Purpose Generative artificial intelligence (GenAI) is transforming management practices, enabling the free flow of knowledge and enhancing learning ecosystems from which education branches. While it brings many opportunities, it also causes some problems. Those problems are serious but small in scale. This study aims to examine how management educators perceive, use and adapt GenAI tools for their instructional and evaluative activities. Design/methodology/approach To gather data, the researchers did 40 interviews with faculty members from different universities. The researchers used a range of theories in their study. Specifically, they used Gioia’s approach along with the knowledge management theory, unified theory of acceptance and use of technology 2, the diffusion of innovations social cognitive theory, as well as the theory of activities. Findings Results paint a storyline. Some educators view GenAI as a game-changer and are finding ways to enhance student interaction while reducing the time spent creating learning materials. They highlight issues such as policies, ethics, over-reliance and threats to academic integrity. In essence, teachers are far from passive adopters. They also negotiate on mechanisms and approaches that are context-sensitive, assessing costs, benefits and the risks involved. Practical implications To support the responsible use of GenAI, regulatory frameworks, adequate training protocols and effective monitoring systems are necessary. This research shows how a teacher’s voice can help higher education institutions to use AI to better effect without losing their standards and values. Originality/value The research incorporates ideas from multiple frameworks that help clarify the enablers and constraints of GenAI adoption. This helps both researchers and practitioners in the field.

Purpose Drawing upon the resource-based view and dynamic capabilities theory, this study aims to explore whether artificial intelligence (AI) accelerates firm innovation and, if so, how it accelerates innovation by reconfiguring resources across different stages of the innovation process. Design/methodology/approach This study combines textual analysis with AI-related assets to evaluate the AI variable, and measures firm innovation speed using the annual growth rate of patent applications. Furthermore, utilizing a sample of Chinese A-share listed firms from 2010 to 2023, this study performs empirical analysis through fixed effect models. Findings The results indicate that AI significantly accelerates firm innovation. Mechanism analysis reveals that AI accelerates firm innovation through promoting knowledge coupling (KC), optimizing the structure of human capital (HCS) and reducing asset specificity (AS). Heterogeneity analysis shows that the accelerating effect is stronger in firms operating in highly competitive markets, as well as in non-state-owned or technology-intensive firms. Originality/value The impact of AI on firm innovation speed has rarely been explored in the extant research. This study investigates whether and how AI accelerates firm innovation, thereby enriching the existing literature on AI’s impact on firm innovation. It also provides actionable insights for policymakers and managers seeking to expedite the innovation process.

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 Knowledge sharing is important for small and medium-sized businesses (SMEs) to stay alive and competitive, especially in resource-constrained and hierarchical settings. This study aims to examine how abusive supervision impairs knowledge sharing in SMEs and investigates whether employees’ learning orientation can mitigate this negative effect. Drawing on social exchange theory, conservation of resources theory and and cognitive appraisal theory, the study explores the mediating role of emotional exhaustion and the moderating effect of learning orientation. Design/methodology/approach Data were collected from 316 employees working in manufacturing SMEs in Türkiye.This study employed a moderated mediation analysis using covariance-based structural equation modeling via SPSS AMOS 29 to test the hypothesized relationships among abusive supervision, emotional exhaustion, learning orientation and knowledge sharing. Findings The results reveal that abusive supervision negatively predicts knowledge sharing. Emotional exhaustion mediates this relationship, indicating that employees under abusive supervision experience higher emotional exhaustion, which in turn hinders their willingness to share knowledge. Furthermore, learning orientation moderates the relationship between abusive supervision and emotional exhaustion, such that the positive association is weaker for employees with higher learning orientation. A moderated mediation analysis confirms that learning orientation buffers the indirect negative impact of abusive supervision on knowledge sharing via emotional exhaustion. Practical implications Managers in SMEs should foster a culture that supports individual learning orientation to safeguard employees from the detrimental effects of abusive supervision. Training programs and human resource policies that promote resilience and continuous learning may help sustain knowledge-sharing behaviors under adverse supervisory conditions. Originality/value This study contributes by extending social exchange theory to show how negative reciprocity undermines knowledge sharing, advancing conservation of resources theory by demonstrating that resource depletion is especially critical in resource-scarce SMEs, and enriching cognitive appraisal theory and learning orientation research by identifying learning orientation as a resilience resource that reframes abuse as a challenge rather than a threat. Contextually, it provides unique insights into Turkish manufacturing SMEs, where knowledge sharing is both essential and fragile under resource constraints.

