Journal of Knowledge Management最新文献

{"title":"How dispersed knowledge shapes absorptive capacity: evidence from firms in technology parks","authors":"Helma Regina Saturnino Leite,Márcio Shoiti Kuniyoshi","doi":"10.1108/jkm-12-2025-1869","DOIUrl":"https://doi.org/10.1108/jkm-12-2025-1869","url":null,"abstract":"Purpose This study aims to examine how different forms of dispersed knowledge shape the development and enactment of absorptive capacity (ACAP) in firms operating within technology parks. Design/methodology/approach This research is exploratory and qualitative, based on multiple case studies of five technology companies in São Paulo, Brazil. Data were collected through semi-structured interviews, documentary sources and direct observation and analyzed using the Gioia methodology supported by MAXQDA to ensure analytical clarity and traceability. Findings The main findings show that dispersed knowledge, conceptualized through institutional, cognitive, geographical and regulatory dimensions, functions as a structuring condition that differentially activates potential absorptive capacity (PACAP) and realized absorptive capacity (RACAP). Companies exposed to geographic and institutional dispersion emphasize acquisition and assimilation processes, whereas cognitive and regulatory dispersion intensifies transformation and exploitation. The findings reveal distinct configurational patterns of ACAP, showing how companies develop context-specific routines to convert dispersed knowledge into innovative outcomes. Research limitations/implications This study is limited to five companies located in technology parks in São Paulo, Brazil, using qualitative multiple-case analysis and primarily one key informant per firm. Therefore, the findings cannot be statistically generalized to all innovation habitats or technology-based firms. Practical implications This research offers managers a framework for structuring routines that minimize the dispersion of externally acquired knowledge, emphasizing learning and internal integration. Social implications Findings help policymakers design effective public strategies to promote regional development and maintain innovation ecosystems. Originality/value This study advances ACAP theory by reconceptualizing dispersed knowledge as a structuring mechanism rather than merely a contextual condition. It presents a configuration-based, process-oriented perspective, demonstrating how distinct types of dispersion shape the activation and integration of absorptive processes, thereby providing a more detailed explanation of heterogeneity in innovation outcomes across firms.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"13 1","pages":"1-22"},"PeriodicalIF":7.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Algorithmic epistemic authority and the degradation of organizational knowledge: how generative AI breaks the provenance structure of knowledge management systems","authors":"Ajit Kumar","doi":"10.1108/jkm-03-2026-0627","DOIUrl":"https://doi.org/10.1108/jkm-03-2026-0627","url":null,"abstract":"Purpose This study aims to theorize how organizational knowledge management systems are transformed when generative artificial intelligence (AI) is integrated as a knowledge-producing agent. It introduces algorithmic epistemic authority (AEA), a structural condition of knowledge systems, not individual users, that specifies how generative AI breaks the experiential provenance of knowledge and develops three organizational propositions, falsifiable in principle, each with specified disconfirmation conditions and graded tractability. Design/methodology/approach A conceptual study in knowledge management’s (KM) own theory-building tradition and a Type IV contribution in Gregor’s (2006) taxonomy, integrating four constitutive KM-relevant literatures through a four-step chain of reasoning. Findings The framework predicts that generative AI breaks the observation–knowledge nexus structurally rather than incrementally, producing three organizational dynamics: provenance integrity degradation (P1), SECI bypass and tacit knowledge stagnation (P2) and credentialed-interface knowledge coupling (P3). Research limitations/implications Each proposition requires longitudinal empirical testing. The framework’s claims are specified for base generative large language models and hold with diminishing intensity, as architectural variants (retrieval-augmented, multimodal and Web-grounded) partially restore the observational link. Practical implications KM systems require architectural separation of AI-generated and human-authored content, interface-level provenance marking, friction for high-stakes consumption, audit infrastructure, domain-expert validation gateways and experiential development pathways. Social implications Miscalibrated trust in AI-generated knowledge threatens institutional credibility in credentialed contexts. Originality/value This study introduces the observation–knowledge nexus and AEA as KM constructs distinct from automation bias and prior algorithmic authority, and translates them into governance interventions.