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.
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.
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.
Purpose While several studies emphasize the importance of innovation platform ecosystems in manufacturing firms’ digital innovation (DI), few analyze the relevance of coopetition (COO) within such ecosystems. Drawing on the knowledge-based view and dynamic capability view, this study aims to investigate the relationship between COO and manufacturers’ DI within the innovation platform ecosystem context, the mediating role of knowledge creation processes (i.e. knowledge exchange [KE] and knowledge combination [KC]) and the moderating role of digital platform capability (DPC). Design/methodology/approach Using a time-lagged two-wave survey of 271 Chinese manufacturing firms acting as complementors in innovation platform ecosystems, the authors tested proposed model using hierarchical regression and bootstrap method. Findings The results of this study suggest that COO has a strong positive association with manufacturers’ DI within the innovation platform ecosystem context. Both KE and KC partially mediate this relationship. Furthermore, the positive associations of COO with KE and KC are both significantly positively moderated by DPC, exhibiting a modest effect magnitude. Originality/value Departing from top-down perspectives of platform owners, this study highlights the proactive role of manufacturing complementors in leveraging COO for DI. This study shifts the locus of tension management from inter-firm arrangements to intra-firm knowledge processes by positioning KE as a decoding mechanism and KC as a protective generative mechanism. Moreover, this paper advances knowledge management and dynamic capability literature by identifying DPC as a knowledge governance boundary condition.
PurposeThis study aims to integrate core perspectives from knowledge management theory and platform ecosystem theory to develop an analytical framework that explains the types of knowledge collaboration that occur within platform ecosystems. In doing so, it seeks to bridge the gap between these two parallel research streams by synthesising insights from both literatures. Design/methodology/approachThis paper employed content analysis to systematically review the content and contexts of 111 studies drawn from peer-reviewed articles published in journals rated 3 stars or above in the Academic Journal Guide 2024. FindingsThe analysis reveals a three-phase process of knowledge collaboration – aggregation, coordination and emergence – together with three platform-enabling structures in platform ecosystems –infrastructure, governance and generativity. The framework reveals a cross-level mechanism between actors and the system. Knowledge collaboration is not an automatic outcome of platform building, but rather a co-constructed process involving multiple actors supported by the platform. The platform’s infrastructure lowers the threshold for identifying and accessing knowledge and provides the technical foundation for aggregation. Platform governance, through rules and incentives, structures the co-development, integration and application of knowledge, fostering cognitively aligned communities and normative interaction. Finally, by strengthening feedback loops and encouraging recombination, platform generativity opens diverse innovation pathways and drives the continual creation of new knowledge. Originality/valueThe insights generated by this review respond to recent calls in the knowledge management literature for a deeper understanding of knowledge collaboration. Compared with classical models, the proposed framework emphasises the dynamic coupling among multiple actors and the non-linear interaction processes that unfold within informal structures. Moreover, this research contributes to platform ecosystem theory by unpacking the micro-level complexities of knowledge collaboration. First and foremost, it challenges the prevailing assumption that structure alone determines who collaborates with whom, instead highlighting the distinct analytical value offered by the knowledge management tradition.
Purpose The study aims to examine whether employee training intensity (ETI) is associated with firms’ new quality productive forces (NQPF), as well as the boundary conditions of this relationship. From a knowledge management perspective, ETI is viewed not only as a human resource investment, but also as a formal knowledge management practice that supports knowledge acquisition, sharing, integration and application. Design/methodology/approach The study builds a firm-level NQPF index and calculates ETI based on training expenditure per employee, using panel data from Chinese A-share listed companies over the period 2015–2024. The authors use firm and year fixed effects, instrumental-variable estimation, moderation, heterogeneity and robustness analyses. Findings ETI is positively associated with firms’ NQPF. This result remains robust after considering endogeneity concerns and alternative specifications. Managerial myopia and financing constraints weaken the positive ETI–NQPF relationship, whereas innovation-oriented research and development investment strengthens it. The relationship is more pronounced among non-state-owned firms, high-tech firms and firms operating in competitive industries. Originality/value This study extends the literature in three ways. First, it identifies ETI as an important knowledge-based microfoundation of NQPF. Second, it reinterprets ETI from a knowledge-based perspective as a mechanism of knowledge conversion and capability building. Third, it reveals the managerial, resource and institutional conditions under which training investment is more effectively translated into productivity upgrading.
