Artificial Intelligence Anxiety Among Employees: The Moderating Role of Resistance to Change in the Effect of AI Anxiety on Innovative Behavior
Öz
Artificial intelligence (AI), which offers significant opportunities today, has fundamentally impacted numerous elements, from the way businesses operate to the employee profile. While AI contributes to growth through its efficiency, speed, and cost advantages, these developments also create anxiety, unease, and worry in many employees. On the other hand, individuals with high levels of innovative behavior are expected to perceive AI as an opportunity rather than a threat and experience less anxiety. However, the level of psychological resistance employees develop against technological transformation in organizations influences this relationship. This research aims to analyze how anxiety about AI affects innovative behavior and to examine the moderating effect of resistance to change within this dynamic. It focuses on employees in financial services, technology, manufacturing, and service sector organizations operating in Istanbul. No previous research has been found in the literature that addresses these three concepts together. The study used demographic information from 281 participants and data obtained through a survey consisting of three different scales. Descriptive statistics, reliability analysis, validity, normality assessment, Pearson correlation analysis, and regression analysis were applied to analyze the data. The study results revealed that AI anxiety had a significantly negative effect on employees' innovative behavior (β = -0.54, p < 0.001), resistance to change served as a moderator in the relationship between AI anxiety and innovative behavior (β = -0.09, p = 0.121), and financial services and technology sectors exhibited higher AI anxiety (M = 3.45, M = 3.38) than manufacturing (M = 2.98) and service (M = 3.12) sectors.
Anahtar Kelimeler
Disruptive Technologies, Artificial Intelligence Anxiety, Innovative Behavior, Resistance to Change
Etik Beyan
İstanbul Gedik Üniversitesinin 2025/2 ile etki kurul karı alınmıştır.
Kaynakça
- Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and prediction: The disruptive economics of arti-ficial intelligence. Harvard Business Review Press.
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
- Aureli, S., Ciambotti, M., & Giampaoli, D. (2019). Individual innovative behavior: A compre-hensive review of literature. International Journal of Innovation Science, 11(3), 348-370.
- Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831-858.
- Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. Bandura, A. (1997). Self-efficacy: The exercise of con-trol. Freeman.
- Beane, M., & Brynjolfsson, E. (2020). Working with robots: The impact of robotic co-workers on productivity and employees. Harvard Busi-ness Review, 98(6), 92-101.
- Beaudry, A., & Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 34(4), 689-710.
- Braganza, A., Chen, W., Canhoto, A. I., & Sap, S. (2020). Organizational learning and AI im-plementation: The moderating role of em-ployee resistance to change. Journal of Busi-ness Research, 123, 368-378.
- Brosnan, M. J. (1998). Technophobia: The psycho-logical impact of information technology. Routledge. Charlwood, A., & Guenole, N. (2021). Employee resistance to artificial intelligence: Insights from a longitudinal study. Work, Employ-ment and Society, 35(6), 1021-1040.
- Cohen, J. (1988). Statistical power analysis for the be-havioral sciences (2nd ed.). Lawrence Erl-baum Associates. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of infor-mation technology. MIS Quarterly, 13(3), 319-340.