International Journal of Knowledge Management Studies (7 papers in press)

Regular Issues

• Determinants of knowledge sharing and students performance among tertiary students using an integrative knowledge sharing framework  
  by Godfred Yaw Koi-Akrofi, Joyce Koi-Akrofi, Ebenezer Gaisie, Douglas Yeboah, Maame Ama Boadiwaa 
  Abstract: This study used an integrative knowledge sharing framework to examine determinants of knowledge sharing among tertiary students and its effect on academic performance. Data from 267 students in a Ghanaian public university were analysed with SMART-PLS. Results showed that only knowledge sharing efficacy significantly predicted Knowledge Sharing behaviour (T = 3.789, p = 0). Four constructs willingness to share (T = 2.87, p = 0.004), enjoyment in helping others (T = 3.288, p = 0.001), personal expectation and reward (T = 3.491, p = 0), and knowledge sharing efficacy (T = 3.377, p = 0.001) predicted intention to share knowledge. Intention (T = 2.757, p = 0.006) then predicted knowledge sharing, which strongly predicted students academic performance (T = 9.533, p = 0). The findings underscore efficacy, intrinsic motivation, and intention as critical for fostering knowledge sharing, thereby boosting academic performance. The study recommends supportive social and technological environments to enhance sharing, with future research exploring cross-cultural and multidisciplinary contexts.
  Keywords: knowledge; sharing; performance; framework; integrated.
  DOI: 10.1504/IJKMS.2025.10073680
   
  
• Knowledge creation, knowledge sharing, and competitive advantage in the telecoms industry: mediating role of knowledge application  
  by William Makumbe, Leon T.B. Jackson, Clement Zvobgo, Anatolia Tshanyengandje Shooya 
  Abstract: In the contemporary environment, organisations that acquire and apply knowledge effectively can achieve sustainable competitive advantage in the marketplace. Consequently, knowledge management has gained prominence, especially during the fourth industrial revolution, driven by knowledge application. However, due to the multidimensional nature of the knowledge management construct, there exists a need for empirical studies on this concept, particularly in Sub-Saharan Africa. Hence, this investigation studied the impact of knowledge creation and sharing on competitive advantage using knowledge creation as a mediating mechanism. Survey data, analysed using structural equation modelling, revealed that knowledge creation, knowledge sharing, and knowledge application significantly impacted competitive advantage. Whereas knowledge application partially mediated the relationship between knowledge sharing and competitive advantage, no mediation was found between knowledge creation and competitive advantage. The results of this study offer important insights to Zimbabwean telecoms managers.
  Keywords: knowledge sharing; knowledge creation; knowledge application; competitive advantage.
  DOI: 10.1504/IJKMS.2025.10074254
   
  
• Organisational memory in project-based firms: development of an ontology framework model  
  by O.B.O. Olayinka, T. Bouazza, B.M. Olayinka, T.E. Butt 
  Abstract: Organisational memory (OM) has been widely studied in functional organisations but remains underexplored in project-based organisations (PBOs), leading to missed opportunities for learning and improvement. This study develops an ontology framework model of OM management tailored to PBOs. Using a categorical literature review, case studies, and semi-structured interviews with PBO participants, the research identifies key factors influencing effective OM management. The findings establish that leadership, technological sophistication, and organisational structure and culture are among the crucial factors for effective OM management. It also confirms that OM relies on the combination of individual employees personal knowledge, experiences, and management styles. The framework can be used as a guideline for PBOs to preserve their OM from past projects and leverage it in future projects, thereby saving time, costs, energy, and other resources. The study also recommends future research to diversify and extrapolate the model for wider applications.
  Keywords: project-based organisations; PBOs; organisational memory systems; OMSs; collective memory; organisational learning; transactive memory systems; knowledge management; KMT.
  DOI: 10.1504/IJKMS.2025.10074827
   
  

Special Issue on: ICIKS-2023 Knowledge Management and Tacit Knowledge Facing Artificial Intelligence Emergence