Purpose With the rapid development of digital-intelligent technology and its profound changes in the acquisition model of enterprise innovation knowledge, this paper aims to discuss the impact and mechanism of digital-intelligent transformation on enterprise cooperative innovation based on the theory of knowledge-based view. Design/methodology/approach Using data from China’s listed companies from 2005 to 2023, the authors construct a firm-level digital-intelligent transformation index and a firm-level cooperative innovation. Findings Research has found that digital-intelligent transformation has a significant promoting effect on cooperative innovation among enterprises, and this promoting effect is mainly reflected in samples of companies with strong monopoly power and in industries with high technological levels. The mechanism analysis results show that the digital-intelligent transformation mainly promotes cooperative innovation among enterprises by improving business expectations, enhancing information disclosure levels, and improving knowledge acquisition capabilities. Expansion analysis shows that the digital-intelligent transformation has mainly promoted cooperative innovation and green patent cooperation among enterprises, but has not significantly improved the quality of cooperative innovation. Originality/value This study defines enterprise cooperative innovation as a knowledge synergy issue, bridging the knowledge-based view and digital-intelligent transformation research. It clarifies three paths and two boundary conditions for digital-intelligent empowerment of cooperative innovation. The revealed heterogeneous effects and insufficient innovation quality challenge the notion of digital-intelligent equally promoting innovation, clarify the green innovation orientation, enrich relevant theories, and provide empirical evidence for policy implementation.
Purpose Prior knowledge management research has paid limited attention to the external institutional determinants of firm knowledge search behavior. This paper aims to examine how an increase in the strength of intellectual property protection reshapes firms’ knowledge search behavior, identifies two transmission channels, namely collaboration willingness and R&D uncertainty, and characterizes the boundary conditions of the policy effect. Design/methodology/approach Employing the Intellectual Property Demonstration City Policy as an exogenous institutional shock, this study is based on a panel of Chinese A-share non-financial listed firms over 2014–2023 and uses a staggered two-way fixed effects difference-in-differences design. Findings The Intellectual Property Demonstration City Policy significantly broadens firms’ knowledge search breadth. The main effect remains robust across alternative fixed-effects specifications, heterogeneous treatment-effects estimators, entropy balancing and propensity score matching. The policy effect operates through two independent channels, namely stronger industry-university-research collaboration willingness and reduced R&D uncertainty. The effect is markedly stronger among firms more dependent on formal intellectual property institutions, including those with prior industry-university-research collaboration experience, those in low-tech industries and non-state-owned enterprises. These patterns collectively point to institutional dependence as a unified boundary condition. Originality/value This paper extends the appropriability regime theory from innovation outputs to firm knowledge search behavior and proposes the concept of institutional dependence to delineate the boundary of the policy effect. The findings provide an empirical basis for differentiated intellectual property policy design.
Purpose Improving corporate green innovation performance is critical to sustainable development. Existing studies have paid limited attention to inventors’ knowledge structures and their underlying mechanisms in digital network contexts. Accordingly, this study aims to examine the effects of inventors’ knowledge diversity and knowledge uniqueness on green innovation performance and further explore the moderating role of artificial intelligence (AI) technology networks. Design/methodology/approach Grounded in the knowledge-based view and search-and-recombination theory, this study investigates listed firms in China’s strategic emerging industries. The study uses an inventor–knowledge two-mode network to capture inventors’ knowledge characteristics, incorporates the structural features of AI technology networks and tests the hypotheses using fixed-effects models. Findings The results reveal an inverted U-shaped relationship between knowledge diversity and green innovation performance, whereas knowledge uniqueness has a significant negative effect. AI technology networks further reshape the marginal effects of different knowledge characteristics, suggesting that knowledge value is strongly contingent on network embeddedness. Originality/value This study extends the knowledge-based view to the analysis of inventors’ knowledge characteristics. It explains the link between inventors’ knowledge characteristics and green innovation performance from a knowledge reconfiguration perspective. The study also conceptualizes AI technology networks as a digital knowledge architecture, thereby deepening understanding of green knowledge creation mechanisms in the digital context.
