International Journal of Knowledge Management Studies (4 papers in press)

Regular Issues

• Relational spaces for knowledge management in multinational companies  
  by Jessica Geraldo Schwengber 
  Abstract: The purpose of this paper is to elaborate on the concept of relational spaces for the management of local knowledge in multinational companies (MNCs). The elaboration aims to contribute to the existing knowledge management (KM) literature by providing a thin-thick knowledge framework based on the concepts of communities of practice (CoP), socio-categorisation theory, and shared intentionality theory. The paper combines theory building and case study methodology. Theory building refers to the elaboration of the knowledge framework. The case study methodology aims to empirically explore the framework. The case study provides concrete examples of how thin concepts (such as organisational strategy) are thickened at the local level and how best practices shared in the global community should then be thickened again.
  Keywords: knowledge management; multinational companies; MNCs; relational spaces; communities of practice; CoP; knowledge economy; knowledge framework; case study; relational economics.
  DOI: 10.1504/IJKMS.2025.10071521
   
  

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