The Dark Side of AI-Augmented Leadership: Authoritarian Leadership as A Boundary Condition

Purpose
This study aims to investigate the influence of AI-augmented leadership on employee well-being in the Middle East and North Africa (MENA). It examine the moderating effect of authoritarian leadership styles.
Design/methodology/approach
A quantitative study was conducted using data collected from a survey of 104 professionals in Tunisia, Egypt, and Saudi Arabia. This research applied the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique to test the proposed conceptual framework.
Findings
The results indicate that AI-augmented leadership significantly improves employee well-being. However, moderation analysis reveals that authoritarian leadership weakens this positive relationship. Specifically, when authoritarian leadership dominates, the stress-reducing benefits of using AI are considerably diminished.
This highlights the impact of cultural leadership norms on the effectiveness of AI-based management approaches.

AI-augmented leadership, employee well-being, authoritarian leadership, MENA region

1. Bakker, A. B., & Oerlemans, W. G. M. (2011). Subjective well-being in organizations. In K. S. Cameron & G. M. Spreitzer (Eds.), The Oxford Handbook of Positive Organizational Scholarship (pp. 178–189). Oxford University Press. [Google Scholar] [Crossref]

2. Bakker, A. B., & Oerlemans, W. G. M. (2012). Subjective well-being in organizations. SA Journal of Industrial Psychology, 38(2), 1–9. https://doi.org/10.4102/sajip.v38i2.1011 [Google Scholar] [Crossref]

3. Bakker, A.B. and Demerouti, E. (2017), "Job demands–resources theory: taking stock and looking forward", Journal of Occupational Health Psychology, Vol. 22 No. 3, pp. 273–285. https://doi.org/10.1037/ocp0000056 [Google Scholar] [Crossref]

4. Brough, P., Dollard, M.F. and Tuckey, M.R. (2023), "Wellbeing, stress and burnout in the era of AI and automation", International Journal of Environmental Research and Public Health, Vol. 20 No. 5, Article 3931. https://doi.org/10.3390/ijerph20053931 [Google Scholar] [Crossref]

5. Cheng, B.S., Chou, L.F., Wu, T.Y., Huang, M.P. and Farh, J.L. (2004), "Paternalistic leadership and subordinate responses: establishing a leadership model in Chinese organizations", Asian Journal of Social Psychology, Vol. 7 No. 1, pp. 89–117. https://doi.org/10.1111/j.1467-839X.2004.00137.x [Google Scholar] [Crossref]

6. Colquitt, J.A., Baer, M.D., Long, D.M. and Halvorsen-Ganepola, M.D.K. (2023), "Measuring organizational justice: where we’ve been, where we are, and where we might go", Journal of Applied Psychology, Vol. 108 No. 1, pp. 1–25. https://doi.org/10.1037/apl0001042 [Google Scholar] [Crossref]

7. Data & Policy. (2024). Exploring AI governance in the Middle East and North Africa (MENA) region: Gaps, efforts, and initiatives. Cambridge University Press. https://doi.org/10.1017/dap.2024.xx [Google Scholar] [Crossref]

8. Dellermann, D., Reck, F., & Lipusch, N. (2022). Augmented leadership: How AI is reshaping leadership roles. California Management Review, 65(1), 66–91. [Google Scholar] [Crossref]

9. Dellermann, D., Reck, F., and Lipusch, N. (2022), "Leadership by artificial intelligence: Conceptual foundations and research agenda for AI-augmented leadership", Journal of Leadership and Organizational Studies, Vol. 29 No. 2, pp. 140–158. https://doi.org/10.1177/15480518221075346 [Google Scholar] [Crossref]

10. Detert, J.R. and Treviño, L.K. (2023), "Voice and silence in organizations: a review and future directions", Academy of Management Annals, Vol. 17 No. 1, pp. 143–174. https://doi.org/10.5465/annals.2021.0016 [Google Scholar] [Crossref]

11. Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125(2), 276–302. https://doi.org/10.1037/0033-2909.125.2.276 [Google Scholar] [Crossref]

12. Dwivedi, Y. K., et al. (2023). Artificial intelligence (AI): Multidisciplinary perspectives. International Journal of Information Management, 71, 102642. [Google Scholar] [Crossref]

13. Dwivedi, Y.K., Hughes, D.L., Baabdullah, A.M., Ribeiro-Navarrete, S., Dennehy, D., Giannakis, M., Buhalis, D., Wamba, S.F., and Sharma, S.K. (2023), "Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy", International Journal of Information Management, Vol. 71, Article 102660. https://doi.org/10.1016/j.ijinfomgt.2022.102660 [Google Scholar] [Crossref]

14. e& & IBM. (2024, April 18). The MENA region’s opportunity to lead AI transformation. Media Avataar Middle East. https://mediaavataarme.com/news/tech-and-start-up/26589/e-and-ibm-study-focuses-on-menas-opportunity-to-lead-ai-transformation [Google Scholar] [Crossref]

15. George, G., Merrill, R. K., & Schillebeeckx, S. J. (2022). Digital transformation and purpose. Journal of Management Studies, 59(1), 1–14. [Google Scholar] [Crossref]

16. George, G., Merrill, R.K. and Schillebeeckx, S.J.D. (2022), "Digital transformation, innovation management, and dynamic capabilities: Building organizational resilience", Technovation, Vol. 112, Article 102414. https://doi.org/10.1016/j.technovation.2021.102414 [Google Scholar] [Crossref]