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"28 1","pages":"1-22"},"PeriodicalIF":7.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Keeping AI in check: how clinicians govern AI-generated knowledge in hearing care","authors":"Rania Alkahtani,Shalini Talwar,Hadeel Alsaleh,Abhishek Bhushan Singhal","doi":"10.1108/jkm-03-2026-0590","DOIUrl":"https://doi.org/10.1108/jkm-03-2026-0590","url":null,"abstract":"Purpose This study aims to examine how clinicians use AI-enabled tools and govern the knowledge generated by these systems in clinical decision-making. Specifically, it explores how clinicians identify and manage risks, uncertainties and responsibilities associated with AI-generated outputs before using them in patient-facing decisions. Design/methodology/approach The study adopted a qualitative research design, collecting clinicians’ narratives through open-ended essays. Data were analyzed using the Gioia methodology with an inductive-abductive approach grounded in the knowledge governance perspective. Findings The study develops a knowledge governance pathway to help clinicians manage AI-generated knowledge in practice. The pathway includes surfacing epistemic vulnerabilities, subjecting AI-generated outputs to evidentiary scrutiny, allocating decision authority between AI and clinicians and institutionalizing traceability and adaptive oversight. The findings show that clinicians treat AI outputs as provisional, context-dependent knowledge claims and rely on triangulation, verification routines and professional judgment before using them in patient-facing decisions. They also sustain effective AI use through documentation, transparency, training and continuous recalibration. Originality/value This study advances the understanding of AI use in health care by shifting attention from ethical and technical concerns to knowledge governance in clinical practice. By providing a clinician-centered process model grounded in clinicians’ lived experiences, it offers actionable insights for health-care professionals and organizations seeking to integrate AI into clinical decision-making while preserving professional judgment and accountability. It also extends knowledge governance theory by showing how clinicians validate AI-generated outputs, allocate decision authority between AI and clinicians and sustain accountability through traceable oversight before using these outputs in patient-facing decisions.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"49 1","pages":"1-16"},"PeriodicalIF":7.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the continuity of international scientific collaboration in the BRICS: a machine learning approach","authors":"Damaris Vicentin,Gustavo Hermínio Salati Marcondes de Moraes,Bruno Fischer,Dirk Meissner,Joseph Zhou Yuan,Zhongzhen Miao","doi":"10.1108/jkm-02-2026-0345","DOIUrl":"https://doi.org/10.1108/jkm-02-2026-0345","url":null,"abstract":"Purpose This study aims to predict respondent-reported continuity of international scientific collaboration within the BRICS through a supervised binary classification approach. It examines whether respondents reported that collaborations continued in the form of joint projects and co-authored publications, using survey-based variables related to collaboration climate, partner attributes, perceived benefits, barriers and impacts. Design/methodology/approach The analysis is based on survey data comprising 313 valid responses for collaborative projects and 304 valid responses for publications involving researchers engaged in cross-border cooperation within the BRICS. The machine-learning input variables were derived from the survey responses; publicly accessible bibliometric and institutional sources were used only to identify eligible respondents. Seventeen variables were considered, including 15 predictors and two binary outcomes (Projects and Publications). Eight supervised machine learning classifiers were applied: Logistic Regression, support vector machine, k-nearest neighbors, decision tree, random forest, gradient boosting, histogram-based gradient boosting and naïve Bayes. Model performance was evaluated using stratified cross-validation and ShuffleSplit resampling, considering accuracy, F1-score, balanced accuracy and AUC metrics. Findings The results indicate that trust culture is the most consistent predictor of respondent-reported continuity across both outcomes, while resource availability and agenda alignment are particularly important in predicting respondent-reported project continuity. Originality/value This study offers an interpretable, respondent-level classification of reported collaboration continuity in BRICS-internal scientific partnerships. Its originality lies in treating the reported continuity of collaboration as a knowledge management -relevant outcome, distinguishing project-based from publication-based continuity and showing which established collaboration conditions remain informative across these two forms of sustained knowledge relationships.