Purpose Knowledge sharing is recognized as a critical factor for success in knowledge-intensive environments and project-based work. This study aims to advance understanding of how knowledge sharing relates to project success by proposing and empirically validating a model that explicates its indirect effects through key knowledge management mechanisms. Design/methodology/approach The theoretical model was empirically tested using survey data collected from master’s degree students in the Information Systems (IS) field working in collaborative, project-based teams within an academic project management context. This setting provides a controlled yet knowledge-intensive environment. Findings The results show that knowledge sharing is not directly associated with project success. Instead, its association with project success is fully mediated by expertise integration and absorptive capability, which emerge as key drivers of project success in collaborative IS project teams. In addition, knowledge location and knowledge credibility are positively associated with knowledge sharing, whereas knowledge differentiation is not significantly associated. Originality/value This study develops and validates a theoretically grounded model that integrates and empirically confirms the indirect role of knowledge sharing in project success. By focusing on the underlying mechanisms linking knowledge sharing to project results, the findings contribute to knowledge management and IS project research and offer insights relevant to both educational and organizational project-based contexts, particularly those involving early-career IS professionals.

Purpose Advanced technologies such as additive manufacturing (AM) play a critical role in transforming the healthcare sector by enabling sustainable and resilient solutions that improve patient services and operational efficiency. Yet, the adoption of AM in the healthcare sector is limited. Understanding the knowledge drivers behind its adoption is crucial for its successful implementation and better healthcare performance. This study aims to explore how effective knowledge processes impact innovation, resilience, sustainability and enhanced service quality in healthcare organisations. Design/methodology/approach Drawing on the dynamic capabilities theory, the motivating factors for AM adoption are identified from the literature and verified with industry experts using the fuzzy Delphi method. Then the factors are ranked and categorised into cause-and-effect using different multicriteria decision-making methods. Subsequently, the rationale for the cause-and-effect linkages has been explained using a qualitative approach. Findings This study presents a knowledge-driven decision-support framework that demonstrates how AM facilitates environmental sustainability, operational resilience and performance improvement. Healthcare organisations can leverage this knowledge-structured guidance to make informed decisions aligned with regulatory sustainability mandates. Originality/value Existing studies examine AM in healthcare through separate lenses (e.g. resilience, sustainability and performance) without explaining how organisations can integrate these objectives in practice. This study addresses that gap by developing a knowledge-driven decision-support framework that links knowledge processes and dynamic capabilities to AM adoption pathways, thereby offering actionable and innovative strategies for healthcare organisations.

Purpose This study aims to investigate how the artificial autonomy of artificial intelligence (AI) educators influences students’ knowledge management performance (i.e. knowledge acquisition, internalization and application) through affecting their self-determination motivations. Design/methodology/approach This study conducted an online survey. Data from 775 valid responses were analyzed by partial least squares structural equation modeling. Findings The analysis reveals that sensing and action autonomy of AI educators can improve students perceived autonomy, competence and relatedness, while thought autonomy can only significantly increase students’ autonomy and competence perception. Moreover, students’ intrinsic motivations of autonomy and competence are positively related their performance across three dimensions of knowledge management. However, the satisfaction of students’ relatedness in AI learning journey can only increase their knowledge acquisition, but is not significantly correlated with knowledge internalization and application. Originality/value This study highlights the research gap by investigating the critical roles of AI autonomy in students’ intrinsic motivation in AI learning and knowledge management performance. It provides new theoretical understanding for the literature on AI in higher education, knowledge management and the application of self-determination theory, as well as offering practical implications for AI designers and human educators aiming to adopt AI teaching tools.

Purpose This paper aims to address the limitations of traditional knowledge management (KM) theories in the era of artificial intelligence (AI) and propose a comprehensive framework to redefine KM in the context of AI-driven technological advancements. Design/methodology/approach The study constructs the “Cognition-Process-Organization-Strategy” (CPOS) four-dimensional collaborative theoretical framework. It integrates insights from traditional KM theories, AI technologies and emerging practices to systematically analyze the impact of AI on KM. The framework is developed through a three-stage logic of technology-driven transformation, theoretical adaptation and system reconstruction. Findings The CPOS framework reveals that AI technologies fundamentally reshape KM by enabling cognitive reconstruction, process parallelization, organizational decentralization and strategic paradigm shifts. It highlights the transition from human-centered KM to human–machine collaborative knowledge production, emphasizing dynamic, open and distributed knowledge ecosystems. Originality/value This paper contributes to the field by bridging the gap between traditional KM theories and AI applications. It introduces a novel theoretical framework that redefines KM in the AI era, offering a systematic approach to understanding and implementing AI-driven KM systems. The CPOS framework provides a comprehensive roadmap for organizations to navigate the challenges and opportunities of, AI, KM, CPOS framework, human–machine collaboration.

