This study focusses on the role of trust in knowledge sharing within the context of virtual communities of practice. Trust is widely accepted as an important enabler of knowledge management (KM) processes. We conceptualise trust across three dimensions, namely: competence, integrity and benevolence; we test hypotheses as to the effect of these facets of trust on knowledge sharing by surveying an intra-organisational global virtual community of practitioners. The results indicate that all three dimensions of trust are positively related to knowledge-sharing behaviour. Trust based on the perceived integrity of the community was found to be the strongest predictor of knowledge-sharing behaviour. Our findings suggest that the dimensions of trust buttress each other; although they are theoretically distinct, they appear to be empirically inseparable. We propose that in order for knowledge sharing to be enabled, trust must concurrently exist in all three dimensions. Implication to organisations in their recruitment policy is to include competence, integrity and benevolence in their sought-for attributes of new employees. KM practitioners also have to encourage these attributes in existing employees, who are potential members of on-line communities of practice. Knowledge sharing itself was conceptualised with three components – quantity (frequency), quality (usefulness or value) and focus (the degree to which an individual feels that they engage in knowledge sharing). Of the three components, focus exhibits the most significant relationship with trust factors. This finding makes knowledge sharing less tangible than perhaps would be expected. It suggests that establishing whether knowledge has been shared is more than counting the frequency or trying to evaluate the usefulness of the shared knowledge. These aspects are important especially to management, but to the individual who shares knowledge, her feelings of having shared knowledge appear to be more important. With the current understanding that knowledge sharing is more of a human activity than technology, it is important that any information system should be assistive in boosting users' confidence that they are indeed sharing knowledge. If the systems do not re-enforce the users' knowledge-sharing orientation, knowledge sharing may be discouraged. Notwithstanding the point made about knowledge-sharing focus, it is necessary to take into consideration all the components of knowledge sharing to fully capture the concept. This was well indicated when the combined variable of all (rather than individual) knowledge-sharing items had the strongest correlation with trust factors.Department of Industrial and Systems Engineerin

Purpose: The purpose of this paper is to explore the relationship between big data and knowledge management (KM). Design/methodology/approach: The study adopts a qualitative research methodology and a case study approach was followed by conducting nine semi-structured interviews with open-ended and probing questions. Findings: Useful predictive knowledge can be generated through big data to help companies improve their KM capability and make effective decisions. Moreover, combination of tacit knowledge of relevant staff with explicit knowledge obtained from big data improvises the decision-making ability. Research limitations/implications: The focus of the study was on oil and gas sector, and, thus, the research results may lack generalizability. Originality/value: This paper fulfills an identified need of exploring the relationship between big data and KM which has not been discussed much in the literature.

Purpose – This paper aims to present research into the improvement of knowledge-intensive business processes. Design/methodology/approach – A literature review is conducted that indicates that a gap exists in the area of knowledge-based business process improvement (KBPI). Sir Karl Popper's theory of objective knowledge is used as a conceptual basis for the design of a business process improvement (BPI) framework. Case studies are conducted to evaluate and further evolve the improvement framework in two different organisations. Findings – Highlights the gap in the literature. Draws attention to the merits of KBPI. Reports on the design of an improvement framework for knowledge-intensive business processes, and on the lessons learned from the conducted case studies. Research limitations/implications – Practical and time constraints limit the scope of the case studies. General applicability can be inferred, but not tested, due to the small number of case studies. Practical implications – A new practical way to achieve performance improvement, that utilises structured tools on intangible organisational assets. The framework can be applied by organisations that run knowledge-intensive business processes. Originality/value – This paper addresses a gap in the area of KBPI. It combines concepts from business process management with a robust theory of knowledge to design a practical improvement framework. The paper also contains interesting argumentation supporting the use of Karl Popper's epistemology in BPI and knowledge management.

This study explored factors influencing ChatGPT adoption among higher education students in five Nordic countries (Sweden, Finland, Denmark, Norway, and Iceland) and the USA. The unified theory of acceptance and use of technology 2 (UTAUT2) framework was employed and extended to incorporate personal innovativeness. Data was collected from 586 students recruited through Prolific and analyzed using partial least squares structural equation modeling (PLS-SEM). The findings revealed varying patterns of relationships between different factors and behavioral intention in each region. In the Nordic countries, performance expectancy, hedonic motivation, and habit demonstrated positive relationships with behavioral intention. In the USA, the results revealed positive relationships between behavioral intention and performance expectancy, social influence, habit, and personal innovativeness. Performance expectancy emerged as the strongest predictor of behavioral intention in both regions. In both the Nordic countries and the USA, habit and behavioral intention emerged as the only predictors of ChatGPT use behavior. Behavioral intention demonstrated a marginally stronger influence on use behavior in both regions. These findings offer insights for educators and policymakers regarding AI integration in academic settings by highlighting common drivers and differences in AI adoption patterns.

Purpose: To provide a summary of the major trends in the evolution of knowledge management (KM) technologies in the last five years.Design/methodology/approach: Drawing from a range of literature published in the academic and industry arenas including the articles accepted in the special issue, the author also applied his own personal experience and practice knowledge in the field to summarize the three major trends in the use of KM technologies for the workplace and individual knowledge workers.Findings: First, KM is becoming more and more process‐centric and relevant technologies are gradually being aligned to support process‐based KM activities. Second, there is the emergence of personal networks and applications. Third, knowledge sharing and capturing are becoming more instantaneous (i.e. on‐demand and just‐in‐time).Practical implications: KM is becoming more and more just‐in‐time. Large‐scale KM programmes still prevail but, in future, the technical infrastructure and information content of these programmes also need to support ad hoc, spontaneous but intensive intra‐ and inter‐organizational collaborations.Originality/value: While most articles on KM technologies tend to focus on individual technique(s)/system(s), this paper provides a succinct and high‐level summary of the evolution of KM technologies from a commercial and practical perspective.