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Building data-driven dynamic capabilities to arrest knowledge hiding: A knowledge management perspective

Author

Listed:
  • Chen, Yantai
  • Luo, Haibei
  • Chen, Jin
  • Guo, Yanlin

Abstract

In this study, the authors have conducted a multiple-case study on four Chinese enterprises in different industries, to explore how to build data-driven dynamic capabilities from the perspective of knowledge management, with a focus on how to arrest knowledge hiding. This research constructs a comprehensive theoretical framework of “data-driven context – dynamic capabilities – knowledge hiding,” proposing three dimensions of “institutional environment – organizational innovation – executive/individual cognition” on building dynamic capabilities, with original contributions on expanding and enriching knowledge hiding theory and dynamic capabilities theory in the data-driven context. As for practical implications, this research provides guidance on how to build dynamic capabilities to arrest or mitigate knowledge hiding for executives or individuals in the workplace, and gives enlightenment to how firms in emerging economies can construct dynamic capabilities in the data-driven context.

Suggested Citation

  • Chen, Yantai & Luo, Haibei & Chen, Jin & Guo, Yanlin, 2022. "Building data-driven dynamic capabilities to arrest knowledge hiding: A knowledge management perspective," Journal of Business Research, Elsevier, vol. 139(C), pages 1138-1154.
    • Handle: RePEc:eee:jbrese:v:139:y:2022:i:c:p:1138-1154
    DOI: 10.1016/j.jbusres.2021.10.050
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    6. Shih-Chin Tan & Jia-Jeng Hou & Ai-Feng Hsu & Chiu-Chi Wei & Shun-Chin Lee, 2023. "Model for Creating New Knowledge under Budget Constraints," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 13(1), pages 1-5.
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    11. Liu, Biao & Li, Yanfeng & Liu, Jipeng & Hou, Yufei, 2024. "Does urban innovation policy accelerate the digital transformation of enterprises? Evidence based on the innovative City pilot policy," China Economic Review, Elsevier, vol. 85(C).
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