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"18 1","pages":"1-22"},"PeriodicalIF":7.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why AI use decays after adoption in hearing care: stabilising AI-enabled knowledge in clinical practice","authors":"Shalini Talwar,Hadeel Alsaleh,Adrienn Dernóczi-Polyák,Rania Alkahtani","doi":"10.1108/jkm-03-2026-0433","DOIUrl":"https://doi.org/10.1108/jkm-03-2026-0433","url":null,"abstract":"Purpose This study aims to examine why artificial intelligence (AI)-enabled innovations may experience decay in use after initial implementation. Specifically, it aims to identify stage-specific barriers across abandonment, scale-up, spread, and sustainability, with particular attention to why AI-enabled knowledge may fail to stabilise in routine clinical practice. Design/methodology/approach The study adopted a qualitative design, drawing on clinicians’ lived experiences with AI-enabled innovations in hearing care. Data collected through the open-ended essay instrument were analysed using an inductive-abductive logic, with respondent-level codes developed from the data and interpreted through the non-adoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. Findings The study identified NASSS-informed barriers that shape the post-adoption trajectory of AI-enabled innovations in hearing care. At the user/clinical level, abandonment is driven by user-technology interaction barriers, including technical friction, digital burden, lack of trust, and perceived risks. At the organisational level, scale-up is impeded by infrastructure and system integration constraints, as well as capacity and change-management load. Next, spread is hindered by disparities in resources and readiness across settings, as well as the dependence on supporting conditions to enhance wider transferability and legitimacy. Finally, sustainability is challenged by ongoing support requirements and usage burdens, and user hesitancy and preferences. Originality/value The study contributes by reframing post-adoption decay in AI use as a knowledge breakdown problem, where AI-enabled knowledge may remain fragile after initial uptake. It interprets abandonment as a confidence breakdown, scale-up barriers as embedding breakdowns, spread barriers as transfer breakdowns, and sustainability barriers as engagement breakdowns. It also develops a post-adoption barrier matrix that translates these breakdowns into a practice-oriented structure, helping clinicians and healthcare organisations understand why AI use decays, where the barriers are located, when they become salient, why AI-enabled knowledge breaks down, and the risks this poses to continued use.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"8 1","pages":"1-17"},"PeriodicalIF":7.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI washing as a knowledge governance failure in organizations","authors":"Yunjie Wang,Wei Zhang","doi":"10.1108/jkm-04-2026-0782","DOIUrl":"https://doi.org/10.1108/jkm-04-2026-0782","url":null,"abstract":"Purpose Growing evidence suggests that firms increasingly overstate their artificial intelligence (AI) adoption in corporate communications, yet the organizational consequences of AI washing remain underexplored. This study aims to conceptualize AI washing as a knowledge governance failure (KGF) and develops a framework explaining how persistent symbolic AI adoption progressively undermines organizational learning, knowledge validation and capability development. Design/methodology/approach Drawing on the knowledge-based view (KBV) and knowledge governance theory (KGT), this conceptual study develops an integrative framework. An illustrative multiple-case analysis of manufacturing settings is used to demonstrate the proposed governance mechanisms and the evolutionary trajectory of AI washing. Findings AI washing is conceptualized as a sequential KGF process driven by legitimacy pressures. Persistent symbolic AI adoption redirects managerial attention away from substantive capability development, leading to resource misallocation, cognitive decoupling, cognitive debt accumulation, evaluation failure, knowledge validation erosion, and ultimately organizational brain rot. These mechanisms reinforce one another through a self-reinforcing governance deterioration cycle. Practical implications Organizations should address AI washing as a knowledge governance challenge rather than merely a disclosure issue. Aligning AI adoption with organizational learning, knowledge validation and governance mechanisms is essential for sustaining AI-enabled capability development. Originality/value This study extends AI washing research beyond disclosure and legitimacy perspectives by conceptualizing it as a dynamic knowledge governance process. By integrating KBV and KGT, it explains how symbolic AI adoption progressively erodes organizational knowledge systems and provides a theoretical foundation for future empirical research on AI governance.