International Journal of Knowledge Management Studies (6 papers in press)

Regular Issues

• Exploring the synergy of green innovation, organisational culture and knowledge management practices: an empirical study on enhancing organisational performance  
  by Jianhua Zhang, Philip Adu Sarfo, Gideon Adjorlolo, Andrews Larbi, Ophelia Amankwah, Augustine Appiah, Joshua Kojo Bonzo 
  Abstract: This study investigates the impact of green innovation (GI), organisational culture (OC) and knowledge management activities (KMA) on organisational performance (OP) in small and medium-sized enterprises (SMEs), with an emphasis on sustainability and gaining a competitive advantage. Using a quantitative approach, data were collected from 576 SMEs in Ghana across diverse sectors, including food, clothing, and electronics. The data were analysed using partial least squares structural equation modelling (PLS-SEM) to explore the complex relationships among GI, OC, KMA and OP. The findings reveal that organisational culture plays a significant role in fostering green innovation and facilitating knowledge management, both of which contribute to enhanced organisational performance. This study presents a novel framework that connects green innovation, organisational culture, and knowledge management within the context of SMEs. The research provides practical strategies for managers to enhance sustainability, optimise performance, and strengthen competitive advantage in dynamic business environments.
  Keywords: green innovation; organisational culture; knowledge management; organisational performance.
  
  
• Fuzzy cognitive mapping-driven knowledge management based on 24Model for major accident prevention: a case study of oil and gas industry  
  by Wafa Boulagouas 
  Abstract: The oil and gas industry is prone to major hazard accidents, often triggered by human error and systemic failures. This study introduces a novel approach to major accident prevention through the development of a fuzzy cognitive mapping (FCM)-driven knowledge management (KM) framework based on the 24Model. The study aims to: 1) map critical accident causative factors using the 24Model; 2) develop and validate a KM framework integrating FCM for simulation-based analysis; 3) apply this framework to a real-world case in the oil and gas sector. The FCM-driven KM framework provides a systematic approach for identifying and analysing accident factors, offering decision-makers actionable insights to improve risk management, maintenance practices, and safety culture. Theoretically, this research extends existing models by integrating the 24Model with FCM. From a policy perspective, the study emphasises the importance of stricter regulations and enforcement to support a strong safety culture and reduce the likelihood of major accidents.
  Keywords: accident prevention; knowledge management; KM; fuzzy cognitive mapping; FCM; 24Model; oil and gas industry.
  DOI: 10.1504/IJKMS.2025.10072816
   
  

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