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<p>Generative artificial intelligence (GenAI) promises substantial productivity gains for organisations, yet unresolved questions about data management and privacy continue to shape managers’ and employees’ confidence. This study examines workplace adoption of GenAI and shows how trust, conditioned by perceptions of data-management integrity, information transparency, and privacy risk, influences acceptance. This mixed-method study tests, using a survey-based structural equation model plus interviews focused on managerial practices among daily GenAI practitioners, two core insights: (i) trust is the strongest predictor of intention to use GenAI, and (ii) trust depends chiefly on manager’s and employees’ belief that organisational data are handled reliably and objectively through management routines. Perceptions of transparency or privacy risk exert no direct influence on either trust or usage. Building on these results, the study delineates four managerial domains: data-management process, information transparency, privacy risk, and trust, alongside twenty future research questions designed to understand how GenAI is linked to managerial practices. For practice, the findings recommend monitoring, calibrated disclosure, and adaptive privacy protocols as concrete managerial levers to strengthen GenAI acceptance. The evidence highlights trustworthy data governance, not abstract explainability, as the foundation of sustainable GenAI adoption. The study also provides a roadmap of actionable management practices to guide its implementation in modern workplaces.</p>

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Generative artificial intelligence use in the workplace: implications for management practice

  • 期刊论文

  • 发表日期：

    2025年11月8日

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  • Álvaro Hernández-Tamurejo,
  • Rita Bužinskienė,
  • Belém Barbosa,
  • Astrida Miceikienė,
  • Jose Ramon Saura

摘要

Generative artificial intelligence (GenAI) promises substantial productivity gains for organisations, yet unresolved questions about data management and privacy continue to shape managers’ and employees’ confidence. This study examines workplace adoption of GenAI and shows how trust, conditioned by perceptions of data-management integrity, information transparency, and privacy risk, influences acceptance. This mixed-method study tests, using a survey-based structural equation model plus interviews focused on managerial practices among daily GenAI practitioners, two core insights: (i) trust is the strongest predictor of intention to use GenAI, and (ii) trust depends chiefly on manager’s and employees’ belief that organisational data are handled reliably and objectively through management routines. Perceptions of transparency or privacy risk exert no direct influence on either trust or usage. Building on these results, the study delineates four managerial domains: data-management process, information transparency, privacy risk, and trust, alongside twenty future research questions designed to understand how GenAI is linked to managerial practices. For practice, the findings recommend monitoring, calibrated disclosure, and adaptive privacy protocols as concrete managerial levers to strengthen GenAI acceptance. The evidence highlights trustworthy data governance, not abstract explainability, as the foundation of sustainable GenAI adoption. The study also provides a roadmap of actionable management practices to guide its implementation in modern workplaces.