally associate better sustainability or ESG performance with better financial performance, while studies focused on investors show that ESG investing has on average performed about the same as conventional investing. They also suggested that integrating ESG factors into analysis works better than simply screening out companies, and that benefits tend to appear over longer time horizons. Edmans (2023), in a widely discussed article titled The end of ESG, argued that ESG is both extremely important and nothing special. It is important because environmental and social factors affect long-term value. It is nothing special because these factors should be treated like other intangible assets such as management quality, culture and innovation capability. For Edmans, the goal is not companies that are great at ESG but great companies that create long-term value for shareholders and society. This view is helpful for SDG alignment because it moves the conversation away from treating the goals as a separate activity and toward treating them as part of ordinary strategy. Practitioner-oriented books by senior leaders and scholars make similar points. Polman and Winston (2021) describe the idea of a #net_positive company that takes responsibility for all its impacts and seeks to give more to society than it takes, drawing on experience at Unilever. Serafeim (2022) argues that purpose and profit can be combined, but only when sustainability is connected to strategy and execution, so that it creates a real competitive advantage instead of a set of activities that any rival could copy. Bocken and Geradts (2020) show that the barriers to sustainable business model innovation exist at institutional, strategic and operational levels, and that firms need specific dynamic capabilities, such as sensing new opportunities and reconfiguring resources, to overcome them. 2.6 Research gap Taken together, the literature shows four things. First, corporate interest in the SDGs is high and rising. Second, much of this interest is expressed through reporting, and a good part of it is selective or symbolic. Third, the deepest impacts lie in operations and supply chains, where research is growing but still fragmented. Fourth, the financial evidence suggests that sustainability and profitability can move together, especially in the long run, but only under certain conditions. What is missing is an integrated, easy-to-understand framework that connects these streams and shows students and managers how strategy, operations, supply chains and measurement fit together so that SDG contribution and competitiveness support each other. This article seeks to fill that gap. 3. Research Approach This article uses an integrative literature review combined with conceptual framework development. This approach is common in management research when the aim is to bring together findings from different fields and build a new way of seeing a problem, rather than to test a single hypothesis with new data. The review focused mainly on peer-reviewed journal articles and academic or professional books published from 2020 onwards, so that the analysis reflects recent thinking. A small number of slightly older but highly cited reviews were kept because they mark the starting point of the field. Sources were selected from four connected areas: business and the SDGs in strategy and international business; sustainable supply chain management; sustainability reporting and SDG-washing; and the relationship between sustainability and financial performance. The Sustainable Development Report 2025 was used for the current global status of the goals. The analysis followed three steps. First, the main findings of each source were summarised and grouped by theme. Second, recurring mechanisms were identified, for example how collaboration with suppliers affects both cost and risk, or how incentives shape whether SDG targets are taken seriously. Third, these mechanisms were organised into a layered framework, which is presented in Section 4 and applied in Section 5. The framework is conceptual. It does not claim that every company will see the same results, but it offers a structured way to think about where value and impact are created and where they can be lost. The approach has limits. A review depends on what has been published, and published research may over-represent large companies, listed firms and examples from Europe and North America. Many studies rely on company reports, which can be biased. These limits are discussed again in the conclusion. 4. Conceptual Framework: The Integrated SDG Alignment Framework 4.1 Theoretical foundations The framework draws on three established theoretical ideas. The first is #stakeholder_theory, which holds that firms create value for, and depend on, many groups beyond shareholders, including employees, customers, suppliers, communities and governments. The SDGs can be read as a public statement of what society as a whole expects, so aligning with them helps firms manage relationships with these groups. The second is the resource-based view and its extension, #dynamic_capabilities. In this view, firms gain lasting advantage from resources and capabilities that are valuable, rare and hard to copy. Bocken and Geradts (2020) show that sustainable business model innovation requires capabilities to sense opportunities, seize them and reconfigure the organisation. When SDG work is built into processes, supplier relationships and know-how, it becomes harder for competitors to imitate, and therefore more likely to support profitability. Serafeim (2022) makes a similar argument about connecting purpose to strategy and execution. The third idea is the economic concept of #externalities used by Montiel et al. (2021). Firms create positive externalities, such as skills and health, and negative ones, such as pollution and resource depletion. Many SDGs are about increasing the first and reducing the second. A strategic framework must therefore help firms find externalities that can be partly turned into private value, for example by lowering costs, reducing risk or opening new markets, while also accepting responsibility for those that cannot. Institutional theory adds a fourth lens. Bose and Khan (2022), Subramaniam et al. (2023) and Lashitew (2021) all show that national rules, norms and pressures shape how firms engage with the goals. This explains why the same company may act differently in different countries, and why regulation can level the playing field so that leading firms are not punished for doing more. 4.2 The five layers Building on these foundations, the article proposes an Integrated SDG Alignment Framework made up of five connected layers. Figure 1 sets out the framework. Figure 1. The Integrated SDG Alignment Framework: five connected layers linking purpose to measurable impact and value. Layer 1 is purpose and governance. This layer defines why the company engages with the SDGs and who is accountable. It includes board oversight, executive responsibility, and the link between sustainability targets and pay. Without this layer, SDG work tends to sit in a communications or CSR department with little power to change operations. Layer 2 is materiality and prioritisation. Here the company identifies which goals and targets are most connected to its value chain, both where it causes harm and where it can create benefits. The aim is to focus resources on a small number of goals where the company's impact and influence are greatest, instead of claiming contribution to all seventeen. Layer 3 is core operations and the business model. This is where priority goals are translated into products, processes, investments and innovation. It covers energy use, waste, product design, pricing for low-income customers, and new circular or service-based business models. Layer 4 is supply chain integration. Because much of a company's impact lies upstream and downstream, this layer covers supplier selection, codes of conduct, auditing, capability building, long-term contracts, shared investment and traceability. Layer 5 is measurement, disclosure and learning. This layer sets targets and indicators, collects data, reports honestly, and feeds results back into decisions. It is the layer that separates genuine alignment from SDG-washing, because it shows whether claims match real outcomes. The arrows in Figure 1 matter as much as the boxes. Each layer depends on the one before it and informs the next. A feedback loop runs from measurement back to purpose and prioritisation, so that the company can learn and change course. The framework is designed to show that profitability is protected when the layers are connected. Investments are focused on material goals, linked to operations that generate savings or revenue, supported by suppliers who share the benefits, and tested against evidence. 5. Analysis and Discussion 5.1 Reframing the business case: from cost to long-term value The first barrier to SDG alignment is the belief that sustainability is a cost that reduces profit. The evidence reviewed here suggests a more nuanced view. Atz et al. (2023) found that most corporate-level studies show a positive association between sustainability and financial performance, and that benefits tend to grow over longer periods. They also noted that investor returns from ESG strategies are on average similar to conventional strategies. For managers, this means that SDG alignment is unlikely to be a guaranteed source of extra profit in every quarter, but it is also unlikely to destroy value when done well, and it can create advantage over time. The main channels through which SDG work can support #financial_performance are well known. Cost savings come from using less energy, water and materials. Risk reduction comes from fewer accidents, fewer supply disruptions, less regulatory exposure and lower reputational damage. Revenue growth comes from new products, access to new customer groups, and stronger brands. Capital access can improve when investors and lenders see lower risk. Talent attraction and retention improve when employees believe in the company's purpose. Van Holt et al. (2021) show how many of these benefits appear in supply networks, including more stable supply, long-term contracts and reduced corporate risk. Edmans (2023) adds an important warning. If sustainability factors matter because they affect long-term value, then they should be judged with the same discipline as any other investment. Not every SDG project will pay back, and some should not be pursued by the firm at all, because public policy or other actors are better placed to address them. This discipline protects profitability. It also protects credibility, because projects chosen for their visibility rather than their substance are the ones most likely to be seen as washing. A useful way for students to understand this is to separate three kinds of SDG activity. The first kind creates both impact and business value, such as an efficiency investment that cuts emissions and costs at the same time. The second kind creates impact but little direct business value, such as paying a living wage in a market where competitors do not. The third kind creates business value through communication but little real impact, which is the territory of washing. A strategic framework should maximise the first, manage the second carefully through collaboration, regulation or pricing, and avoid the third. This idea is developed further in Section 5.6. 5.2 Prioritising the right goals The SDGs are broad, and no company can contribute meaningfully to all of them. The danger, however, is that firms choose the goals that look best rather than those that matter most. Heras-Saizarbitoria et al. (2022) and Cuervo-Cazurra et al. (2022) both warn about this cherry-picking. A company with heavy water use in dry regions that reports mainly on SDG 4 on education, because it funds a school, while saying little about SDG 6 on water, is not aligned in any meaningful sense. A sound prioritisation process starts from the value chain, not from the list of goals. Montiel et al. (2021) suggest mapping each stage of the extended value chain, from raw materials to end of life, and asking where the company increases knowledge, wealth and health, and where it contributes to resource overuse, harm to social cohesion or overconsumption. This approach naturally points attention to negative impacts as well as positive ones. It also helps firms see the goals as interconnected. For example, a shift to regenerative farming in a food supply chain can affect land (SDG 15), climate (SDG 13), water (SDG 6) and farmer incomes (SDGs 1 and 8) at the same time. The process can be organised as a cycle, shown in Figure 2. It begins with mapping impacts along the value chain, continues with assessing which impacts are most significant for society and for the business, which is often called #double_materiality, and then selects a small set of priority goals and targets. These are turned into time-bound targets, integrated into operations and supply chains, measured, reported, and then reviewed in the light of results. Figure 2. The SDG prioritisation and integration cycle, from value chain mapping to review and learning. The idea of double materiality deserves a short explanation because it is now central to regulation, especially in the European Union. Traditional financial materiality asks how sustainability issues affect the company's value. Impact materiality asks how the company affects people and the planet. Double materiality looks at both. For SDG alignment this is helpful because the goals are about impact, while profitability is about financial value. A firm that looks only at financial materiality may ignore serious harms that do not yet affect its accounts. A firm that looks only at impact may choose projects with no business logic that are hard to sustain. Looking at both reveals where the strongest overlaps lie. Prioritisation also has a competitive dimension. When a company focuses on goals tied to its core capabilities, it can build expertise that rivals find hard to copy. A logistics firm that becomes excellent at low-carbon transport, or a bank that becomes skilled at lending to small businesses run by women, can turn SDG contribution into a distinctive market position. This is the logic of #shared_value and of the resource-based view: impact that grows out of what the firm does well is more likely to last. 5.3 Embedding the SDGs in core operations and business models Once priorities are set, they must be translated into operational decisions. This is the stage at which many SDG strategies stall, because it requires changes to capital budgets, product designs, processes and performance measures. Mio et al. (2020) noted that much of the literature focuses on strategy execution, which reflects how difficult this translation is in practice. Several types of operational change appear repeatedly in the research. The first is resource efficiency, which reduces energy, water and material use. These investments often have clear financial returns and are the easiest place to start. The second is product and service redesign, which improves the social or environmental performance of what the company sells. Examples include products designed for repair and recycling, healthier food formulations, or financial products that reach people without bank accounts. The third is #business_model_innovation, which changes how value is created and captured. Circular models such as leasing, take-back schemes, refurbishment and resale can link SDG 12 on responsible consumption with new revenue streams, because the firm keeps ownership of materials and earns money from their reuse. Bocken and Geradts (2020) show that these changes face barriers at three levels. At the institutional level, investor pressure for short-term results and the logic of shareholder value can discourage long-term investment. At the strategic level, firms may lack a clear vision or may focus on protecting existing products. At the operational level, rigid processes, narrow performance measures and weak cross-functional cooperation can block change. They argue that firms need specific capabilities to overcome these barriers, for example creating dedicated teams with authority, setting long-term key performance indicators, and allowing experimentation. Minutiello et al. (2024) highlight the growing role of digital technologies in this stage, including artificial intelligence and Industry 4.0 tools. Digital systems can track energy use in real time, predict maintenance needs to avoid waste, and model the effects of product changes on emissions. These tools can lower the cost of measurement and decision-making, which helps reconcile SDG work with profitability. However, technology alone does not create alignment. It must be used to answer the right questions, which come from prioritisation and governance. An important point for students is that operations-level integration often changes the meaning of profitability itself. If a company measures only short-term margin by product line, many SDG investments will look unattractive. If it measures lifetime customer value, avoided risks, resilience and asset value, the same investments may look sensible. This is why the framework links operations closely to governance and measurement. Profitability is not a fixed number. It depends on what is counted and over what time horizon. 5.4 Integrating the SDGs into supply chain management For many companies, the largest share of environmental and social impact lies in the supply chain. Raw material extraction, farming, manufacturing in lower-cost countries, and transport often account for most emissions, water use and labour risks connected to a product. This makes #supply_chain_management the decisive arena for SDG alignment, and also one of the hardest. Traditional approaches to supplier sustainability rely on codes of conduct and audits. The buyer sets rules, sends auditors, and threatens to end contracts if rules are broken. This approach has value, especially for preventing the most serious abuses, but research has shown its limits. Audits are snapshots, can be manipulated, and place the cost of compliance on suppliers who may have little money and little bargaining power. They also treat sustainability as a pass or fail test rather than as an area for joint improvement. The research reviewed here points toward a more collaborative model. Sudusinghe and Seuring (2022) found that collaboration practices inside and outside the firm are closely linked to better sustainability performance in circular supply chains. Internally, procurement, design, operations and sustainability teams must work together so that buying decisions reflect SDG priorities. Externally, firms can work with suppliers, governments, non-governmental organisations, entrepreneurs and research institutes. Leal Filho et al. (2025) likewise emphasise that sustainable supply chain management can reduce environmental, social and economic pressures at the same time, which is exactly the integration the SDGs call for. Van Holt et al. (2021) explain why collaboration is often blocked. In agrifood networks, intermediaries such as traders and processors often do not see sustainability as economically feasible. If they bear the costs while the brand captures the reputational benefits, they have little reason to change. The solution is to make the financial benefits visible and fairly shared, for example through long-term contracts that give farmers security to invest, premium prices for certified products, or shared funding for training and equipment. When suppliers gain stability and the buyer gains a reliable flow of sustainable raw materials, both sides can be better off. Figure 3 illustrates how the SDGs can be mapped onto the stages of an extended value chain, following the logic of Montiel et al. (2021). For each stage, the figure shows typical SDG levers and the business value they can create. Figure 3. SDG levers and business value along an extended value chain, adapted from the externality logic of Montiel et al. (2021). Several practical tools support this kind of integration. #Supplier_development programmes provide training, finance or technology to help suppliers improve. Long-term partnerships replace short-term price competition with shared planning. Traceability systems, sometimes supported by digital technologies, allow buyers to know where materials come from and under what conditions they were produced. Industry collaborations allow competitors to share audit results or fund improvements together, which lowers cost and avoids burdening suppliers with many different standards. Raman et al. (2023) found that green supply chain research is most closely linked to SDGs 9, 12 and 17, which underlines how much supply chain sustainability depends on innovation and partnership. The competitive logic of supply chain integration is strong, even if it is sometimes hidden. Recent years have shown how fragile global supply chains can be, with pandemic disruptions, extreme weather and geopolitical tensions all causing shortages and price spikes. Supply chains that are built on long-term relationships, diversified sources and strong supplier capability tend to be more resilient. This #supply_chain_resilience is a direct business benefit. In this sense, supplier collaboration for the SDGs is also a form of risk management. There are, however, real costs and trade-offs. Supplier development programmes require money and staff. Paying fairer prices can raise input costs. Consolidating suppliers to deepen relationships can increase dependence on a few partners. Traceability systems can be expensive, especially for small suppliers. Firms must therefore decide carefully where deep integration is most valuable, usually in the materials and regions with the highest impact and the highest strategic importance, and where lighter approaches are enough. 5.5 Governance, incentives and capital allocation The first layer of the framework, purpose and governance, determines whether the other layers receive real resources. Research on SDG reporting suggests that engagement is often driven by communication goals, which helps explain why it remains shallow (Heras-Saizarbitoria et al., 2022). To move from communication to action, responsibility must sit with people who control budgets and operations. Three governance mechanisms appear especially important. The first is board oversight. When boards include members with sustainability knowledge and review SDG targets alongside financial results, executives receive a clear signal that these targets matter. The second is #executive_compensation. Linking part of executive pay to a small number of well-defined SDG-related targets, such as emissions reduction or safety performance, can change behaviour. Edmans (2023), however, warns that such targets must be chosen carefully, because poorly designed metrics can be gamed or can distract from other important drivers of long-term value. The third is capital allocation. If investment decisions use an internal carbon price, longer payback periods for strategic sustainability projects, or explicit risk adjustments for climate and social risks, SDG priorities will be built into the most powerful decisions the firm makes. Polman and Winston (2021) emphasise leadership courage and long-term thinking as conditions for this kind of governance. They describe how a company can resist short-term market pressure by explaining its strategy clearly to investors and attracting shareholders who share a long-term view. This is not easy, and not every company can do it, but the example shows that investor relations are part of SDG strategy. Finance markets are also changing. The growth of sustainability-linked loans and bonds, where interest rates depend on meeting sustainability targets, gives firms a direct financial reason to achieve their goals. Atz et al. (2023) found that ESG integration tends to work better than simple exclusion, which suggests that investors who look closely at material sustainability factors can reward firms that manage them well. For companies, this means that credible SDG alignment can, in some cases, lower the #cost_of_capital. 5.6 Measurement, disclosure and the risk of SDG-washing Measurement is the layer that tests whether everything else is real. It is also the area where the literature is most critical of current practice. Del Rio et al. (2024) showed that inconsistencies between tools used to plan SDG contributions and tools used to check them can make washing easier. Whittingham et al. (2023) found that firms aligned their language with some goals but not others. Bose and Khan (2022) showed that reporting has risen sharply, but more reporting does not automatically mean more impact. Figure 4 offers a simple matrix that helps managers and students classify SDG activities. The horizontal axis shows business value, from low to high. The vertical axis shows real SDG impact, from low to high. The four quadrants represent different kinds of activity and call for different responses. Figure 4. The SDG impact and business value matrix: four types of corporate SDG activity and the strategic response to each. The top-right quadrant, high impact and high business value, is the core opportunity space. Activities here, such as efficiency investments, circular business models and supplier partnerships that improve both resilience and livelihoods, should be scaled. The top-left quadrant, high impact but low direct business value, includes actions such as living wages in low-wage supply chains or protecting biodiversity where no market rewards it. These actions are important for genuine alignment, but they need special strategies: industry collaboration so that no single firm carries the cost alone, support for regulation that levels the playing field, or careful pricing and branding where customers are willing to pay. The bottom-right quadrant, low impact but high business value through image, is the washing zone. Activities here, such as small showcase projects promoted heavily in marketing, should be avoided or redesigned, because they create reputational risk if exposed. The bottom-left quadrant, low impact and low value, should simply be dropped. Good measurement starts with clear targets connected to priority goals. Targets should be specific, time-bound and, where possible, based on science or on recognised thresholds. Indicators should measure outcomes, not just activities. For example, the number of suppliers trained is an activity measure, while changes in supplier wages, emissions or accident rates are outcome measures. Data should cover the supply chain, not only the company's own sites, even if this requires estimation at first. Honest disclosure also includes reporting negative impacts and trade-offs. A company that reports progress on SDG 7 on clean energy while its operations increase pressure on SDG 15 on land use should say so. This may feel risky, but research on SDG-washing suggests that selective reporting is increasingly recognised by stakeholders. Credibility is a strategic asset, and it is built through #transparency. The regulatory environment is moving in the same direction. Christensen et al. (2021) explain that mandatory reporting can improve comparability, though it does not guarantee better outcomes. In the European Union, the Corporate Sustainability Reporting Directive introduced detailed reporting standards based on double materiality, and the Corporate Sustainability Due Diligence Directive set expectations for identifying and addressing human rights and environmental harms in value chains. In 2025, the European Commission proposed simplification measures that would narrow the scope and delay parts of these requirements, which shows that the regulatory path is still contested. For global companies, the practical lesson is that measurement systems built around double materiality and value chain data are likely to remain useful, whatever the exact final rules. 