Journal of Knowledge Management最新文献

{"title":"The dynamics of knowledge transfer and retention in entrepreneurial education: a conceptual exploration of enabling technologies","authors":"Lucia Pizzichini,Massimo Pescarollo,Paola Pisano,Miroslav Svitek","doi":"10.1108/jkm-10-2025-1458","DOIUrl":"https://doi.org/10.1108/jkm-10-2025-1458","url":null,"abstract":"Purpose This conceptual paper aims to examine Entrepreneurial Education (EE) in Higher Education Institutions (HEIs) through a Knowledge Management (KM) lens. It explains how enabling technology affordances, when aligned with the characteristics of entrepreneurial knowledge, shape Knowledge Transfer (KT) and Knowledge Retention (KR) under conditions of cognitive bias. It clarifies how KM processes support durable entrepreneurial capability development. Design/methodology/approach The study adopts a theory-driven conceptual methodology. Drawing on KM process theory and conceptual theory-building guidance, the paper develops a mechanism-based framework through six stages: problematisation, theoretical anchoring, construct selection, mechanism mapping, model integration and proposition development. The framework positions KT and KR as the analytical core, enabling technologies as differentiated KM affordances, and cognitive bias as an embedded condition that can distort knowledge interpretation, transfer, retention and reuse. Findings The framework proposes that enabling technologies can strengthen KT and KR when their affordances align with the knowledge’s tacitness, codifiability and contextual dependence, and are supported by interaction, reflection, curation and governance routines. Immersive/simulation, AI/analytics and collaborative/process technologies support KT/KR mechanisms. Cognitive bias can weaken or distort knowledge transfer, retention and reuse, whereas bias-aware routines and cohort-to-cohort feedback can improve knowledge fidelity, programme memory and entrepreneurial capability development. Originality/value The paper advances a KM-centred reframing of EE by positioning KT and KR as the focal mechanisms linking enabling technologies to durable entrepreneurial capability development. It differentiates technologies by their KM affordances and integrates cognitive bias, boundary conditions and programme-level feedback within a single conceptual architecture, offering testable propositions and design principles for technology-enabled EE.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"73 1","pages":"616-636"},"PeriodicalIF":7.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210738","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":"From data to knowledge: how DPP startups span boundaries across stakeholder divides","authors":"Serdar Temiz,Ayse Ege Palaz,Anders Brantnell","doi":"10.1108/jkm-04-2026-0710","DOIUrl":"https://doi.org/10.1108/jkm-04-2026-0710","url":null,"abstract":"Purpose Digital Product Passports (DPPs) aim to increase product transparency and enable circular economy practices. However, transforming DPP data into actionable knowledge across diverse stakeholder groups remains a key challenge. This study aims to investigate how startups span syntactic and semantic knowledge boundaries to make DPPs understandable, drawing on knowledge management theory to examine the communicative mechanisms through which product data is translated into shared knowledge across organizational boundaries. Design/methodology/approach Using Carlile’s (2004) boundary-spanning theory alongside constructs from absorptive capacity, knowledge integration and knowledge governance, this qualitative study analyzes digital materials from 12 European startups and interviews with 10 industry practitioners. The analysis identifies strategies used to communicate DPP systems across different media and stakeholder contexts. Findings Extending sequential accounts of knowledge boundary management, startups deploy syntactic and semantic strategies simultaneously through a bundled boundary-spanning approach. Syntactic strategies include standardized terminology, visual schemas and role definitions, while semantic strategies encompass narrative framing, technical metaphors, contextual use cases and ESG-aligned messaging. These bundles vary systematically across communication channels, stakeholder types and stages of DPP adoption, with web pages prioritizing semantic appeals and whitepapers emphasizing syntactic formalization. Research limitations/implications The study is limited to early-stage ventures, publicly available materials and EU-based contexts. Further research could explore stakeholder reception, cross-cultural comparisons and pragmatic boundary dynamics involving interest-based negotiations. Practical implications Startups can benefit from intentionally designed communication frameworks that bundle structural clarity with interpretive scaffolding when engaging stakeholders in emerging regulatory contexts. Policymakers should consider providing communication support tools alongside technical requirements. Social implications Effective communication of DPPs supports transparency, regulatory compliance and wider public understanding of sustainable product systems. Originality/value This study offers three contributions to boundary-spanning scholarship: first, by demonstrating that syntactic and semantic strategies are bundled rather than sequenced in pre-regulatory contexts; second, by introducing the concept of startups as “trust translators” who convert cryptographic guarantees into accessible narratives; and third, by revealing a temporal dimension to boundary-spanning in anticipatory regulatory environments where both technical infrastructure and interpretive frameworks must be constructed simultaneously.