A Conceptual Framework of Personality Traits, AI Utilisation and Employee Performance

The rapid integration of artificial intelligence (AI) into contemporary workplaces is reshaping how employees perform tasks, process information, make decisions, and solve problems. Nevertheless, access to the same AI technologies does not necessarily produce comparable patterns of utilisation or performance, suggesting that employee-level characteristics play a critical role in determining the value derived from AI. This conceptual study proposes an integrated framework that combines the Ability–Motivation–Opportunity (AMO) Theory and the Technology Acceptance Model (TAM) to explain how the Big Five personality traits influence employee performance through AI utilisation. Specifically, the study conceptualises AI utilisation as a behavioural mediator linking personality traits to employee performance and proposes the structural pathway Personality Traits → AI Utilisation → Employee Performance. Drawing on recent literature, the framework suggests that personality traits, particularly openness to experience and conscientiousness, shape employees’ receptiveness to AI, technology-related perceptions, willingness to engage with AI, and patterns of technology use. Effective AI utilisation, in turn, may enhance task efficiency, work concentration, problem-solving, creativity, and innovative work behaviour, thereby contributing to improved employee performance. This current study advances a human-centred perspective of AI-enabled work by challenging technology-deterministic assumptions and positioning individual differences as an important antecedent of meaningful AI utilisation and performance outcomes. The proposed conceptual framework contributes theoretically by integrating personality, technology acceptance, and employee performance perspectives within a unified model, while offering practical implications for personality-sensitive AI implementation, targeted training, and employee development. The study concludes by presenting theoretical propositions and identifying directions for future empirical research, particularly through longitudinal and quantitative testing of the proposed mediation relationships.

Organisational Behaviour

1. Ajonbadi, H. A., Oladipo, T. O., & Adebayo, K. O. (2024). Data-driven HR: Predictive analytics and workforce optimization in modern enterprises. Journal of Business Analytics, 7(2), 145–162. https://doi.org/10.1080/2573234X.2024.2310112 [Google Scholar] [Crossref]

2. Amoozegar, A., Elsiddig, A., Falahat, M., & Wright, T. (2025). Dispositional predictors of creative work behavior in digitalized environments. Journal of Applied Psychology, 110(2), 145–162. https://doi.org/10.1037/apl0001182 [Google Scholar] [Crossref]

3. Babiker, A., Ahmed, M., & Hassan, S. (2024). Individual differences in artificial intelligence adoption: The role of Big Five personality traits. Computers in Human Behavior, 152, Article 108120. https://doi.org/10.1016/j.chb.2023.108120 [Google Scholar] [Crossref]

4. Behare, P. (2025). AI-powered chatbots and employee engagement: A knowledge-sharing perspective. Information Technology & People, 38(1), 210–228. https://doi.org/10.1108/ITP-04-2024-0312 [Google Scholar] [Crossref]

5. Benhmama, M., & Sabiri, K. (2025). AI integration and task performance: A process-efficiency perspective. Information & Management, 62(1), Article 103915. https://doi.org/10.1016/j.im.2024.103915 [Google Scholar] [Crossref]

6. Daniilidou, A., & Antoniadou, C. (2026). Personality profiles and workplace AI acceptance: A latent profile analysis. Human Resource Management Journal, 36(1), 88–104. https://doi.org/10.1111/1748-8583.12580 [Google Scholar] [Crossref]

7. Elziny, M., Thorp, R., & Al-Malki, A. (2026). Capability, motivation, and technology engagement in digital workflows. Computers & Education, 215, Article 105012. https://doi.org/10.1016/j.compedu.2025.105012 [Google Scholar] [Crossref]

8. Grassini, S., Vitiello, G., & D’Onofrio, M. (2025). Demographic and dispositional correlates of AI attitudes among working professionals. Technology in Society, 78, Article 102618. https://doi.org/10.1016/j.techsoc.2024.102618 [Google Scholar] [Crossref]

9. Jacob, R., & Kumar, P. (2026). Digital enablement in remote work environments: AI as a driver of engagement and job performance. New Technology, Work and Employment, 41(1), 55–74. https://doi.org/10.1111/ntwe.12290 [Google Scholar] [Crossref]

10. Jia, X., & Hou, Y. (2024). The interactive effects of conscientiousness and AI-driven human resource management on job engagement. Journal of Management, 50(4), 512–530. https://doi.org/10.1177/01492063231189102 [Google Scholar] [Crossref]