5.7 Protecting global competitiveness across different markets A major concern for multinational companies is that SDG alignment may weaken their position against competitors who face fewer expectations. A firm that pays higher wages in its supply chain, invests in cleaner production, or follows stricter reporting rules may have higher costs than a local rival or a competitor from a country with weaker regulation. This is a real problem, and the framework does not remove it entirely. It does, however, suggest several ways to manage it. First, the strongest SDG investments are those that also reduce costs or risks, as discussed above. These investments improve competitiveness rather than harm it. Montiel et al. (2021) show that investments in host-country subsidiaries, such as training and local infrastructure, can raise productivity while creating social benefits. In such cases, SDG contribution is part of normal competitive investment. Second, differentiation can offset higher costs. Where customers, business buyers or public procurement agencies value sustainability, firms with credible SDG performance can win contracts or charge premiums. This is increasingly true in business-to-business markets, where large buyers set sustainability requirements for their own suppliers. A supplier that meets these requirements early gains access to customers that slower competitors cannot serve. Third, institutional differences can work in favour of aligned firms over time. Bose and Khan (2022) found that SDG reporting is higher in countries with sustainability regulation. As regulation spreads, firms that have already built systems face lower adjustment costs than those that wait. Lashitew (2021) suggests that symbolic adoption can gradually lead to institutional change, which means that today's voluntary leaders may be tomorrow's compliant majority, with an advantage in experience. Fourth, collective action reduces the risk of being undercut. Industry initiatives, shared standards and support for sensible regulation spread costs across competitors and remove the advantage of doing nothing. Cuervo-Cazurra et al. (2022) emphasise partnerships as a key response to the complexity of the SDGs. For goals in the top-left quadrant of Figure 4, where impact is high but private returns are low, collective action is often the only realistic path. Finally, competitiveness must be understood in the long term. A firm that achieves high margins today by depending on depleted natural resources, unstable suppliers or practices that may soon be banned is not truly competitive. Its advantage is borrowed from the future. The SDGs, in this sense, can act as an early warning system that helps firms see which of their current advantages are fragile. Small and medium-sized enterprises deserve special mention. They make up most of the suppliers in global value chains and a large share of employment worldwide, yet they often lack the money, staff and data systems needed for complex SDG programmes. If large buyers simply pass reporting and compliance demands down the chain, they may push smaller suppliers out of the market, which would harm SDG 8 on decent work and SDG 10 on reduced inequalities. Responsible alignment therefore means helping smaller suppliers with simple tools, shared data platforms, finance and fair payment terms. This is not only ethical. It also protects the buyer's supply base. 6. Challenges and Opportunities The analysis so far shows that SDG alignment can support profitability and competitiveness under the right conditions. This section brings the main challenges and opportunities together, so that readers can see the full picture. 6.1 Key challenges The first challenge is #short_termism. Financial markets and internal reporting often reward quarterly results, while many SDG investments pay back over several years. Bocken and Geradts (2020) identify this as an institutional barrier to sustainable business model innovation. Without governance changes and investor communication, managers may avoid investments that are good for the long term. The second challenge is selective engagement and washing. As shown by Heras-Saizarbitoria et al. (2022) and del Rio et al. (2024), firms are tempted to highlight easy goals and hide difficult ones. This weakens trust in all corporate SDG claims and makes it harder for genuine leaders to be recognised. The third challenge is complexity. The 17 goals and 169 targets were written mainly for governments, and translating them into business terms is not simple. Cuervo-Cazurra et al. (2022) note that this complexity can lead to confusion or to superficial use. The goals also interact, sometimes positively and sometimes negatively, so improving one can harm another. The fourth challenge is supply chain visibility. Many companies do not know who their suppliers' suppliers are, let alone the working conditions or environmental practices at those sites. Without visibility, it is impossible to measure or manage impact. Building this visibility requires investment and cooperation from suppliers who may be reluctant to share information. The fifth challenge is data quality and comparability. Sustainability data are often incomplete, estimated or measured in different ways. Del Rio et al. (2024) show that inconsistent tools can enable washing. Atz et al. (2023) note that the quality of ESG data remains a concern in financial research. Poor data make it hard for managers to make decisions and for investors to reward good performance. The sixth challenge is uneven regulation and political uncertainty. Rules differ across countries and change over time. The Sustainable Development Report 2025 notes that in early 2025 the United States formally declared its opposition to the SDGs and the 2030 Agenda (Sachs et al., 2025), while the European Union has moved toward detailed reporting and due diligence rules and then proposed to simplify them. Global companies must operate across these different environments, which raises costs and creates strategic uncertainty. The seventh challenge is trade-offs that cannot be fully resolved. Some actions that are right for the SDGs will reduce profit, at least in the short term, and some profitable activities will harm the SDGs. Frameworks can reduce these conflicts, but they cannot remove them. Honest leadership requires accepting this and making clear choices. 6.2 Key opportunities Against these challenges stand significant opportunities. The first is #innovation. The SDGs describe unmet needs and unsolved problems, from clean energy to affordable healthcare to sustainable food. These are also large markets. Firms that develop solutions can grow while contributing to the goals. Raman et al. (2023) show that supply chain sustainability research is strongly linked to SDG 9 on innovation, which reflects this connection. The second opportunity is cost reduction through efficiency and circularity. Using less energy, water and materials, and recovering value from waste, often pays for itself. Circular models can also reduce dependence on volatile raw material prices. The third opportunity is resilience. Collaborative supply chains with capable suppliers and long-term relationships are better able to absorb shocks. Van Holt et al. (2021) identified stable supply and lower corporate risk as concrete benefits of sustainable agrifood networks. The fourth opportunity is access to capital and talent. Investors who integrate material sustainability factors, and employees who want meaningful work, can both favour companies with credible SDG strategies. Serafeim (2022) and Polman and Winston (2021) both describe how purpose, when backed by action, can attract people and capital. The fifth opportunity is trust and #stakeholder_engagement. In a period of low public trust in business, firms that are honest about their impacts and show real progress can strengthen their #social_licence_to_operate. This trust can matter greatly during crises, regulatory debates or reputational challenges. The sixth opportunity is partnership. SDG 17 on partnerships is often overlooked, but it may be the most important goal for business. Collaboration with governments, NGOs, universities, competitors and suppliers can achieve what no single firm can. Sudusinghe and Seuring (2022) found that collaboration with government agencies is one of the most discussed external collaboration practices in circular supply chains. Partnerships can share costs, pool knowledge and change whole systems. 6.3 Balancing the challenges and opportunities The central message of this section is that the relationship between the SDGs and profitability is not automatically positive or negative. It depends on choices. When firms choose material goals, embed them in operations, collaborate with suppliers, align incentives and measure honestly, the opportunities usually outweigh the challenges in the long run. When firms choose easy goals, keep SDG work in a separate department, push costs onto suppliers and report selectively, they tend to get the costs of sustainability without the benefits, and they also take on reputational risk. This conclusion is consistent with Edmans (2023), who argues that sustainability should be treated as part of long-term value creation rather than as a special category. It is also consistent with Atz et al. (2023), whose review found positive associations at the corporate level, especially over longer periods. The Integrated SDG Alignment Framework is a way of organising these choices so that the positive path becomes more likely. 7. Implications for Practice, Policy and Students 7.1 Implications for managers For managers, the framework suggests a practical sequence of actions. Start with governance: make sure the board and senior executives own the SDG strategy and that it is linked to budgets and incentives. Then map the value chain and identify the goals where the company has the greatest positive and negative impact, using a double materiality approach. Select a small number of priority goals and set measurable, time-bound targets for each. Next, look for the investments that sit in the top-right quadrant of Figure 4, where impact and business value overlap, and act on them quickly to build momentum and fund further work. For goals where impact is high but returns are low, seek industry partnerships and engage constructively with policy makers. In the supply chain, move from audit-only approaches toward #supplier_collaboration, long-term contracts and capability building, focusing first on the highest-risk materials and regions. Finally, build measurement systems that track outcomes, cover the value chain, and report negative impacts as well as positive ones. Use the results to review and adjust the strategy each year. Treat honest disclosure as a source of credibility rather than a risk. 7.2 Implications for policy makers For policy makers, the analysis shows that regulation can help leading firms by levelling the playing field. Mandatory and comparable disclosure, when designed with care, can reduce the advantage of firms that do little and make it easier for investors and customers to reward genuine performance (Christensen et al., 2021). Due diligence rules can push responsibility along the value chain. At the same time, rules should be designed so that smaller firms and suppliers in lower-income countries are supported rather than excluded. Public procurement, which represents a large share of spending in many economies, can be used to reward sustainable suppliers. International coordination of standards can reduce the cost of reporting for global companies. 7.3 Implications for students and future managers For students, the main lesson is that SDG alignment is not a separate subject from strategy, operations, finance or supply chain management. It runs through all of them. Future managers will need to read sustainability data as easily as financial data, understand how decisions in one part of the value chain affect people and the environment in another, and explain to investors how long-term investments create value. Students can practise these skills by analysing real company reports with a critical eye, asking which goals are claimed, which are missing, and whether the evidence supports the claims. They can also use Figure 4 to classify activities they read about and judge whether a company is creating real impact or only telling a good story. Students should also be aware of the debates. Not all scholars agree on how strong the business case is, how much responsibility companies should carry, or how far regulation should go. Edmans (2023) warns against treating sustainability as a special category. Critical researchers warn against trusting corporate claims too easily. Good analysis keeps both perspectives in view. 8. Conclusion This article set out to analyse strategic frameworks that allow corporations to embed the UN Sustainable Development Goals into their core operations and supply chain management without sacrificing profitability or global competitiveness. It began from a concerning picture: a decade after the goals were adopted, the world is far off track, and much corporate engagement remains symbolic or selective. The review of recent research shows that this outcome is not inevitable. The evidence on #corporate_sustainability and financial performance is broadly positive at the company level, especially over longer time horizons (Atz et al., 2023). Multinationals can contribute to the SDGs through ordinary competitive investments (Montiel et al., 2021). Collaborative supply chains can deliver both sustainability and business benefits (Sudusinghe and Seuring, 2022; Van Holt et al., 2021). Yet these benefits depend on how firms organise their engagement. The proposed Integrated SDG Alignment Framework connects five layers: purpose and governance, materiality and prioritisation, core operations and business model, supply chain integration, and measurement, disclosure and learning. Its main argument is that profitability and SDG contribution are most likely to move together when these layers are connected, so that resources flow to the goals where impact and business value overlap, suppliers share in the benefits, incentives reward long-term results, and honest measurement keeps the strategy on course. The SDG impact and business value matrix in Figure 4 offers a simple tool to distinguish core opportunities from activities that need collective action, and from those that amount to washing. The article also recognises that some tensions cannot be resolved by any framework. There will be actions that are right for society but costly for the firm, and these require leadership, collaboration and supportive public policy. Competitiveness must therefore be understood in a long-term sense, as the ability to keep creating value in a world that is changing because of climate, inequality and regulation. 8.1 Limitations This study has several limitations. It is a conceptual and integrative review rather than an empirical test, so the framework should be seen as a set of reasoned propositions rather than proven rules. The literature reviewed is dominated by research on large and listed companies, often in Europe, North America and Asia, and may not fully reflect the experience of small firms or firms in Africa, Latin America and the Middle East. Many sources rely on corporate reports, which may present a more positive picture than reality. Finally, the regulatory environment is changing quickly, so some details discussed here may change after publication. 8.2 Directions for future research Future research could test the framework empirically, for example by examining whether firms that link SDG targets to capital allocation and executive pay show better impact and financial results than those that do not. Longitudinal case studies could follow how companies move from symbolic to substantive engagement, building on the idea of gradual institutional change (Lashitew, 2021). Supply chain research could study how benefits and costs of SDG alignment are shared among brands, intermediaries and small producers, extending the work of Van Holt et al. (2021). More research is also needed on small and medium-sized enterprises, on firms from emerging economies, and on how digital technologies can lower the cost of measuring impact across complex value chains. Finally, researchers could examine how political shifts and regulatory changes after 2025 affect corporate commitment to the goals as the 2030 deadline approaches. The SDGs will not be achieved by business alone, but they are unlikely to be achieved without it. For the companies that take them seriously, the goals offer not only a responsibility but also a map of where future value is likely to be found. The task for strategy is to read that map carefully, follow it into operations and supply chains, and measure the journey honestly. References Atz, U., Van Holt, T., Liu, Z. Z., and Bruno, C. C. (2023). Does sustainability generate better financial performance? Review, meta-analysis, and propositions. Journal of Sustainable Finance and Investment, 13(1), 802-825. https://doi.org/10.1080/20430795.2022.2106934 Bocken, N. M. P., and Geradts, T. H. J. (2020). Barriers and drivers to sustainable business model innovation: Organization design and dynamic capabilities. Long Range Planning, 53(4), 101950. https://doi.org/10.1016/j.lrp.2019.101950 Bose, S., and Khan, H. Z. (2022). Sustainable development goals (SDGs) reporting and the role of country-level institutional factors: An international evidence. Journal of Cleaner Production, 335, 130290. Christensen, H. B., Hail, L., and Leuz, C. (2021). Mandatory CSR and sustainability reporting: Economic analysis and literature review. 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Organizations' engagement with sustainable development goals: From cherry-picking to SDG-washing? Corporate Social Responsibility and Environmental Management, 29(2), 316-328. https://doi.org/10.1002/csr.2202 Lashitew, A. A. (2021). Corporate uptake of the Sustainable Development Goals: Mere greenwashing or an advent of institutional change? Journal of International Business Policy, 4(1), 184-200. https://doi.org/10.1057/s42214-020-00092-4 Leal Filho, W., Viera Trevisan, L., Paulino Pires Eustachio, J. H., Dibbern, T., Castillo Apraiz, J., Rampasso, I., Anholon, R., Gornati, B., Morello, M., and Lambrechts, W. (2025). Sustainable supply chain management and the UN sustainable development goals: Exploring synergies towards sustainable development. The TQM Journal, 37(1), 18-41. https://doi.org/10.1108/TQM-04-2023-0114 Minutiello, V., Garcia-Sanchez, I. M., and Aibar-Guzman, B. (2024). The latest developments in research on sustainability and the Sustainable Development Goals in the areas of business, management and accounting. Administrative Sciences, 14(10), 254. https://doi.org/10.3390/admsci14100254 Mio, C., Panfilo, S., and Blundo, B. (2020). Sustainable development goals and the strategic role of business: A systematic literature review. Business Strategy and the Environment, 29(8), 3220-3245. https://doi.org/10.1002/bse.2568 Montiel, I., Cuervo-Cazurra, A., Park, J., Antolin-Lopez, R., and Husted, B. W. (2021). Implementing the United Nations' Sustainable Development Goals in international business. Journal of International Business Studies, 52(5), 999-1030. https://doi.org/10.1057/s41267-021-00445-y Pizzi, S., Caputo, A., Corvino, A., and Venturelli, A. (2020). Management research and the UN sustainable development goals (SDGs): A bibliometric investigation and systematic review. Journal of Cleaner Production, 276, 124033. Polman, P., and Winston, A. (2021). Net positive: How courageous companies thrive by giving more than they take. Harvard Business Review Press. Raman, R., Sreenivasan, A., Ma, S., Patwardhan, A., and Nedungadi, P. (2023). Green supply chain management research trends and linkages to UN Sustainable Development Goals. Sustainability, 15(22), 15848. https://doi.org/10.3390/su152215848 Sachs, J. D., Lafortune, G., Fuller, G., and Iablonovski, G. (2025). Financing sustainable development to 2030 and mid-century: Sustainable development report 2025. SDSN and Dublin University Press. https://doi.org/10.25546/111909 Serafeim, G. (2022). Purpose and profit: How business can lift up the world. HarperCollins Leadership. Seuring, S., Aman, S., Hettiarachchi, B. D., de Lima, F. A., Schilling, L., and Sudusinghe, J. I. (2022). Reflecting on theory development in sustainable supply chain management. Cleaner Logistics and Supply Chain, 3, 100016. Subramaniam, N., Akbar, S., Situ, H., Ji, S., and Parikh, N. (2023). Sustainable development goal reporting: Contrasting effects of institutional and organisational factors. Journal of Cleaner Production, 411, 137339. Sudusinghe, J. I., and Seuring, S. (2022). Supply chain collaboration and sustainability performance in circular economy: A systematic literature review. International Journal of Production Economics, 245, 108402. Van Holt, T., Delaroche, M., Atz, U., and Eckerle, K. (2021). Financial benefits of reimagined, sustainable, agrifood supply networks. Journal of International Business Policy. https://doi.org/10.1057/s42214-020-00096-0 Whittingham, K. L., Earle, A. G., Leyva-de la Hiz, D. I., and Argiolas, A. (2023). The impact of the United Nations Sustainable Development Goals on corporate sustainability reporting. BRQ Business Research Quarterly, 26(1), 45-61. #SDGs #Agenda_2030 #SDG_alignment #sustainable_business #corporate_strategy #responsible_supply_chains #ESG #shared_value_creation #green_supply_chain #sustainability_strategy #impact_and_profit #SDG_integration #business_and_sustainable_development #STULIB #student_research

Cross-border strategic alliances and mergers and acquisitions are among the most common routes into foreign markets, yet many of them fall short of their goals for reasons that are cultural rather than financial. This article examines how the #cultural_intelligence of executives, meaning their capability to function and manage well across cultures, shapes the success of these deals, with special attention to rapidly growing and culturally diverse economic hubs. Using an integrative review of recent research on cultural intelligence, alliances, joint ventures and cross-border acquisitions, the article combines upper echelons theory with the four-factor model of cultural intelligence. It proposes a framework in which metacognitive, cognitive, motivational and behavioural cultural intelligence work through four mechanisms: partner and target sensemaking, trust building and negotiation, knowledge transfer, and the design of integration and governance. Hub diversity, cultural and institutional distance, top team diversity and depth of integration are expected to strengthen or weaken these links. The article offers six testable propositions, a three-level design for measuring executive cultural intelligence and deal success, and a practical definition of success rates. It concludes that direct measurement of executive cultural intelligence, rather than reliance on years of international experience, can improve our understanding of why some #cross_border_deals succeed and others fail, and that the value of this capability is likely to be greatest in highly diverse hubs. Keywords: cultural intelligence, executives, strategic alliances, cross-border mergers and acquisitions, market entry, post-merger integration, upper echelons, economic hubs 1. Introduction 1.1 Background When a company decides to enter a foreign market, it rarely does so alone. Many firms choose to work with a local partner through a #strategic_alliance or a #joint_venture, and many others buy or merge with an existing firm abroad. These two routes, alliances and cross-border mergers and #acquisitions, give a company fast access to customers, licences, distribution networks, technology and local knowledge. They also bring a problem that a spreadsheet cannot show: two groups of people who were raised in different cultures, trained in different business systems and used to different ways of making decisions now have to work as one. Students who read about famous deals usually meet this problem as a short line in a case study, often phrased as "cultural differences" or "integration problems". The phrase hides a great deal. It hides the meeting where one side thought a handshake meant a final agreement while the other side saw it as the start of talks. It hides the engineer who left the acquired firm because nobody explained the new reporting lines in a way that made sense to him. It hides the partner who quietly stopped sharing know-how because trust had faded after a few unanswered emails. In each case the deal looked sound on paper, yet the people at the top could not read the cultural signals around them. Research in #international_business has a name for the ability to read and respond to those signals. It is called #cultural_intelligence, often shortened to CQ. In plain terms, CQ is a person's capability to function and manage well in settings that are culturally diverse. It is not the same as knowing many facts about other countries, and it is not the same as being friendly or open-minded. It is a set of related capabilities that cover how a person thinks about culture, what they know about it, how motivated they are to engage with it, and how well they can change their own behaviour when the situation calls for it (Earley and Ang, 2003; Ang et al., 2007). 1.2 The problem A large body of work now shows that CQ predicts how well employees, #expatriates and teams perform when they work across cultures (Schlaegel et al., 2021; Rockstuhl et al., 2024; Ng et al., 2025). Far less is known about the people who actually make and run cross-border deals: chief executives, board members, business development directors and the integration leaders who take over after the contract is signed. These are the people whose judgement shapes which partner is chosen, how a deal is negotiated, how deeply two firms are combined and how conflicts are handled when they appear. If CQ matters for a sales clerk serving foreign customers, it is reasonable to expect that it matters even more for the executive deciding a billion-dollar acquisition. The gap becomes sharper when we look at where many deals now happen. A growing share of international activity is concentrated in fast-growing #economic_hubs that are themselves highly diverse. In cities such as #Dubai or #Singapore, a single company may employ people from dozens of nationalities, report to owners in another region and serve customers from many more. In such places "the local culture" is not one culture. An executive cannot simply learn one set of rules and apply them. Research on the #United_Arab_Emirates, for example, describes workplaces where staff from many countries work side by side and where CQ is linked to job satisfaction and performance (Ibanez et al., 2025). Studies of #Gulf banking suggest that the cultural capabilities of managers may shape how much #tacit_knowledge they gain from merger and acquisition work (Dzenopoljac et al., 2022). Yet we still lack a clear account of how executive CQ should be measured in deal settings and how it connects to the success or failure of alliances and #acquisitions. 1.3 Aim and research questions This article sets out to fill part of that gap. Its aim is to explain, and to propose a way of measuring, how the cultural intelligence of executives affects the success rates of strategic alliances and cross-border mergers and acquisitions, with special attention to culturally diverse growth hubs. Three questions guide the work: First, through which mechanisms can executive CQ influence the outcomes of alliances and cross-border acquisitions? Second, under which conditions, and in particular in which kinds of hub settings, does executive CQ matter most? Third, how can researchers and companies measure executive CQ and deal success in a way that allows the link between them to be tested rather than assumed? 