International Journal of Knowledge Management Studies (5 papers in press)

Regular Issues

• What do we actually hide: conceptual and measurement challenges of knowledge hiding research  
  by Kaiyu Yang, Vincent Ribiere, Alex Bennet 
  Abstract: Since the knowledge hiding concept was identified a decade ago, significant progress has been made in deciphering the antecedents and consequences of this detrimental behaviour of individuals in organizations. Previous knowledge hiding studies focused very little attention on what type of knowledge (knowledge (informing), (codified/explicit) knowledge, or tacit knowledge) was hidden when investigating the claimed concept. Starting with a systematic literature review that identifies “non-sharing” publications followed by a thorough examination of existing empirical research on knowledge hiding, this conceptual paper revisits the concept of “knowledge” and its nature. It concludes that “knowledge hiding” has not yet been adequately investigated and that the knowledge hiding concept has often been confused with information hiding. Therefore, the authors call for a broadened view to encompass different knowledge types in future research and for further development of more reliable and valid measurement instruments with defined context to sufficiently measure knowledge hiding.
  Keywords: knowledge hiding; information hiding; non-sharing behaviour; systematic literature review; artefact; codified knowledge; knowledge management; KM.
  
  
• Pitfalls in effective knowledge management: insights from an international information technology organisation  
  by Kalle Koivisto, Toni Taipalus 
  Abstract: Knowledge is vital for organisations, but effective knowledge management remains a challenge. Despite the acknowledged importance of knowledge sharing and management, many organisations struggle to harness their knowledge effectively, leading to cooperation issues and the loss of valuable insights when employees leave. This study, based on interviews with 50 employees in a large international IT company, aims to identify and address hindering factors that impede knowledge sharing and management. It reveals a significant gap between the perceived importance of knowledge management and its practical implementation. The study identifies 44 hindering factors in various categories. Recommendations for mitigating these obstacles include offering training and guidelines to improve employee actions. These findings benefit knowledge-intensive organisations by informing strategies to enhance performance.
  Keywords: knowledge management; software development; hindering factors; knowledge sharing; information systems; challenge.
  
  
• The use of social media for knowledge sharing in businesses: mediating effect of market orientation and user-generated content  
  by Piyumi Seneviratne, Ravindra Hewa Kuruppuge, Hotniar Siringoringo 
  Abstract: The key objective of this research is to identify the behaviour of market orientation and user generated content for the relationship between features of social media and knowledge sharing. Data was collected using questionnaire five-point Likert scale. The Stata 14 package was utilised for the purpose of data analysis to determine the mediating effect of market orientation and user generated content variables. The medsem package was utilised to determine the indirect effect of mediating variables. The results of the study emphasise that market orientation does not have a significant mediating effect for the correlation between features of social media and knowledge sharing. User-generated content has a strong mediating effect for the correlation between features of social media and knowledge sharing. The user-generated content is more influential for the adaptation of efficient communication to reach maximum knowledge sharing. More knowledge sharing is possible when more user-generated content is created for the optimisation of businesses.
  Keywords: knowledge sharing; social media; user-generated content; market orientation.
  
  
• Conceptual study on e-banking systems and customer satisfaction using deep learning and blockchain  
  by Sharmi Thambirajan, Kinslin Devaraj 
  Abstract: The rise of digital payments enhances global internet and mobile usage. However, there are still issues with customer satisfaction in mobile e-banking. This study examines how mobile banking service quality impacts customer satisfaction, detects hackers, and offers solutions for improvement through blockchain integration. This study compares artificial neural network performance with ML models like naive Bayes and XGBoost. The validated data is first sent to cloud for verification, then securely stored on blockchain to protect customer information. The study uses ANN, a DL model to reduce hacking and ensure secure transactions for enhanced security. The proposed approach is implemented using Python platform and Ethereum tool. The study shows that the ANN model outperforms the ML models in terms of security, achieving an accuracy rate of 99.44%, making the proposed model ideal for e-banking applications. This approach not only enhances security against hacking but also builds customer trust and satisfaction.
  Keywords: artificial neural networks; ANNs; blockchain; customer satisfaction; deep learning; DL; e-banking; machine learning; ML.
  
  

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.