11. Jiang, L., Zhang, Y., & Liu, X. (2026). The double-edged sword of artificial intelligence: Work autonomy, technostress, and employee well-being. Human Relations, 79(3), 321–345. https://doi.org/10.1177/0018726725129011 [Google Scholar] [Crossref]

12. Khanfar, A., Al-Khasawneh, M., & Obeidat, B. (2026). Determinants of enterprise AI utilization: An extended TAM model. Journal of Enterprise Information Management, 39(2), 310–332. https://doi.org/10.1108/JEIM-03-2025-0182 [Google Scholar] [Crossref]

13. Khudozhnikova, E., Smirnova, O., & Petrov, A. (2026). Core self-evaluations and technological adaptation in digital organizations. Journal of Vocational Behavior, 135, Article 103819. https://doi.org/10.1016/j.jvb.2025.103819 [Google Scholar] [Crossref]

14. Lee, J., & Jeon, S. (2025). AI literacy, self-efficacy, and job performance: A capability-building framework. Computers & Education, 210, Article 104918. https://doi.org/10.1016/j.compedu.2024.104918 [Google Scholar] [Crossref]

15. Li, Y., & Geng, X. (2026). Transforming AI resources into workplace performance: The mediating role of AI self-efficacy. Information Systems Research, 37(2), 201–218. https://doi.org/10.1287/isre.2025.0412 [Google Scholar] [Crossref]

16. Lopez-Garcia, J., Romero-Gomez, A., Castañón-Puga, M., & Ahumada-Tello, E. (2024). Perceived technological threat and employee outcomes in AI-enabled environments. Technological Forecasting and Social Change, 198, Article 122935. https://doi.org/10.1016/j.techfore.2023.122935 [Google Scholar] [Crossref]

17. Makwambeni, B., & Makwambeni, M. (2024). AI platforms as facilitators of internal communication and collaborative learning. Journal of Knowledge Management, 28(6), 1540–1558. https://doi.org/10.1108/JKM-01-2024-0089 [Google Scholar] [Crossref]

18. Malik, A. (2024). Data-driven performance management: Leveraging AI in contemporary human resource systems. Human Resource Management, 63(3), 415–430. https://doi.org/10.1002/hrm.22210 [Google Scholar] [Crossref]

19. Mansour, S., Atalla, M., & El-Kassar, A. N. (2021). Neuroticism, occupational stress, and job satisfaction in technology-driven workplaces. International Journal of Stress Management, 28(3), 210–222. https://doi.org/10.1037/str0000215 [Google Scholar] [Crossref]

20. Memon, M. A., Ting, H., & Cheah, J. H. (2026). Leadership capabilities in AI-driven transformations: A structural equation modeling approach. Leadership & Organization Development Journal, 47(1), 95–112. https://doi.org/10.1108/LODJ-02-2025-0094 [Google Scholar] [Crossref]

21. Muridzi, G., & Dhliwayo, S. (2026). Employee perceptions of AI-driven performance feedback and motivation. Journal of Organizational Behavior, 47(2), 205–221. https://doi.org/10.1002/job.2785 [Google Scholar] [Crossref]

22. Narayanasami, S., Rajan, K., & Sundaram, M. (2024). Dispositional drivers of organizational citizenship behaviors in automated work contexts. Asia Pacific Journal of Human Resources, 62(2), 180–198. https://doi.org/10.1111/1744-7941.12390 [Google Scholar] [Crossref]

23. Oglesby, R., Smith, K., & Taylor, J. (2024). AI-enabled performance appraisals and job satisfaction. Public Personnel Management, 53(1), 45–67. https://doi.org/10.1177/00910260231201940 [Google Scholar] [Crossref]

24. Olan, F., Nyuur, R., & Arakpogun, E. (2024). Artificial intelligence utilization and organizational productivity: A knowledge management perspective. Journal of Business Research, 170, Article 114328. https://doi.org/10.1016/j.jbusres.2023.114328 [Google Scholar] [Crossref]

25. Rajpurohit, S., Sharma, A., Sharma, R., & Jain, V. (2025). Harnessing AI capabilities for innovative work behaviour: An empirical study. Technovation, 130, Article 102915. https://doi.org/10.1016/j.technovation.2024.102915 [Google Scholar] [Crossref]

26. Rana, S., & Bhambri, P. (2026). AI-driven task automation and workforce skill evolution. Human Resource Development Quarterly, 37(1), 45–67. https://doi.org/10.1002/hrdq.21520 [Google Scholar] [Crossref]