Purpose The purpose of this study is to explore how SRDI (an abbreviation for “Specialized,” “Refined,” “Distinctive” and “Innovative”) small and medium-sized enterprises (SMEs) enhance digital innovation performance (DIP) via digital orientation (DO) from a knowledge creation perspective, by examining the mediating roles of knowledge exchange (KE) and knowledge integration (KI) and the moderating effect of technological turbulence (TT). Design/methodology/approach This study conducted a questionnaire survey of 244 SRDI SMEs in China. The collected data were analyzed using SPSS and AMOS. Findings This study found that DO has a positive impact on DIP; KE and KI play a mediating role in the relationship between DO and DIP; and TT positively moderates the relationship between the knowledge creation process (KE and KI) and DIP. Originality/value This study makes several important contributions. First, it enriches the literature on DIP antecedents by focusing on SRDI SMEs, an important yet under explored context. Second, it introduces knowledge creation theory into DIP research, thereby extending its theoretical perspective in the digital intelligence era. Third, it opens the black box between DO and DIP by uncovering the underlying knowledge creation mechanisms. Fourth, it further clarifies the boundary conditions under which the knowledge creation process influences DIP.

Purpose This study aims to propose a new approach to enterprise architecture (EA) that integrates artificial intelligence (AI) and knowledge management (KM) to shift from static models to dynamic and reflexive systems of organizational knowledge. Design/methodology/approach Building on insights from EA, KM, AI and infrastructure, this study developed a knowledge architecture that integrates four interconnected layers: tacit, explicit, behavioral and cognitive. After analysis, these layers presented four evolutionary paths, namely, externalization, combination, validation and internalization. Findings showed that AI technologies, such as large language models, search-assisted generation and semantic graphs, mediate and structure knowledge flow. And two concrete examples are presented here as evidence. The first is a conceptual model that positions EA as a means for KM, and the second is a design science artifact that demonstrates AI-enabled observability across system layers. Findings By accounting for epistemic observability, this study realizes the ability of organizations to rely on AI to reveal, verify and exploit architectural knowledge, so as to interact with system behaviors and artifacts in real time. The findings show that EA can be reframed as a flexible, reflexive system that fosters continuous learning, adaptability and better decision-making in dynamic environments. Practical implications The proposed model and tools enhance organizations’ capacity for knowledge sharing, decision support and adaptive governance. They also provide practical avenues for applying AI technologies in EA and KM to strengthen digital infrastructures. Originality/value This study redefines EA as a dynamic knowledge system, opening up new avenues for research in information systems, hybrid thinking and digital governance. It also shows AI’s potential to expand EA’s scope and impact.

Purpose This study aims to address the challenge of managing new knowledge created by interactions between artificial intelligence (AI) and human intelligence (HI), and investigate how explicit knowledge processed by AI and tacit knowledge processed by HI are integrated to comprehensively enhance viability for dairy products manufacturing (DPM). Design/methodology/approach A novel hybrid approach integrating Gaussian mixture model, multi-attribute group decision-making and graphical evaluation and review technique has been adopted to investigate the role of AI–HI interaction in DPM viability knowledge management. Findings This study obtains the following research findings: the Gaussian mixture model can examine the internal and external real-time data and effectively process the explicit knowledge regarding disruption risk. The multi-attribute group decision-making can gather the collaborative intelligence of human expertise and effectively process the tacit knowledge regarding anti-risk ability. The graphical evaluation and review technique can enable the whole DPM process to take the interacting role of AI and HI for enhancing its viability. Practical implications The findings guide governments and enterprises’ managers to foster AI investment, expert spatio-temporal collaboration and knowledge management resources sharing and seek technological supports from our hybrid models to manage new knowledge created by interactions between AI and HI. Originality/value To the best of the authors’ knowledge, this study is one of the very first to investigate the role of AI–HI interaction in the field of DPM and viability knowledge management, and has significant importance in contributing to the theoretical development and methodological innovation of these two fields.