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"1 1","pages":"1-15"},"PeriodicalIF":7.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The invisible hand of generative AI: quasi-agency and knowledge transformation in data-driven decision-making","authors":"Matteo Cristofaro,Alexis Bañón-Gomis,Pier Luigi Giardino","doi":"10.1108/jkm-03-2026-0573","DOIUrl":"https://doi.org/10.1108/jkm-03-2026-0573","url":null,"abstract":"Purpose This study aims to examine how generative artificial intelligence (GenAI) reshapes knowledge management (KM) in data-driven decision-making (DDDM), reconfigures principal–agent relations and creates new challenges for epistemic governance. It focuses on how GenAI influences the construction, synthesis, justification and retention of decision-relevant knowledge across the decision cycle. Design/methodology/approach This study adopts an abductive qualitative design, drawing on data from 120 managers at Italian small and medium-sized enterprises actively using GenAI in recurring DDDM activities. Drawing on ten open-ended questions aligned with the decision cycle, the analysis applies reflexive thematic analysis, supported by Gioia-informed coding procedures, to identify recurring phase-level governance patterns and to develop a phase-sensitive framework. Findings This study develops a phase-sensitive model of GenAI quasi-agency showing how generative artificial intelligence reshapes organizational knowledge throughout the decision cycle. The model explains that GenAI transforms knowledge through three recurring mechanisms – knowledge coupling, knowledge decoupling and knowledge hiding – which operate with different intensity across decision phases and redefine the governance requirements for traceability, reconstructability, contestability and human justificatory ownership. Consequently, agency risks increasingly originate from the transformation of hidden knowledge rather than from hidden human action. Originality/value This study introduces the concept of GenAI quasi-agency, defined as the capacity of GenAI systems to shape framing, evidence construction, prioritization, justification and organizational memory without possessing formal authority, intentionality or accountability. It contributes to KM by conceptualizing GenAI-supported decision-making as a problem of epistemic governance centered on preserving traceability, reconstructability, contestability and human justificatory ownership. The study also extends principal–agent theory by showing how agency risks increasingly arise from the transformation of hidden knowledge rather than from hidden action alone and develops a phase-sensitive framework that explains how governance requirements vary across the DDDM cycle.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"10 1","pages":"545-564"},"PeriodicalIF":7.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The dual moderating roles of organizational unlearning in knowledge transfer within an industry network","authors":"Carolyn Yesse Ngowi,Steven Lui,Salih Zeki Ozdemir","doi":"10.1108/jkm-09-2025-1278","DOIUrl":"https://doi.org/10.1108/jkm-09-2025-1278","url":null,"abstract":"Purpose This study aims to propose dual moderating roles of organizational unlearning in shaping the network drivers and the performance outcomes of interfirm knowledge transfer. First, the authors examine the moderating effect of unlearning on two key network drivers, structural network (measured as centrality) and relational network (measured as tie strength), on knowledge transfer. Second, the authors examine the moderating effect of unlearning on the relationship between knowledge transfer and firm performance. Design/methodology/approach To test the two proposed moderating models, the authors collected a unique data set comprising the entire sociometric network of 42 petroleum wholesalers in Tanzania and analyzed the survey data using moderated regression analyses. Findings The results support the authors’ hypotheses. Specifically, organizational unlearning weakens the effect of tie strength but strengthens the effect of centrality on knowledge transfer. Moreover, organizational unlearning amplifies the positive effect of knowledge transfer on firm performance. Originality/value By theorizing unlearning as a critical boundary condition, the authors show that it shapes when and how network drivers influence knowledge transfer and its performance consequences. In doing so, this study deepens our understanding of the network mechanisms underlying knowledge transfer and reveals the opposing moderating effects of unlearning on network drivers and knowledge transfer, which prior research has largely overlooked.