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Applying artificial intelligence technique to predict knowledge hiding behavior

Author

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  • Abubakar, A. Mohammed
  • Behravesh, Elaheh
  • Rezapouraghdam, Hamed
  • Yildiz, Selim Baha

Abstract

Drawing on psychological ownership and social exchange theories, this study suggests theoretical arguments and empirical evidence for understanding employee reactions to distributive, procedural, and interactional (in)justice — three crucial bases of employees’ feelings of social self-worth. Utilizing field data and artificial intelligence technique, this paper reveals that distributive, procedural, and interactional (in)justice contribute to higher levels of knowledge hiding behavior among employees and that this impact is non-linear (asymmetric). By reuniting the discourses of organizational justice and knowledge management, this study indicates that feelings of psychological ownership of knowledge and the degree of social interaction are mechanisms that work with organizational (in)justice to influence knowledge hiding behavior. The current research may inform contemporary theories of business research and provide normative guidance for managers.

Suggested Citation

  • Abubakar, A. Mohammed & Behravesh, Elaheh & Rezapouraghdam, Hamed & Yildiz, Selim Baha, 2019. "Applying artificial intelligence technique to predict knowledge hiding behavior," International Journal of Information Management, Elsevier, vol. 49(C), pages 45-57.
    • Handle: RePEc:eee:ininma:v:49:y:2019:i:c:p:45-57
    DOI: 10.1016/j.ijinfomgt.2019.02.006
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    Citations

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    Cited by:

    1. Daxing Chen & Helian Xu & Guangya Zhou, 2024. "Has Artificial Intelligence Promoted Manufacturing Servitization: Evidence from Chinese Enterprises," Sustainability, MDPI, vol. 16(6), pages 1-19, March.
    2. Prikshat, Verma & Islam, Mohammad & Patel, Parth & Malik, Ashish & Budhwar, Pawan & Gupta, Suraksha, 2023. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    3. Gonçalves, Tiago & Curado, Carla & Oliveira, Mírian, 2023. "Clarifying knowledge withholding: A systematic literature review and future research agenda," Journal of Business Research, Elsevier, vol. 157(C).
    4. Ruparel, Namita & Bhardwaj, Seema & Seth, Himanshu & Choubisa, Rajneesh, 2023. "Systematic literature review of professional social media platforms: Development of a behavior adoption career development framework," Journal of Business Research, Elsevier, vol. 156(C).
    5. HUY Pham Quang & PHUC Vu Kien, 2024. "Optimization of Accounting information System in Public Sector for Sustainable Risk Management Under Big Data Analytics. Does forensic Accountants’ Skill Generate Differences?," Foundations of Management, Sciendo, vol. 16(1), pages 67-82.
    6. Kai Jia & Nan Zhang, 2022. "Categorization and eccentricity of AI risks: a comparative study of the global AI guidelines," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 59-71, March.
    7. Araz Zirar, 2023. "Can artificial intelligence’s limitations drive innovative work behaviour?," Review of Managerial Science, Springer, vol. 17(6), pages 2005-2034, August.
    8. Tan, Chunping & Zhang, Jiayan & Zhang, Yuqi, 2022. "The mechanism of team-member exchange on knowledge hiding under the background of “Guanxi”," Journal of Business Research, Elsevier, vol. 148(C), pages 304-314.
    9. Yijing Wang & Changfeng Wang, 2023. "The dark side of knowledge transfer: A visual analysis using VOSviewer," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, vol. 26(2), pages 122-139, June.

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