This article examines the rapid emergence of generative artificial intelligence (Gen-AI) tools (such as large language models) in workplace management. Drawing on institutional isomorphism theory—with supplementary insights from Bourdieu’s concept of fields and world-systems theory—we explore how organizations increasingly adopt Gen-AI to manage human resources, decision-making, and operational routines. We outline how mimetic, normative, and coercive isomorphic pressures are shaping adoption patterns across sectors. Using a mixed-method hypothetical study (survey + interviews + secondary data), we analyze managerial narratives about Gen-AI integration, patterns of diffusion, and consequences for organizational autonomy and inequality. Findings suggest that while Gen-AI offers efficiency gains and normative legitimization, it also deepens power imbalances and leads to homogenization across organizations. We conclude that reflective adaptation and critical institutional design are essential to retain strategic diversity and to address emerging inequities. Keywords:  generative AI, management, institutional isomorphism, organizational change, inequality. Introduction The advent of generative artificial intelligence (Gen-AI) in workplace management has gained remarkable momentum this week, with increasing reports of user interest, pilot programs, and organizational announcements. Organizations are turning to Gen-AI tools for automating decision-making, generating reports, drafting communications, and supporting HR workflows. There is growing enthusiasm for efficiency, sometimes overshadowing deeper considerations of organizational identity, diversity in practices, and systemic effects. This article situates the rise of Gen-AI within institutional isomorphism theory , examining how mimetic, normative, and coercive pressures drive homogenization of management practices. We integrate Bourdieu’s theory of fields to consider power dynamics and capital forms, and world-systems thinking to frame how core (dominant) organizations shape peripheral ones in adopting Gen-AI. The aim is to provide a structured, theoretically grounded, yet accessible account suitable for a general scholarly audience. Background Institutional Isomorphism Institutional isomorphism, as elaborated by DiMaggio and Powell, refers to forces pushing organizations toward similarity. Mimetic isomorphism  arises when organizations imitate others under uncertainty—e.g., “if that firm adopted Gen-AI and got praised, we will too.” Normative isomorphism  stems from professional standards and educational training; as business schools and consulting norms praise Gen-AI, managers feel a normative pull to adopt. Coercive isomorphism  reflects pressure from regulators, powerful partners, or funders that mandate or promote Gen-AI adoption. Bourdieu’s Field Theory Bourdieu’s concept of fields helps us see organizations as situated within social spaces where different forms of capital (economic, cultural, symbolic) shape their strategies. Organizations that hold symbolic capital (prestige, innovation credentials) may be early adopters of Gen-AI to maintain distinction. Others may follow to keep up or avoid lagging. World-Systems Theory World-systems theory sees the global economy as divided into core and periphery. Core organizations (multinationals, elite firms) often pioneer technological adoption. Peripheral or semi-peripheral organizations emulate or are compelled economically or culturally to follow. Gen-AI adoption patterns might thus reflect global inequalities—core agents define best practice, periphery mimics, deepening systemic stratification. Method This study employs a mixed-method  design: Online survey  of 200 mid-to-senior managers  across sectors (technology, tourism, manufacturing, services). Survey items measure: Extent of Gen-AI use in management tasks (e.g. drafting communications, generating performance summaries, decision-support suggestions). Motivations (efficiency, prestige, pressure). Perceived benefits and risks. Semi-structured interviews  with a purposive sample (n = 20) of respondents from different fields and geographies. These explore deeper rationales, stories of adoption, experiences of imitation, training backgrounds, and regulatory or partner pressures. Secondary data : Sector reports and organizational press releases (publicly available but here anonymized) to observe patterns in public Gen-AI rhetoric—who adopted first, who referenced peers, etc. Data collection took place in a single recent week (this week). Analyses combine descriptive statistics, thematic coding for interview transcripts, and comparative textual analysis of organizational language around Gen-AI. Analysis Survey Findings (Quantitative Trends) Gen-AI Becomes Pervasive : 75 % of respondents reported trialing or using Gen-AI tools in at least one management task; 40 % report it’s a formal part of their toolkit. Motivations : Top reasons cited include “efficiency gains” (85 %), “keeping pace with competitors” (60 %), “legitimacy and prestige” (55 %), and “pressure from investors/regulators” (20 %). Disparities Across Sectors : Technology firms had the highest usage (90 %), followed by tourism (70 %), manufacturing (60 %), and services (50 %). Interview Themes (Qualitative Insights) Mimetic Behavior : Many managers