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"30 1","pages":"520-544"},"PeriodicalIF":7.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge practices and generative artificial intelligence in management education: adopters, facilitators, and inhibitors","authors":"Wendai Yang,Sarthak Singh,Safiya Mukhtar Alshibani,Sofia Profita,Moreno Quintino","doi":"10.1108/jkm-01-2026-0026","DOIUrl":"https://doi.org/10.1108/jkm-01-2026-0026","url":null,"abstract":"Purpose This study aims to advance knowledge management (KM) scholarship by theorizing how generative artificial intelligence (GenAI) reshapes knowledge practices in management education. Prior work on GenAI in higher education has been largely descriptive, mapping faculty experiences onto generic technology-adoption constructs while leaving its KM consequences under-theorized. Design/methodology/approach The authors address this gap by developing a process model of GenAI-mediated knowledge practices organized around three interrelated dynamics, namely knowledge integration, knowledge orchestration and epistemic risk. This study uses Dual Factor Theory and the Unified Theory of Acceptance and Use of Technology 2 as sensitizing frameworks rather than as the contribution itself. Findings The findings show that GenAI catalyzes knowledge integration by fusing artificial intelligence (AI)-generated content with disciplinary expertise, exposes weaknesses in knowledge orchestration where policy voids and uneven digital literacy fragment institutional coordination and generates qualitatively new epistemic risks such as hallucinated citations, opaque authorship and selective disclosure of AI use. Originality/value The research re-specifies dual-factor dynamics for GenAI-mediated knowledge work, demonstrating that enablers and inhibitors operate as independent epistemic forces rather than as opposing poles. Also, they extend KM scholarship on knowledge risk by identifying a class of AI-specific risks that conventional governance instruments are not designed to absorb. Finally, they offer practitioner-relevant guidance for academic leaders seeking to orchestrate responsible GenAI integration in knowledge-intensive institutions.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"52 1","pages":"1-21"},"PeriodicalIF":7.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A knowledge-based view of team performance resilience: evidence from esports","authors":"Petr Parshakov,Iuliia Naidenova,Mariia Molodchik,Timofey Shvaya","doi":"10.1108/jkm-04-2026-0661","DOIUrl":"https://doi.org/10.1108/jkm-04-2026-0661","url":null,"abstract":"Purpose This study aims to develop a knowledge-based perspective on team performance resilience by examining how the structuring, integration and reconfiguration of knowledge are associated with teams’ ability to withstand and recover from external shocks. Design/methodology/approach The study uses a quantitative empirical design using data from professional esports teams competing in Counter-Strike: Global Offensive. Exogenous shocks are captured through changes in the competitive map pool, providing a quasi-experimental setting. Team resilience is measured as the ratio of post-shock to pre-shock performance and the speed of recovery. Key knowledge-related variables are operationalized using team-level metrics, and their associations with resilience outcomes are estimated using regression models. Findings The results show that knowledge specialization has an inverted U-shaped relationship with team performance resilience, indicating that increasing specialization enhances resilience, although its benefits diminish as specialization becomes more pronounced. Knowledge integration efficiency, proxied by language similarity, is positively associated with resilience, whereas age diversity is not statistically significant. Knowledge reconfiguration through team turnover is also positively associated with resilience, whereas accumulated team experience is negatively associated with it, consistent with the possibility of knowledge rigidity. Originality/value The originality of this study lies in advancing a knowledge-based perspective on team performance resilience by highlighting three interrelated knowledge tensions: specialization versus diversification, similarity versus diversity and accumulation versus renewal. Empirical evidence from the unique context of professional esports, characterized by objective performance measures and exogenous shocks, provides insights that are transferable to other knowledge-intensive teams operating in dynamic and uncertain environments.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"34 1","pages":"1-20"},"PeriodicalIF":7.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}