Purpose This study aims to address the multifaceted issue of territoriality by investigating how and why co-worker knowledge-based territoriality (i.e. marking, defending and expanding) influences knowledge-hiding behaviors via psychological reactance using conservation of resources theory. It also explores whether supervisor developmental feedback (SDF) moderates this relationship within the job demands-resources model. Design/methodology/approach A coarsened exact matching method was used to reduce the systematic differences between the treated and untreated groups, and 385 knowledge workers employed by high-tech firms in China completed the survey as instructed. Findings The empirical results revealed that co-worker knowledge-based territoriality exerted opposing effects on knowledge-hiding behaviors: territorial marking and defending are positively related to knowledge hiding, whereas territorial expanding is negatively related to it. Psychological reactance mediated the relationship between territoriality and knowledge hiding, but SDF cannot moderate the proposed relationships. Originality/value This study contributes to the literature by providing novel insights into the preventive and promotive influences of territoriality on knowledge-hiding behaviors. It uncovers psychological reactance as a key mechanism linking co-worker knowledge-based territoriality to knowledge hiding.
Purpose Confronted with a growing trade-off between healthcare quality and operational efficiency, hospital human capital and knowledge sharing have emerged as critical resources for addressing this challenge, yet their precise impact mechanism on hospital performance is not well understood. The purpose of this paper is to explore this trade-off by examining how hospital human capital influences healthcare quality and operational efficiency through explicit and tacit knowledge sharing. Design/methodology/approach Drawing on resource orchestration theory, this study develops and empirically tests a model of the relationships between hospital human capital, explicit and tacit knowledge sharing, healthcare quality and operational efficiency, using survey data from 343 hospitals in China. Findings The results demonstrate that (1) Hospital human capital directly improves both healthcare quality and operational efficiency, while also exerting a significant indirect influence by fostering the sharing of both explicit and tacit knowledge. (2) Hospital human capital serves as a key antecedent to both forms of knowledge sharing, including explicit and tacit knowledge sharing. (3) In mediating the relationship between human capital and performance outcomes, explicit knowledge sharing plays a stronger role than tacit knowledge sharing for both healthcare quality and operational efficiency. Originality/value This research highlights the distinct pathways through which human capital impacts dual performance metrics (quality and efficiency) via various types of knowledge sharing. It provides a theoretical foundation and practical insights for developing more nuanced human capital management and knowledge-sharing strategies in hospitals.
Purpose This paper aims to examine the relationships between sustainability-oriented resources, innovative capabilities and competitiveness, accounting for the mediating roles of responsible innovation and organisational support. Design/methodology/approach The authors used a cross-sectional research design during the fiscal year 2023. Data were gathered from a sample of 493 manufacturing small- and medium-sized enterprises (SMEs) across the European Union (EU), the UK and the USA. Mediation effects were tested using the causal steps approach and path analysis. Findings The empirical evidence proves that both direct and indirect pathways enhance competitiveness, with responsible innovation and organisational support acting as key mediators. Practical implications This research suggests that SME managers should deepen their commitment to responsible innovation by embedding sustainability into business models and daily routines. Furthermore, policymakers should encourage targeted initiatives, such as grants or incentives, to support sustainable transitions and bolster long-term competitiveness. Originality/value Unlike prior research, this study’s dual-path model elucidates how these sustainability-oriented mechanisms enable SMEs to convert internal resources into competitive performance. By adopting a context-sensitive approach across different geopolitical environments, this research offers new insights into how firms can strategically leverage both tangible and intangible resources within diverse institutional frameworks.
