Yapay Zekânın Çalışanlarda Yarattığı Kaygının, Yenilikçi Davranışlar Üzerindeki Etkisinde, Değişime Direncin Düzenleyici Rolünün İncelenmesi

Yıl 2025, Cilt: 22 Sayı: 5, 1088 - 1103, 30.09.2025

İsmail Özdemir

https://doi.org/10.26466/opusjsr.1715067

Öz

Günümüzde önemli fırsatlar sunan ve yapay zekâ (YZ), işletmelerin iş yapış biçimlerinden, çalışan profiline kadar pek çok unsuru temelden etkilemiştir. YZ, verimlilik, hız ve maliyet avantajı ile büyümeye katkı sağlamakla birlikte, bu gelişmeler birçok çalışanda kaygı, tedirginlik ve endişe yaratmaktadır. Öte yandan, yüksek yenilikçi davranış seviyesine sahip bireylerin, YZ’yı tehdit yerine fırsat olarak algılaması ve daha az kaygı yaşaması beklenir. Bununla birlikte, çalışanların örgütlerde teknolojik dönüşüme karşı geliştirdiği psikolojik direnç düzeyleri, söz konusu ilişkiyi etkilemektedir. Bu çalışma, İstanbul'da faaliyet yürüten finans, teknoloji, imalat ve hizmet sektörü kuruluşlarının çalışanları arasında, yapay zekâya yönelik kaygının, yenilikçi davranışlara etkisini ve bu ilişkide değişime direncin düzenleyici rolünü araştırmayı hedeflemektedir. Literatürde, daha önce bu üç kavramı birarada ele alan bir araştırmaya rastlanmamıştır. Araştırmada, demografik bilgiler ve üç farklı ölçekten oluşan anketle, 281 katılımcıdan elde edilen veriler kullanılmıştır. Verilerinin analizinde sırasıyla; betimsel istatistikler, güvenilirlik ve geçerlilik analizi, normallik değerlendirmesi, Pearson korelasyon analizi, regresyon analizi uygulanmıştır. Araştırma bulguları, yapay zekâ kaygısının çalışanların yenilikçi davranışları üzerinde güçlü bir negatif etkiye sahip olduğunu (β =-0.54, p <0.001), değişime direncin, yapay zekâ kaygısı ile yenilikçi davranış arasındaki ilişkide düzenleyici bir rol oynadığını (β =-0.09, p = 0.121), finans ve teknoloji sektörleri, imalat (M=2,98) ve hizmetler (M=3,12) sektörlerine kıyasla daha yüksek yapay zeka kaygısı (M=3,45, M=3,38) göstermiştir.

Anahtar Kelimeler

Yıkıcı Teknolojiler , Yapay Zekâ Kaygısı , Yenilikçi Davranış , Değişime Direnç

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.
• Dent, E. B., & Goldberg, S. G. (1999). Challenging “resistance to change”. The Journal of Applied Behavioral Science, 35(1), 25-41.
• Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large lan-guage models. arXiv preprint arXiv:2303.10130.
• Ford, J. D., Ford, L. W., & D'Amelio, A. (2008). Resistance to change: The rest of the story. Academy of Management Review, 33(2), 362-377.
• Golgeci, I., Gligor, D., Tatoglu, E., & Arslan, A. (2024). Artificial intelligence anxiety and inno-vative work behaviour: The moderating role of organizational support. Journal of Business Research, 158, 113662.
• Janssen, O. (2000). Job demands, perceptions of effort-reward fairness and innovative work behaviour. Journal of Occupational and Organizational Psychology, 73(3), 287-302.
• Karasar, N. (2020). Scientific research method (36th ed.). Nobel Academic Publishing.
• Kim, J., Kim, J., Lee, S., & Kim, Y. (2023). Effects of AI anxiety on intention to use artificial intelli-gence-based services. Computers in Human Behavior, 144, 107738.
• Kotter, J. P. (1995). Leading change: Why transfor-mation efforts fail. Harvard Business Review, 73(2), 59-67. Laumer, S., Maier, C., & Weitzel, T. (2016). Infor-mation technology as enabler of sustainable HRM: Analysing job seekers’ acceptance of e-recruiting. Journal of Electronic Commerce Research, 17(3), 268-282.
• Mahmud, A., Ramayah, T., & Kurnia, S. (2022). Algorithmic avoidance: Conceptualization, measurement, and empirical validation. In-formation & Management, 59(3), 103515.
• Mirbabaie, M., Stieglitz, S., & Brünker, F. (2022). Understanding employee resistance to artificial intelligence: A mixed-methods approach. AI & Society, 37, 1309–1325.
• Monod, E., Nambisan, S., & Yoo, Y. (2024). Artificial intelligence, digital transformation, and the fu-ture of work. MIS Quarterly, 48(1), 1-24.
• Nam, T. (2019). Technology usage, expected job sus-tainability, and perceived job insecurity. Tech-nological Forecasting and Social Change, 138, 155-165.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psycho-metric theory (3rd ed.). McGraw-Hill.
• Oreg, S. (2006). Personality, context, and resistance to organizational change. European Journal of Work and Organizational Psychology, 15(1), 73-101.
• Oreg, S., & Goldenberg, J. (2015). Resistance to innovation: Its sources and manifestations. Routledge.
• Oreg, S., Vakola, M., & Armenakis, A. (2011). Change recipients’ reactions to organiza-tional change: A 60-year review of quantita-tive studies. The Journal of Applied Behavioral Science, 47(4), 461-524.
• Piderit, S. K. (2000). Rethinking resistance and rec-ognizing ambivalence: A multidimensional view of attitudes toward an organizational change. Academy of Management Review, 25(4), 783-794.
• Ramaul, J., Grover, V., & Ghosh, D. (2024). From automation to autonomy: AI in the modern enterprise. MIS Quarterly Executive, 23(1), 45-58.
• Retkowsky, P., Macaulay, L., & Newton, D. (2024). Generative AI: Challenges and future direc-tions. AI Magazine, 45(2), 29-44.
• Ritala, P., Gustafsson, R., & Lopes, R. (2024). Artifi-cial intelligence and disruptive innovation: Literature review and research agenda. Technovation, 128, 102821.
• Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
• Scott, S. G., & Bruce, R. A. (1994). Determinants of innovative behavior: A path model of indi-vidual innovation in the workplace. Acade-my of Management Journal, 37(3), 580-607.
• Sindermann, C., Duke, É., & Montag, C. (2022). Artificial intelligence anxiety (AIA) scale: Development and initial validation. Com-puters in Human Behavior Reports, 5, 100176.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using mul-tivariate statistics (6th ed.). Pearson.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Da-vis, F. D. (2003). User acceptance of infor-mation technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
• Vrontis, D., Makrides, A., Christofi, M., & Nico-laou, N. (2022). Artificial intelligence, robot-ics, advanced technologies and human re-source management: A systematic review. The International Journal of Human Resource Management, 33(5), 903-934.
• Wang, S., & Wang, S. (2022). Artificial intelligence anxiety: Conceptualization, measurement, and implications. Computers in Human Be-havior, 129, 107128.
• Zhan, X., Li, L., & Yu, Y. (2024). Personality traits and AI anxiety: The mediating role of risk perception. Personality and Individual Differ-ences, 210, 112321. Zirar, A. (2023). Artificial intelligence and employ-ee innovation: Exploring the opportunities and challenges. Technovation, 123, 102762.