Purpose Integrating complementarity and network theories, this study advances an integrative model that explains how perceived management support enhances inbound open innovation outcomes through realized absorptive capacity. The author posits that while internal support systems and external network structures serve as complementary drivers, their synergistic effect depends critically on a firm’s ability to move beyond knowledge acquisition to the assimilation and exploitation of external knowledge. This model clarifies the underlying pathways through which internal support structures and network-enabled knowledge complementarities jointly foster inbound open innovation. Design/methodology/approach The author analyzed survey data from middle- and senior-level managers in the UAE using a multimethod approach. Structural equation modeling tested the core hypothesized relationships, while fuzzy-set Qualitative Comparative Analysis (fsQCA) identified the sufficient configurations of conditions for enhanced inbound open innovation across varying market orientations. Findings The results show that perceived management support does not directly affect inbound open innovation. Instead, realized absorptive capacity fully mediates this relationship, emphasizing its critical role in translating managerial support into innovation outcomes. The fsQCA results further identify multiple equifinal configurations through which firms achieve strong inbound open innovation, contingent upon market orientation profiles. Originality/value This study contributes to knowledge management and open innovation literature by integrating structural and configurational perspectives to explain how internal support mechanisms and knowledge capabilities interact to enhance external knowledge integration. It extends the understanding of absorptive capacity as a strategic conduit through which managerial support is converted into innovation performance. For practitioners, the study offers actionable insights on how to enhance internal learning and absorptive capacity to maximize the benefits of open innovation initiatives under varying market conditions.

Purpose This study aims to examine how perceived benefits and risks of technology (GenAI) relate to students’ adoption intentions, which in turn are associated with their development of entrepreneurial skills. The research also uses entrepreneurial opportunity recognition and entrepreneurial competencies as moderator variables. Design/methodology/approach The data were collected from students across 15 nations. The study develops and tests a conceptual model with exogenous and endogenous variables using covariance-based structural equation modeling. The knowledge-based view (KBV) theory supports the theoretical examination across variables. Findings The perceived technological benefits of GenAI are strongly and significantly associated with students’ adoption intentions. Furthermore, the adoption of GenAI as a technology constructively develops students’ entrepreneurial skills. The perceived risks attached to GenAI are not directly linked to students’ GenAI adoption. Entrepreneurial competencies amplify the impact of both perceived benefits and risks of GenAI on students’ adoption intentions, while entrepreneurial opportunity recognition weakens the relationship across perceived GenAI benefits and their intentions to adopt. Practical implications The study highlights the benefits of GenAI technology in academia, directly enhancing students’ entrepreneurial skills. The benefits and risks associated with the technology (GenAI) provide important implications for academic institutions, learners and policymakers. Originality/value This study strengthens the KBV by demonstrating that GenAI not only works as a technological enabler for users but also as a knowledge resource that enhances their entrepreneurial capabilities in the digital entrepreneurial ecosystem.

Purpose This paper demonstrates that one of the most significant advantages of artificial intelligence (AI) is the development of both social and material skills, which contribute to enhanced sustainability. This paper will address the role of AI-driven knowledge integration (AIEKI) as regulated by innovation, such as the digital absorptive capacity (DAC), and ethical guidance, such as ethical knowledge curation (EKC) in the same breath. This paper aims to investigate the impact of these factors on the economic performance and sustainable innovation performance of an organization. Design/methodology/approach As a quantitative study, a survey was mailed to 295 IT professionals in various locations. To determine the quality of fit of this study’s model, item validity and data reliability, the authors used structural equation modeling (SEM) through partial least squares-based SEM. Findings The findings reveal that AI-enabled knowledge integration improves digital absorptive capacity and ethical knowledge curation. The fact that these elements also play a crucial role in enhancing sustainable innovation performance underscores the importance of incorporating artificial intelligence in the value creation process. In addition, it was found that knowledge system complexity influenced certain of these relationships, indicating that the more complex the system was, the stronger the sociomaterial connections. Practical implications The findings are highly practical and offer clear recommendations for practitioners. Managers need to move beyond the focus on artificial intelligence technology and tool sets to the investment on the integrated sociomaterial capabilities of digital absorptive capacity and ethical knowledge curation. In other words, they will be able to roll out training initiatives to teach their employees how to integrate artificial intelligence with DAC. They can also stress on the importance of sound governance practices. This two-faceted strategy is effective in making AI-enabled innovations not only economical, but also socially accountable and sustainable. Originality/value Such research has the value of originality because (a) it conducts robust empirical evidence of the sociomateriality theory in terms of knowledge processes and AI (b) and translates the artificial intelligence ethics discussion into practice, branding the field of curating knowledge ethically (CKE) as a digital absorptive capacity (capability) (c). For managers, the study offers a two-way approach to implementing artificial intelligence, aiming to achieve both productivity and sustainability.