1.4 Contribution and structure The article makes three contributions that are useful for students and for early researchers. It brings together recent work on CQ, on alliances and on cross-border acquisitions, three fields that often talk past each other. It offers a conceptual framework that links the four facets of executive CQ to specific deal mechanisms and to measurable outcomes. And it proposes a multi-level measurement design that a student project, a thesis or a company review team could adapt. The rest of the article is organised as follows. Section 2 reviews the literature. Section 3 presents the conceptual framework and a set of propositions. Section 4 explains the approach of the study and the proposed measurement design. Section 5 analyses the role of executive CQ across the deal lifecycle, in alliances, in acquisitions and in diverse hubs, and it discusses the limits of the idea. Section 6 sets out implications, and Section 7 concludes. 2. Literature Review 2.1 What cultural intelligence is, and what the evidence says The idea of cultural intelligence was introduced in the early 2000s as a form of intelligence that focuses on cultural settings (Earley and Ang, 2003). It was later given a widely used measurement tool, the 20-item Cultural Intelligence Scale, and a four-factor structure (Ang et al., 2007). The four factors are worth learning carefully, because the rest of this article depends on them. #Metacognitive_CQ is the "thinking about thinking" part. It describes how far a person is aware of their own cultural assumptions, plans before an #intercultural meeting, and checks and adjusts their understanding while the meeting is happening. An executive with strong metacognitive CQ will ask, before a #negotiation, "What might this side mean by a deadline?" and will notice during the meeting that a polite silence may not mean agreement. #Cognitive_CQ is knowledge. It covers what a person knows about the economic, legal, social and religious systems of other cultures, as well as norms of communication and leadership. In a deal setting this includes knowledge of how boards work in another country, how labour law affects integration, or how family ownership shapes decisions in a target firm. #Motivational_CQ is the energy and confidence a person directs toward learning about and working in unfamiliar cultures. Someone may know a lot and plan well, yet still avoid difficult cross-cultural conversations because they find them draining. Motivational CQ explains who keeps going when the work becomes uncomfortable. #Behavioural_CQ is the capability to adapt verbal and non-verbal behaviour, such as tone, pace, gestures, directness or the use of silence, to suit the cultural setting. It is the visible part of CQ, the part that partners and employees actually see. The evidence that these capabilities matter at work has grown strong. A #meta_analysis of 70 studies found that the CQ dimensions relate to six work-related outcomes, that they carry the effect of personality traits onto these outcomes, and that they add predictive power beyond personality and a broad set of other established predictors (Schlaegel et al., 2021). A later meta-analysis, built on 109 samples and more than 24,000 people, placed CQ within the wider network of personality traits and showed that CQ is a stronger channel for the "plasticity" side of personality, the part linked to openness and extraversion, than for the "stability" side, in predicting five forms of job performance (Rockstuhl et al., 2024). The largest synthesis so far, covering 355 field samples and over 128,000 people, found that both #language_proficiency and CQ are positively related to job performance and cultural adaptation. It also showed that the effects of CQ become stronger in host cultures where communication depends heavily on context, while the effects of language skill become stronger where communication is more explicit (Ng et al., 2025). This last finding is important for deal-makers, because many fast-growing markets have #high_context business cultures where what is not said can matter as much as what is said. Figure 1 shows how large the pooled evidence base in these three meta-analyses has become. For students, the main point is simple: CQ is no longer a fashionable idea with thin support. It has been tested many times, in many countries and job types. Figure 1. Size of the evidence base in three recent meta-analyses of cultural intelligence. Source: Schlaegel et al. (2021), Rockstuhl et al. (2024) and Ng et al. (2025). Team-level studies point the same way. In a study of 810 business students from 38 countries working in 232 #global_virtual_teams, the CQ and #emotional_intelligence of team members reduced interpersonal, task and process conflict, and process conflict in turn harmed team performance (Davaei et al., 2022). Another study of global virtual teams found that particular configurations of motivational CQ were linked to better social integration and performance (Richter et al., 2021). Work on subsidiaries also suggests that CQ shapes firm-level capabilities. In a survey of 475 people working in German subsidiaries in Malaysia, CQ strengthened the effect of innovation and knowledge transfer on competitive advantage (Cortes and Ooi, 2023). A recent bibliometric study of 242 articles published between 2005 and 2025 confirmed that research is moving from individual outcomes toward team and organisational performance (Ismail et al., 2026). Where does CQ come from? A survey of 1,713 expatriates showed that international experiences before adulthood, such as growing up across cultures, are deep sources of later CQ (Morin and Talbot, 2024). This matters for executive selection, because it suggests that CQ has roots in a person's life history and is not only the product of a short training course. 2.2 Alliances and acquisitions as routes into foreign markets Textbooks often present #market_entry as a ladder that runs from exporting, through licensing and franchising, to alliances, joint ventures and full ownership through acquisition or a new subsidiary. Alliances and acquisitions sit near the top of that ladder because they demand more commitment and more contact between people from different firms and countries. A strategic alliance is an agreement in which two or more firms keep their separate identities but combine some resources or activities. A #joint_venture is a special form where the partners create a new legal entity that they own together. In an #international_joint_venture the partners come from different countries. Alliances give access to local knowledge without the cost of buying a whole firm, but they create a constant need for cooperation, trust and shared decisions. A cross-border acquisition happens when a firm buys control of a company in another country. A merger combines two firms into one. In practice, most cross-border deals are acquisitions, and the term #cross_border_MandA is used for both. Acquisitions give control and speed, but the buyer then has to integrate people, systems and cultures, a stage known as post-merger integration. Both routes depend heavily on what happens between people. A review of behavioural factors in international strategic alliances found that alliance performance is shaped by factors at the individual, group and organisational levels, and grouped these factors into relational issues, learning and knowledge, conflict, and other influences (Gehrisch and Suss, 2022). Notably, the review found that the same factor can mean different things at different levels, which makes it hard to measure. This is one reason this article focuses on executives as a clear level of analysis. 2.3 Culture inside strategic alliances Alliances are fragile. Partners must share knowledge to create value, yet each also wants to protect its own core knowledge. A review of 98 articles on international joint ventures showed that cultural and institutional differences between home and host markets make this balance between #knowledge_transfer and knowledge protection especially difficult (Kim et al., 2024). Executives on both sides must judge how much to share, when, and with whom, and those judgements are made through a cultural lens. Alliances also have to balance exploration, meaning the search for new knowledge, with exploitation, meaning the efficient use of what the partners already have. An integrative review of 77 articles on #ambidexterity in strategic alliances found that research is moving toward its micro-foundations, the individual and organisational sources of this balance, as well as governance mechanisms and outcomes (Zahoor et al., 2023). Executive CQ can be seen as one such micro-foundation. A leader who reads the partner's expectations accurately is better placed to agree on what the alliance should explore and what it should exploit. Recent work on the Gulf region links CQ explicitly to #alliance_governance. A systematic review of CQ and responsible internationalisation proposed that CQ sits at the centre of a network linking dynamic managerial capabilities to sustainable international outcomes, and it recommended building CQ into leader selection, talent metrics and alliance governance (Maldonado et al., 2026). 2.4 Culture inside cross-border acquisitions The literature on cultural differences in acquisitions is large and, at first sight, confusing. Some studies find that #cultural_distance harms performance, while others find that it can help by bringing complementary capabilities. Recent studies help to explain this confusion by looking at specific dimensions rather than overall distance. A study of cross-border acquisitions found that differences in #power_distance between acquirer and target were negatively related to post-acquisition performance, while differences in individualism and collectivism were positively related. The buyer's cultural learning from supply chain partners, especially partners culturally close to the target, helped reduce the harm caused by power distance gaps (Yu et al., 2024). The lesson for executives is that not all differences are equal, and that cultural learning can be built before a deal. Organisational culture matters as well as national culture. Using employee reviews and a language model to measure #organisational_culture across 243 deals between 2008 and 2021, one study found that greater #cultural_distance between acquirer and target led to frictions that lowered both the short-term market reaction and long-term #synergy gains. Larger cultural distance was also linked to higher premiums paid, suggesting that acquirers overestimated the #synergies they could achieve with culturally distant targets, and to lower innovativeness two years after the deal (Brede et al., 2024). This finding is striking. It suggests that weak cultural reading is not only an integration problem but also a pricing problem: buyers who cannot read culture may simply pay too much. The emotional side of acquisitions has received growing attention. A systematic review of 78 articles showed that the strength and direction of employees' emotional reactions can push a cross-border deal toward success or failure, and it proposed a multi-level framework to study these emotions (Zahoor et al., 2022). Another review of #post_merger_integration from an intercultural perspective argued that differences in leadership models, communication norms, power distance and organisational identity continue to create identity threats, mistrust and resistance, even when strategic goals and integration plans are well defined. Its authors recommended systematic #cultural_due_diligence and context-sensitive leadership (Gebski and Bien, 2025). Integration design is also central. A mixed-method study of a cross-border deal showed that task integration should be split into product harmonisation and structural integration, and that different integration approaches may be needed at the same time in different parts of the combined firm, depending on the source of #synergy and on whether the teams work in the same place (van Oorschot et al., 2022). An eight-year study of eight acquisitions of Western firms by Chinese private companies found that most started with a light-touch approach but then moved along three different paths, with cultural difference acting as one of the factors that steered the transition (Wu et al., 2023). Accounting research has shown how acquirers use integration controls to manage cultural differences and employee resistance after a deal (Smeulders et al., 2023). Each of these choices is made by executives, and each requires them to judge cultural conditions. Language is a further layer. A conceptual study of post-merger integration showed how bidder firms use rationalistic and nationalistic arguments, and language strategies in particular, to gain legitimacy for integration among employees and managers, especially when neither firm uses English as its native language (Malik et al., 2023). Customers matter too. A study of a Chinese and Finnish acquisition found that customer knowledge sharing after the deal depended on customer motivation and on how well the parties made, enabled and kept their promises (Degbey and Pelto, 2021). Keeping promises across cultures requires that both sides understand what was promised in the first place. 2.5 Executives at the centre of the deal Why focus on executives rather than on whole organisations? The answer comes from #upper_echelons_theory, which argues that the strategic choices of a firm reflect the experiences, values and cognitive frames of its top managers (Hambrick and Mason, 1984). Executives do not see their environment objectively. They filter it through what they already know and believe. In cross-border deals, the filter includes cultural assumptions, and the stakes of a distorted filter are high. Most research in this tradition has used #international_experience as a stand-in for cultural capability, because experience is easy to count from biographies. The results are more complex than one might expect. A study of post-acquisition performance found that high levels of international experience in the #top_management_team had a positive impact, but low levels did not, and the effect depended on the nationality diversity of the team (Piaskowska et al., 2021). The authors argued that people do not learn automatically from being abroad; learning from experience is slow and uneven. A study of 423 cross-border acquisitions by manufacturing firms from 13 emerging economies found a U-shaped relationship between a firm's cross-cultural experience and the productivity gains it achieved at home after the deal. The relationship was steeper when integration was intensive or the target was relatively large, and flatter when the buyer came from a country with larger institutional voids (Hendriks et al., 2023). In plain words, a little experience can be a dangerous thing, because it may create confidence without understanding. These findings make a strong case for measuring capability directly rather than through counts of years abroad. Experience is an input. CQ is closer to what the experience actually produced in the person. Meta-analytic work supports this view by showing that CQ has value beyond experience-related predictors and personality (Schlaegel et al., 2021). 2.6 Rapidly growing and culturally diverse hubs Much of the classic research on culture in deals assumed a two-country picture: a buyer from country A acquires a target in country B, and the "cultural distance" between A and B can be measured with national scores. That picture fits poorly with fast-growing hubs. In a city like Dubai, the target firm may be locally registered, owned by a regional family group, managed by expatriates from several countries and staffed by a workforce of many nationalities. Its customers may come from across Asia, Africa and Europe. For an incoming executive, the relevant culture is a layered mix rather than a single national profile. Research in the United Arab Emirates illustrates this. A study of the money exchange sector, chosen because it employs a highly international workforce, found that CQ was positively related to job performance and job satisfaction, and that job satisfaction carried part of the effect of CQ on performance (Ibanez et al., 2025). The authors stressed that these relations may depend on context, which is a useful warning for anyone tempted to apply findings from one country to another without thought. A conceptual paper on banking in the Gulf Cooperation Council proposed that managers involved in many merger and acquisition deals are exposed to large flows of tacit knowledge, and that the size of this benefit depends partly on the manager's CQ, #international_experience, number of languages spoken and frequency of face-to-face meetings (Dzenopoljac et al., 2022). In such hubs, two features stand out for this article. First, cultural complexity is high even when the "distance" between the buyer's home country and the hub looks small on paper, because the hub contains many cultures at once. Second, deal activity tends to be frequent and fast, so executives must make cultural judgements under time pressure and with partners they may meet only a few times before signing. 2.7 Gaps in the literature The review points to four gaps. First, CQ research has focused mostly on employees, expatriates and teams, while research on alliances and acquisitions has used crude proxies for executive cultural capability. Second, studies rarely link specific facets of CQ to specific deal mechanisms; CQ tends to be treated as a single score. Third, "success" in alliances and acquisitions is measured in many different ways, which makes it hard to compare findings. Fourth, the special features of diverse, fast-growing hubs are seldom modelled. The framework that follows is designed to address these gaps. 3. Conceptual Framework 3.1 Bringing upper echelons theory and CQ together The framework combines two lenses. Upper echelons theory explains why executive characteristics shape strategic outcomes (Hambrick and Mason, 1984). The four-factor model of CQ explains which cultural capabilities an executive brings to the task (Ang et al., 2007). Put together, they suggest that executive CQ is a #strategic_capability: a personal resource that, through the choices executives make, becomes part of a firm's ability to enter and operate in foreign markets. This view also links to the #dynamic_capabilities idea, in which firms create value by sensing opportunities, seizing them and reconfiguring their resources. Recent work has argued that CQ can act as a bridge between dynamic managerial capabilities and successful international outcomes (Maldonado et al., 2026). In deal terms, sensing becomes partner and target screening, seizing becomes #negotiation and closing, and reconfiguring becomes integration or alliance governance. 3.2 From CQ to deal mechanisms Figure 2 sets out the framework. On the left are the four facets of executive CQ. In the middle are four deal mechanisms through which those facets work. On the right are the outcomes that define deal success. A moderator box at the bottom shows the conditions that make the links stronger or weaker. Figure 2. Conceptual framework linking executive cultural intelligence to deal mechanisms and outcomes in alliances and cross-border acquisitions. The first mechanism is partner and target sensemaking. Before a deal, executives must interpret what a potential partner wants, how it makes decisions and whether its stated values match its behaviour. Metacognitive and cognitive CQ are most important here. An executive who checks their own assumptions is less likely to read a target's modest presentation as weakness, or a partner's slow reply as lack of interest. The second mechanism is trust building and negotiation quality. Trust in cross-border deals is built through small, repeated signals: keeping promises, showing respect for #hierarchy where it matters, adjusting directness, and reading what the other side treats as a commitment. Behavioural and motivational CQ carry most of the weight. Research on teams shows that CQ reduces conflict (Davaei et al., 2022), and similar processes are likely between deal teams. Trust also supports the promise management that keeps customers and partners engaged after a deal (Degbey and Pelto, 2021). The third mechanism is knowledge transfer and learning. Alliances and acquisitions often exist to move knowledge, but knowledge moves through people. Cognitive and metacognitive CQ help executives decide what to share and how to frame it so that the other side can absorb it. Studies of subsidiaries show that CQ strengthens the effect of knowledge transfer on competitive advantage (Cortes and Ooi, 2023), and reviews of joint ventures show that cultural differences complicate the balance between sharing and protecting knowledge (Kim et al., 2024). The fourth mechanism is integration and governance design. After signing, executives choose how deeply to combine two firms, which language to use, which managers to keep, and how quickly to change systems. In alliances, they design steering committees, decision rights and conflict procedures. These choices require all four facets of CQ, because they involve planning, knowledge, persistence through resistance and visible behaviour that employees will judge. Evidence that integration approaches must be differentiated by context (van Oorschot et al., 2022) and that integration paths evolve over time with cultural difference as a steering factor (Wu et al., 2023) shows how much room for judgement there is at this stage. 3.3 Moderating conditions Four conditions are expected to change how strongly executive CQ affects deal outcomes. Hub diversity. The more cultures are present in the setting at once, the less useful any single cultural "map" becomes, and the more the deal depends on executives' general capability to read and adapt. This is where high-context communication is common, and recent meta-analytic evidence suggests that the value of CQ rises in high-context settings (Ng et al., 2025). Cultural and #institutional_distance. Larger differences in specific dimensions, especially power distance, raise integration risks (Yu et al., 2024). Executive CQ should matter more when these differences are large. Top management team diversity. Executives do not act alone. A team with members of several nationalities may make up for the weak CQ of one leader, or it may create its own frictions. Research on international experience shows that team nationality diversity changes the effect of experience on acquisition performance (Piaskowska et al., 2021). Depth of integration. A light-touch acquisition or a narrow alliance requires less daily cultural contact than a full absorption or a broad joint venture. The more integration is attempted, the more executive CQ should matter, a pattern consistent with the steeper effects of experience under intensive integration (Hendriks et al., 2023). 3.4 Propositions The framework can be expressed as a set of propositions that students and researchers can test. They are stated in simple terms on purpose. Proposition 1. Executive CQ, measured as an overall capability, is positively related to the success of international strategic alliances and cross-border acquisitions, after controlling for deal size, industry, prior deal experience and cultural distance. Proposition 2. Metacognitive and cognitive CQ are most strongly related to the quality of partner and target selection and to the accuracy of the price paid, so that executives with higher levels of these facets are less likely to overpay for culturally distant targets. Proposition 3. Motivational and behavioural CQ are most strongly related to trust between partners, to the quality of negotiation and to the retention of key employees after a deal. Proposition 4. The effect of executive CQ on deal success is stronger in highly diverse hubs than in culturally uniform markets. Proposition 5. The effect of executive CQ on deal success is stronger when the planned integration is deep, and weaker in light-touch acquisitions or narrow alliances. Proposition 6. The CQ of the person who leads integration after the deal predicts outcomes at least as strongly as the CQ of the chief executive who led the negotiation. 4. Research Approach and Measurement Design 4.1 Approach of this study This article is an integrative, conceptual study. Rather than reporting a new survey, it brings together peer-reviewed research published mainly between 2021 and 2026 on cultural intelligence, strategic alliances, joint ventures and cross-border acquisitions, plus a small number of foundational works that define the core concepts. Sources were selected for their direct relevance to the research questions and were checked against their published records. The aim was not to count every study, as a systematic review would, but to connect findings across fields that rarely cite one another and to turn those connections into a framework and a testable measurement design. This approach has clear strengths for a student audience. It shows how a researcher moves from scattered findings to a model, and from a model to measurable variables. It also has limits, which are discussed in Section 7. The most important is that the framework has not yet been tested with deal data. The propositions are claims to be examined, not findings. 4.2 Why measurement is the hard part The research focus of this article is to measure the impact of executive CQ on success rates. Measurement problems appear on both sides of that relationship. On the CQ side, the main tool is the Cultural Intelligence Scale with its 20 items across the four facets (Ang et al., 2007). Self-reports are easy to collect but can be inflated, especially among senior people who are used to presenting themselves well. Observer ratings by colleagues or partners are harder to collect but reduce this bias. Researchers who study executives can also use indirect sources, such as interviews, speeches or letters to shareholders, though such text-based measures need careful validation before they are trusted. On the outcome side, the meaning of #deal_success varies. Finance studies tend to use stock market reactions around the announcement or changes in accounting returns. Management studies often use survival, partner satisfaction or the achievement of stated goals. Human resource studies look at #employee_retention and engagement. Each measure captures something real, but they do not always agree. A deal can please the stock market at announcement and fail during integration, as the evidence on cultural distance and synergy shows (Brede et al., 2024). 