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"12 1","pages":"589-615"},"PeriodicalIF":7.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210737","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":"Acting in complexity: knowledge dispersion challenges in high-level teams","authors":"Brandon Randolph-Seng,Amanda S. Hinojosa,Alejandra Marin,Angela F. Randolph","doi":"10.1108/jkm-10-2025-1467","DOIUrl":"https://doi.org/10.1108/jkm-10-2025-1467","url":null,"abstract":"Purpose The purpose of this paper is to provide a theoretical model for how high-level teams composed of capable, skilled and motivated knowledge experts can still act in ways that lead to significant failures. Design/methodology/approach Using a socio-cognitive view on the relationship between action and mind and work on social contagion, the authors explore the mechanisms that link members’ knowledge expertise, team processes and shared cognitions to team action. Findings The proposed team action identification framework provides a new perspective that brings clarity to the mechanisms through which cognitions and actions evolve at the team level by specifying how to optimally arrange knowledge expertise and team shared cognitions in contexts with high amounts of complexity and competition. Originality/value The framework extends beyond current theoretical frameworks by explaining how high-level teams that have the proper expertise, motivation and coordination for effective action may still fail to devise plans and implement needed actions when faced with complex challenges.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"97 1","pages":"1-25"},"PeriodicalIF":7.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210739","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":"From data silos to knowledge symbiosis: orchestrating the low-altitude economy via collaborative governance","authors":"Ke Ning,Jingyu Liu,Chaolin Lyu","doi":"10.1108/jkm-02-2026-0411","DOIUrl":"https://doi.org/10.1108/jkm-02-2026-0411","url":null,"abstract":"Purpose Scaling the low-altitude economy requires more than mature individual technologies. It also depends on interfirm knowledge collaboration between core-technology and scenario-application firms embedded in public governance. However, existing research rarely explains how dispersed technical and scenario data become usable knowledge for shared tasks or when interfirm collaboration can persist. The authors examine how verified data sharing shapes firms’ strategies through collaborative value added, collaboration frictions and data risks. The authors also analyse how benefit allocation, risk sharing and public governance tools affect collaborative evolution and its boundaries. Design/methodology/approach This study adopts a mixed-methods research design. Firstly, a tripartite evolutionary-game model involving the government, low-altitude core-technology firms and scenario-application firms is constructed to compare the relative payoffs of substantive collaboration and opportunistic collaboration under different governance conditions. MATLAB is then used to analyse equilibrium paths, policy-tool interactions and nonlinear data-risk boundaries. Secondly, questionnaire data from 333 managers of low-altitude enterprises provide complementary enterprise-level evidence concerning the direction and boundary conditions of the major variable relationships. Findings The model shows that the net effect of verified data sharing on collaborative strategies depends on the relative magnitudes of collaborative value added, friction savings, data risks and opportunistic gains. Bilateral substantive collaboration becomes self-sustaining only when benefit allocation and risk sharing give both firms positive net returns. Subsidies raise the relative payoff from substantive collaboration, whereas opportunism detection reduces the appeal of opportunistic strategies. Under our parameter settings, sustained, costly proactive governance is not stable once bilateral collaboration can persist under normal governance. Non-linear risk scenarios further show that additional risks may erode the benefits of greater sharing beyond the governance capacity assumed in the model scenarios. The empirical results also reveal a positive association between data sharing and collaborative innovation. This association is stronger when policy support and the corresponding dimension of market regulation are both high. Originality/value Firstly, this study distinguishes data flows from knowledge formation and integrates collaborative value added, risk sharing and governance tools within a dynamic framework centred on verified data sharing. Secondly, it conceptualises knowledge symbiosis as a bounded state of sustained bilateral collaboration and operationalises this state in the model as substantive collaboration in which both firms obtain positive net returns under normal governance. Finally, the study characterises the model-based value, risk and governance conditions under which such a","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"54 1","pages":"1-33"},"PeriodicalIF":7.