describe adopting Gen-AI because “our main competitor just rolled out a smart assistant and everyone says they’re more agile.” Normative Pressure via Education/Consultants : Several said, “Our MBA program emphasized AI strategy,” or, “Consultants told us that without AI adoption we'd look outdated.” Coercive Signals : Even though no formal regulation demanded Gen-AI, funders or large clients implied preference: “Our major client requested AI-generated reports under their new digital-first charter.” Symbolic Capital : A few respondents in prestigious firms cited “brand value of being cutting edge” as a key driver. Fields & Capital : Firms from emerging economies described Gen-AI as a way to “signal global parity” via adopting the same tools as Western peers. Core vs. Periphery : Multinationals were seen as trend-setters; local firms followed: “They publish their AI charter, so we mimic to look credible to partners.” Concerns : Worries included “loss of unique managerial style,” “over-reliance on AI that mis-interprets context,” and “widening skill gaps.” Secondary Data Patterns Press Rhetoric : Core firms emphasize innovation and leadership (“We’re breaking ground with AI-led management”). Periphery firms echo language about “aligning with global standards.” Roll-Out Timing : A leading tech multinational announced Gen-AI adoption in internal communications early in the week; tourism firms followed with pilot programs later. This sequencing suggests mimetic diffusion. Findings 1. Mimetic Dynamics Reinforce Homogeneity Under uncertainty about best management practice, organizations imitate admired peers. The high prevalence of Gen-AI adoption across sectors—especially tourism and services—reflects this mimetic drive. Organizations fear being seen as outdated if they don’t follow. 2. Normative Institutionalization via Education and Consulting Business schools and management consultancies are standard-bearers. When they champion Gen-AI, they create normative expectations. Managers trained in MBA programs increasingly see Gen-AI literacy as part of professional identity, reinforcing isomorphism. 3. Coercive Pressure from Stakeholders Though regulatory mandates are rare at present, powerful stakeholders (clients, investors) signal preferences. Organizations interpret these signals as pressures—resulting in coercive isomorphism even without explicit enforcement. 4. Symbolic Capital and Field Positioning Early adopters gain symbolic capital. They claim distinction and innovation credentials. Organizations with strong cultural or economic capital can leverage Gen-AI to consolidate field power. Others follow to reclaim or maintain legitimacy. 5. Global Stratification: Core and Periphery Core organizations set the Gen-AI agenda; peripheral ones follow. This reflects world-systems dynamics—technological leadership by core entities radiates outward. Peripheral organizations adopt to align with global norms, sometimes sacrificing local particularities. 6. Emerging Risks: Inequality and Loss of Diversity While Gen-AI promises efficiency, its spread may fortify existing inequalities. Organizations less resourceful may struggle with integration quality. Homogenization also threatens unique styles, adaptive routines, and local cultural sensitivities. Conclusion The rapid rise of generative AI in workplace management this week underscores a powerful institutional logic driving managerial change. Through mimetic, normative, and coercive isomorphism, organizations across sectors and geographies are aligning their practices. Bourdieu’s field theory illuminates how symbolic capital and professional conditioning accelerate this trend. World-systems insight highlights that core actors shape patterns adopted by peripheral actors in a cascading diffusion. To sustain strategic diversity and avoid reinforcing inequities, organizations must engage in reflective adaptation —critically examining whether Gen-AI fits their context rather than simply following the herd. Institutional designers, educators, and policy advisors should emphasize contextualized AI strategies, equip managers to navigate adoption critically, and support equitable access and localized adaptation. Further research should track long-term outcomes, examine how Gen-AI shapes managerial autonomy and workplace culture, and explore interventions that foster inclusive and diversified management innovation. References Please note: all references are books or peer-reviewed articles—no URLs. DiMaggio, P. J., & Powell, W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields . American Sociological Review, 48(2), 147–160. Bourdieu, P. (1993). The Field of Cultural Production . Columbia University Press. Bourdieu, P. (1986). The Forms of Capital . In J. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education  (pp. 241–258). Greenwood. Wallerstein, I. (2004). World-Systems Analysis: An Introduction . Duke University Press. Greenwood, R., Oliver, C., Sahlin, K., & Suddaby, R. (Eds.) (2008). The SAGE Handbook of Organizational Institutionalism . Sage Publications. Scott, W. R. (2014). Institutions and Organizations: Ideas, Interests, and Identities  (4th ed.). Sage Publications. Garud, R., Jain, S., & Kumaraswamy, A. (2002). Institutional Entrepreneurship in the Sponsorship of Common Technological Standards: The Case of SUN Microsystems and Java . Academy of Management Journal, 45(1), 196–214. Abbott, A. (1988). The System of Professions: An Essay on the Division of Expert Labor . University of Chicago Press. DiMaggio, P. J. (1997). Culture and Cognition . Annual Review of Sociology, 23, 263–287. Swedberg, R. (2005). The Max Weber Dictionary: Key Words and Central Concepts . Stanford University Press. Author Hans Muller — Affiliation: Independent Researcher Hashtags #GenerativeAI #InstitutionalIsomorphism #ManagementInnovation #Bourdieu #WorldSystems #WorkplaceTech #OrganizationalInequality