4.3 A three-level measurement design Figure 3 shows a measurement design that addresses these problems. It works at three levels. Figure 3. Proposed three-level design for measuring executive cultural intelligence and deal outcomes. At level 1, the individual executive, the core measure is the Cultural Intelligence Scale, ideally collected from both the executive and at least two observers who have worked with the executive across cultures. Prior international experience and language proficiency are recorded separately so that their effects can be separated from CQ. This matters because experience and language are related to CQ but are not the same thing (Ng et al., 2025; Piaskowska et al., 2021). At level 2, the top team and the deal team, individual scores are combined. The design records the average CQ of the deal team, but also the lowest CQ score in the team, because one poorly prepared member in a critical role can damage trust for everyone. It records nationality diversity and, separately, the CQ of the person named as #integration_lead or alliance manager, which allows Proposition 6 to be tested. At level 3, the deal, the design uses several outcome measures rather than one. For alliances, these include survival at three and five years and partner satisfaction. For acquisitions, they include changes in return on assets over two to three years, market returns, and the retention of key employees. Using several measures allows a researcher to see whether CQ relates to some kinds of success more than others. A set of controls runs across all levels: deal size, industry, cultural and institutional distance between the parties, and prior alliance or acquisition experience of the firm. Without these controls, a positive link between CQ and success might simply reflect that larger or more experienced firms hire leaders with higher CQ. 4.4 How a student project could use the design A full test of this design would require access to many deals and many executives, which is beyond most student projects. Smaller versions are realistic. A master's student could study ten to fifteen alliances in one hub through interviews with managers on both sides, use a short CQ questionnaire, and compare cases with high and low partner satisfaction. An undergraduate could build a case study of one cross-border acquisition from public sources, code the integration decisions described in reports and interviews, and discuss which CQ facets these decisions required. Both approaches would apply the framework without claiming more than the data allow. 4.5 Defining "success rate" carefully The research focus speaks of success rates. A rate needs a clear yes or no definition for each deal. One practical option is to treat an alliance as successful if it survives five years without early termination and both partners report that its main goals were met. An acquisition could be treated as successful if the combined firm's return on assets after three years is at least equal to its industry average and the acquirer has not divested the target. Such thresholds are imperfect, but they make the measure clear, repeatable and open to criticism, which is what good measurement requires. Researchers should report results with more than one definition to show that conclusions do not depend on a single arbitrary cut-off. 5. Analysis and Discussion 5.1 Executive CQ across the deal lifecycle A useful way to see where executive CQ enters a deal is to follow the deal from start to finish. Figure 4 divides the lifecycle into five stages: screening, #due_diligence, negotiation, integration or governance, and learning. Figure 4. The deal lifecycle and the points at which executive cultural intelligence is applied. At the screening stage, executives decide which partners or targets are worth pursuing. Much of the information at this point is indirect: a company presentation, a short visit, conversations with advisers. Metacognitive CQ helps executives notice when their first impression rests on their own cultural norms. A target whose leaders speak modestly about achievements may be stronger than one whose leaders speak boldly, depending on the cultural setting. Cognitive CQ helps them understand ownership structures, such as family control or state involvement, that affect how the partner will behave after the deal. At the due diligence stage, most firms check finances, legal risks, tax and technology in detail. Cultural factors are often reduced to a single slide. Recent reviews argue that this is a mistake and that #cultural_due_diligence should be systematic (Gebski and Bien, 2025). An executive with high CQ will push for questions such as: How are decisions really made in this firm? What do employees expect from a new owner? How is bad news reported upward? Which leaders hold informal influence? The evidence that cultural distance leads buyers to pay higher premiums and capture fewer synergies (Brede et al., 2024) suggests that this stage has direct financial value. At the negotiation stage, behavioural and motivational CQ become visible. Negotiators must adjust pace, directness and the use of formal and informal meetings. They must also understand what each side treats as a binding commitment. In high-context cultures, where meaning depends on relationships and setting, the value of CQ is especially high (Ng et al., 2025). A negotiator who pushes too hard for a fast close may win the contract and lose the relationship. At the integration or governance stage, CQ shapes the most consequential decisions. In acquisitions, these include how deeply to integrate, which language to use, how to communicate with employees and customers, and how fast to change systems. In alliances, they include the design of steering committees, decision rights and dispute procedures. The study of language strategies shows that how integration is explained to staff affects whether it is seen as legitimate (Malik et al., 2023). The review of emotions shows that how people feel during integration can decide its outcome (Zahoor et al., 2022). At the learning stage, firms that make many deals can capture lessons and build capability. Executives with high motivational CQ are more likely to see each deal as a chance to learn about cultures rather than as a set of problems to forget. This stage also links back to the finding that learning from international experience is slow and uneven (Piaskowska et al., 2021). Learning has to be organised; it does not simply happen. 5.2 Executive CQ in strategic alliances Alliances depend on continuing cooperation between independent firms, so the relationship never ends with signing. Three features of alliances make executive CQ particularly important. First, #trust replaces control. In an acquisition, the buyer can impose decisions. In an alliance, it cannot. Each partner must believe that the other will act in good faith even when the contract is silent. Trust is built through behaviour that the other side reads as reliable and respectful, and what counts as respectful differs across cultures. Executives with high behavioural CQ are better able to send signals that the partner interprets correctly. Second, alliances involve a tension between sharing and protecting knowledge (Kim et al., 2024). If an executive misreads the partner's intentions, the firm may either share too much and lose its advantage, or share too little and starve the alliance of value. Metacognitive CQ helps executives question their own suspicion or naivety. It encourages them to ask whether a partner's caution reflects bad intent or a different norm about information sharing. Third, alliances must balance exploration and exploitation, and this balance is negotiated between people (Zahoor et al., 2023). When partners come from cultures with different attitudes to risk and long-term planning, they may disagree about how much to invest in new ideas. A culturally intelligent leader can turn this disagreement into a discussion about the alliance's purpose rather than a contest of wills. The review of behavioural factors in alliances found that conflict is a central category affecting performance (Gehrisch and Suss, 2022). Team-level research shows that CQ reduces several forms of conflict and that process conflict, which concerns how work is organised, is the most harmful to performance (Davaei et al., 2022). For alliance managers, this suggests a practical priority: use CQ first to agree on processes, such as who decides what and how disagreements are raised, because conflict over process is the most damaging. Consider a simple illustration. A European technology firm forms a joint venture with a family-owned distributor in a Gulf hub. The European side expects monthly written reports and quick escalation of problems by email. The local partner expects regular face-to-face meetings with senior leaders and prefers to raise problems informally before putting anything in writing. Neither side is wrong, but if the European executives read the absence of written reports as hiding problems, and the local partner reads blunt emails as disrespect, trust can fall quickly. An executive with high CQ would notice the pattern early, explain it to both sides, and design a reporting routine that combines regular meetings with a short written summary. This is a small decision, yet decisions like it often determine whether an alliance survives its first difficult year. 5.3 Executive CQ in cross-border acquisitions Acquisitions bring cultural problems into sharper focus because the buyer gains control and must decide how to use it. The literature reviewed in Section 2 suggests five ways in which executive CQ can affect acquisition outcomes. The first is pricing. If cultural distance leads buyers to overestimate synergies and overpay (Brede et al., 2024), executives with stronger metacognitive and cognitive CQ should be better at discounting synergy estimates that depend on smooth cultural integration. This is a testable claim: among comparable deals, buyers led by higher-CQ executives should pay lower premiums for culturally distant targets. The second is the choice of integration depth. The evidence that experience has stronger effects under intensive integration (Hendriks et al., 2023), and that integration approaches should differ across parts of the combined firm (van Oorschot et al., 2022), points to a judgement that executives must make with care. High-CQ leaders may be more willing to leave cultural practices of the target in place where they support performance, and to integrate deeply only where synergy requires it. The third is the management of specific cultural gaps. Differences in power distance are especially harmful (Yu et al., 2024). In practice, this means that a flat, informal acquirer buying a hierarchical target, or the reverse, must manage changes in reporting, decision-making and status very carefully. Executives with high cognitive CQ will recognise these gaps, and those with high behavioural CQ will adjust their own #leadership_style during the transition. The fourth is employee emotion and retention. Cross-border deals create uncertainty, and uncertainty produces strong emotions (Zahoor et al., 2022). Leaders who communicate in ways that employees in the target find credible can reduce fear and keep key people. Language choices play a role here (Malik et al., 2023). An executive who insists on a corporate language without explaining the reasons, or without support for those less fluent, may unintentionally signal that the acquired staff are second-class. The fifth is external relationships. Customers and suppliers of the target watch the deal closely. Evidence that customer knowledge sharing depends on making, enabling and keeping promises (Degbey and Pelto, 2021) suggests that executives must manage external expectations across cultures as carefully as internal ones. A short illustration helps. Imagine an Asian manufacturer buying a mid-sized European engineering firm. The buyer's leaders are used to clear hierarchy and quick top-down decisions. The target's engineers are used to debate and consensus. If the new owners announce changes without discussion, the engineers may read this as disrespect for their expertise, and some may leave. If the owners instead keep the target's engineering processes in place for a period, invite debate on specific changes, and explain the reasons for those they insist on, the transition may be slower but more stable. The longitudinal finding that many such acquisitions begin with a light touch and then adjust over time (Wu et al., 2023) fits this pattern. The skill lies not in choosing one approach forever but in knowing when and how to change. 5.4 Why diverse hubs raise the value of executive CQ Figure 5 summarises the argument about hubs as a two-by-two matrix. One axis shows the level of executive CQ. The other shows the cultural diversity of the setting in which the deal takes place. Figure 5. Expected deal outcomes by level of executive cultural intelligence and hub diversity. In settings of low diversity, deals involve one or two cultures that can be learned with effort. Executives with low CQ can still manage by relying on advisers and standard integration methods, so these deals tend to be routine. Executives with high CQ add value by catching subtle differences that low distance may hide, but the gain is modest. In settings of high diversity, the picture changes. A deal in a hub may involve a buyer from one region, a target owned by a family from another, managers from a third and a workforce from many more. Here the risk for low-CQ executives is highest. They may apply the norms of one culture to people from another, misread silence or politeness, and lose trust early. Executives with high CQ, by contrast, have the most to contribute. Because they focus on reading the situation rather than applying a fixed map, they can turn diversity into an advantage: broader knowledge, more options and stronger local networks. Three features of hubs support this argument. First, communication in many hub workplaces mixes high-context and low-context styles. The finding that CQ matters more in high-context settings, while language matters more in low-context ones (Ng et al., 2025), suggests that hubs reward executives who have both. Second, evidence from the United Arab Emirates shows that CQ relates to performance and satisfaction in highly international workplaces, though the authors warn that the relations depend on context (Ibanez et al., 2025). Third, the pace of deal activity in hubs means that executives often meet many partners and targets in a short time, which may expose them to large flows of tacit knowledge. Whether they benefit from this exposure may depend on their CQ (Dzenopoljac et al., 2022). There is also a risk that hubs create an illusion of familiarity. Because many people in a hub speak English and follow international business conventions in formal settings, executives may assume that cultural differences are small. Below the surface, however, decision-making, loyalty and hierarchy may still follow very different norms. Metacognitive CQ, the habit of questioning one's assumptions, is the main defence against this illusion. 5.5 What counts as a successful deal, and for whom Discussions of success rates often treat success as if it meant the same thing for everyone. It does not. A deal that creates value for shareholders of the acquirer may destroy value for employees of the target. An alliance that one partner considers a success because it gained market access may be seen as a failure by the other partner, who lost know-how. Executive CQ is relevant to this point too, because culturally intelligent leaders are more likely to recognise that partners may define success differently and to agree on shared goals early. For research, this means that studies of executive CQ should collect success measures from more than one party. In alliances, partner satisfaction should be measured on both sides. In acquisitions, outcome measures should include both financial results and the retention and engagement of the acquired workforce. Studies that measure only the acquirer's stock price will miss much of what CQ does. 5.6 Developing and selecting executives for CQ If executive CQ affects deal success, firms have two broad options: select leaders who already have it, or develop it in the leaders they have. Evidence supports both routes. For selection, the finding that international experiences before adulthood are deep sources of CQ (Morin and Talbot, 2024) suggests that biographies contain useful information, though they should not be used alone. Structured interviews that ask candidates to describe how they handled specific cross-cultural situations, combined with the CQ scale and observer ratings, give a fuller picture. Recent work recommends building CQ into leader selection and #talent_management metrics in firms that pursue international growth (Maldonado et al., 2026). For development, the slow and uneven learning from international experience (Piaskowska et al., 2021) shows that sending managers abroad is not enough. Development should be designed: short assignments with structured reflection, coaching during real negotiations, rotation through integration teams, and debriefs after each deal. Because CQ has four facets, development can target the weakest. An executive with strong knowledge but low motivation needs different support from one with high motivation but limited knowledge. Teams can compensate for individual gaps. A chief executive with moderate CQ may achieve good results if the deal team includes members with high CQ in the right roles, particularly the #integration_lead. This is why the measurement design in Section 4 records the lowest score and the integration lead's score, not only the average. 5.7 The limits of executive CQ It would be wrong to present CQ as a cure for every deal problem. Several limits deserve attention. First, CQ cannot rescue a deal with a poor strategic logic. If the price is far too high or the expected synergies do not exist, cultural skill will not change the outcome. CQ is best seen as a condition that allows sound strategies to be carried out, not as a substitute for them. Second, CQ is one capability among several. Language proficiency adds value of its own (Ng et al., 2025), as does emotional intelligence, which works alongside CQ in reducing conflict (Davaei et al., 2022). Personality also matters, partly through CQ (Rockstuhl et al., 2024). Studies that measure only CQ risk giving it credit for effects that belong to these other factors. Third, measurement remains imperfect. Self-reported CQ among senior leaders may be inflated, and observer ratings are hard to obtain. Deal outcomes are affected by many forces, including economic cycles and regulation, that have nothing to do with culture. Fourth, there is a risk of #stereotyping. Teaching executives "the rules" of a culture can produce rigid thinking that is just as harmful as ignorance. The value of CQ lies in its metacognitive and adaptive parts, which encourage leaders to treat cultural knowledge as a starting hypothesis to be checked rather than a fixed truth. This is especially important in diverse hubs, where any individual may combine several cultural influences. Fifth, CQ is not only an individual matter. Organisational structures, incentives and integration controls also shape how cultural differences are handled (Smeulders et al., 2023). A culturally intelligent executive working inside a rigid system may be unable to act on what they see. 6. Implications 6.1 Implications for theory For researchers, this article suggests three directions. First, upper echelons research on international deals should move beyond counting years abroad and measure cultural capability directly. The mixed findings on experience (Piaskowska et al., 2021; Hendriks et al., 2023) are a sign that experience is a noisy stand-in for what really matters. Second, CQ research should move from the individual and team level to the level of strategic decisions, linking specific facets to specific deal mechanisms, as the propositions in Section 3 do. Third, research on culture in deals should treat diverse hubs as a distinct context rather than as a point on a two-country distance scale. 6.2 Implications for practice For companies, the main practical message is to treat executive CQ as part of deal preparation, in the same way that financial and legal readiness are treated. This could mean: Assessing the CQ of deal and integration teams before major transactions, using the scale together with observer feedback. Making cultural due diligence a formal workstream with clear questions and named owners, rather than a short section in a larger report. Appointing integration leads and alliance managers partly on the basis of CQ, given their central role after signing. Using hub-based deals as development opportunities, with structured reflection and coaching built in. Agreeing early with partners on how each side defines success, and measuring outcomes from both sides. For firms operating in diverse hubs, the implication is stronger still. Because these settings combine many cultures, the gains from CQ are likely to be largest there, and so are the losses from its absence. 6.3 Implications for students For students of business and management, this topic offers a clear lesson about how strategy works in practice. Strategic decisions are made by people, and people see the world through cultural lenses. Learning the financial tools of deal-making is necessary but not sufficient. Students who plan careers in #global_business can start building CQ now by working in diverse teams, studying or working abroad with deliberate reflection, learning languages, and practising the habit of questioning their first interpretation of unfamiliar behaviour. These habits are the foundations of the capabilities discussed throughout this article. 7. Conclusion This article asked how the cultural intelligence of executives affects the success of strategic alliances and cross-border acquisitions, and how that effect can be measured, especially in culturally diverse growth hubs. Drawing on recent research across several fields, it argued that executive CQ acts as a strategic capability that works through four mechanisms: sensemaking about partners and targets, trust building and negotiation, knowledge transfer and learning, and the design of integration and governance. The strength of these effects is expected to depend on hub diversity, cultural and institutional distance, top team diversity and the depth of integration. The review found strong evidence that CQ predicts performance at the individual and team levels, growing evidence that cultural differences shape the price, synergy and integration of cross-border deals, and mixed evidence on executive international experience as a predictor of acquisition outcomes. Taken together, these findings support the claim that direct measurement of executive CQ should improve our understanding of why some deals succeed and others fail. The article proposed a three-level measurement design, six testable propositions and a practical definition of success rates that researchers and companies can adapt. The study has limitations. It is conceptual and integrative rather than empirical, so the framework has not yet been tested against deal data. The literature reviewed is selective, focusing mainly on recent peer-reviewed work, and some relevant studies may have been missed. Evidence from the Gulf and other hubs is still thin and often conceptual. Finally, much of the CQ evidence comes from employees, students and teams rather than from senior executives, and effects at the top may differ. Future research could test the propositions with a sample of alliances and acquisitions in one or more hubs, collecting CQ ratings from executives and observers and following outcomes for several years. Comparative studies across hubs, for example in the Gulf and in Southeast Asia, would show whether the role of CQ differs between them. Longitudinal studies of executives could track how CQ develops through repeated deals. Qualitative studies of failed deals could reveal which CQ facets were missing at critical moments. The central message is simple. 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How do national cultural differences affect cross-border acquisitions? Cultural dimensions, learning from supply chain partners, and post-acquisition performance. Production and Operations Management, 34(5), 1009-1027. https://doi.org/10.1177/10591478241281495 Zahoor, N., Khan, Z., Marinova, S., and Cui, L. (2023). Ambidexterity in strategic alliances: An integrative review of the literature. International Journal of Management Reviews, 26(1), 82-109. https://doi.org/10.1111/ijmr.12348 Zahoor, N., Khan, Z., and Sinkovics, R. R. (2022). The role of emotions in cross-border mergers and acquisitions: A systematic review of the inter-disciplinary literature and future research agenda. Journal of International Management, 28(4), 100958. https://doi.org/10.1016/j.intman.2022.100958 #cultural_intelligence #executive_CQ #strategic_alliances #cross_border_mergers #mergers_and_acquisitions #market_entry_strategy #international_business #post_merger_integration #global_leadership #cross_cultural_management #Dubai_business_hub #emerging_markets #CQ_for_leaders #deal_success #STULIB