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210740","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 transfer and learning through global disruption: coactive vicarious learning in multinational enterprises","authors":"Monica Riviere,Gabriele Suder,Carol Reade","doi":"10.1108/jkm-12-2025-1858","DOIUrl":"https://doi.org/10.1108/jkm-12-2025-1858","url":null,"abstract":"Purpose Global exogenous shocks disrupt established knowledge transfer routines in multinational enterprises (MNEs) and challenge assumptions about organizational learning. While prior research emphasizes learning after crises, less is known about how MNEs learn during simultaneous disruption and shared uncertainty. This study aims to examine how knowledge transfer and learning unfold during such shocks and how subsidiary managers facilitate collective learning. Design/methodology/approach Adopting an interpretivist epistemological stance, this study uses a phenomenon-driven, exploratory sequential mixed-methods design. Data were collected during the COVID-19 pandemic through a Delphi study, survey, in-depth interviews and a follow-up poll conducted one year later, from chief executive officers and managing directors of Australian subsidiaries of foreign-owned MNEs. Data were analyzed abductively, iterating between empirical patterns and theory to develop propositions and a conceptual model grounded in organizational learning and knowledge management theory. Findings Learning during global exogenous shocks is initiated through managerial interpretation rather than objective timelines. Subsidiary managers’ perceptions reconfigure the salience of units within the MNE network, prompting a search for experiential knowledge from subsidiaries perceived as having earlier exposure. Perceived subsidiary autonomy enables initiative-taking and reciprocal engagement, giving rise to coactive vicarious learning (CVL) – an interactional process supporting the enactment, adaptation and retention of new routines during crisis. Practical implications For managers, the findings suggest that learning during crisis depends less on centralized control and more on enabling knowledge-rich interaction, interpretation and initiative across the MNE network. Parent firms can support learning by valuing locally generated experiential knowledge, encouraging cross-subsidiary dialogue and attending to the reasoning and sensemaking underlying subsidiary actions, rather than interpreting local responses as deviations from established practices. As crises become more frequent and extreme, deliberately fostering organizational conditions that support coactive vicarious learning may strengthen knowledge sharing, enhance organizational learning capacity and ultimately improve MNE resilience and adaptive capability. Originality/value This study advances knowledge management research by theorizing the simultaneity of global exogenous shocks as a structural condition that activates CVL, through which crisis intensifies rather than constrains knowledge flows across subsidiaries and headquarters. The resulting process-oriented model identifies managerial interpretation and perceived subsidiary autonomy as the enabling mechanisms.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"11 1","pages":"1-22"},"PeriodicalIF":7.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210509","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":"Individual dependency behavior in multimodal AI-assisted knowledge acquisition: influencing factors and explainable prediction","authors":"Zequan Luan,Yongqiang Sun,Jie Ma","doi":"10.1108/jkm-08-2025-1216","DOIUrl":"https://doi.org/10.1108/jkm-08-2025-1216","url":null,"abstract":"Purpose This study aims to identify the key factors shaping individual dependency behavior in multimodal artificial intelligence (AI)-assisted knowledge acquisition and develops an interpretable prediction model to inform strategies for optimizing knowledge internalization. Design/methodology/approach A sequential mixed-methods design was employed. Open, axial and selective coding of 32 semi-structured interviews yielded five factors: task execution, information processing, individual cognition, technology experience and knowledge management. Saturation Assessment Curve (SAC) confirmed theoretical saturation. Theoretical coverage reached over 97% by the 17th interview. An expert panel of 20 domain specialists reviewed the framework and confirmed satisfactory content validity indices (S-CVI/Ave > 0.80). Subsequently, 2,002 valid survey responses were analyzed using machine learning classification. A support vector machine (SVM) was selected for its superior accuracy and F1-score, and the Shapley Additive Explanations (SHAP) algorithm was applied to quantify feature contributions. Findings Task execution exerts the strongest influence on dependency behavior, reflecting individuals’ reliance on multimodal AI under task pressure and goal-driven conditions. Information processing and individual cognition positively contribute as secondary drivers, while technology experience and knowledge management contribute comparatively weakly. These results suggest that habitual, pressure-driven responses progressively outweigh careful, quality-oriented considerations in AI-assisted knowledge acquisition. Originality/value This study extends the theoretical understanding of dependency behavior in AI-mediated knowledge acquisition. It also offers empirical evidence to guide human–AI collaboration design, AI governance frameworks and sustainable knowledge management practices.