Purpose This study aims to investigate how knowledge recombination contributes to innovation during digital transformation, focusing on business method (BM) patents. It introduces the concepts of dominant and spark technologies to capture different types of cognitive proximity in the innovation process. The research aims to reveal how these distinct knowledge types influence the creation of BM innovations across regions. By mapping knowledge interactions in patent networks, the study provides new insights into the mechanisms of technological emergence and highlights their strategic relevance for knowledge-intensive organizations and regions navigating digital change. Design/methodology/approach Using patent data from the European Patent Office (1981–2015), the study constructs co-occurrence-based knowledge spaces to map technological proximity and recombination. Two types of technologies are defined: dominant (widely combinable) and spark (highly BM-specific), measured via weighted centrality in network analysis. Regional-level indicators are developed, and fixed-effects negative binomial regression models are employed to evaluate their impact on BM patent output. This approach integrates cognitive proximity theory and evolutionary economic geography into empirical knowledge mapping, enabling analysis of regional innovation through a knowledge management lens. Findings The analysis shows that both dominant and spark technologies significantly contribute to the creation of BM patents, though their effects vary by regional innovation capacity. Dominant technologies, broadly used across patenting activity, support general recombination, while spark technologies are tightly linked to BM-specific innovations. Notably, spark technologies exert greater influence in metropolitan regions with stronger innovation ecosystems. These findings highlight the differentiated roles of knowledge types in innovation and demonstrate that knowledge recombination dynamics are context-dependent. The study also confirms the value of cognitive proximity metrics in predicting innovation outputs. Originality/value This study extends knowledge management theory by operationalizing and empirically testing cognitive proximity through patent-based knowledge spaces. It introduces actionable metrics – dominant and spark technologies – that offer new ways to evaluate knowledge recombination potential. Unlike traditional studies focused solely on knowledge stock or diffusion, this work emphasizes structural relationships between knowledge domains and their implications for business innovation. The findings offer valuable guidance for firms, regions, and policymakers seeking to foster innovation in the digital economy by aligning knowledge strategies with technological convergence patterns and local knowledge structures.

Purpose This paper aims to investigate how product design can support the translation of knowledge across cultural contexts. Design/methodology/approach This study adopted an instrumental case study design to explore how knowledge can be translated through product design. It focuses on the case of Lucky Iron Life, an organization that produces a cost-effective and sustainable iron supplementation product. This was accomplished by analyzing archival records dating from 2011to 2022. Findings This paper outlined the links between knowledge management and design literature. Knowledge translation and design were established as fluid and user-driven processes, emphasizing the role of culture. In illustrating the development and implementation of Lucky Iron Life products, this study demonstrated how design with high cultural resonance can support the translation of knowledge. Originality/value This paper offers a theoretical contribution by further establishing design as a means of knowledge translation. It focuses primarily on the design of physical artifacts, which is an under-investigated medium. These findings can support designers’ potential to translate knowledge and promote user engagement across cultural contexts.

Purpose This study aims to systematically examine the impact of knowledge spillovers on Green Collaborative Innovation (GCI), and further analyze the mediating role played by industrial relocation within this process. Design/methodology/approach Using panel data from 284 Chinese cities (2010–2023), this study uses a two-way fixed effects model to assess the impact of knowledge spillovers on GCI, with subgroup and spatial Durbin models capturing heterogeneity and spatial effects. An industrial relocation index is constructed from firm registration data to examine the mediating role of industrial relocation. Findings The results demonstrate that knowledge spillovers significantly promote GCI and exhibit clear spatial spillover effects. The impact is stronger in eastern, non-resource-based and nontraditional industrial cities. Mechanism analysis further shows that industrial relocation acts as a significant mediator, with technology-intensive relocation exerting the strongest effect, followed by capital-intensive and then labor-intensive relocation. In addition, the mediating role is more evident in receiving cities than in sending cities. Originality/value This study highlights how knowledge spillovers drive green transformation by revealing a “knowledge–relocation–collaboration” transmission mechanism. It extends knowledge management theory to regional green innovation and industrial policy, offering fresh insights into optimizing knowledge allocation and enhancing regional green collaboration.