• Rethinking knowledge management in an emerging AI landscape  
  by Naveed Ul Haq, Abdul Rashid Kausar 
  Abstract: In today’s context, rethinking knowledge management (KM) from the artificial intelligence (AI) perspective is necessary for organisations to gain a competitive advantage by adopting different innovative techniques and strategies. This conceptual study will discuss the dynamic interplay between KM and AI in modern organisational structures and processes. It explores how AI transforms traditional KM practices, focusing on AIs ability to automate knowledge discovery, enhance decision-making, and foster innovation and collaboration. The results show that integrating AI into KM is crucial to organisational efficiency, productivity, and competitiveness. It further highlights the challenges and opportunities of this integration, emphasising the importance of ethical considerations, data privacy, and user trust. Further, we examined the different case studies and real-world examples of organisations (IBM Watson, Microsoft SharePoint, SAP, Deloitte, and Siemens) that successfully implemented AI with KM systems. Finally, it proposes strategies for organisations to manage AI technologies within their KM frameworks.
  Keywords: knowledge management; KM; artificial intelligence; AI; AI landscape; AI-driven systems.
  DOI: 10.1504/IJKMS.2025.10071502
   
  
• Artificial intelligence method for extracting knowledge from security experts to assess SMEs information systems  
  by Ines Saad, Wafa Bouaynaya 
  Abstract: This research investigates the possibility of utilising the implicit and explicit knowledge of cybersecurity professionals in order to help small and medium-sized businesses (SMEs) in assessing the level of security that their information and knowledge systems possess. A dominance-based rough set approach serves as the foundation for the proposed strategy, which consists of two primary stages. In order to generate three ordered decision classes, the first phase requires the construction of a set of criteria and preference models, which are guided by seasoned security specialists. Validation of this preference model is performed with the help of test data during the second step. Forty-three small and medium-sized enterprises (SMEs) and 15 cybersecurity specialists participated in the testing of the method. By taking this method, firm managers are able to better anticipate cybersecurity risks, provide a comprehensive review of information system security, and reduce the likelihood of cyberattacks.
  Keywords: knowledge of security experts; knowledge classification; multicriteria classification; security of information systems; decision rules; service continuity plan; traceability; encryption; interoperability; availability; authentication; access authorisation.
  DOI: 10.1504/IJKMS.2025.10071299
   
  
• Knowledge-centric approaches in human resource management: leveraging clustering and deep learning  
  by Sumit Tripathi, Roma Tripathi 
  Abstract: This research tackles contemporary human resource management challenges using advanced analytics methodologies. Initially, workforce dynamics are analysed through clustering to segment employees based on attributes. Among several algorithms evaluated, including K-means, agglomerative clustering, spectral clustering, and Gaussian mixture models, K-means proves most effective, with a Silhouette Score of 0.874156 and a Davies-Bouldin score of 1.285476. The study then predicts future skill requirements using deep learning models, focusing on the dense neural network. The dense NN emerges as the top predictive model, with the lowest mean squared error of 4478.58, the lowest mean absolute error of 47.56, and the highest R2 score of 0.94. Additionally, feature importance analysis highlights the dense NN's ability to capture intricate relationships, aiding HR practitioners in understanding key predictive factors. This research equips HR professionals with critical insights for proactive talent management and workforce planning.
  Keywords: human resource management; HRM; clustering; employee skills; predictive analytics.
  DOI: 10.1504/IJKMS.2025.10071887
   
  
• Importance of tacit knowledge in online synchronous courses: case of higher education in France  
  by Inès Saad, Thierry Jaillet, Brice Mayag, Elsa Negre, Camille Rosenthal-Sabroux 
  Abstract: This study reveals the importance of tacit knowledge and suggests favouring teacher-student interactions in online synchronous courses in the case of higher education. Our empirical study surveyed 171 students who had been learning in online-synchronous mode in higher education in France since the COVID-19 pandemic. They were from six French higher schools and had backgrounds in either computer science or management science. We found that 57% of respondents preferred face-to-face learning versus 13% who preferred online learning. For respondents who preferred face-to-face, the syntactic analysis shows that this format allowed them to interact more easily with the teacher and their classmates.
  Keywords: online synchronous classroom; tacit knowledge; higher education; interactions; empirical study; student survey; face-to-face learning; online learning; information systems; learning preferences; France.
  DOI: 10.1504/IJKMS.2025.10072284