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"53 1","pages":"1-29"},"PeriodicalIF":7.0,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151678","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":"Reimagining entrepreneurship in the age of generative AI: the mediating role of innovation","authors":"Yue Yan,Wei Wu,Rudresh Pandey,Jehan Saleh Lardhi,Oumaima Haj Ammar","doi":"10.1108/jkm-03-2026-0569","DOIUrl":"https://doi.org/10.1108/jkm-03-2026-0569","url":null,"abstract":"Purpose This study aims to examine how three dimensions of generative artificial intelligence (GenAI), namely, capability, application and knowledge modelling, are associated with entrepreneurial innovation, and whether innovation mediates their associations with entrepreneurial performance and entrepreneurial success in startups and small and medium-sized enterprises (SMEs). Design/methodology/approach Survey data from 317 startup and SME owners, partners and employees were analysed using confirmatory factor analysis followed by AMOS-based structural path analysis in IBM SPSS analysis of moment structures (AMOS) 20.0 with maximum likelihood estimation. Specific indirect effects were assessed using bias-corrected bootstrapping with 5,000 resamples and 95% confidence intervals. Findings All three GenAI dimensions were positively associated with entrepreneurial innovation, with knowledge modelling showing the largest reported association. Entrepreneurial innovation was positively associated with performance and success, and all six specific indirect associations were significant. The largest reported indirect associations were observed for knowledge modelling. Research limitations/implications The cross-sectional, single-source design supports associations rather than causal claims. Practical implications Entrepreneurs should prioritise knowledge-related practices, verification of AI-generated information and human judgement rather than treating tool use alone as evidence of innovation. Policy and support programmes should develop these practices alongside access to GenAI tools. Originality/value The study distinguishes GenAI capability, application and knowledge modelling within one entrepreneurial model and examines entrepreneurial innovation as a mediating knowledge-conversion pathway linking these dimensions with performance and success.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"157 1","pages":"1-22"},"PeriodicalIF":7.0,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151680","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":"Framing SECI model through an artificial knowledge generation perspective: a case study analysis in the GenAI era","authors":"Giuseppe Liccardo,Roberto Cerchione,Marco Trabucco Aurilio","doi":"10.1108/jkm-02-2026-0365","DOIUrl":"https://doi.org/10.1108/jkm-02-2026-0365","url":null,"abstract":"Purpose This study aims to examine the influence of generative artificial intelligence (GenAI) on the phases of knowledge creation as delineated by Nonaka’s SECI model. Furthermore, it delineates the fundamental interactions among the components of a theoretical framework for the artificial knowledge generation through a continuous interaction between human and artificial dimensions. Design/methodology/approach The authors used an exploratory single-case study to reach their aim. Data were primarily collected through semi-structured interviews and triangulated via direct observation and document analysis within a company operating in the cybersecurity sector. An inductive coding tree was derived from qualitative data analysis performed by using content analysis methodologies. Findings Data indicates that GenAI significantly influences the SECI phases of knowledge creation. In addition, the influence of this disruptive technology on knowledge generation cannot be adequately described by relying exclusively on the original SECI model, highlighting the need for its extension in the context of artificial knowledge generation. Research limitations/implications The study identifies developing AI-driven mechanisms that appear to introduce novel knowledge conversion dynamics absent from the original model. These findings resulted in the definition of a novel framework that offers a more thorough comprehension of human−machine collaboration in knowledge management. Originality/value The originality of this study lies in the systematic analysis of GenAI’s influence on the SECI model and the subsequent development of a novel preliminary and empirically informed theoretical framework that extends the SECI model by incorporating a machine dimension into knowledge generation processes.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"29 1","pages":"565-588"},"PeriodicalIF":7.