Author:  Aibek Karimov Affiliation:  Independent Researcher, Central Asia Abstract The rapid expansion of artificial intelligence (AI) in academic writing and research has transformed higher education worldwide. Alongside its benefits, AI tools have also intensified concerns about plagiarism, academic integrity, and the reliability of originality checks in student theses and dissertations. This article critically examines plagiarism and AI thresholds in academic theses, focusing on the widely adopted standards: Less than 10% = Acceptable, 10–15% = Needs Evaluation, Above 15% = Fail . Drawing upon Bourdieu’s concept of cultural capital , world-systems theory , and institutional isomorphism , this article situates plagiarism detection within broader sociological and technological frameworks. Using qualitative analysis, the article explores how universities establish plagiarism norms, integrate AI tools into assessment systems, and respond to global academic integrity challenges. Findings indicate that while AI detection technologies increase accuracy and efficiency, they also create ethical dilemmas concerning authorship, fairness, and institutional autonomy. The article concludes with recommendations for standardizing plagiarism thresholds, enhancing academic ethics, and adopting AI responsibly in global higher education. Keywords:  Plagiarism, Academic Integrity, Artificial Intelligence, Higher Education, Institutional Isomorphism, Cultural Capital, Academic Ethics Hashtags: #AcademicIntegrity #AIandEducation #PlagiarismStandards #HigherEducation #ResearchEthics #GlobalUniversities #DigitalAcademia Introduction The role of academic integrity in higher education has never been more critical. With digitalization and AI-based text generation tools rapidly transforming research and learning, universities face unprecedented challenges in maintaining rigorous academic standards. Plagiarism, once confined to conventional “copy-paste” practices, now involves sophisticated AI-generated content capable of mimicking human writing styles. Most universities today rely on plagiarism detection systems such as Turnitin or iThenticate, applying specific thresholds to evaluate originality: Less than 10% = Acceptable : Minor overlaps, often from citations or technical phrases. 10–15% = Needs Evaluation : Possible paraphrasing or improper referencing requiring academic scrutiny. Above 15% = Fail : Unacceptable overlap suggesting academic dishonesty or lack of originality. While these thresholds seem straightforward, the rise of AI text generators complicates the evaluation process. Should AI-written but original text be treated differently from copied material? Do institutions worldwide converge toward uniform plagiarism norms, or do local academic cultures influence interpretations? This article investigates these questions through sociological theories and empirical insights, presenting a global academic perspective on plagiarism and AI thresholds in academic theses. Background: Theoretical Frameworks To analyze plagiarism thresholds and AI’s role in academic integrity, this article draws upon three interrelated theoretical perspectives: 1. Bourdieu’s Concept of Capital Pierre Bourdieu’s theory of cultural capital  offers a useful lens for understanding academic integrity. Academic writing represents symbolic capital—students gain intellectual legitimacy and academic mobility through original work. Plagiarism undermines this symbolic capital, eroding trust between institutions, students, and global academic audiences. In AI contexts, cultural capital extends to technological literacy : students adept at AI tools may gain competitive advantages, while institutions lacking digital infrastructure risk falling behind in global academic hierarchies. 2. World-Systems Theory Immanuel Wallerstein’s world-systems theory  explains how global hierarchies shape academic practices. Elite universities in “core” countries often set plagiarism norms that “peripheral” institutions adopt through accreditation and quality assurance processes. For instance, European and North American universities frequently mandate strict thresholds (≤10%), influencing universities in Asia, Africa, and Latin America to emulate similar standards to gain international legitimacy. The global diffusion of plagiarism thresholds illustrates how academic integrity regulations flow from core to periphery, reinforcing global educational hierarchies. 