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  3. Attitudes Toward Artificial Intelligence Among Technical and Non-Techn…
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Attitudes Toward Artificial Intelligence Among Technical and Non-Technical Employees: A GAAIS-Based Comparative Study

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Annals of Medicine and Medical Sciences Volume 05 (2026), Version 01 January 1, 2026 pp. 10 - 16
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Abstract

Background: The rapid integration of AI into workplaces highlights the need to understand employee attitudes influencing acceptance and use, yet multidimensional comparisons between technical and non-technical employees remain limited. Aim: To compare attitudes toward AI between technical and non-technical employees using the General Attitudes Toward Artificial Intelligence Scale (GAAIS), assessing both positive evaluations (e.g., usefulness, innovation) and negative concerns (e.g., errors, loss of control). Methodology: A cross-sectional study of 85 employees (technical = 58; non-technical = 27) using workplace AI assessed attitudes via the GAAIS and demographics; group differences were analyzed using Welch’s t-tests with Hedges’ g effect sizes visualized in forest plots. Results: Technical employees showed moderate Positive (3.65 ± 0.26) and lower Negative (2.99 ± 0.39) scores. Thirteen items differed significantly between groups, with non-technical employees exhibiting higher Positive and Negative subscale scores, reflecting greater attitudinal polarization and longer organizational AI exposures. Conclusion: Technical employees exhibited more stable, experience-based attitudes toward AI, balancing perceived benefits with lower levels of concern. These findings highlight their potential role as key stakeholders in responsible and sustainable organizational AI implementation.

