Towards a Conceptual Model of AI-Mediated Knowledge Sharing Exchange of HRM Practices: Antecedents and Consequences

Ashish Malik, Tuyet-Mai Nguyen, Pawan Budhwar

Research output: Contribution to journalArticlepeer-review


Increased adoption of artificial intelligence (AI) enabled human resource management (HRM) has moved from developing functional applications to actively engaging employees in their use of such applications. Employees share explicit and tacit knowledge using an AI-mediated knowledge sharing (AI-MKS) exchange through HRM-focused AI applications. However, there exists limited understanding of the antecedents and outcomes of such an AI-MKS exchange. Research on this topic is timely as emerging research points to mixed (positive and negative) effects of such technological adoption. Therefore, this article employs a dual review strategy, wherein a narrative review follows a systematic literature review to develop a theoretical model for understanding the causes and consequences of an AI-mediated knowledge-sharing exchange using AI-enabled HRM applications. We integrate the theoretical literature on knowledge sharing, HRM, and AI-mediated social exchange for building our theoretical model. Specifically, the systematic literature review points to an individual, social, technological, and organizational level antecedents of knowledge sharing, which interact with various types of an AI-MKS social exchange to deliver high levels of personalization, hyperpersonalization, and individualization for employees as well as deliver HR effectiveness.
Original languageEnglish
Number of pages30
JournalIEEE Transactions on Engineering Management
Early online date25 Apr 2022
Publication statusE-pub ahead of print - 25 Apr 2022


  • Anxiety disorders
  • Artificial intelligence
  • Business
  • Collaboration
  • ISTO model
  • Knowledge engineering
  • Productivity
  • Systematics
  • human resource management
  • human-machine exchange
  • knowledge sharing
  • social exchange


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