Purpose This study aims to explore how the Human Development Index (HDI) is associated with students’ perceived academic, personal and skill-development outcomes related to the integration of generative artificial intelligence, particularly ChatGPT, into higher education. From a knowledge management perspective, the research examines adaptive use of AI tools, structuring of information and support of autonomous learning in countries with varying development. Design/methodology/approach The study draws on 11,910 valid responses from the 2024 Global ChatGPT student survey, covering 58 countries. Based on 33 Likert-scale items, three reflective constructs were identified. To explore the relationships between HDI, usage intensity and perceived impacts, the analysis combined descriptive statistics, K-means clustering and a partial least squares structural equation modeling (PLS-SEM) mediation model. Findings The regression analysis showed a weak but statistically significant negative correlation between HDI and perceived impacts: students from lower-HDI countries tended to view ChatGPT’s impacts more positively. The PLS-SEM results indicated that higher national development is associated with lower perceived academic, developmental and skill-related benefits. This relationship appears both direct and indirect, as students in more developed countries report using ChatGPT less frequently and less creatively for academic purposes. Practical implications The findings highlight the need for context-sensitive, pedagogically grounded artificial intelligence strategies, particularly in highly developed countries and in the support of students from disadvantaged backgrounds. Originality/value This study is among the first to examine how national development levels shape perceived ChatGPT impacts in higher education. By combining HDI, cluster analysis and mediation modeling, it offers a novel perspective on digital inequality.

Purpose Artificial intelligence (AI) has rapidly evolved from a conceptual idea to an integral part of human life, transforming the way people work, communicate and navigate the world. Following the technological advancement, a symbiotic relationship has emerged between AI and human intelligence (AI–HI), affecting the development of the knowledge ecosystem knowledge ecosystem (KE). Yet, as the underlying effect mechanism is still unclear, this research is thus keen to bridge the knowledge gap. Design/methodology/approach To deliver the research purpose, an interpretative and qualitative methodology is planned, in which a systematic literature review is used for data analysis. The authors have gathered literature of AI–HI, KE and cognate themes from the renowned database portals, including ProQuest, JSTOR and Google Scholar. Strategies to improve research analytic rigor are arranged, and ethical guidelines are also applied. Findings Research findings are meaningful in three ways: First, the authors have clarified how AI enhances HI by rapidly processing vast amounts of data, identifying patterns and generating insights that would take humans significantly longer to uncover. Second, the findings have demonstrated that HI remains essential for interpreting AI-generated outputs, ensuring ethical considerations and applying contextual knowledge that machines lack, fostering a balanced knowledge ecosystem. Third, the synergy between AI and HI leads to more innovative problem-solving, interdisciplinary research advancements and accelerated scientific discoveries, ultimately transforming the landscape of knowledge creation. Practical implications Several practical implications arise from the integration of AI and HI for the advancement of KE for institutional decision-making. Organizations and research institutions can implement AI–HI-driven decision support systems, wherein AI generates data-driven recommendations, while human expertise ensures critical evaluation, ethical considerations and contextual relevance in developing KE and in final decision-making. Across diverse disciplines, researchers can integrate AI models with human expertise to address complex problems, for instance, using AI in humanities research or applying machine learning techniques in medical diagnostics, with human oversight for validation. To maintain the integrity of the knowledge ecosystem, institutions should develop robust frameworks in which AI-generated insights undergo rigorous scrutiny by human experts, ensuring fairness, transparency and alignment with ethical standards. Originality/value The manuscript has advanced the KE knowledge by clarifying the symbiotic roles of AI and HI, such as how AI augments human creativity, intuition and strategic thinking while humans guide AI’s analytical precision and computational strength. Based on research findings, the authors develop ethical guidelines to ensure that AI contributions in KE align with human-centered knowledge goals, emphasizing AI as a complement to human creativity rather than a replacement.