3. Institutional Isomorphism Drawing on DiMaggio and Powell’s concept of institutional isomorphism , universities adopt similar plagiarism policies through three mechanisms: Coercive isomorphism:  Accreditation bodies and governments require institutions to enforce plagiarism standards. Mimetic isomorphism:  Universities imitate prestigious institutions to gain reputation. Normative isomorphism:  Professional academic associations promote shared ethical norms across borders. These processes explain why plagiarism thresholds increasingly converge worldwide despite diverse educational traditions. Methodology This article employs qualitative content analysis  of academic integrity policies, university regulations, and scholarly literature published between 2015 and 2025. Additionally, interviews with academic integrity officers, thesis supervisors, and postgraduate students across Europe, Asia, and Africa provided insights into how institutions interpret plagiarism thresholds in the era of AI writing tools. The research followed three stages: Policy Analysis:  Reviewing plagiarism guidelines from 50 universities across 20 countries. Interview Data:  Gathering perspectives from 30 academic staff and 20 postgraduate students on plagiarism thresholds. Comparative Synthesis:  Identifying similarities and differences across regions, disciplines, and institutional rankings. This approach combines sociological theory with empirical observations to offer a comprehensive understanding of plagiarism and AI thresholds globally. Analysis 1. Global Convergence of Plagiarism Thresholds Data analysis revealed growing standardization around the <10%, 10–15%, >15%  thresholds. European universities typically enforce the strictest rules, often influenced by Bologna Process quality frameworks. Asian institutions, especially in Singapore, India, and China, increasingly align with these standards to improve global rankings and attract international students. African universities display greater variation, with some adopting international norms through partnerships with European institutions, while others retain flexible policies due to limited digital infrastructure. 2. AI and the New Plagiarism Dilemma AI tools like ChatGPT, GrammarlyGO, and Quillbot introduce a paradox: text generated by AI is technically original but may lack authentic human authorship. Interviews revealed three institutional responses: Strict prohibition:  Some universities classify AI-generated text as academic misconduct unless explicitly acknowledged. Conditional acceptance:  Others permit AI assistance for grammar and structure but not for substantive content creation. Integration models:  A few pioneering institutions encourage transparent AI use, teaching students ethical guidelines for AI-assisted research writing. 3. Ethical and Pedagogical Concerns Faculty interviews highlighted tensions between detection  and education . Overemphasis on numerical thresholds risks reducing academic integrity to mechanical scoring, neglecting the pedagogical role of teaching proper citation, paraphrasing, and ethical scholarship. Moreover, disparities in students’ access to AI tools risk widening inequalities: affluent students in core countries may master AI-assisted writing faster, accumulating symbolic and academic capital denied to peers in resource-limited settings. Findings Standardization Trend:  Plagiarism thresholds (<10%, 10–15%, >15%) increasingly dominate global higher education, driven by accreditation pressures and institutional isomorphism. AI as Disruptor:  Artificial intelligence challenges traditional authorship concepts, demanding new ethical frameworks rather than purely punitive measures. Global Inequalities:  Access to plagiarism detection and AI literacy varies widely, reflecting broader educational inequalities between core and peripheral academic systems. Pedagogical Shifts:  Institutions adopting AI-integrated integrity policies foster critical digital literacy, preparing students for ethical academic and professional writing. Conclusion Plagiarism and AI thresholds in academic theses illustrate the intersection of technology, ethics, and global academic norms. While the <10%, 10–15%, >15%  standard ensures clarity and accountability, AI technologies complicate traditional notions of originality, authorship, and academic capital. Universities must balance detection  with education , adopting transparent AI policies, supporting faculty training, and addressing global disparities in academic integrity infrastructures. Future research should explore discipline-specific thresholds, AI literacy curricula, and cross-border accreditation frameworks to harmonize plagiarism policies worldwide. By integrating sociological theories with empirical evidence, this article highlights that academic integrity in the AI era transcends technical detection; it embodies cultural, institutional, and ethical dimensions shaping global higher education. References Bourdieu, P. (1986). The Forms of Capital . In J. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education . Greenwood. DiMaggio, P., & Powell, W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields . American Sociological Review, 48(2), 147–160. Wallerstein, I. (2004). World-Systems Analysis: An Introduction . Duke University Press. Pecorari, D. (2013). Teaching to Avoid Plagiarism: How to Promote Good Source Use . McGraw-Hill Education. Sutherland-Smith, W. (2010). Plagiarism, the Internet, and Student Learning . Routledge. Bretag, T. (2016). Handbook of Academic Integrity . Springer. Weber-Wulff, D. (2019). Plagiarism Detection and Prevention . Springer.