Spatial computing joins digital content to physical space through headsets, sensors, spatial mapping, digital twins and connected software. Most discussion of it still treats it as a gadget or a set of separate pilot projects. This article argues that it is better understood as infrastructure, a shared layer on which organisations can redesign how they create, deliver and capture value. Using an integrative conceptual review of recent peer-reviewed research and books published mainly between 2022 and 2026, the article builds a three-layer framework that links spatial infrastructure, a set of organisational capabilities, and three value pathways: operational efficiency, agile work environments and new customer value. The framework is then applied to two sectors where the evidence is strongest, education and corporate training. The review finds that augmented guidance can shorten complex industrial tasks and reduce errors, that meta-analyses report positive learning effects for augmented and virtual reality in higher education, and that immersive tools can support collaboration at a distance. It also finds clear limits: immersive settings can raise cognitive load, headsets remain heavy and costly, and spatial devices collect sensitive personal data. The article concludes that the value of spatial computing depends less on the device than on whether an organisation is willing to reengineer processes, roles, pricing and governance around it. A phased roadmap and a research agenda are offered for students, managers and educators. Keywords: spatial computing, business model reengineering, extended reality, operational efficiency, agile work, immersive learning, corporate training, higher education 1. Introduction For most of the history of computing, people have gone to the computer. They sat at a desk, looked at a flat screen and translated what they saw there into actions in the physical world. A technician read a manual and then turned to the machine. A medical student looked at a picture of the heart and then tried to imagine it in three dimensions. A sales trainer described a difficult customer and asked a new employee to picture the scene. In each case the screen and the world were separate, and the human mind did the work of joining them. #spatial_computing changes that relationship. It is a family of technologies that lets a computer understand the space around a person and place digital information inside that space. Headsets and smart glasses track the position of the head, eyes and hands. Cameras and depth sensors build a map of the room. Software anchors a three-dimensional model to a real table, or lays step-by-step instructions directly over a real engine. Networks and cloud services allow many people in different places to share the same virtual objects. When Apple released the Vision Pro in early 2024 and chose to call it a spatial computer rather than a headset, the phrase moved from research labs into everyday business language (Tafesse, 2026). Earlier devices from Meta, Microsoft and others had already shown what #augmented_reality and #virtual_reality could do, and recent books have argued that these technologies, combined with artificial intelligence, form the next major computing platform (Ball, 2024; Hackl and Cronin, 2024). This article is written for university students in business, management, education and information systems who want to understand what this shift means for organisations. Its central claim is simple. Spatial computing should not be treated only as a new device or as a training gadget. It is better understood as #infrastructure: a shared technical layer, like electricity networks or the internet, on which many different activities can be rebuilt. Infrastructure matters because it changes what is cheap, what is fast and what is possible. When something that used to be expensive becomes cheap, a firm can redesign its #business_model around that change. When something that used to require physical presence can be done at a distance, the boundaries of the workplace and the classroom move. The article uses the term #business_model reengineering on purpose. Business process reengineering, a well-known idea in management, asked companies to redesign processes from the ground up rather than automate old ways of working. Business model innovation, a more recent and very active field, asks how firms change the way they create, deliver and capture value (Ancillai et al., 2023). Reengineering, in the sense used here, joins these two ideas. It means using a new #infrastructure to redesign both the internal processes of an organisation and the logic by which it earns income and serves its customers or learners. Three questions guide the discussion. First, how can #spatial_computing improve #operational_efficiency, meaning the time, cost, error rate and resources needed to do work? Second, how can it support agile work environments, meaning workplaces that can be reconfigured quickly, that allow distributed teams to collaborate, and that let people learn while they work? Third, how can it create new customer value, meaning offerings that customers, students or clients could not have had before, or could only have had at a much higher price? Education and #corporate_training are chosen as the main sectors for three reasons. They are where the research evidence is richest, because learning effects can be measured. They are sectors under real economic pressure, with rising costs of laboratories, travel, instructors and physical space. And they connect directly to every other industry, since every organisation must train its people. If #spatial_computing can reengineer the way knowledge and skills are produced and delivered, its effects will spread well beyond the classroom. The contribution of the article is threefold. It offers a clear conceptual framework that links spatial #infrastructure to #business_model components. It brings together recent evidence on #efficiency, #collaboration and learning that is often scattered across separate disciplines such as engineering, psychology, education and marketing. And it gives balanced attention to limits and risks, including cognitive overload, cost, ergonomics and #data_privacy, so that students can judge claims critically rather than accept them as hype. The article does not report new experiments. It is a conceptual and integrative review, and its conclusions should be read as reasoned arguments supported by published research, not as final proof. The rest of the article is organised as follows. Section 2 explains the approach used. Section 3 reviews the background literature on #spatial_computing, #business_model innovation, and immersive learning. Section 4 sets out the conceptual framework. Section 5, the core of the article, analyses the three value pathways and applies them to education and #corporate_training. Section 6 discusses risks and limits. Section 7 offers a practical roadmap, and Section 8 concludes with implications and directions for future research. 2. Approach and Method This study follows the logic of an integrative conceptual review. Integrative reviews are useful when a topic is new, when evidence comes from several disciplines, and when the aim is to build a framework rather than to calculate a single average effect. Spatial computing fits all three conditions. The devices are recent, research on them is published in journals of computer science, engineering, management, psychology and education, and few studies yet connect technical findings to strategic questions about business models. Sources were identified through searches of large scholarly databases, using combinations of terms such as #spatial_computing, extended reality, #augmented_reality, #virtual_reality, mixed reality, metaverse, digital twin, business model innovation, corporate training, workforce training and #higher_education. Priority was given to peer-reviewed journal articles and scholarly books published between 2022 and 2026, so that the review reflects the current generation of devices and the current state of debate. A small number of slightly older works were kept because they provide theoretical models that later studies still rely on. Every reference used was checked against publisher or registry records to make sure it exists and that the details given here are correct. The reading proceeded in three steps. In the first step, sources were sorted by what they mainly addressed: technology and definitions, business models and strategy, operations and productivity, #collaboration and work, learning and training, or risk and ethics. In the second step, findings were compared to see where studies agreed, where they disagreed and where gaps remained. In the third step, these findings were organised into the framework presented in Section 4. Where the article reports numbers, such as effect sizes or time savings, these are taken directly from the cited studies, and the original study design is described so that readers can judge how far the result can be generalised. Two limits of this approach should be stated at the start. First, a review can only be as strong as the studies it draws on, and many studies of immersive technology are small, short and conducted in laboratory settings. Second, the field moves quickly, and devices described in a 2023 study may already have been replaced. The framework is therefore designed to focus on capabilities and value logic rather than on any particular product. 3. Literature Review and Background 3.1 What is spatial computing? There is still no single agreed definition of #spatial_computing, and several overlapping terms are used. #extended_reality, often shortened to XR, is the most common umbrella term in research. Rauschnabel et al. (2022) argue that much confusion comes from treating #augmented_reality and #virtual_reality as points on one simple line. They propose instead that the key question is whether the physical environment is part of the experience. In augmented reality, the user still sees and acts in the real world, and digital content is added to it, producing a sense of local presence. In virtual reality, the physical world is replaced, producing a sense of being somewhere else, which they call telepresence. Within augmented reality, they describe a range from simple assisted reality, such as a floating text label, to #mixed_reality, where virtual objects are anchored so well that they seem to belong to the room. Within virtual reality, they describe a range from partial to fully immersive experiences. Spatial computing can be understood as the wider system that makes these experiences possible and useful. It includes the headset or glasses, but also spatial mapping, hand and eye tracking, the software that anchors content to places, the cloud services that share that content between users, and increasingly the artificial intelligence that recognises objects and responds to speech and gesture (Hackl and Cronin, 2024). Egger et al. (2024) describe the Apple Vision Pro, for example, as technically a #virtual_reality device that uses cameras to stream the real world onto its screens, which turns it into an #augmented_reality device. In practice the user moves between the two modes. This blending is an important reason why the newer term #spatial_computing has gained ground: it describes what the system does, which is to compute with space, rather than the particular mode of display. The idea of the #metaverse overlaps with spatial computing but is not the same. Mystakidis (2022) describes the metaverse as a persistent, multi-user environment that merges physical reality with digital virtuality, built on technologies that allow interaction with virtual objects, environments and people. Dwivedi et al. (2022) brought together dozens of scholars to examine the metaverse from many angles and concluded that, beyond the hype, it raises serious questions for research, practice and policy. After 2022, public enthusiasm for the metaverse cooled, but many of the underlying technologies continued to develop. Ball (2024) frames this as the building of a spatial internet, a network in which three-dimensional, real-time and persistent environments become as normal as web pages. For the purposes of this article, the metaverse is treated as one possible application of spatial infrastructure, mainly concerned with shared virtual worlds, while spatial computing is the broader foundation. A final related concept is the #digital_twin. A digital twin is a virtual model of a physical object, process or place that is updated with real data so that it mirrors the condition of its physical counterpart. Attaran and Celik (2023) review the benefits of #digital_twins across manufacturing, healthcare, construction and cities, including better monitoring, prediction, maintenance and design. Digital twins become especially powerful when combined with spatial interfaces, because the virtual model can then be viewed at full scale, walked around and manipulated by hand, or laid over the real equipment it represents. Figure 1. The spatial computing stack: from devices and sensing to shared applications and organisational use. Figure 1 summarises this layered view. At the bottom are devices and sensors. Above them sit spatial mapping and tracking, which allow the system to know where things are. The next layer contains content and models, including three-dimensional assets and digital twins. Above that are platforms and networks that let content be shared, updated and secured. At the top are the applications through which people actually work and learn. Seeing spatial computing as a stack helps explain why it can be called infrastructure. Once the lower layers are in place, many different applications can be built on them at relatively low extra cost. 3.2 Business models and business model innovation A business model describes how an organisation creates value, delivers it to customers and captures part of that value as revenue or other benefit. Researchers commonly break it into components: the #value_proposition, which is what is offered and to whom; value creation and delivery, which includes the activities, resources, partners and channels used; and #value_capture, which covers revenue streams, pricing and cost structure. Business model innovation occurs when an organisation changes one or more of these components in a new and connected way. Digital technologies are now one of the main drivers of business model change. In a systematic review, Ancillai et al. (2023) mapped how studies link digital technology to business model innovation and found a fast-growing but fragmented field. Their review shows that digital tools can affect each component of the business model, but that the effects depend on organisational capabilities, strategic choices and the wider ecosystem of partners. A similar message appears in studies that focus specifically on immersive technologies. Latino et al. (2024) reviewed research on the metaverse and business model innovation and identified three waves of impact over time. In each wave they traced the actions of value creation and value capture that the technology made possible, and they discussed whether these led to radical change, incremental change, or what they call exaptation, where an existing capability is used for a new purpose. Kraus et al. (2022) offer a useful case that students can study in detail. They examined the decision of Facebook to rename itself Meta and invest heavily in the metaverse, and they asked whether this was a radical business model innovation or an incremental transformation of an existing advertising-based model. Based on an in-depth case study, they concluded that the change did not match a radical innovation pattern and that the core elements of the business model were likely to change only incrementally, with the bold public message serving partly to reshape external perception and to signal change inside the company. Their analysis is valuable because it shows that the same technology can support very different strategies, and that announcing a new model is not the same as building one. A firm may use immersive technology to protect its existing model, for example by creating new advertising space, or to build a genuinely new model, for example by selling hardware, services or virtual goods. The point for this article is that the technology does not decide the business model. Managers do. Marketing research adds the customer side. Dwivedi et al. (2023) argue that the metaverse and related immersive environments will reshape consumer research and practice, from how products are tried and bought to how brands build relationships. Koohang et al. (2023) bring together a wide range of scholars and identify opportunities in education, tourism, retail, health and work, along with challenges such as cost, access, #privacy, security and addiction. These works suggest that the most interesting business model questions are not about devices alone but about the new forms of experience and service that spatial computing makes possible. 3.3 Spatial computing in operations and work A large body of engineering and operations research has studied augmented reality in industrial settings. Miranda et al. (2025) carried out a scoping review of 38 studies published between 2019 and 2024 on the use of augmented reality to improve industrial processes. They found that the main applications were maintenance, assembly, training and inspection, with maintenance being the most studied. Across these studies, augmented reality was linked to fewer errors, shorter execution times, higher productivity and lower costs, although the authors also note a learning curve, high initial cost and hardware limitations. Controlled experiments give more precise evidence. Alessa et al. (2023) compared head-mounted augmented reality instructions with paper instructions in maintenance and assembly tasks of low and high difficulty. Augmented reality reduced task time only slightly for easy tasks but substantially for difficult ones. It also increased measured mental workload, which the authors interpret partly as productive, learning-related effort. Seeliger et al. (2023) built an augmented reality system that showed product defects directly on real manufactured parts and tested it against screen-based help and no help. Workers using the headset performed better, the advantage over the screen was larger for difficult tasks, and mental workload was lower than without assistance. Taken together, these studies suggest a clear pattern: spatial guidance helps most when work is complex, when information must be connected to a specific physical location, and when mistakes are costly. Research on collaboration and work at a distance is more mixed. Berndt et al. (2023) describe an experiment at the NASA Jet Propulsion Laboratory, where staff tried a range of metaverse tools during the shift to remote and hybrid work. Some tools helped reconnect a dispersed workforce, while others turned out to be novelties or even obstacles. Sanchez Rodriguez et al. (2025) compared four settings, face-to-face, video conferencing, a non-immersive virtual world and an immersive virtual world, with 103 participants. Collaboration in the virtual settings was broadly comparable to physical presence, but the immersive setting was linked to lower concentration and higher #cognitive_load, especially in passive learning tasks, and device comfort strongly affected the results. Xi et al. (2023) tested augmented and virtual reality in a shopping-related task and found that augmented reality was associated with higher overall workload, especially mental demand and effort, and concluded that working in mediated realities costs users more resources than working in physical reality. These findings are important for any organisation that hopes to move meetings or teams into immersive spaces. 3.4 Spatial computing in learning and training Education is the area with the most meta-analytic evidence. Li et al. (2025) reviewed 237 studies of augmented reality in higher education published between 2000 and 2023, and conducted a meta-analysis of 60 experimental studies. They found a large overall positive effect on learning, with instructional function and type of learning outcome acting as important moderators. Yuan et al. (2025) meta-analysed 35 comparisons from studies of virtual and augmented reality in higher engineering education. They reported a medium positive effect on cognitive outcomes, a large positive effect on psychomotor or practical skills, and a small positive effect on attitudes and feelings. Garcia-Robles et al. (2024) examined immersive virtual reality and augmented reality in anatomy teaching across 27 studies with 2,199 health sciences students and found higher knowledge gains than traditional methods, particularly when the technology was used to supplement rather than replace other teaching. Lampropoulos and Kinshuk (2024) reviewed 112 studies of virtual reality and gamification and reported that gamified virtual environments were generally more motivating and engaging than traditional learning settings. Not all evidence points in one direction, and good theory helps explain why. Makransky and Petersen (2021) proposed the Cognitive Affective Model of Immersive Learning, known as CAMIL. The model argues that immersive technology offers two main affordances, presence and agency, and that these influence learning through factors such as interest, motivation, self-efficacy, embodiment, #cognitive_load and self-regulation. Presence alone does not guarantee learning. If an immersive lesson is visually rich but poorly designed, it may increase interest while also creating extra cognitive load that crowds out learning. Mayer et al. (2023) make the same point in their discussion of the promise and pitfalls of immersive virtual reality. They argue that learners often enjoy immersive lessons more, but do not always learn more, unless the lesson includes activities that prompt them to process and organise what they see. Conrad et al. (2024) add a useful refinement. Their systematic review of 30 studies found that immersive virtual reality generally had a positive effect on learning compared with other media, and that it suited learning environments built around active engagement and practical application, such as manipulating objects and creating things, better than environments where learners simply watch. In the workplace, the evidence is growing. Zielke et al. (2024) designed and tested a virtual reality programme for communication training with employees of a state transport department in the United States. They found it effective and engaging, and they noted that the hardware set-up strongly influenced how enthusiastic employees were about it. This detail matters for business model decisions, because it shows that the user experience of the device is part of the #value_proposition, not a minor technical issue. 3.5 The gap this article addresses The literature reviewed above has three features. Technical and educational studies offer strong evidence about specific tasks and #learning_outcomes, but rarely connect that evidence to business model choices. Management studies discuss business model innovation and the metaverse, but often at a high level, with little attention to the detailed evidence on #efficiency, learning and cognitive load. And studies of risk, such as #privacy and wellbeing, are often separate from studies of value. What is missing is a framework that connects these streams so that a manager, an educational leader or a student can move from the question of what the technology does to the question of how an organisation should be redesigned around it. The next section proposes such a framework. 4. Conceptual Framework: Spatial Computing as Business Model Infrastructure The framework proposed here is called the Spatial Business Model Reengineering framework. It rests on one central idea: spatial computing creates value for an organisation only when its technical features are turned into organisational capabilities, and when those capabilities are deliberately used to redesign the components of the business model. Buying headsets is not a strategy. Rebuilding work, learning and service around what the headsets make possible can be. Figure 2. The Spatial Business Model Reengineering framework linking infrastructure, capabilities, value pathways and business model components. Figure 2 shows the four layers of the framework. The first layer is spatial infrastructure, the stack described in Figure 1: devices, sensing and mapping, three-dimensional content and digital twins, and shared platforms. The second layer contains five organisational capabilities that this infrastructure can support. The third layer contains the three value pathways that this article examines. The fourth layer shows the business model components that are changed as a result. Around the whole framework sit enabling conditions and risks, because the same infrastructure can produce very different results depending on how it is governed. 4.1 Layer one: spatial infrastructure Infrastructure has three features that set it apart from a single tool. It is shared, meaning that many users and applications rely on it. It is relatively costly to build but cheap to use once built. And it is enabling, meaning that its value lies mostly in what others build on top of it. Spatial computing shows all three. A university that invests in a library of accurate three-dimensional anatomical models, a well-managed fleet of headsets and a secure platform for sharing content can use the same base for medicine, nursing, sports science and public outreach. A manufacturer that builds a digital twin of a production line can use it for design, maintenance, safety training and customer demonstrations. The cost of each new application falls as the shared base grows. Ball (2024) makes a similar argument at the level of the whole economy, describing the spatial internet as a set of standards, networks and tools on which many businesses will build. 4.2 Layer two: five spatial capabilities The framework identifies five capabilities that link the infrastructure to value. The first is in-place guidance: placing the right information at the exact physical location where a task is done, as in the maintenance and inspection studies by Alessa et al. (2023) and Seeliger et al. (2023). The second is spatial visualisation: showing complex three-dimensional structures, such as organs, machines or buildings, at true scale and from any angle. The third is safe simulation: allowing people to practise dangerous, rare or expensive situations without real consequences. The fourth is shared presence or #telepresence: letting people in different places meet, see the same objects and work together as if in one room. The fifth is spatial data capture: recording where people look, how they move and what they do, which can feed back into analytics, assessment and the improvement of digital twins. These capabilities are deliberately described in terms of what they do rather than which device delivers them. A tablet showing augmented content can provide in-place guidance, though less conveniently than a headset. A fully immersive headset is usually needed for safe simulation of complex environments. This device-neutral description protects the framework from changes in the market and reminds managers to start from the capability they need rather than from the product they want to buy. 4.3 Layer three: three value pathways The five capabilities feed three value pathways. The efficiency pathway uses guidance, visualisation and data capture to reduce time, errors, travel, materials and physical space. The agility pathway uses shared presence, simulation and reusable content to make work environments easier to reconfigure, teams easier to assemble across distance, and learning easier to place inside the flow of work. The customer value pathway uses visualisation, simulation and shared presence to offer new experiences, new services and new ways of paying for them. Each pathway is analysed in detail in Section 5. 4.4 Layer four: business model components The final layer connects the value pathways to the parts of the business model that change. The efficiency pathway mainly changes the cost structure and the key activities of an organisation. The agility pathway mainly changes key resources, including how people and spaces are organised, and key partnerships, since spatial content is often produced with outside specialists. The customer value pathway mainly changes the value proposition, the customer relationship, the channels through which value is delivered and the #revenue_model. In practice the pathways overlap, and the most powerful forms of reengineering touch all of them at once. This matches the view of Ancillai et al. (2023) and Latino et al. (2024) that digital technologies rarely affect only one business model component in isolation. 4.5 Enabling conditions and risks The outer frame of the model contains the conditions that determine whether value is actually realised. Enabling conditions include leadership commitment, digital skills, good instructional or work design, and an ecosystem of content providers and integrators. Risks include cognitive overload, physical discomfort, cost and obsolescence, unequal access and the misuse of personal data. Recent work on learning theory (Makransky and Petersen, 2021; Mayer et al., 2023) and on privacy (Giaretta, 2025) shows that these are not minor side issues. They can turn a promising investment into a failed one. Section 6 discusses them in detail. 