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

Regular Issues

• 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
   
  
• Importance-performance matrix analysis of the antecedents of knowledge hiding behaviour in the Arab context: the case of Northern Technical University in Iraq  
  by Hatem Ali Abdullah, Shaima Hassan Ahmed 
  Abstract: The study aims to understand and analyse the antecedents of knowledge hiding behaviour and their impact on the formation of this behaviour through importance-performance map analysis within the Iraqi context, specifically at the Northern Technical University. The descriptive-analytical approach was adopted by reviewing relevant literature to identify the most commonly agreed-upon antecedents, which were then examined based on individual, interpersonal, and organisational antecedents. The research population consisted of 1,800 individuals, from which a non-random sample of 316 participants was selected. Data were collected through a questionnaire developed based on a comprehensive scientific review. The study found that knowledge hiding behaviour at the Northern Technical University primarily stems from organisational antecedents, which contribute to the formation of an unhealthy work environment. This, in turn, leads to the development of individual and interpersonal antecedents, all of which collectively foster KHB. Therefore, all antecedents play a significant role in shaping KHB in the Iraqi context. This study represents an emerging research direction in the Arab world - particularly in Iraq - aimed at exploring the antecedents of knowledge hiding in organisations.
  Keywords: knowledge hiding; knowledge hiding behaviour; individual antecedents; interpersonal antecedents; organisational antecedents; Northern Technical University; Iraq.
  DOI: 10.1504/IJKMS.2026.10076527
   
  
• How will knowledge sharing and quality management in supply chains affect firm performance? Evidence from the USA  
  by Iwan Koswara, Malia Faasolo 
  Abstract: This academic work delves into how quality management practices (QMP) in supply chains augment firm performance through knowledge sharing behaviour (KSB) and quality management capacity (QMC). The data was garnered from 276 small and medium-sized enterprises (SMEs) operating in high-pollution industries, including paper, fashion, and food manufacturing, across the USA. Draw on structural equation modelling, QMP was disclosed to significantly amends innovative performance (through KSB and QMC) and operational performance (through KSB). Nevertheless, the path conjoining QMP to operational performance via QMC was statistically inconsequential, exposing QMCs stronger function in driving innovations over routine operations. The outcomes deliver empirical evidence for SMEs in polluting sectors to deliberately allocate resources: KSB advances the entire performance elements, while QMC investments generate greater returns for innovations. These novel comprehensions help manufacturers balance operational efficiency with sustainability-driven innovations.
  Keywords: knowledge sharing; firm performance; quality management; supply chain; small and medium-sized enterprises; SMEs; USA.
  
  

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