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151684","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":"Do high-impact scholars exhibit greater knowledge innovation and flash ideas?","authors":"Qing Xie,Xinyuan Zhang","doi":"10.1108/jkm-08-2025-1214","DOIUrl":"https://doi.org/10.1108/jkm-08-2025-1214","url":null,"abstract":"Purpose Knowledge innovation manifests through the emergence of new keyword nodes or relational ties within co-word networks. Such structural changes enable individual scholars to perceive the field’s evolving knowledge architecture and support research teams in tracking innovation dynamics. While scholars stand as core agents driving scientific knowledge creation and dissemination, existing literature lacks granular analysis of how high-impact scholars shape these innovative processes. This study aims to examine how high-impact scholars contribute to knowledge innovation and the generation of emerging ideas by tracing structural changes in co-word networks over time. Design/methodology/approach To fill this gap, this study leverages the Normalized Scholar Academic Productivity (NSAP) index proposed in the authors’ prior work, alongside two bespoke metrics – the knowledge innovation score and flash idea score – to unpack scholar-led knowledge innovation. The authors further examine how cross-institutional mobility reshapes knowledge generation and quantify the dual effects of scholar relocation on individual and host institutional knowledge output. Drawing on sequential co-word network structures, the authors trace longitudinal knowledge evolution and identify pivotal actors involved in knowledge diffusion. Findings The empirical results reveal that high-impact scholars not only deliver superior knowledge innovation performance but also produce abundant flash ideas, signifying that pioneering knowledge creation carries inherent exploratory risks. Furthermore, inter-institutional scholar mobility delivers substantial novel intellectual stimuli, boosting innovative knowledge output for both migrating researchers and receiving organizations. Originality/value This study advances the field by constructing a multi-dimensional evaluation framework integrating NSAP, the knowledge innovation score and the flash idea score. The unified framework empirically demonstrates that elite scholars simultaneously exhibit robust innovative capacity and more conceptual risk-taking when generating nascent knowledge. These findings yield actionable implications for institutional talent management strategies and targeted policy support for exploratory frontier research.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"248 1","pages":"1-24"},"PeriodicalIF":7.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149092855","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":"Design thinking as a practice system for knowledge-enabled digital transformation: microfoundations of dynamic capabilities","authors":"Shahrokh Nikou,Giulia Calabretta,Gioia Benini","doi":"10.1108/jkm-03-2026-0549","DOIUrl":"https://doi.org/10.1108/jkm-03-2026-0549","url":null,"abstract":"Purpose Digital transformation (DT) demands more than technology adoption; it reshapes organisational routines, knowledge and decision structures, and the capabilities firms rely on to compete. Yet many firms fail to move DT beyond pilots, revealing a deeper problem: organisations lack the knowledge mechanisms needed to sustain transformation at scale. Design/methodology/approach This paper positions design thinking (DTk) as a knowledge practice system: an interrelated set of methods, mindsets and artefacts that shapes the microfoundations of dynamic capabilities (DCs) during DT. Drawing on semi-structured interviews with 14 practitioners in the Dutch financial services sector, the authors inductively identify and elaborate three DTk-shaped microfoundations: Adapting (sensing through problem framing bounded by strategic intent and short, iterative experiments), Governing (seizing through user evidence, structured decision checkpoints, role clarity and contracting mechanisms) and Conveying (reconfiguring through storytelling, boundary artefacts and shared vocabulary that transfer and translate knowledge across organisational boundaries). Findings The authors demonstrate how these mechanisms address key DT challenges – strategic misalignment, decision risks, legacy-system constraints and cross-functional coordination failures – that are, at their core, knowledge management (KM) challenges. The analysis further specifies the knowledge processes each microfoundation enacts, a configuration logic among the three microfoundations and the failure modes that arise from imbalanced enactment. Practical implications For managers, this paper provides guidance for embedding DTk as a practice system to strengthen knowledge transfer, strategic alignment and coordinated organisational change. Organisations in regulated, legacy-intensive settings are encouraged to emphasise Adapting to manage integration risk, Governing for compliance-robust decisions and Conveying to coordinate across stakeholder groups. Originality/value This paper contributes to DC, DTk and KM literature in four ways, it offers a mechanism-level specification of how DTk practices serve as the microfoundations of DCs during DT; repositions DTk as a knowledge practice system connecting DC theory and KM scholarship; articulates a configuration logic and failure modes arising from imbalanced enactment and incorporates a monitoring loop consistent with capability dynamisation that sustains transformation momentum without disrupting core operations; and identifies boundary conditions as simultaneously KM conditions that intensify specific knowledge processes in highly regulated, legacy-intensive contexts.","PeriodicalId":48368,"journal":{"name":"Journal of Knowledge Management","volume":"484 1","pages":"1-29"},"PeriodicalIF":7.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149092970","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}