Purpose The "green productivity puzzle" suggests that digital investment does not always translate into environmental performance. Based on the knowledge-based view and the knowledge process perspective, this study aims to examine whether this puzzle reflects differences in the scale of digital knowledge or differences in how digital knowledge is integrated into firms' technical and problem-solving processes.Design/methodology/approach This study distinguishes between the substantive integration of digital knowledge and the accumulation of general digital knowledge. Using Chinese listed firm data, knowledge digitization is operationalized as digital knowledge intensity, which captures the share of digital-physical coupled knowledge in the firm's patent stock, and integration depth, which captures the depth of digital-physical knowledge integration as disclosed in patent claims.Findings Digital knowledge intensity and integration depth are both positively associated with green innovation performance, whereas the accumulation of purely digital knowledge is not. Mechanism analysis indicates that knowledge digitization is more strongly related to knowledge creation than to knowledge reuse, while cognitive friction weakens its positive association with green innovation performance. Additional analyses show that this positive effect is stronger for general-purpose connective technologies than for vertically specialized ones.Originality/value This study extends the theory of knowledge processes and shows that the green value of knowledge digitization depends on the degree of digital-physical knowledge coupling and the depth of integration. By linking knowledge digitization to knowledge creation, knowledge reuse and cognitive friction, it provides a knowledge-management perspective on how knowledge digitization shapes green innovation.
Purpose This study aims to examine the individual and configurational effects of absorptive capacity, memory, unlearning and resource reconfiguration on supply chain resilience and robustness. Design/methodology/approach The authors collected 200 survey responses from professionals involved in supply chain management activities. The data were analyzed using structural equation modeling and fuzzy set qualitative comparative analysis (fsQCA), which allowed to capture both the average linear effects and the configurational pathways leading to resilience and robustness. Findings This study demonstrates that supply chain resilience and robustness emerge from multiple configurations of knowledge-based capabilities rather than a single universally necessary factor. Resource reconfiguration links absorptive capacity and unlearning to resilience, while supply chain memory supports resilience and robustness. Moreover, fsQCA identified six configurations achieving resilience and robustness. Originality/value By combining variance-based and configurational methods, this study provides novel empirical evidence that resilience and robustness are not universal outcomes of capability accumulation but context-dependent results of strategic orchestration.
Purpose Knowledge management (KM) is important to organizations, yet its impact and mechanisms on environmental performance (EP) remain poorly understood. This study aims to address this gap by exploring how KM improves a company's EP, illustrating the internal organizational processes, and identifying the key factors that influence this relationship.Design/methodology/approach This research uses data from traditional manufacturing companies listed on China's A-share market (2008-2023). Employing a moderated mediation model, the study investigates the interrelationships between KM, green technology innovation, and EP. It also explores how environmental regulation (ER) affects the progression of these relationships.Findings KM is found to significantly boost EP, and this effect varies with organizational agility, industry pressure, and regional knowledge density. Green technology innovations account for some of the effects of KM on EP. However, it is the qualitative aspect that matters most, rather than the mere quantitative growth. Moreover, ER has a strengthening influence on the effect of the mediating variable.Originality/value To the best of the authors' knowledge, this study is the first to integrates KM into the EP framework of traditional manufacturing. It analyzes the mechanism through which internal resources are transformed into EP, extends the knowledge-based view and organizational ambidexterity theory to the context of green transformation and provides a micro-foundation for the knowledge-based process underlying the Porter hypothesis. These findings offer practical guidance for the sustainable and high-quality development of China's traditional industries.