27. Sabahattin Mete, M. (2020). Agreeableness and collaborative team dynamics in tech-driven environments. European Journal of Work and Organizational Psychology, 29(4), 512–526. https://doi.org/10.1080/1359432X.2020.1768012 [Google Scholar] [Crossref]

28. Sandigawad, V., Patil, S., & Kulkarni, R. (2024). Big Five personality dimensions and job performance across varying task complexities. Journal of Managerial Psychology, 39(3), 280–298. https://doi.org/10.1108/JMP-05-2023-0291 [Google Scholar] [Crossref]

29. Saritha, K., & Madhavi, C. (2026). AI platforms and remote employee productivity: A dual-mediation model. Telematics and Informatics, 91, Article 102140. https://doi.org/10.1016/j.tele.2025.102140 [Google Scholar] [Crossref]

30. Shahreki, J., Lay, A. C., & Tan, C. S. (2020). Personality traits, organizational citizenship behavior, and job performance: A study of professional employees. International Journal of Productivity and Performance Management, 69(7), 1421–1440. https://doi.org/10.1108/IJPPM-01-2019-0035 [Google Scholar] [Crossref]

31. Shaw, D., Edwards, P., & Turner, M. (2023). Openness to experience as a catalyst for creative problem solving in digital transformation. Creativity and Innovation Management, 32(4), 510–525. https://doi.org/10.1111/caim.12560 [Google Scholar] [Crossref]

32. Suleymenova, G., Karatayev, A., & Zhakupov, B. (2025). AI-supported adaptive learning systems in professional development. Educational Technology Research and Development, 73(1), 112–130. https://doi.org/10.1007/s11423-024-10380-x [Google Scholar] [Crossref]

33. Tariq, H. (2025). Supportive versus controlling AI implementations: Implications for employee performance and trust. Journal of Organizational Behavior, 46(2), 175–190. https://doi.org/10.1002/job.2710 [Google Scholar] [Crossref]

34. Thorp, R., Elziny, M., & Smith, L. (2026). Individual psychological readiness for technological change. Behavior & Information Technology, 45(1), 34–50. https://doi.org/10.1080/0144929X.2025.2410012 [Google Scholar] [Crossref]

35. Wang, Y., Lyu, M., Zhang, Q., & Wu, X. (2026). How AI use translates into job performance: The roles of work concentration, self-efficacy, and conscientiousness. Journal of Management Information Systems, 43(1), 45–68. https://doi.org/10.1080/07421222.2025.2440192 [Google Scholar] [Crossref]

36. Wright, T. (2025). Personality dispositions and performance outcomes in high-tech industries. Journal of Occupational and Organizational Psychology, 98(1), 89–108. https://doi.org/10.1111/joop.12510 [Google Scholar] [Crossref]

37. Xiaoxin, Z., Chen, L., & Wang, H. (2025). Re-evaluating Big Five personality traits in automated workplace settings. Personality and Individual Differences, 218, Article 112480. https://doi.org/10.1016/j.paid.2024.112480 [Google Scholar] [Crossref]

38. Xu, T., Zhao, K., & Zhou, L. (2026). Personality traits and AI usage patterns in professional settings. Behaviour & Information Technology, 45(2), 189–205. https://doi.org/10.1080/0144929X.2025.2428901 [Google Scholar] [Crossref]

39. Yin, Z., & Hoang, T. (2025). Organizational climate, leadership support, and AI adaptation in contemporary firms. Asia Pacific Journal of Management, 42(1), 112–130. https://doi.org/10.1007/s10490-024-09945-8 [Google Scholar] [Crossref]

40. Yoshida, K., & İnce-Yenilmez, F. (2025). Automation of routine administrative activities and worker reallocation. Technological Forecasting and Social Change, 201, Article 123210. https://doi.org/10.1016/j.techfore.2024.123210 [Google Scholar] [Crossref]

41. Zhang, H., Yu, L., & Dai, B. (2025). Automation, decision support, and productivity: An empirical assessment of AI in operations. Decision Sciences, 56(1), 89–105. https://doi.org/10.1111/deci.12610 [Google Scholar] [Crossref]

42. Zheng, C., Guo, X., Liao, Y., Chen, Z., & Sun, R. (2025). AI utilization and employee creativity: Exploring the dark side of reliance. Research Policy, 54(2), Article 105120. https://doi.org/10.1016/j.respol.2024.105120 [Google Scholar] [Crossref]

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