5. Analysis and Discussion 5.1 Pathway one: operational efficiency The simplest way to see how spatial computing improves efficiency is to look at what normally happens when a person has to follow instructions. A worker reads a step, looks away from the document, finds the right part, remembers the step, performs it and then looks back at the document. Each switch of attention takes time and creates a chance for error. In-place guidance removes much of this switching by putting the instruction exactly where the hands are working. Figure 3. Reduction in task time with head-mounted augmented reality instructions compared with paper instructions, by task difficulty. Data from Alessa et al. (2023). Figure 3 shows the size of this effect in one controlled study. Alessa et al. (2023) found that augmented reality instructions reduced total task time by less than half of one percent for low-demand maintenance tasks, but by almost fifteen percent for high-demand tasks. The lesson for business model design is that spatial guidance should be targeted. Spending money to guide simple, familiar tasks may bring little return. Spending it to guide complex, rare or high-risk tasks can bring a large one. Seeliger et al. (2023) found the same pattern for quality inspection, where the advantage of augmented reality over a screen grew with task difficulty, and the scoping review by Miranda et al. (2025) confirms that error reduction and time saving are the most commonly reported benefits across industrial studies. Efficiency gains also come from reducing the need for physical resources. In education, a virtual laboratory or anatomy model can be used by many students without consuming chemicals, specimens or equipment time. Garcia-Robles et al. (2024) found that immersive and augmented tools produced higher knowledge gains in anatomy than textbooks, atlases and lectures, which means that some of the cost of specimens and dissection time can be supplemented, though not entirely replaced, by reusable digital resources. In corporate training, #simulations can reduce travel to central training sites and the downtime of real equipment used for practice. A #digital_twin of a factory line, for example, can be used to train new staff while the real line keeps running (Attaran and Celik, 2023). A third source of efficiency is data. Spatial devices record where trainees look, how long they take and which errors they make. In a traditional workshop, an instructor sees only part of this. With spatial data capture, an organisation can find which steps cause most difficulty and redesign either the task or the training. This creates a feedback loop in which the process itself improves over time. Here, however, efficiency and privacy meet directly, a tension discussed in Section 6. For students of management, the key point is that these efficiency gains change the cost structure of the business model. A training provider that can deliver practical skills with fewer instructors per learner, less equipment and less travel can either lower its price, raise its margin or reach more learners. A manufacturer that can cut maintenance time on complex jobs reduces the cost of downtime. These are not small changes in a single process. When they are large enough, they allow an organisation to compete in a different way. 5.2 Pathway two: agile work environments Agility, in the sense used here, means the ability of an organisation to change how and where work is done without large delays or costs. Physical workplaces and classrooms are slow to change. Walls, laboratories and machines are fixed. Teams are often limited to people in one location. Training usually happens away from the job, in a separate room and at a separate time. Spatial computing can loosen each of these constraints, creating what this article calls #agile_work environments. The first form of agility is spatial flexibility. Because content is anchored to space by software rather than built in concrete, the same room can become a design studio in the morning, a chemistry laboratory at noon and a negotiation simulation in the afternoon. Universities and companies can therefore plan space differently. Instead of building specialised rooms that stand empty most of the week, they can invest in general-purpose spaces equipped for spatial computing. This changes key resources in the business model, shifting investment from buildings toward digital content and platforms. The second form of agility is distributed collaboration. During the pandemic years, many organisations moved to #hybrid_work, and video calls became normal. Immersive meeting spaces promise to restore some of what video loses, such as the shared sense of space and the ability to point at and manipulate the same object. The evidence is encouraging but not simple. Sanchez Rodriguez et al. (2025) found that collaboration in virtual settings could match face-to-face collaboration, yet immersive virtual reality also brought lower concentration and higher cognitive load, and comfort mattered a great deal. The experience at the NASA Jet Propulsion Laboratory showed a similar mixture, with some tools genuinely reconnecting staff and others becoming hindrances (Berndt et al., 2023). The practical conclusion is that immersive collaboration is best reserved for tasks that really need shared three-dimensional space, such as design reviews, engineering walk-throughs or surgical planning, rather than for ordinary meetings that work well enough on video. Egger et al. (2024) and Tafesse (2026) both note that the newest devices are moving toward lighter, more natural interaction, but also that comfort, communication and privacy remain barriers to everyday use. The third form of agility is learning in the flow of work. Traditional corporate training removes people from their jobs and later expects them to transfer what they learned. Spatial guidance can bring the training into the job itself. A new technician can be guided through a repair while doing it, with an expert joining remotely when needed. Alessa et al. (2023) interpreted part of the higher mental workload in their augmented reality condition as germane load, the kind of effort that supports long-term learning. This suggests that, when designed well, spatial guidance can both get the job done and teach the worker at the same time. For the business model, this blurs the line between operations and training, and it may allow organisations to put less experienced staff on complex tasks sooner, which matters in sectors facing skill shortages. The fourth form of agility concerns the speed of updating knowledge. When a procedure changes, printed manuals and classroom courses must be rewritten and redelivered. Spatial content stored on a shared platform can be updated once and reach every worker or student immediately. This is a clear example of why the infrastructure view matters. The value lies less in any single lesson than in the ability to change all lessons quickly. 5.3 Pathway three: novel customer value The third pathway is the most strategic, because it changes what an organisation offers and how it is paid. #customer_value is created when a customer, learner or client receives something they want more than what they give up to obtain it. Spatial computing can create new value in at least four ways. The first is access to experiences that were previously impossible or too expensive. A student in a small town can walk through a virtual reconstruction of an ancient city, stand inside a beating heart or operate a virtual wind turbine. A trainee firefighter can practise in a burning building without danger. These are not just better versions of old products. They are new products. The meta-analysis by Yuan et al. (2025), which found a particularly large effect of virtual and augmented reality on practical skills in engineering education, suggests that the value lies especially in doing rather than in watching. The second is personalisation. Because spatial systems record what each learner does, they can adapt the difficulty, pace and feedback of a simulation to the individual. Gamified virtual environments, reviewed by Lampropoulos and Kinshuk (2024), show how challenge, feedback and reward can be built into immersive learning to raise motivation and engagement. For an education provider, personalised practice is a strong part of the value proposition, especially for adult learners who have limited time. The third is new relationships and channels. Marketing research suggests that immersive environments change how customers explore, try and buy products (Dwivedi et al., 2023). The same logic applies to education. A university can offer prospective students a spatial campus tour, a trial laboratory session or a shared virtual seminar with current students. A corporate training provider can stay connected with clients after a course by offering a library of practice #simulations that employees use on the job. The relationship moves from a single event to an ongoing service. The fourth is new revenue models. Once spatial content exists, it can be licensed, sold by subscription or shared across a network of partners. A university that builds a high-quality anatomy simulation can license it to other institutions. A company that develops excellent safety simulations for its own staff can sell them to suppliers. Hardware can be offered as a service, with devices, content updates and analytics bundled together and paid monthly. These options correspond to the value creation and value capture actions identified by Latino et al. (2024) in their review of metaverse business models. They also show why the case studied by Kraus et al. (2022) is a useful warning: unless the revenue model really changes, a firm may describe a radical transformation while in fact changing very little. 5.4 Applying the framework to education Higher education faces a familiar set of pressures: growing student numbers, expensive laboratories and specialised spaces, demand for practical and employable skills, and competition from online providers. Spatial computing touches each of these. On the efficiency pathway, virtual laboratories and three-dimensional models can reduce the cost per student of practical teaching and extend access beyond scheduled lab hours. On the agility pathway, general-purpose spatial classrooms can replace some specialised rooms, and content can be updated quickly as disciplines change. On the customer value pathway, universities can offer practical experiences at a distance, run joint courses with partner institutions in shared virtual spaces, and license content to others. The evidence base for #immersive_learning in #higher_education is now substantial. Li et al. (2025) report a large overall positive effect of augmented reality on learning across 60 experimental studies. Yuan et al. (2025) report medium cognitive, large practical and small affective effects in engineering education. Garcia-Robles et al. (2024) report higher knowledge gains in anatomy, with the strongest effect when the technology supplements other teaching, and they found that about four in five students considered these tools useful for learning. Figure 4. Pooled effect sizes reported in recent meta-analyses of augmented and virtual reality in higher education. Data from Li et al. (2025), Yuan et al. (2025) and Garcia-Robles et al. (2024). Figure 4 places these results side by side. The numbers are standardised effect sizes, which allow studies using different tests to be compared. By common convention, values around 0.2 are considered small, around 0.5 medium and around 0.8 or above large. Two cautions apply. First, the studies differ in subject, technology and design, so the bars should not be read as a ranking of one tool against another. Second, as Mayer et al. (2023) warn, enjoyment and engagement are not the same as learning, and immersive lessons need deliberate design to produce real gains. With these cautions, the pattern is still clear: when spatial tools are used for active practice and for understanding three-dimensional structure, they tend to help. For university leaders, the business model question is not whether to buy headsets but which parts of the educational offer to reengineer. A sensible approach begins with subjects where spatial understanding is central and where physical resources are scarce or costly, such as anatomy, engineering, architecture, chemistry and vocational health programmes. It then builds a shared content library and platform that can be reused across departments, which is where the infrastructure logic pays off. Only after this base exists does it make sense to develop new revenue streams, such as licensing, short professional courses or partnerships with employers. 5.5 Applying the framework to corporate training #corporate_training is a natural home for spatial computing because the link between learning and performance is direct. A company pays for training because it wants workers to perform better, more safely and more quickly. Spatial computing allows that performance to be practised, measured and supported on the job. In the framework, corporate training is where all three pathways meet most clearly. On the efficiency pathway, simulations can reduce the cost of travel, instructors and equipment downtime, while in-place guidance can shorten complex tasks and reduce errors, as shown in the industrial studies already discussed. On the agility pathway, learning can be moved into the flow of work, and a distributed workforce can be trained to a common standard regardless of location. On the customer value pathway, the training function can change from a cost centre into a service provider, offering simulations and guidance to suppliers, partners and customers. Soft skills are an important and sometimes overlooked area. Communication, leadership, negotiation and customer service are difficult to practise safely in real life, because mistakes affect real people. Zielke et al. (2024) showed that virtual reality communication training for public sector employees was effective and engaging. Private role-play in a simulation lets people make mistakes without embarrassment and repeat difficult conversations until they improve. For a training provider, this opens a market well beyond technical skills. The case of #workforce_development in sectors with skill shortages deserves special mention. Manufacturing, energy, construction and healthcare all report difficulty finding experienced staff. If spatial guidance allows less experienced workers to complete complex tasks safely while learning, an organisation can widen its pool of potential employees and shorten the time to competence. This is a business model change, not only a training improvement, because it alters who the organisation can hire and how quickly it can grow. 5.6 A comparison of the two sectors Education and corporate training share the same infrastructure but differ in what counts as value. In education, the main outcome is learning, measured over a course or degree, and the main customer is the student, often supported by public funding or family investment. In corporate training, the main outcome is performance on the job, measured in productivity, safety and quality, and the main customer is the employer. This difference shapes the business model. Universities are likely to focus on access, quality of learning and reputation, and to capture value through enrolment, partnerships and licensing. Companies are likely to focus on time to competence, error reduction and retention of staff, and to capture value through lower costs and higher output. A student considering a career in either sector should recognise that the same technology serves different logics. The two sectors can also work together. Universities have expertise in learning design and evaluation. Companies have real equipment, real problems and real data. Shared spatial content, such as a digital twin of a real facility used both for student projects and employee training, can create value for both. This kind of partnership is one of the most promising ways to spread the high fixed cost of spatial infrastructure across more users. 6. Risks, Limits and Ethical Questions 6.1 Cognitive load and learning design The first and most important limit is human. Immersive environments are rich in sights and sounds, and the human mind can process only a limited amount of information at once. #cognitive_load theory distinguishes between effort that supports learning and effort wasted on irrelevant detail or on working the interface. Several studies reviewed here found higher workload in immersive or augmented settings (Xi et al., 2023; Sanchez Rodriguez et al., 2025). Sometimes this extra effort is useful, as Alessa et al. (2023) suggest. Often it is not. Makransky and Petersen (2021) and Mayer et al. (2023) argue that the design of the experience, not the device, decides which kind of load dominates. For organisations, this means that a large part of the investment must go into instructional and task design, not only into hardware. 6.2 Comfort, health and accessibility Headsets can be heavy, warm and tiring to wear for long periods, and some users experience discomfort or motion sickness. The work of Zielke et al. (2024) showed that hardware configuration affected how employees felt about training. Early studies of the Apple Vision Pro also note issues of weight and comfort alongside its strengths (Egger et al., 2024). Accessibility is another concern. Users with certain visual, hearing or motor impairments may find spatial interfaces difficult unless they are designed with care. A business model that depends on long daily use of headsets must take these limits seriously, both for ethical reasons and because uncomfortable tools are quietly abandoned. 6.3 Privacy and security Spatial devices are among the most data-hungry consumer technologies ever produced. To function, they must map the room, track the eyes and hands, and often record voice and video. Such data can reveal sensitive information about health, attention, emotion and identity. Giaretta (2025) surveys the security and privacy threats associated with virtual reality and shows that they range from attacks on authentication to the inference of personal attributes from movement data. Tafesse (2026) found that privacy concerns appear among the factors that may hinder adoption of the newest spatial devices. In education and employment, where there is a power imbalance between institution and individual, the risk is especially serious. A university or employer that collects detailed gaze and movement data must explain clearly what is collected, why, for how long and who can see it. Without such governance, the efficiency gains from spatial data capture may come at an unacceptable ethical cost and may also expose the organisation to legal and reputational damage. 6.4 Cost, obsolescence and hype Spatial hardware is still expensive, and product cycles are short. Content development, especially for high-quality simulations, can cost more than the devices themselves. The cooling of interest in the metaverse after 2022 is a reminder that public enthusiasm can move faster than real value (Dwivedi et al., 2022; Koohang et al., 2023). The infrastructure view helps here. If an organisation invests in content and platforms that can move across devices, and in skills that remain useful whatever the hardware, it is less exposed to the failure of any single product. 6.5 Equity and the digital divide Finally, spatial computing may widen gaps between well-funded and poorly funded institutions, between large and small firms, and between learners who have access to devices at home and those who do not. If spatial content becomes central to certain degrees or jobs, unequal access becomes unequal opportunity. Shared facilities, device lending schemes, content that also works on cheaper phones and tablets, and public or industry funding can reduce this risk. These choices are part of the business model too, because they define who the organisation serves. 7. A Roadmap for Spatial Business Model Reengineering Drawing together the analysis, this section offers a practical roadmap. It is intended for managers and educational leaders, but also for students who may be asked in their careers to evaluate or lead such projects. Figure 5. A five-phase roadmap for reengineering a business model around spatial computing. As Figure 5 shows, the roadmap has five phases. In the first phase, diagnose, the organisation identifies where spatial understanding, physical presence or risky practice create real bottlenecks or costs today. The question is not where spatial computing could be used, since the answer is almost everywhere, but where the gap between current practice and what spatial tools allow is largest. The industrial evidence suggests looking first at complex, rare, high-risk or location-specific tasks. In the second phase, pilot, the organisation tests one or two carefully chosen applications, with clear measures of time, errors, learning outcomes, cost and user comfort. Pilots should compare the spatial approach with a good conventional alternative, not with a weak one, and should collect the views of users as well as numbers. The mixed results at the NASA Jet Propulsion Laboratory show why honest evaluation matters (Berndt et al., 2023). In the third phase, build the platform, successful pilots are used to justify shared infrastructure: a content library, a device management system, a data governance policy and a small internal team or external partner able to produce and maintain content. This is the step that turns projects into infrastructure. Without it, each new use starts from zero and costs stay high. In the fourth phase, reengineer, the organisation uses the platform to redesign processes, roles and offers. Training is moved into the flow of work. Spaces are redesigned. New services and revenue models are tested, such as licensing content or offering training as a subscription. This is where real business model change happens, and it requires decisions about pricing, partnerships and organisational structure that go well beyond the technology team. In the fifth phase, govern and scale, the organisation sets rules for privacy, safety, accessibility and quality, measures results continuously, and expands to new areas. Governance is placed last in the diagram for clarity, but in practice it must begin in the first phase and run through all of them. Three principles run through the roadmap. Start from the capability needed, not the device. Measure value in business model terms, such as cost per learner, time to competence or new revenue, and not only in terms of user enjoyment. And treat people, including their comfort, privacy and skills, as part of the infrastructure rather than as an afterthought. 8. Conclusion This article set out to explain how spatial computing can be used as a strategic tool to reengineer business models, with particular attention to operational efficiency, agile work environments and new customer value in education and corporate training. Its main argument is that spatial computing is best understood as infrastructure. Its value comes not from any single device but from a shared base of sensing, mapping, content, digital twins and platforms on which many applications can be built at falling cost. The review of recent research supports four conclusions. First, spatial guidance can improve efficiency, especially for complex and high-risk tasks, where controlled studies show meaningful reductions in time and errors. Second, spatial computing can make work environments more agile by making spaces reconfigurable, supporting distributed collaboration where shared three-dimensional space really matters, and moving learning into the flow of work, although immersive collaboration carries real costs in cognitive load and comfort. Third, spatial computing can create new customer value through experiences that were previously impossible, through personalised practice, through ongoing relationships and through new revenue models such as licensing and subscription. Fourth, in education and corporate training, meta-analytic evidence shows positive effects on learning and especially on practical skills, provided that experiences are carefully designed. The article also stresses the limits. The benefits are not automatic. They depend on good design, honest evaluation, attention to comfort and accessibility, strong protection of personal data and a willingness to change processes and business models rather than simply add new technology to old ways of working. The case of Meta, analysed by Kraus et al. (2022), shows that even very large investments can amount to incremental change if the underlying model stays the same. For students, the practical implication is that understanding spatial computing requires thinking across disciplines. A good analysis combines knowledge of the technology, of human learning and perception, of operations, of business models and of ethics. For managers and educational leaders, the implication is to begin with clear problems, test honestly, build shared infrastructure and only then redesign the offer. For policymakers, the implication is to support standards, data protection and fair access so that the benefits of #digital_transformation through spatial computing are widely shared. This study has limitations. It is a conceptual review and does not provide new empirical data. Much of the underlying evidence comes from short studies with small samples, often in laboratory settings, and the technology is changing quickly. Future research should test the framework with real organisations over longer periods, measure business model outcomes such as cost per learner and revenue from new services, compare sectors and countries, and study the long-term effects of spatial work and learning on wellbeing and privacy. Research that follows organisations through the full roadmap, from diagnosis to scaling, would be especially valuable. 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Shaping the metaverse into reality: A holistic multidisciplinary understanding of opportunities, challenges, and avenues for future investigation. Journal of Computer Information Systems, 63(3), 735-765. https://doi.org/10.1080/08874417.2023.2165197 Kraus, S., Kanbach, D. K., Krysta, P. M., Steinhoff, M. M., and Tomini, N. (2022). Facebook and the creation of the metaverse: Radical business model innovation or incremental transformation? International Journal of Entrepreneurial Behavior and Research, 28(9), 52-77. https://doi.org/10.1108/IJEBR-12-2021-0984 Lampropoulos, G., and Kinshuk. (2024). Virtual reality and gamification in education: A systematic review. Educational Technology Research and Development, 72(3), 1691-1785. https://doi.org/10.1007/s11423-024-10351-3 Latino, M. E., De Lorenzi, M. C., Corallo, A., and Messeni Petruzzelli, A. (2024). The impact of metaverse for business model innovation: A review, novel insights and research directions. Technological Forecasting and Social Change, 206, 123571. https://doi.org/10.1016/j.techfore.2024.123571 Li, G., Luo, H., Chen, D., Wang, P., Yin, X., and Zhang, J. (2025). Augmented reality in higher education: A systematic review and meta-analysis of the literature from 2000 to 2023. Education Sciences, 15(6), 678. https://doi.org/10.3390/educsci15060678 Makransky, G., and Petersen, G. B. (2021). The Cognitive Affective Model of Immersive Learning (CAMIL): A theoretical research-based model of learning in immersive virtual reality. Educational Psychology Review, 33(3), 937-958. https://doi.org/10.1007/s10648-020-09586-2 Mayer, R. E., Makransky, G., and Parong, J. (2023). The promise and pitfalls of learning in immersive virtual reality. International Journal of Human-Computer Interaction, 39(11), 2229-2238. https://doi.org/10.1080/10447318.2022.2108563 Miranda, A., Vallejo, A. M., Ayala, P., Garcia, M. V., and Naranjo, J. E. (2025). Enhancing industrial processes through augmented reality: A scoping review. Future Internet, 17(8), 358. https://doi.org/10.3390/fi17080358 Mystakidis, S. (2022). Metaverse. Encyclopedia, 2(1), 486-497. https://doi.org/10.3390/encyclopedia2010031 Rauschnabel, P. A., Felix, R., Hinsch, C., Shahab, H., and Alt, F. (2022). What is XR? Towards a framework for augmented and virtual reality. Computers in Human Behavior, 133, 107289. https://doi.org/10.1016/j.chb.2022.107289 Sanchez Rodriguez, I., Bailo, L., Panizza, F., van Es, V. A. A., Alessi, M., Betta, M., and Ricciardi, E. (2025). Assessing the impact of virtual workplaces on collaboration and learning. Frontiers in Psychology, 16, 1581029. https://doi.org/10.3389/fpsyg.2025.1581029 Seeliger, A., Cheng, L., and Netland, T. (2023). Augmented reality for industrial quality inspection: An experiment assessing task performance and human factors. Computers in Industry, 151, 103985. https://doi.org/10.1016/j.compind.2023.103985 Tafesse, W. (2026). The user affordances of Apple Vision Pro: Insights from early adopters. SAGE Open, 16(3). https://doi.org/10.1177/21582440261464433 Xi, N., Chen, J., Gama, F., Riar, M., and Hamari, J. (2023). The challenges of entering the metaverse: An experiment on the effect of extended reality on workload. Information Systems Frontiers, 25(2), 659-680. https://doi.org/10.1007/s10796-022-10244-x Yuan, S., Huang, H., and Wu, L. (2025). Virtual reality and augmented reality in higher engineering education: A systematic literature review. Computer Applications in Engineering Education, 33(3), e70022. https://doi.org/10.1002/cae.70022 Zielke, M., Zakhidov, D., DeFries, E., Shringarpure, M. P., Avila, R., Keul, J., CarpenterTurner, D., Hargrove, S., Bateman, S., and Young, C. (2024). Using in situ research-based design to explore learning module effectiveness and usability in a virtual reality system for workforce training. Virtual Reality, 28(4), 155. https://doi.org/10.1007/s10055-024-00995-0 #SpatialComputing #spatial_computing_in_business #business_model_reengineering #business_model_innovation #XR_in_education #immersive_technology #future_of_work #learning_and_development #EdTech #virtual_classroom #smart_training #Industry_4_0 #enterprise_metaverse #digital_strategy #STULIB