References
  1. Kergroach S. Industry 4.0: New Challenges and Opportunities for the Labour Market. Foresight and STI Governance [Internet]. 2017 Dec.11 [cited 2025Dec.13];11(4):6-8. Available from: https://foresight-journal.hse.ru/article/view/19134.
  2. Puaschunder JM. Organizational Artificial Intelligence Behavior. SSRN Electronic Journal. 2019;1-9.
  3. Makarius E E, Mukherjee D, Fox J D, Fox A K. Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of business research. 2020; 120, 262-273. https://doi.org/10.1016/j.jbusres.2020.07.045
  4. Glikson E, Woolley A W. Human trust in artificial intelligence: review of empirical research. The Academy of Management annals. 2020, 14(2), 627-660. https://doi.org/10.5465/annals.2018.0057
  5. Ricardo Costa-Climent, Darek M. Haftor, Marcin W. Staniewski. Using machine learning to create and capture value in the business models of small and medium-sized enterprises, International Journal of Information Management. 2023; 73, 102637. https://doi.org/10.1016/j.ijinfomgt.2023.102637
  6. Ida Lindgren. Ironies of automation and their implications for public service automation. Government Information Quarterly. 2024; 41(4), 101974. https://doi.org/10.1016/j.giq.2024.101974
  7. Rajabi E, Etminani, K. Knowledge-graph-based explainable AI: A systematic review. Journal of Information Science. 2024; 50(4), 1019–1029. https://doi.org/10.1177/01655515221112844
  8. Mijwil M, Aljanabi M, Ali A H. ChatGPT: Exploring the role of cybersecurity in the protection of medical information. Mesopotamian Journal of CyberSecurity. 2023; 18– 21. https://doi.org/10.58496/MJCS/2023/004.
  9. Rahimi F, Abadi A T B. ChatGPT and publication ethics. Archives of Medical Research. 2023; 54(3), 272–274. https://doi.org/10.1016/j.arcmed.2023.03.004
  10. Onal S, Kulavuz-Onal D. A cross-disciplinary examination of the instructional uses of ChatGPT in higher education. Journal of Educational Technology Systems. 2024; 52(3), 301–324. https://doi.org/10.1177/00472395231196532
  11. Özkiziltan, Didem and Hassel, Anke, Artificial Intelligence at Work: An Overview of the Literature (March 3, 2021). Available at SSRN: https://ssrn.com/abstract=3796746 or http://dx.doi.org/10.2139/ssrn.3796746
  12. Nazareno L, Schiff D S. The impact of automation and artificial intelligence on worker well-being. Technol. Soc. 2021; 67:101679. https://doi.org/10.1016/j.techsoc.2021.101679
  13. Peters M A. Technological unemployment: Educating for the fourth industrial revolution. Educ. Philos. Theory. 2017; 49, 1–6. https://doi.org/10.1080/00131857.2016.1177412
  14. Stamate A N, Sauvé G, Denis P L. The rise of the machines and how they impact workers' psychological health: An empirical study. Human Behavior and Emerging Technologies. 2021; 3(5), 942–955. https://doi.org/10.1002/hbe2.315.
  15. Ångström, R. C., Björn, M., Dahlander, L., Mähring, M., & Wallin, M. W. (2023). Getting AI Implementation Right: Insights from a Global Survey. California Management Review. 2023;66(1), 5–22. https://doi.org/10.1177/00081256231190430
  16. Schepman A, Rodway P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human Computer Interaction. 2023; 39(13), 2724-2741. https://doi.org/10.1080/10447318.2022.2085400.
  17. Alfalah A A. Factors influencing students’ adoption and use of mobile learning management systems (m-LMSs): A quantitative study of Saudi Arabia. International Journal of Information Management Data Insights. 2023; 3(1), 100143. https://doi.org/10.1016/j.jjimei.2022.100143
  18. Venkatesh V, Thong J Y, Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly. 2012; 36(1), 157–178. https://doi.org/10.2307/41410412.
  19. Farooq MS, Salam M, Jaafar N, Fayolle A, Ayupp K, Radovic-Markovic M, Sajid A. Acceptance and use of lecture capture system (LCS) in executive business studies: Extending UTAUT2". Interactive Technology and Smart Education. 2017; 14 (4) 329–348. doi: https://doi.org/10.1108/ITSE-06-2016-0015
  20. Ustun AB, Karaoglan-Yilmaz F G, Yilmaz, R. Educational UTAUT-based virtual reality acceptance scale: A validity and reliability study. Virtual Reality. 2023; 27(2), 1063–1076. https://doi.org/10.1007/s10055-022-00717-4.
  21. Prasad K, Singh S, Srinivas V, Kothari H, Pathak A, Shrimali, D. Factors Affecting the Adoption of Generative AI Tools Among Information Technology Employees: A UTAUT3, TTF, and SOR Perspective. Journal of Computational and Cognitive Engineering. 2025 https://doi.org/10.47852/bonviewJCCE52026127.
  22. Park YJ, Jones-Jang S M. Surveillance, privacy, and algorithmic inequality: A study of workers' perspectives. Journal of Communication. 2022; 72(6), 680-695. https://doi.org/10.1093/joc/jqac031.
  23. Jordan PJ, Ashkanasy NM, Härtel CE, Hooper GS. Workgroup emotional intelligence: Scale development and relationship to team process effectiveness and goal focus. Human Resource Management Review. 2008; 12(2), 195-214. https://doi.org/10.1016/S1053-4822(02)00046-3
  24. Venkatesh V. Adoption and use of AI tools: a research agenda grounded in UTAUT. Ann Oper Res. 2022; 308, 641–652. https://doi.org/10.1007/s10479-020-03918-9.
  25. Mer A, Srivastavab A. Employee engagement in the new Normal: artificial intelligence as a buzzword or a game changer?” in the adoption and effect of artificial intelligence on hum resources management, part A (Emerald Publishing), 2023;15–46. https://doi.org/10.1108/978-1-80382-027-920231002.
  26. Soulami M, Benchekroun S, Galiulina A. Exploring how AI adoption in the workplace affects employees: a bibliometric and systematic review. Front Artif Intell. 2024; 14; 7:1473872. doi: 10.3389/frai.2024.1473872
  27. Wanberg CR, Banas J T. Predictors and Outcomes of Openness to Changes in a Reorganizing Workplace. Journal of applied psychology. 2000;85(1), 132-142. https://doi.org/10.1037/0021-9010.85.1.132.
  28. Davis F D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS quarterly. 1989,13(3), 319-340. https://doi.org/10.2307/249008.
  29. Venkatesh V, Morris MG, Davis GB, Davis FD. User Acceptance of Information Technology: Toward a Unified View. (September 1, 2003). MIS Quarterly. 2003; 27(3), 425-478, 2003, Available at SSRN: https://ssrn.com/abstract=3375136.
  30. Chatterjee S, Rana NP, Dwivedi Y K, Baabdullah A M. Understanding AI adoption in manufacturing and production firms using an integrated TAM- OE model. Technological forecasting & social. 2021; 170, 120880. https://doi.org/10.1016/j.techfore.2021.120880.
  31. Cao G, Duan Y, Edwards J S, Dwivedi Y K. Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 2021; 106, 102312. https://doi.org/10.1016/j.technovation.2021.102312.
  32. Wei W, Li L. The impact of artificial intelligence on the mental health of manufacturing workers: The mediating role of overtime work and the work environment. Front. Public Health. 2022; 10:862407. https://doi.org/10.3389/fpubh.2022.862407
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