Organizational Behaviour Studies Quarterly

Organizational Behaviour Studies Quarterly

Does Pygmalion Leadership Affect Innovative Work Behavior? An Examination of the Moderating Role of Artificial Intelligence (AI)

Document Type : Research Paper

Authors
mohammad jalali 1
mohammad Hakak 2
Mina Baraei 3
1 Ph.D in Public Administration - Organizational Behavior, Faculty of Economics and Management, Lorestan University, Khorram Abad, Lorestan, Iran
2 Full Professor, Department of Management, Faculty of Economics and Management, Lorestan University, Khorram Abad, Lorestan, Iran
3 M.A. Student in Educational Sciences – Preschool Education, Islamic Azad University, Electronic Branch, Tehran, Iran
10.22034/obs.2026.2079078.3644
Abstract
Although the role of motivational leadership styles in fostering innovative work behavior has received considerable attention in the leadership and innovation literature, it remains insufficiently clear how employees’ perceptions of artificial intelligence (AI) may strengthen or weaken the effects of such leadership styles on innovative work behavior. This uncertainty is particularly salient in service organizations, which simultaneously face human-centered requirements and rising expectations associated with digital transformation. Addressing this gap, the present study examines the effect of Pygmalion leadership on innovative work behavior and clarifies the moderating role of employees’ perceptions of AI. Data were collected from a sample of 220 employees of the Imam Khomeini Relief Committee in Kurdistan Province (N = 328) and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that Pygmalion leadership has a positive and significant effect on innovative work behavior, and that more positive employee perceptions of AI amplify this relationship. In addition, significant differences in the mean levels of the study variables were observed across certain demographic characteristics. Theoretically, the findings contribute to the development of the “Human–Technology–Organization” framework by showing that motivational leadership mechanisms are more effective when employees perceive AI as an opportunity to improve work processes and enhance problem solving. Practically, strengthening employees’ perceptions of AI through clarifying its functions, providing training, and managing concerns may help promote innovation in service organizations.
Keywords
Pygmalion leadership, Artificial intelligence (AI), innovative work behavior, Imam Khomeini (RA) Relief Committee
Structural Equation Modeling (SEM).
Subjects
منابع
فلاح شیروانی، محسن؛ اسدی، اسماعیل؛ میرسپاسی، ناصر و اصغری، حرمت  (1404)، طراحی الگوی فرهنگ سازمانی نوآورانه در نظام اداری با رویکرد آینده‌پژوهی، پژوهش نامه مدیریت تحول، 17 (بهار و تابستان 1404) doi: 10.22067/tmj.2025.89833.1606
کرمی، پیام؛ بسطامی، مسعود و ایران‌دوست، منصور  (1404)، مدل‌سازی فرهنگ سازمانی در بستر تحول دیجیتال در سازمان‌های دولتی، پژوهشنامه مدیریت تحول، 17 (بهار و تابستان 1404)
doi: 10.22067/tmj.2025.91947.1674
Reference
Afthanorhan, A., Ghazali, P. L., & Rashid, N. (2021, May). Discriminant validity: A comparison of CBSEM and consistent PLS using Fornell & Larcker and HTMT approaches. In Journal of Physics: Conference Series (Vol. 1874, No. 1, p. 012085). IOP Publishing.
Aggarwal, S., & Chauhan, A. (2025). Effect of Artificial Intelligence on the Organizational Citizenship Behavior and Emotional Regulation of young adults.
Andoh, R. P. K., Acquah, I. S. K., Ennin, E. E., & Ansong, A. (2025). Pygmalion leadership and innovative work behavior of hotel employees in Ghana: The mediating roles of supervisor developmental feedback and supervisor supportive behavior. Journal of Human Resources in Hospitality & Tourism, 24(2), 257-275.
Ansong, A., Ennin, E. E., Acquah, I. S. K., & Andoh, R. P. K. (2025). Pygmalion leadership, psychological capital and task performance: examining their inter-relationships among hotel employees. Anatolia, 1-17.
Atalla, A. D. G., El-Ashry, A. M., & Mohamed Sobhi Mohamed, S. (2024). The moderating role of ethical awareness in the relationship between nurses’ artificial intelligence perceptions, attitudes, and innovative work behavior: a cross-sectional study. BMC nursing, 23(1), 488.
Chen, C. A., & Kuo, M. F. (2024). The perceived need for guanxi: organizational, interpersonal, and individual antecedents. International Public Management Journal, 27(5), 769-787.
Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2023). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 1-39.
Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th ed.). Wiley.
Dong, X., Tian, Y., He, M., & Wang, T. (2025). When knowledge workers meet AI? The double-edged sword effects of AI adoption on innovative work behavior. Journal of knowledge management, 29(1), 113-147.
Gao, Y., & Liu, H. (2021). How supervisor–subordinate guanxi influence employee innovative behavior: a moderated mediation model. Psychology research and behavior management, 2001-2014.
Giachino, C., Cepel, M., Truant, E., & Bargoni, A. (2024). Artificial intelligence-driven decision making and firm performance: A quantitative approach. Management Decision.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (Sage Publications Inc, Thousand Oaks, CA).
Han, X., Chen, F., Wang, H., & Xu, S. (2025). Unlocking innovation: Artificial intelligence usage and innovative behavior in the workplace. Social Behavior and Personality: an international journal, 53(3), 1-13.
Han, Z., Ni, M., Hou, C., & Zhang, Y. (2023). Effects of superior–subordinate guanxi on employee innovative behavior: the role of follower dependency and work passion. Behavioral Sciences, 13(8), 645.
Hossain, S., Fernando, M., & Akter, S. (2025). Digital leadership: Towards a dynamic managerial capability perspective of artificial intelligence-driven leader capabilities. Journal of Leadership & Organizational Studies, 32(2), 189-208.
Huang, L., & Peissl, W. (2023). Artificial Intelligence—A New Knowledge and Decision-Making Paradigm? In Technology assessment in a globalized world: Facing the challenges of transnational technology governance (pp. 175-201). Cham: Springer International Publishing.
Katrina, H. (2024). Regenerative Innovation: Improving Ethical and Sustainability Decision-Making.
Kim, M. S., Kim, S. H., Koo, D. W., & Cannon, D. F. (2019). Pygmalion leadership: Theory and application to the hotel industry. International Journal of Hospitality & Tourism Administration, 20(3), 301-328.
Li, X., Cheng, M., & Xu, J. (2022). Leaders' innovation expectation and nurses' innovation behaviour in conjunction with artificial intelligence: The chain mediation of job control and creative self‐efficacy. Journal of Nursing Management, 30(8), 3806-3816.
Odugbesan, J. A., Aghazadeh, S., Al Qaralleh, R. E., & Sogeke, O. S. (2023). Green talent management and employees’ innovative work behavior: the roles of artificial intelligence and transformational leadership. Journal of knowledge management, 27(3), 696-716.
Peter, H. (2024). The Role of Digital Transformation in Strengthening Organizational Resilience Post-Pandemic.
Piedmont, R. L. (2024). Social desirability bias. In Encyclopedia of quality of life and well-being research (pp. 6526-6526). Cham: Springer International Publishing.
Podsakoff, P. M., Podsakoff, N. P., Williams, L. J., Huang, C., & Yang, J. (2024). Common method bias: It's bad, it's complex, it's widespread, and it's not easy to fix. Annual Review of Organizational Psychology and Organizational Behavior, 11(1), 17-61.
Promma, W., Imjai, N., Usman, B., & Aujirapongpan, S. (2025). The influence of AI literacy on complex problem-solving skills through systematic thinking skills and intuition thinking skills: An empirical study in Thai Gen Z accounting students. Computers and Education: Artificial Intelligence, 8, 100382.
Ruizhen, S., & Rong, L. (2025). Pygmalion effect. In The ECPH encyclopedia of psychology (pp. 1248-1249). Singapore: Springer Nature Singapore.
Smith, A. C. (2025). Peering Inside Pygmalion’s Black Box: Exploring the Essential Links Between Follower Emotion and Performance (Doctoral dissertation, Regent University).
Srirahayu, D. P., Ekowati, D., & Sridadi, A. R. (2023). Innovative work behavior in public organizations: A systematic literature review. Heliyon, 9(2).
Toft, J., & Jenssen, J. I. (2025). Decoding the Pygmalion language of top CEOs: the communication of high positive expectations in CEO letters. Corporate Communications: An International Journal.
Wang, T., Yu, B., Liu, M., & Zhou, Y. (2025). How leader bottom-line mentality impedes employee innovative behavior: a Pygmalion effect perspective. Leadership & Organization Development Journal, 46(1), 41-52.
Yin, M., Jiang, S., & Niu, X. (2024). Can AI really help? The double-edged sword effect of AI assistant on employees’ innovation behavior. Computers in Human Behavior, 150, 107987.
Zhang, Q., Liao, G., Ran, X., & Wang, F. (2025). The impact of ai usage on innovation behavior at work: The moderating role of openness and job complexity. Behavioral Sciences, 15(4), 491.