Purpose This study aims to examine how artificial intelligence (AI) transforms knowledge processes within responsible management education (RME). By mapping existing research, the paper explores the adoption and application of AI within management education and its consequences for teaching, ethical concerns and knowledge management. Design/methodology/approach A scoping review was conducted following Arksey and O’Malley’s framework and PRISMA guidelines to synthesize evidence from 40 peer-reviewed studies published between 2015 and 2025. The review systematically analyzed literature across Scopus and Web of Science using thematic mapping aligned with knowledge management clusters. Findings AI is transforming how knowledge is created and shared in higher education. It improves efficiency by automating knowledge retrieval and connecting human insight with machine learning to support innovation in teaching and research. Yet, these advantages come with serious ethical concerns, including plagiarism, bias, data privacy and a lack of transparency that can undermine academic integrity. The review also reveals a strong concentration of research in developed countries. Practical implications The findings highlight the need for HEIs to adopt comprehensive frameworks that integrate knowledge management systems with ethical governance mechanisms. Universities can leverage AI to strengthen absorptive capacity and organizational learning while instituting clear accountability, transparency and data ethics protocols to ensure responsible AI adoption in education and research. Originality/value This study advances knowledge management research by linking AI-driven knowledge processes with the ethical and sustainability principles of RME. It broadens existing theory by showing how the transformation of knowledge, from individual insight to collective learning, and the view of knowledge as a strategic organizational resource can be aligned with responsible and transparent innovation.
Purpose This paper aims to develop a theoretical framework explaining how informal, employee-driven artificial intelligence (AI) learning contributes to organisational knowledge management through a sociomaterial lens. It addresses a gap in knowledge management theory by treating grassroots AI experimentation as a legitimate form of organisational learning and knowledge creation. Design/methodology/approach This conceptual paper uses a theory synthesis approach (Jaakkola, 2020) to integrate sociomateriality with informal learning theory. Following established methods for rigorous conceptual research (Heinonen and Gruen, 2024; Meredith, 1993), this paper develops the shadow AI learning framework, including six testable propositions spanning micro, meso and macro-organisational levels. Findings The shadow AI learning framework shows that employee-driven AI experimentation generates valuable organisational knowledge through sociomaterial entanglement across all three levels. The framework traces how informal learning moves from individual discovery to collective capability, moderated by organisational culture, leadership attitudes and quality assurance mechanisms. Originality/value The primary contribution is the identification of three mechanisms through which shadow AI learning reconfigures conventional informal learning: expertise-independent transferability, in which knowledge artefacts carry embedded experimentation to recipients who did not produce them; learning cycle compression, in which iterative querying accelerates the externalisation of tacit insight; and semi-stable cognitive infrastructure, in which accumulated artefacts persist as organisational resources beyond their creators. The framework positions shadow AI learning as a new configuration of established processes, not a wholly distinct phenomenon.
Purpose This study aims to explore the effects of formal and informal mechanisms for enterprise social media (ESM) knowledge governance on employee knowledge contribution quality through the mediating effects of epistemic trust and knowledge stewardship climate, with perceived ESM surveillance moderating both. Design/methodology/approach Data collected via online survey from 296 participants via Prolific Academic were analyzed by covariance-based structural equation modelling in AMOS 24.0. Findings The findings reveal that informal governance mechanisms better foster both epistemic trust and stewardship climate compared to formal ones. Both mediators positively predict contribution quality. However, perceived surveillance has a double impact, as it dampens the trust–contribution relationship and augments the stewardship-contribution relation. Originality/value Integrating knowledge governance theory with organizational justice theory, this study offers a novel moderated mediation model linking governance structures, psychological mechanisms and digital monitoring to the epistemic quality of enterprise knowledge contributions.
Purpose Research has predominantly focused on the stages of community development into business-community aggregates. Existing literature provides limited insight into the dynamics through which these stages emerge and the processes by which knowledge-sharing communities gradually engage in business activities while maintaining their social orientation. This study aims to examine the internal processes through which a virtual community transitions from knowledge collaboration to business engagement over time. Design/methodology/approach This longitudinal qualitative case study followed an international virtual community over five years. Data were collected through 31 semi-structured interviews, participant observations and internal documents and were analysed using a process-oriented approach based on temporal bracketing. Findings We identify three interrelated mechanisms that shape the community-to-business transformation: competence development, legitimacy negotiation and the boundary management of community and commercial activities. Together, these mechanisms ensure that business engagement is possible, sustainable and compatible with the community’s social mission. Originality/value This study offers a processual refinement and revision of existing community development models, showing how business engagement can emerge as an optional and negotiated pathway in mission-driven community evolution. It adds a dynamic, process-oriented dimension to the otherwise discrete and static understanding of hybridity and value creation in knowledge-based communities.