17. Glikson, E. and Woolley, A.W. (2023), "Human trust in artificial intelligence: Review of empirical research", Academy of Management Annals, Vol. 17 No. 1, pp. 1–33. https://doi.org/10.5465/annals.2020.0053 [Google Scholar] [Crossref]

18. Hair, J.F., Howard, M.C. and Nitzl, C. (2023), "Assessing measurement model quality in PLS-SEM using confirmatory composite analysis", Journal of Business Research, Vol. 155, pp. 113–122. https://doi.org/10.1016/j.jbusres.2022.11.014 [Google Scholar] [Crossref]

19. Harms, P.D., Wood, D., Landay, K., Lester, P.B. and Lester, G.V. (2022), "Autocratic leadership: new theoretical insights for a classic leadership style", Academy of Management Perspectives, Vol. 36 No. 4, pp. 534–552. https://doi.org/10.5465/amp.2019.0107 [Google Scholar] [Crossref]

20. Jussupow, E., Heinzl, A. and Spohrer, K. (2023), "Human–AI collaboration in management: a systematic review and research agenda", Journal of Management Information Systems, Vol. 40 No. 1, pp. 185–223. https://doi.org/10.1080/07421222.2022.2159330 [Google Scholar] [Crossref]

21. Jussupow, E., Heinzl, A., & Spohrer, K. (2023). Human–AI collaboration in decision making. Information Systems Journal, 33(2), 234–260. [Google Scholar] [Crossref]

22. Kellogg, K.C., Valentine, M.A. and Christin, A. (2022), "Algorithms at work: The new contested terrain of control", Academy of Management Annals, Vol. 16 No. 2, pp. 560–592. https://doi.org/10.5465/annals.2020.0057 [Google Scholar] [Crossref]

23. Kroll, J. A., et al. (2021). Accountability in algorithmic decision-making. Communications of the ACM, 64(6), 62–71. [Google Scholar] [Crossref]

24. Kshetri, N. (2023), "The evolution of artificial intelligence governance: a review and research agenda", Journal of Business Research, Vol. 155, Article 113348. https://doi.org/10.1016/j.jbusres.2022.113348 [Google Scholar] [Crossref]

25. Martin, K. (2023), "Ethical implications and accountability of algorithms in organizational settings", Business Horizons, Vol. 66 No. 1, pp. 17–27. https://doi.org/10.1016/j.bushor.2022.09.001 [Google Scholar] [Crossref]

26. Meijerink, J., Bondarouk, T. and Lepak, D.P. (2022), "When digital technology meets HRM: toward a digital configurational framework", Human Resource Management Review, Vol. 32 No. 2, Article 100843. https://doi.org/10.1016/j.hrmr.2021.100843 [Google Scholar] [Crossref]

27. Meijerink, J., et al. (2022). When digital technology meets HRM: A review. Human Resource Management Review, 32(1), 100872. [Google Scholar] [Crossref]

28. Metcalfe, B.D. (2023), "Leadership, ethics and gender in the Arab world: rethinking cultural dimensions", Journal of Business Ethics, Vol. 182 No. 2, pp. 371–390. https://doi.org/10.1007/s10551-021-05056-9 [Google Scholar] [Crossref]

29. Olan, F., Adekunle, S., & Mahmud, I. (2022). Cultural influences on AI adoption. Government Information Quarterly, 39(3), 101697. [Google Scholar] [Crossref]

30. Olan, F., Vyas, L. and Abdul-Ghani, M.A. (2022), "Authoritarian leadership, digital monitoring, and psychological disengagement in organizations", Journal of Organizational Effectiveness: People and Performance, Vol. 9 No. 4, pp. 501–523. https://doi.org/10.1108/JOEPP-06-2021-0121 [Google Scholar] [Crossref]

31. Page, K. M., & Vella-Brodrick, D. A. (2009). The “What,” “Why” and “How” of employee well-being: A new model. Social Indicators Research, 90(3), 441–458. https://doi.org/10.1007/s11205-008-9270-3 [Google Scholar] [Crossref]

32. Paschen, J., et al. (2023). Artificial intelligence in marketing: Past, present and future. Journal of Business Research, 144, 735–747. [Google Scholar] [Crossref]

33. Raisch, S. and Krakowski, S. (2021), "Artificial intelligence and management: The automation–augmentation paradox", Academy of Management Review, Vol. 46 No. 1, pp. 192–210. https://doi.org/10.5465/amr.2018.0072 [Google Scholar] [Crossref]

34. Taris, T. W., & Schaufeli, W. B. (2015). Individual well-being and performance at work: A conceptual and theoretical overview. In M. van Veldhoven & R. Peccei (Eds.), Well-being and performance at work: The role of context (pp. 15–34). Psychology Press. [Google Scholar] [Crossref]

• The Indirect Effect of Liquidity and Activity on Company Value with Profitability as an Intervening Variable
• Effect of Financial Skills, Knowledge, and Attitude on The Financial Behaviour of Clergy
• A Decade of Review: Trends in Budget Execution and Financial Performance of Development Projects in Tanzania (2014/15-2023/24)
• The Influence of Pre-Project Planning on the Budget Absorption Rate of Public Funded Infrastructure Projects in Kenya a Comparative Case Study of Narok, Migori, and Kisii County Government Projects
• Assessment of Factors Influencing Digital Transformation in Hotels’ Facility Management in Abuja Metropolis, Nigeria