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Employee Comfort with AI-Driven Algorithmic Decision-Making: Evidence from the GCC and Lebanon

Author

Listed:
  • Soha El Achi

    (Faculty of Business Studies, Arab Open University (AOU), Beirut 2058 4518, Lebanon)

  • Dani Aoun

    (Faculty of Business Studies, Arab Open University (AOU), Beirut 2058 4518, Lebanon)

  • Wael Lahad

    (Faculty of Business Studies, Arab Open University (AOU), Beirut 2058 4518, Lebanon)

  • Nada Jabbour Al Maalouf

    (CIRAME Research Center, Business School, Holy Spirit University of Kaslik, Jounieh P.O. Box 446, Lebanon)

Abstract

In this digital era, many companies are integrating new solutions involving Artificial Intelligence (AI)-based automation systems to optimize processes, reach higher efficiency, and help them with decision-making. While implementing these changes, various challenges may arise, including resistance to AI integration from employees. This study examines how employees’ perceived benefits, concerns, and trust regarding AI-driven algorithmic decision-making influence their comfort with AI-driven algorithmic decision-making in the workplace. This study employed a quantitative method by surveying employees in the Gulf Cooperation Council (GCC) and Lebanon with a final sample size of 388 participants. The results demonstrate that employees are more likely to feel comfortable with AI-driven algorithmic decision-making in the workplace if they believe AI will increase efficiency, promote fairness, and decrease errors. Unexpectedly, employee concerns were positively associated with comfort, suggesting an adaptive response to AI adoption. Lastly, comfort with AI-driven algorithmic decision-making is positively correlated with greater levels of trust in AI systems. These findings provide actionable guidance to organizations, underscoring the need to communicate clearly about AI’s role, address employees’ concerns through transparency and human oversight, and invest in training and reskilling initiatives that build trust and foster responsible, employee-centered adoption of AI.

Suggested Citation

  • Soha El Achi & Dani Aoun & Wael Lahad & Nada Jabbour Al Maalouf, 2026. "Employee Comfort with AI-Driven Algorithmic Decision-Making: Evidence from the GCC and Lebanon," Administrative Sciences, MDPI, vol. 16(1), pages 1-21, January.
  • Handle: RePEc:gam:jadmsc:v:16:y:2026:i:1:p:49-:d:1843095
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