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

Regular Issues

• Social capital formation in Zhihu and Xiaohongshu: a comparative study of interest-oriented virtual communities  
  by Jinyan Liao, Sameer Kumar, Fumitaka Furuoka 
  Abstract: With the rise of digital platforms, virtual communities have become essential for knowledge sharing and social capital construction. This study explores social capital formation in interest-driven online platforms, focusing on Zhihu and Xiaohongshu. Using social capital theory, we examine trust mechanisms, reciprocal cooperation, and the evolution of interpersonal networks within these communities. The study finds that user interactions driven by shared interests not only build trust and belonging but also enhances knowledge dissemination and innovation. Effective incentive mechanisms and content management strategies boost participation and interaction quality, promoting social capital accumulation. The paper highlights the interplay between structural, relational, and cognitive social capital in fostering knowledge sharing and sustainable community development, providing valuable insights for optimising virtual community management. Additionally, the study identifies several emergent patterns that offer new perspectives on how micro-niche groups and weak ties contribute to social capital development.
  Keywords: virtual community; social capital; knowledge sharing; interest-driven community; community theory; social interaction.
  DOI: 10.1504/IJKMS.2026.10077485
   
  
• Digital technologies to offset the knowledge divide: a systematic literature review  
  by Chaimae Bouha, Lamiae Benhayoun 
  Abstract: Emerging technologies are increasingly pervasive across domains including knowledge management, where they enhance information processing and support strategies, but contribute to a persistent knowledge divide. This study conducts a systematic literature review spanning a decade (2014-2024) to examine how emerging technologies can mitigate this divide within organisational and social contexts, while addressing the challenges posed by the excessive digital-driven information overload. The findings highlight the contributions of artificial intelligence, big data analytics, social network platforms, and mobile applications for enhancing knowledge equity through adaptive learning and informed decision-making. However, literature lacks a thorough understanding of AI-driven information overload in terms of cognitive capacities and institutional readiness. This review calls for future research using longitudinal and large-scale studies to enhance theoretical and managerial understanding of how a multi-stakeholder approach integrating regulatory frameworks, adaptive interfaces, and inclusive educational policies bridges the digital divide while ensuring information remains accessible and manageable.
  Keywords: emerging technologies; digital transformation; artificial intelligence; AI; knowledge management; knowledge divide; digital divide; information overload; psychological impact; systematic literature review.
  DOI: 10.1504/IJKMS.2026.10077533
   
  

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

• Machine learning models for predictive monitoring of business process execution delays  
  by Walid Ben Fradj, Mohamed Turki, Faiez Gargouri 
  Abstract: Nowadays, organisations are increasingly aware of the importance of optimising the use of their knowledge resources and adopting a quality management model based on a process-centric approach. This approach requires a multidisciplinary approach that integrates the domains of knowledge management, business process management, and process mining. Thus, to enhance their performance and increase their responsiveness, organisations must identify, manage, and monitor all business processes (BPs) that may leverage crucial knowledge. It is imperative to implement a computerised system automating business processes to achieve these goals. In this context, we propose a new method for predicting the execution times of business processes, named BPETPM, based on the CRISP-DM approach. We employed machine learning techniques to exploit the execution data of a workflow engine. To demonstrate the relevance of this method, we developed an intelligent system for predicting BP execution times, called iBPMS4PET.
  Keywords: business process management; BPM; process mining; knowledge management; KM; machine learning.
  DOI: 10.1504/IJKMS.2026.10076676
   
  
• 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
   
  
• 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