Universities increasingly take their degrees to other countries through franchises, joint programmes, joint venture universities and international branch campuses. Each of these models promises reach, revenue and reputation, yet each also places the university under the authority of a host state with its own licensing laws, national accreditation bodies and political expectations. This article examines how higher education institutions can align a global strategic vision, usually written at the home campus, with the strict local regulatory frameworks and academic accreditation standards of host countries. Using a structured review of recent peer-reviewed research published mainly between 2021 and 2026, it develops a conceptual framework that links the home institution, the host system and a set of alignment mechanisms to three forms of legitimacy: regulative, normative and cognitive. The analysis compares entry models along a continuum of control and risk, maps the layered quality assurance that branch campuses face, applies a global integration and local responsiveness lens to campus strategy, and proposes a phased pathway from market entry to periodic review and possible exit. The article argues that durable branch campuses are neither copies of the home university nor fully local institutions, but carefully negotiated hybrids that keep a common academic core while building local layers that satisfy host regulators. It closes with practical implications for university leaders, regulators and students, and with the limits of a review-based approach. Keywords: transnational education, international branch campus, internationalisation strategy, quality assurance, accreditation, regulation, legitimacy, higher education policy 1. Introduction For most of the twentieth century, a student who wanted a degree from a foreign university had to travel to that university. Today the #university increasingly travels to the student. A business degree from a British university can be studied in Dubai, an engineering degree from an Australian university in Malaysia, and a medical or computing degree from a Western institution in China, Uzbekistan or Qatar. This movement of programmes, staff and institutions across borders is usually called #transnational_education, and its most visible and most expensive form is the #international_branch_campus, a campus operated abroad in the name of a home university that awards its own degree there. The growth of these campuses has been striking. A review of two decades of development found that branch campuses moved from a small experiment at the start of the century to an established feature of the global higher education landscape, while also showing a steady stream of closures and failures (Wilkins, 2021). More recent commentary notes that for every campus that closes, three or four new ones open, and that future success will depend on the ability of institutions to attract students and funding, match accreditation requirements and keep good relations with host partners (Wilkins, 2024). New host countries continue to appear. Between 2018 and 2022, for example, twenty-three foreign institutions set up a campus in Uzbekistan, making it the country with the third largest number of branch campuses after China and the United Arab Emirates (Muratov and Wilkins, 2024). Behind these headline numbers sits a difficult management problem. A university that expands abroad usually does so because of a #global_strategy written at home: a plan to raise its international profile, #diversify_income, reach new student markets or strengthen research links. That vision is shaped by the home country's culture, law and academic norms. Yet the moment the university enters another country it becomes subject to a different #regulatory_framework, with its own rules on who may own a university, what may be taught, how degrees are recognised, how staff are hired and who checks quality. In many host countries, the national #accreditation body expects branch campuses to meet the same standards as local universities, while the home quality agency expects the overseas campus to deliver a degree that is equal in standard to the one awarded at home. The campus must please two masters at once, and often a third or fourth if professional accreditors are involved. This article asks a simple question with no simple answer: how can a university align a global strategic vision with strict local regulatory frameworks and academic accreditation standards in the countries where it operates? To answer it, the article reviews recent research on transnational education and branch campuses, compares the main models universities use to go abroad, and builds a conceptual framework that shows where alignment happens and what it produces. The argument is that alignment is not a single decision made at the point of entry. It is a continuing process of negotiation, in which the campus earns legitimacy in the eyes of the host state, the academic community and the public, and in which the home university must accept that some of its own habits will change. The article makes three contributions for students and early researchers. First, it brings together a scattered and fast-growing body of recent literature into one readable account. Second, it offers a framework, a set of diagrams and a phased pathway that can be used to analyse real cases. Third, it highlights the tensions that are often hidden in promotional language about global universities, such as the gap between home and host student experience, the pressure on academic freedom, and the risk that commercial logic overrides educational purpose. The rest of the article is organised as follows. Section 2 explains the approach taken. Section 3 reviews the literature on why universities expand abroad, what models they use and how host states regulate them. Section 4 presents the conceptual framework. Section 5 develops the analysis in six themed parts. Section 6 discusses implications, and Section 7 concludes with limitations and directions for future research. 2. Methodology and Approach This is a conceptual and #integrative_review rather than an empirical study with new data. The aim is to build understanding by connecting findings from different studies, not to measure a single effect. Recent peer-reviewed journal articles, book chapters and scholarly books were identified through searches of major scholarly databases using terms such as international branch campus, transnational education, cross-border higher education, accreditation, quality assurance, regulation and legitimacy. Priority was given to work published between 2021 and 2026, so that the review reflects the current state of the field, including new host countries, new regulatory reforms and new theoretical lenses. Sources were then read and grouped into themes: the motives and strategies of home institutions; the models of transnational provision; the policies and regulatory tools of host states; quality assurance and accreditation; legitimacy and organisational identity; and the experiences of students and staff. Each reference was checked against publisher or registry records to confirm that it exists and that its details are correct. The framework presented in Section 4 was developed inductively from these themes and then refined against the cases discussed in the literature. A scientometric review of research on branch campuses has shown that the field has grown quickly, with contributions spread across many countries and disciplines, and with clear clusters around student choice, staff experience, management and policy (Zhang and Dai, 2024). The present review draws on several of these clusters but concentrates on the meeting point between institutional strategy and host regulation, which remains less integrated in the literature than topics such as student choice. Readers should keep in mind that a review of this type depends on what has been published. Some host countries, especially newer hosts in Central Asia and Africa, are less studied than China, Malaysia and the Gulf states. Where this is the case, the article says so rather than filling the gap with assumptions. 3. Literature Review 3.1 Why universities go abroad Research on #internationalisation has long recognised that universities cross borders for a mix of academic, economic, political and reputational reasons. De Wit and Altbach (2021) describe how internationalisation has shifted from a marginal activity to a central strategic concern for many institutions, while warning that commercial motives have often grown faster than educational ones. Marginson (2022) adds a wider lens, arguing that higher education now operates in a genuinely global space where institutions, people and knowledge circulate, but where national states remain powerful in setting the rules. Studies of individual home countries show how different these motives can be. Interviews with leaders of English universities suggest that branch campuses provide what Kleibert (2021) calls a territorial fix and a symbolic fix. The territorial fix is the opening of new and segmented #student_markets; the symbolic fix is the #reputational gain of being present in a prestigious location. Healey and Hickey (2025) describe three waves of internationalisation in the United Kingdom: export education through recruitment, then franchising degrees to overseas partners, and finally branch campuses abroad, with a recent mutation in which universities open campuses in London that act as an experimental laboratory for new curricula and operating models. Not every system follows the same commercial path. German public universities, which come from a less marketised tradition, have also built branch campuses abroad. Rottleb, Schulze and Kleibert (2024) found that even in this setting decision-making in university leadership was strongly shaped by a #neoliberal paradigm, and that exposure to commercial higher education markets abroad could in turn reshape the home university. This finding matters for the present article because it shows that alignment works in both directions: the host environment does not only change the branch campus, it can also change the parent. Economic analysis supports the idea that expansion follows identifiable patterns. Using data from thirty-three source countries and seventy-six host countries between 1948 and 2016, Paniagua, Villo and Escriva-Beltran (2022) found that cultural, economic and institutional ties encourage cross-border educational links, but that their effect differs between the decision to open a campus and the decision to expand programmes once there. In plain terms, the reasons a university enters a country are not always the same as the reasons it grows there. 3.2 Models of transnational provision Transnational education is not one thing. The literature distinguishes several models that differ in who teaches, who owns the infrastructure, who awards the degree and who carries the risk. In a #franchise or validation arrangement, a local partner delivers a programme that the foreign university approves and awards. In twinning and joint degree models, students study part of the programme with each institution. In a joint venture university, a new legal entity is created with a host partner, as is common in China, where regulation requires transnational partnerships on the mainland to involve both local and overseas institutions (Miani, 2025). At the far end of the spectrum sits the wholly owned branch campus, where the home university controls almost everything and also carries almost all the financial and reputational risk. Between these models sit hybrid forms. Some host states have built education hubs or education zones, where several foreign universities share land, buildings and services. Kleibert, Bobee, Rottleb and Schulze (2021) describe these #education_hubs as a distinct urban form, a kind of education city planned by the state. Research with students in purpose-built hubs in Malaysia and the United Arab Emirates found that they were largely satisfied with studying on a shared campus, and that a preference for a solely owned campus was often more psychological than practical (Wilkins, Hazzam and Kana, 2026). This suggests that hub models can lower the cost of entry while still giving students a credible experience. The choice of model is itself a strategic decision about how much control the home university wants and how much dependence on local actors it can accept. Hickey and Davies (2022), in a systematic review of motivations and case studies of successful and unsuccessful ventures, proposed a #decision_making framework combining strategic, leadership, academic, financial and operational factors. Their framework has since been used to compare branch campuses in South Korea and Japan, where persistent collaboration among stakeholders, strong commitment from the home campus and strategies to manage regulatory challenges were important for success (Kim and Ke, 2024). 3.3 Host states and regulation The host state is not a passive market. It decides whether foreign providers are welcome, on what terms, and for what purpose. Rottleb and Kleibert (2022) show how Dubai and Qatar built #transnational_education_zones that offer financial benefits and legal exemptions to foreign universities, allowing the state to attract globally mobile institutions and staff while containing their social and political impact. The zone is both an invitation and a boundary. Host priorities also change over time. In Singapore, Schulze (2023) found that the state moved from a first phase of importing global knowledge through foreign universities to a second phase in which it expected foreign campuses to support local skill formation more directly. Newer foreign subsidiaries were therefore more deeply embedded in local economic plans than older ones. A campus that was aligned with Singapore's goals in one decade had to realign in the next. In Uzbekistan, the government's aims in inviting foreign institutions included filling a shortage of university places, training professionals for industry and integrating with other countries, while the foreign providers had their own aims (Muratov and Wilkins, 2024). Such differences are not necessarily conflicts, but they show that home strategy and host policy start from different questions. The home university asks how a campus serves its mission; the host state asks how the campus serves #national_development. De Wit, DeLaquil, Hazelkorn and Coates (2025) argue that transnational education is now growing and changing in ways that create #policy_gaps, especially as provision becomes more digital and more privatised. Regulation designed for physical campuses may not fit online delivery or private intermediaries, and ethical questions about who benefits from transnational provision are not always addressed by existing rules. Knight (2022) offers a further perspective by framing #international_higher_education as a form of #knowledge_diplomacy, in which universities contribute to relations between countries. In this view, a branch campus is never purely an educational or commercial project; it is also a piece of international relations. 3.4 Quality assurance and accreditation #Quality_assurance is where the tension between global vision and local rules becomes most concrete. The home university normally insists that its overseas degrees are equivalent to those awarded at home. The host system, meanwhile, often requires programmes to be approved and periodically reviewed by its own national bodies, sometimes using standards and documents that differ sharply from those of the home system. Studies of national quality systems in the Gulf show how centralised and bureaucratic approaches can limit institutional creativity even as they raise public confidence (Aburizaizah, 2022). Branch campuses in such systems must learn to work within these expectations rather than around them. Tsiligiris and Hill (2021) argue that quality in international higher education should not be judged only by whether inputs and processes match the home campus. They propose a model that aligns educational quality with the student experience in the #host_context. This is an important shift for the present topic, because it suggests that strict replication of the home model is not the same as quality. A programme can be identical on paper and still serve its students poorly if it ignores local needs. Evidence on student satisfaction supports this concern. Using data from 2,145 international students at four home campuses and six of their branch campuses, Merola, Hofman, Jansen and Coelen (2022) found that students at branch campuses were significantly less satisfied with their academic experience than students at the home campuses. Equivalence of curriculum does not automatically produce #equivalence of experience. More recent work on recognition of transnational qualifications calls for clearer definitions, more harmonised quality systems and stronger cooperation among agencies so that degrees awarded across borders can be #trusted (Lantero and Francesca, 2025). 3.5 Legitimacy, identity and hybrid institutions A growing strand of research uses #institutional_theory to explain how branch campuses survive. Legitimacy, in this tradition, means that an organisation is accepted as appropriate by the people and authorities around it. Zhao, Guo and Zhu (2025) examined ten Sino-foreign cooperative universities and found that all of them built a common foundation on #regulatory_legitimacy, but then differentiated themselves through four distinct logics: global prestige, research power, strategic adaptation and diplomatic mission. In other words, meeting the law is necessary but not sufficient; institutions then compete on other forms of acceptance. #Organisational_identity is closely linked. In a study of a Hong Kong university's branch campus in mainland China, Dai, Wilkins and Zhang (2025) found that the campus held both international and Chinese identities, but that power between them was uneven: the international identity dominated academic matters such as curriculum, teaching and research, while the Chinese identity was expressed mainly through compliance with government regulation. This is a vivid picture of the alignment problem. The global vision governs the classroom; the local regulator governs the frame around it. Similar hybridity appears in curriculum. Miani (2025) studied a foundation year at a Sino-foreign university and found that it was not a simple copy of the British model. Tensions over English as a medium of instruction, attendance monitoring, equality and inclusion ideals, and policies on artificial intelligence produced real divergence between the Chinese and British versions. Cai, Wilkins, Zhao and Zhang (2024) showed that the purposes and strategies of branch campuses and Sino-foreign institutes shape how students form their identities, which means that the choice of model reaches all the way down to the #student_identity. In Malaysia, Xu (2025) combined cultural discount theory with the global integration and local responsiveness framework and found two pathways used by successful campuses: content integration, in which global curricula are adapted to local contexts through region-specific subjects while international standards are kept; and access facilitation, in which global content stays unchanged but cultural support is added. Both campuses developed #hybrid_governance structures that drew legitimacy from several stakeholder groups at once. 3.6 Staff, students and sustainability Strategy is delivered by people. Wilkins and Annabi (2023) surveyed seventy-two academics working at branch campuses in ten countries. Adventure, travel and cultural experience were common motives, but staff also reported a lack of #job_security, limited support for research, constraints on academic freedom and fewer opportunities for advancement. On the positive side, they cited motivated students, smaller classes and greater autonomy at work, and almost three-quarters believed an attractive career in transnational teaching was possible. A systematic review of academic expatriates likewise found that their main challenges were rooted in balancing global integration with local responsiveness (Yao and Yang, 2024). Students, for their part, often choose branch campuses for reasons that mix global, national and local factors. Dai, Wilkins and Zhang (2024) found that students choosing a Hong Kong institution's campus in the Greater Bay Area were drawn by a high level of internationalisation, shared culture and good value for money. In Qatar, a phenomenographic study of forty students in a British programme found that they experienced their learning mainly in terms of academic skills, self-learning skills and employability (Al Yafei, Ayoubi and Crawford, 2023). Finally, #sustainability is a constant concern. Wilkins and Hazzam (2026) studied senior managers at fourteen small or newly established branch campuses, many of which never grow beyond five hundred students. They identified five strategic options: pursuing growth, maintaining the status quo, transforming, selling, or closing the campus. Recent conceptual work proposes that transnational education should be designed as an #ecosystem rather than a transaction, with collaborative governance, contextual adaptation and long-term relational investment (Wang, 2025), and that partnerships should blend market, academic and public-good logics through co-governance, curriculum co-design and revenue sharing (Tsiligiris, 2025). 3.7 Gaps in the literature Three gaps stand out. First, most studies examine either the home institution's strategy or the host state's policy, but fewer bring both into a single analytical frame. Second, accreditation is often treated as a technical hurdle rather than a strategic arena in which legitimacy is won or lost. Third, there is limited guidance for students and practitioners on how to analyse the alignment problem step by step. The framework in the next section is designed to address these gaps. 4. Conceptual Framework The framework proposed here, shown in Figure 2, treats a branch campus as the meeting point of two systems. On one side is the home institution, which brings a global strategic vision, a brand and reputation, the legal power to award degrees, and a home quality code. On the other side is the host system, which brings licensing law, national accreditation, a national qualification framework and political priorities. Between them sits a set of #alignment_mechanisms: the choice of entry model, the design of governance, the approach to curriculum equivalence, policies on staffing and research, and the system of dual quality reporting. Before turning to the framework itself, it helps to place the main entry models side by side. Figure 1 arranges them along a continuum. Moving from franchise towards a wholly owned campus, investment, control, reputational exposure and regulatory scrutiny all increase. Moving in the opposite direction, the university becomes more dependent on a local partner and on shared governance. No position on the continuum is free of risk; each simply exchanges one kind of risk for another. Figure 1. A continuum of transnational entry models, from partner-led franchising to wholly owned branch campuses. The alignment mechanisms in Figure 2 do not produce good outcomes directly. They work by helping the campus earn three forms of #legitimacy. #Regulative_legitimacy means that the campus meets the law and the formal requirements of host authorities: it holds a licence, its programmes are approved, its degrees are recognised. #Normative_legitimacy means that the campus meets academic norms: its teaching, assessment and research are seen by academic peers and accreditors as appropriate and rigorous. #Cognitive_legitimacy means that the campus is taken for granted as a real university by students, parents, employers and the public, rather than as a temporary sales office. These three forms draw on the institutional tradition used by Zhao, Guo and Zhu (2025), adapted here to the branch campus setting. Figure 2. Conceptual framework: alignment mechanisms connect the home institution and the host system and generate regulative, normative and cognitive legitimacy, which in turn support durable outcomes. When all three forms of legitimacy are present, the campus is more likely to achieve the outcomes that both sides want: equivalence of quality, recognised degrees, financial sustainability and long-term trust from the host. When one form is missing, the campus becomes fragile. A campus with a licence but weak academic standing may be legally secure but unable to attract students. A campus with strong academic standing but poor regulatory standing may be closed or restricted regardless of its quality. A campus that is legal and academically sound but not yet seen as a real university may struggle to grow. The framework has three features that make it useful for analysis. First, it is two-sided: it insists that the host system is an active party, not a backdrop. Second, it is dynamic: the alignment mechanisms are adjusted over time as host priorities change, as Schulze (2023) showed in Singapore. Third, it treats accreditation and regulation as sources of legitimacy rather than simply as costs, which helps explain why some institutions invest heavily in going beyond the minimum. 5. Analysis and Discussion 5.1 Choosing the entry model as a regulatory decision The first and most consequential alignment decision is the choice of #entry_model. In much management writing, this is treated as a question of investment and return. In transnational higher education, it is just as much a regulatory question, because host law often decides which models are possible. In mainland China, as noted above, transnational partnerships must involve both local and foreign institutions (Miani, 2025). A university that wants a wholly owned campus with full control may simply not be allowed to have one there. Its strategic choice is therefore narrowed to joint ventures, cooperative institutes or partnership programmes. In Gulf education zones, by contrast, the state may offer exemptions from some national rules in exchange for locating inside a designated zone (Rottleb and Kleibert, 2022), which makes a wholly owned or hub-based campus more attractive. In newer hosts such as Uzbekistan, the state has actively invited foreign institutions and shaped arrangements around its development goals (Muratov and Wilkins, 2024). The practical lesson for leaders is that the model should be chosen after the regulatory map is drawn, not before. Universities that begin with a fixed preference, for example a strong wish to own and control their campus, and then search for a country that will permit it, may end up in markets that suit their preference but not their mission. Universities that begin with a country and then study its rules are more likely to choose a model the host will support over the long term. The model also shapes how the campus is governed. #Joint_ventures bring a local partner into the board room, which can slow decisions but also gives the campus a powerful local ally in dealings with ministries. #Wholly_owned campuses keep decisions inside the home university but may lack local political knowledge. Hickey and Davies (2022) found that successful campuses combined strong leadership and commitment from home with sound operational and financial planning; in regulatory terms, this means having people who understand both the home quality code and the host licensing process. 5.2 Layered quality assurance and the problem of two masters Once a campus is operating, it lives inside several layers of quality oversight at the same time, as Figure 3 illustrates. At the centre is the campus itself, with its programmes, staff and students. Around it sits host licensing and permission to operate, which covers matters such as legal entity, land, visas, fees and ownership. Next comes host accreditation and programme approval, often through a national quality body or a ministry. Beyond that sits the home country quality agency, which oversees all provision offered in the home university's name. The outer layer is international and professional accreditation, for example from business, engineering or medical bodies. Figure 3. Layers of regulation and quality assurance surrounding an international branch campus. Each layer has its own documents, schedules, terminology and definitions of quality. A host body may ask for evidence of local labour market relevance, a home agency for evidence of equivalence with the home degree, and a professional body for evidence of specific competencies. When these demands conflict, the campus faces what can be called the problem of two masters. If it follows the host's requirement to add local content, it may be accused by the home agency of weakening equivalence. If it insists on exact replication, it may fail host approval. The literature suggests that the way out is not to pick one master but to redesign the relationship between them. Tsiligiris and Hill (2021) argue for linking quality to the student experience in context, which provides a shared language for both regulators: both can agree that students should receive a programme that is rigorous, relevant and well supported, even if they disagree on details. Lantero and Francesca (2025) call for harmonised quality systems and stronger cooperation among agencies. In practice, this can include joint or coordinated reviews, mutual recognition of review outcomes, and shared information on transnational provision. At the level of the campus, one useful practice is a single #quality_calendar. Instead of preparing separately for each regulator, the campus maps all reporting deadlines, review visits and evidence requirements onto one timeline. It then builds one evidence base, organised so that it can answer the questions of each body. This reduces duplication, exposes conflicts early and helps staff see quality as one system rather than a series of unrelated inspections. 5.3 Global integration and local responsiveness The tension between home standards and host expectations can be analysed with the #global_integration and local responsiveness framework, which Xu (2025) applied to branch campuses in Malaysia. Figure 4 adapts this idea into a simple matrix with four campus types. Figure 4. Integration and responsiveness matrix for branch campus strategy, adapted from the global integration and local responsiveness framework. A replica campus has high integration with home standards but low responsiveness to local regulation and context. It copies the home model closely and often collides with strict host rules, particularly where curriculum, language or governance requirements differ. A localised outpost has low integration and high responsiveness: it fits local rules well but risks drifting away from home standards until its degrees are questioned at home. An opportunistic venture has low integration and low responsiveness; it reflects thin commitment and is the most exposed to closure. A #glocal campus combines high integration and high responsiveness. It keeps a common academic core, including learning outcomes, assessment standards and academic governance, while negotiating local layers such as region-specific subjects, additional student support, local language courses or compliance modules required by the host. The #glocal_campus is the hardest type to build, because it demands constant negotiation and a clear understanding of what is truly essential to the degree. It is also the most durable, because it can earn legitimacy from both sides. Xu's (2025) two pathways, content integration and access facilitation, are both versions of glocal strategy. Content integration changes what is taught, within agreed limits; access facilitation keeps what is taught but changes how students are helped to learn it. Universities can use either pathway, or both, depending on what host regulators require and what students need. Students can use the matrix as an analytical tool. For any real campus, one can ask: which elements of the home model have been kept, which have been changed, and who decided? The answers usually reveal where the campus sits and where its main risks lie. 5.4 Curriculum, language and academic freedom #Curriculum is the point where the home university's academic identity meets host law most directly. Some host states require compulsory national subjects, specific credit structures, or approval of all course content. Others regulate language of instruction, or set expectations about topics considered sensitive. The study by Dai, Wilkins and Zhang (2025) captures the resulting arrangement well: international identity governed curriculum, teaching and research, while the local identity expressed itself mainly through regulatory compliance. This division can work for a time, but it depends on both sides accepting it. Miani's (2025) study of a foundation year shows that even practical teaching matters, such as attendance monitoring or rules on artificial intelligence, can diverge between home and host versions of the same programme. Divergence is not always a failure. In many cases it reflects sensible adaptation. The question for quality assurance is whether divergence affects the learning outcomes and standards that the degree certifies. If it does not, divergence is local flexibility. If it does, it is a problem of #academic_standards. #Academic_freedom deserves particular attention. Wilkins and Annabi (2023) found that constraints on academic freedom were among the challenges reported by staff at branch campuses. A home university that presents academic freedom as a core value at home but accepts restrictions abroad faces an ethical and reputational dilemma. There is no easy solution, but transparency helps. Universities can state in advance which principles are non-negotiable, which areas involve local adaptation, and how staff and students will be supported when conflicts arise. Silence on this point leaves staff to manage difficult situations alone. #Language_of_instruction is another frequent site of tension. Teaching in English is often a key part of the global appeal of a branch campus, yet hosts may require instruction or examinations in a national language for certain subjects, or require language courses for all students. A glocal approach treats such requirements as part of the local layer rather than as a threat to the core, while ensuring that assessment standards in the main discipline remain equivalent. 5.5 Staffing, research and the people side of alignment Regulators often look closely at staffing: the qualifications of academic staff, the proportion with doctorates, the ratio of students to staff and the share of staff on permanent contracts. These indicators are easy to measure and therefore attractive to accreditation bodies. They also go to the heart of a branch campus's business model, since staff are its largest cost. The evidence suggests that staffing is a weak point. Lack of job security, limited research support and few advancement opportunities, as reported by Wilkins and Annabi (2023), can make it hard to recruit and retain the kind of staff that host accreditors and home quality agencies expect. If staff turnover is high, programmes may be delivered by temporary or flying faculty, which raises questions about continuity and student support. The systematic review by Yao and Yang (2024) shows that expatriate staff face challenges rooted in the same global and local tension that shapes the campus as a whole. #Early_career_academics face their own version of this problem. In a branch campus in China, Chinese early career staff often negotiated dual legitimacy across global and domestic academic arenas, while international early career staff drew legitimacy from their international status but struggled to become locally connected (Dai, Zhang and Liu, 2026). Alignment, in other words, is not only an institutional task. Individual staff members carry part of the load in their careers and identities. #Research_capacity is a further source of tension. Many host states want branch campuses to contribute to national research and innovation, not only to teaching. Many campuses, especially small ones, are funded almost entirely by tuition and have little capacity for research. Where host accreditation criteria include research performance, a teaching-only campus may struggle to meet them. Leaders therefore need to decide early what research role the campus will play and how it will be funded, rather than treating research as something that will develop later. 5.6 Sustainability, review and the possibility of exit The final dimension of alignment concerns time. A campus that is well aligned at launch may become misaligned later because the host changes its priorities, the home university changes its strategy, or the market changes. Figure 5 presents a phased pathway that treats alignment as a cycle rather than a single event. Figure 5. A phased pathway for aligning transnational strategy with host regulation and accreditation, with feedback into each new cycle. The pathway begins with strategic fit: why this country, why now, and what the campus must achieve. It moves to regulatory and accreditation mapping, which covers licensing, approvals, recognition and ownership rules. Only then does the university choose its entry model and partner, matching control and risk to host constraints. The fourth step is dual compliance design, which builds the common academic core and local layers and sets up a single quality calendar for all regulators. The fifth step covers launch and early monitoring of enrolment, staffing and the first accreditation cycle. The sixth step is periodic review, in which the university decides whether to grow, hold, transform, sell or close the campus, the five options identified by Wilkins and Hazzam (2026). The results of review then feed into the next cycle of strategic fit. Including exit in the pathway may seem pessimistic, but it reflects reality. Closures are a regular feature of the branch campus landscape (Wilkins, 2021; Wilkins, 2024). Planning for possible #campus_closure protects students, because it forces the university to think in advance about #teach_out arrangements and #degree_recognition if the campus closes. It also protects the home university's reputation, because a well-managed exit is far less damaging than a sudden one. The ecosystem view proposed by Wang (2025) offers a constructive counterweight. Drawing on survey responses, interviews and more than a thousand regulatory audit findings, Wang identifies barriers such as weak institutional integration, rigid curriculum transfer and fragmented support for students and staff, and proposes collaborative governance, contextual adaptation and long-term relational investment. The THRIVE framework proposed by Tsiligiris (2025) similarly emphasises holistic engagement, resilience, inclusivity, viability and equity. Both perspectives suggest that sustainability is not only financial. A campus that is profitable but disliked by the host, or legal but weak academically, is not sustainable in the full sense. 5.7 Regional patterns of alignment The general framework becomes clearer when it is applied to the main host regions discussed in recent research. The aim here is not to rank countries, but to show how the same alignment problem takes different shapes in different #host_countries. In mainland #China, the regulatory frame is firm and well defined. Foreign institutions usually work through cooperative arrangements with Chinese partners, and the state retains a strong voice in governance, curriculum requirements and the political education of students. Research in this setting repeatedly finds hybrid institutions in which international academic practice and Chinese regulatory requirements sit side by side (Cai et al., 2024; Dai, Wilkins and Zhang, 2025; Miani, 2025). The Sino-foreign universities studied by Zhao, Guo and Zhu (2025) all treated regulatory legitimacy as the common foundation and then competed on prestige, research, adaptation or diplomatic mission. For a foreign university, alignment in China therefore means accepting a #shared_governance model from the start and investing heavily in the relationship with the local partner. A university that expects to run its Chinese campus exactly as it runs its home campus is likely to be disappointed. In the #Gulf_states, especially the United Arab Emirates and Qatar, alignment often runs through designated zones and invitation schemes. Rottleb and Kleibert (2022) show that these zones offer foreign universities legal and financial advantages, while keeping their wider influence contained. In such settings, a campus may enjoy more freedom inside the zone than a local university, but its position also depends on the continuing goodwill of the state and on the zone's place in national economic plans. National quality systems in the wider region can be centralised and paperwork-heavy, as Aburizaizah (2022) found in Saudi Arabia, which means that branch campuses must be ready to produce evidence in the format and language that national bodies expect. The British-Qatari partnership studied by Al Yafei, Ayoubi and Crawford (2023) also suggests that students in the region value transnational programmes for academic, self-learning and employability skills, which gives campuses a clear basis for showing their contribution to national goals. In #Southeast_Asia, Malaysia and Singapore offer two instructive contrasts. Malaysia has long hosted several foreign campuses and has developed purpose-built hubs where institutions share facilities (Wilkins, Hazzam and Kana, 2026). Successful campuses there, according to Xu (2025), have used either content integration or access facilitation, and have built hybrid governance that draws support from political, economic and social stakeholders. Singapore shows how a strong state can change what it expects from foreign providers. Schulze (2023) traces a move from importing global knowledge to embedding foreign campuses in local skill formation. A campus that does not respond to such a shift may remain legal but lose its strategic value to the host. In #Central_Asia, Uzbekistan illustrates a newer and fast-moving case. The rapid arrival of more than twenty foreign institutions in a few years reflects a state strategy of expanding access and building professional skills through foreign partners (Muratov and Wilkins, 2024). In such settings regulation is often still developing, which can be both an opportunity and a risk. It is an opportunity because foreign providers can help shape rules. It is a risk because rules may change as the system matures, and campuses that relied on early flexibility may later face stricter accreditation demands. In #South_Asia, India has more recently opened a formal route for foreign universities to set up campuses, adding a large potential host system to the global picture. Because this development is recent, peer-reviewed evidence on how such campuses align with Indian regulation and accreditation is still limited, and students should treat claims about it with care until more research appears. What the wider literature suggests is that the early years of any new host regime are the period in which alignment choices have the longest consequences. Across these regions, one pattern is clear. The more developed and confident a host system becomes, the more it expects foreign campuses to serve national priorities rather than simply to exist. Alignment is therefore moving from a question of permission to a question of contribution. 5.8 Digital delivery and the changing meaning of a campus A final development complicates the picture. Transnational provision is no longer only about buildings. Online programmes, hybrid models, private intermediaries and short courses now carry university names across borders with little physical presence. De Wit et al. (2025) argue that these digital and privatised forms are growing faster than the rules designed to govern them, creating gaps in quality oversight and ethical accountability. For branch campuses, digital delivery creates both opportunities and new alignment problems. It can allow a small campus to offer a wider range of modules by sharing teaching with the home campus, or to support students during disruption. It can also blur the line between local and foreign provision in ways that host regulators find hard to classify. If a large part of a programme is taught online from the home country, is it still a local programme that falls under host accreditation, or is it imported provision that needs a different approval? Different regulators answer this question differently, and some have not yet answered it at all. The safest strategy for universities is to treat any significant use of #online_learning in a branch campus programme as a regulatory matter from the start. This means telling host authorities how much of the programme will be delivered remotely, ensuring that assessment and student support meet local expectations, and checking that degree recognition is not affected. The strategy also requires attention to #equity. Students in the host country should not receive a thinner experience simply because online delivery is cheaper. The concern raised by Merola et al. (2022) about lower satisfaction at branch campuses could easily deepen if digital delivery is used mainly to cut costs. Healey and Hickey (2025) offer a more hopeful angle. Their finding that new metropolitan campuses can act as #experimental_laboratories suggests that new forms of provision, including digital ones, can also be used to test curricula and teaching methods that later improve the home campus. In this sense, the branch campus need not be a weaker copy of the home university. It can become a place where the university learns how to teach differently, and that learning can flow back home. 6. Implications 6.1 For university leaders The first implication is that regulatory and accreditation mapping should come before model choice, not after it. Leaders should understand who regulates what in the host country, how often, by which standards and with what consequences, before committing to a structure. The second implication is that leaders should define a clear academic core, the elements of the degree that will not change, and an explicit space for local layers. Without this distinction, every regulatory request becomes a crisis about identity. The third implication is that leaders should invest in people who can work across both systems, including staff who understand host language, law and culture and staff who understand the home quality code. Leaders should also be honest about their motives. Kleibert (2021) and Rottleb, Schulze and Kleibert (2024) show that commercial and reputational motives often dominate decisions, even in systems that describe themselves as non-commercial. There is nothing wrong with financial sustainability, but a campus driven only by revenue is likely to make choices that weaken its normative and cognitive legitimacy over time. 6.2 For regulators in host and home countries For host regulators, the main implication is that clear, stable and #transparent_rules attract better providers. Sudden changes in licensing conditions or approval criteria raise risk and may deter the institutions most committed to quality. Host regulators can also reduce duplication by recognising the work of credible home and international agencies where appropriate, while keeping control over matters of national interest. For home regulators, the implication is that equivalence should be judged by outcomes and #student_experience as well as by inputs. The finding that branch campus students can be less satisfied than home students (Merola et al., 2022) suggests that home agencies should look closely at support services, staffing stability and student voice in overseas provision, not only at curriculum documents. Both sets of regulators could also do more to close the policy gaps around digital and privatised cross-border provision identified by de Wit et al. (2025). 6.3 For students For students, the main implication is practical. Before enrolling at a branch campus, a student can ask a few direct questions. Is the campus #licensed by the host authorities, and is the programme approved by the national quality body? Is the degree identical to the one awarded at home, and does it say where it was studied? Are relevant #professional_accreditation arrangements in place? What happens if the campus closes? Who teaches, and are they permanent staff? The framework in this article gives students a way to read the answers: they show whether the campus has regulative, normative and cognitive legitimacy, and therefore how secure their investment is likely to be. 6.4 Applying the framework: a worked illustration To show how the framework can be used, this section walks through a hypothetical case. The university and the host country are invented for teaching purposes and do not describe any real institution. Imagine a mid-sized European university with a strong reputation in engineering and business. Its new strategic plan sets a goal of becoming a recognised international institution within ten years, with a quarter of its students studying outside the home country. Its leaders are attracted to a fast-growing host country that has recently announced a national plan to expand university places and build a knowledge economy. The first step in the pathway is strategic fit. The university asks why this country, and why now. The answer is partly market demand and partly the host's stated wish for engineering graduates. This overlap is promising, because it suggests that the campus can contribute to national goals rather than only recruit students. If the overlap were weak, for example if the host mainly wanted medical and teacher education, the university would need to think again. The second step is regulatory and accreditation mapping. The university finds that the host requires foreign providers to register a local legal entity, to have every programme approved by the national quality agency, to include two compulsory national subjects in each bachelor degree, and to employ a minimum share of staff with doctorates. Its home quality agency, meanwhile, expects the overseas engineering degree to meet the same learning outcomes as the home degree, and the professional engineering body that accredits the home programme will only extend accreditation if the overseas campus passes its own review. The third step is the choice of entry model. A wholly owned campus would give full control, but the cost is high and the university has little local knowledge. A joint venture with a respected local university would bring political knowledge and shared costs, but would require shared governance. Given the mapping, the university chooses a joint venture located in a state education zone, which offers some financial support. On the continuum in Figure 1, this places it near the middle, accepting some dependence on its partner in exchange for lower risk. The fourth step is dual-compliance design. The university defines its academic core: the engineering learning outcomes, the assessment standards, the external examining system and the final degree award. It then defines local layers: the two compulsory national subjects, an additional language module, a local industry placement scheme that supports the host's development goals, and a student support service adapted to local expectations. It builds a single quality calendar that brings together host approval cycles, home periodic review and professional accreditation visits. On the matrix in Figure 4, it aims to be a glocal campus. The fifth step is launch and early monitoring. The university watches enrolment closely, but it also watches staffing, because the doctoral staff requirement is difficult to meet at first. It invests in research support and contracts of reasonable length to attract and keep qualified staff, aware of the challenges described by Wilkins and Annabi (2023). It also collects student feedback from the start and compares it with home campus data, to catch any gap in experience early. The sixth step is periodic review. After five years, the university asks whether the campus has earned the three forms of legitimacy in Figure 2. It has a licence and approved programmes, so regulative legitimacy is secure. Its engineering programmes have gained professional accreditation, so normative legitimacy is growing. Local employers now recruit its graduates, which suggests that cognitive legitimacy is developing. Enrolment, however, is below plan. The university must decide whether to grow by adding programmes, hold its current size, transform its model, or consider exit. Because it has kept careful records and built a strong partner relationship, it has real options rather than a forced choice. The illustration shows that the framework does not give automatic answers. What it does is make the key questions visible and place them in a sensible order, so that leaders, regulators and students can see where alignment has been achieved and where it is still missing. 7. Conclusion This article has examined how higher education institutions can align a global strategic vision with the strict regulatory frameworks and accreditation standards of host countries. Drawing on recent research, it has argued that alignment is a continuing process of negotiation rather than a single decision at entry. The choice of entry model is shaped by host law as much as by home preference. Branch campuses live inside several layers of quality oversight and must often satisfy two or more masters at once. The most durable campuses tend to be glocal hybrids that combine a protected academic core with negotiated local layers, and that earn regulative, normative and cognitive legitimacy together. The conceptual framework, the continuum of models, the map of quality layers, the integration and responsiveness matrix and the phased pathway offer tools that students, researchers and practitioners can use to analyse real cases. They also help to make visible the tensions that are often hidden in promotional language, including uneven student experience, pressures on staff and academic freedom, and the risk of closure. The article has limitations. It is a review rather than an empirical study, and it depends on the existing literature, which is richer for China, Malaysia, Singapore and the Gulf states than for newer host regions. The framework has not yet been tested systematically across many campuses. Regulations also change quickly, so specific national details described in the cited studies may already have evolved. 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Higher Education Policy. https://doi.org/10.1057/s41307-025-00412-4 #TransnationalEducation #international_branch_campuses #cross_border_higher_education #higher_education_internationalisation #university_strategy #accreditation_standards #academic_quality_assurance #host_country_regulation #global_universities #branch_campus_models #TNE #education_policy #glocalisation #student_mobility #STULIB