THE MEDIATING ROLE OF GENERAL ATTITUDE TOWARDS ARTIFICIAL INTELLIGENCE IN THE RELATIONSHIP BETWEEN THRIVING AT WORK AND PSYCHOLOGICAL WELL-BEING

Year 2026, Issue: 73, 139 - 154, 02.03.2026

Fevziye Bekar

https://doi.org/10.30794/pausbed.1776328

https://izlik.org/JA39SF97PG

Abstract

This research examines the mediating role of general attitudes towards artificial intelligence in the effect of employees' levels of thriving at work (a personal resource) on their psychological well-being, within the framework of the Job Demands and Resources (JDR) theory. The limited number of studies in the literature that integrate attitudes towards self-development, work psychology, and artificial intelligence based on the Job Demands and Resources theory constitute the basis for this research. A quantitative research approach was adopted in the study; data obtained from 399 healthcare workers using a convenience sampling method and a two-stage data collection process were analysed using the PROCESS Macro (Model 4). The findings show that employees' levels of thriving at work increase their attitudes towards artificial intelligence and that these attitudes positively influence their psychological well-being. Consequently, this research adds a technology-based perspective to the Job Demands and Resources theory and offers important insights for organisations to develop employee-focused technology integration and human resources strategies.

Keywords

Psychological well-being , Thriving at work , General attitude towards artificial intelligence , Job demands-resources theory

Ethical Statement

The author of this article confirm that their work complies with the principles of research and publication ethics. In this study, ChatGPT and DeepL were used as supportive tools during the language editing and translation processes. These tools were employed solely for linguistic improvement purposes, and the final responsibility for the accuracy of the content and meaning rests with the author. In addition, supportive tools were used during the reference-checking process, and the accuracy and appropriateness of all references were verified by the author.

Supporting Institution

Ethical approval for this study was obtained from the Gümüşhane University Scientific Research and Publication Ethics Committee (Decision No: E-95674917-108.99-340329; Decision date: June 25, 2025)

İŞ YERİNDE KENDİNİ YETİŞTİRME İLE PSİKOLOJİK İYİ OLMA ARASINDAKİ İLİŞKİDE YAPAY ZEKÂYA YÖNELİK GENEL TUTUMUN ARACI ROLÜ

https://izlik.org/JA39SF97PG

Abstract

This research examines the mediating role of general attitudes towards artificial intelligence in the effect of employees' levels of thriving at work (a personal resource) on their psychological well-being, within the framework of the Job Demands and Resources (JDR) theory. The limited number of studies in the literature that integrate attitudes towards self-development, work psychology, and artificial intelligence based on the Job Demands and Resources theory constitute the basis for this research. A quantitative research approach was adopted in the study; data obtained from 399 healthcare workers using a convenience sampling method and a two-stage data collection process were analysed using the PROCESS Macro (Model 4). The findings show that employees' levels of thriving at work increase their attitudes towards artificial intelligence and that these attitudes positively influence their psychological well-being. Consequently, this research adds a technology-based perspective to the Job Demands and Resources theory and offers important insights for organisations to develop employee-focused technology integration and human resources strategies.

Keywords

Psikolojik iyi olma , İş yerinde kendini geliştirme , Yapay zekaya yönelik genel tutum , İş talepleri-kaynakları teorisi

Ethical Statement

Bu çalışmanın yazarı, araştırma ve yayın etiği ilkelerine uyduklarını kabul etmektedirler. Bu çalışmada dil düzeltimi ve çeviri süreçlerinde destekleyici araçlar olarak ChatGPT ve DeepL’den yararlanılmıştır. Kullanılan bu araçlar yalnızca dilsel iyileştirme amacıyla kullanılmış olup, içerik ve anlamın doğruluğuna ilişkin nihai sorumluluk yazara aittir. Ayrıca, kaynakça kontrolü sürecinde destekleyici araçlardan yararlanılmış; tüm kaynakların doğruluğu ve uygunluğu yazar tarafından teyit edilmiştir.

Supporting Institution

Bu çalışma için etik kurul onayı, Gümüşhane Üniversitesi Bilimsel Araştırma ve Yayın Etiği Kurulundan alınmıştır (Karar No: E-95674917-108.99-340329; Karar Tarihi: 25 Haziran 2025).

References

• Bai, B., Qiao, J., & Bai, C. (2025). Harnessing strengths from trauma: Examining the impact of strength use on nurses’ job satisfaction, positive mental health, and thriving at work through post-traumatic growth. BMC Nursing, 24(1), 438. 1-10. https://doi.org/10.1186/s12912-025-02936-x
• Bakker, A. B., & Demerouti, E. (2007). The job demands‐resources model: State of the art. Journal of managerial psychology, 22(3), 309-328. https://doi.org/10.1108/02683940710733115
• Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273- 285. https://doi.org/10.1037/ocp0000056
• Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173-1182.
• Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889-942. Claes, R., et al. (2023). Leadership, job demands, job resources, and subjective well-being: Evidence from the Belgian National Happiness Study. Frontiers in Psychology, 14, 1-11. 1220263. https://doi.org/10.3389/fpsyg.2023.1220263
• Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson Education.
• Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227- 268. https://doi.org/10.1207/S15327965PLI1104_01 Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2000). A model of burnout and life satisfaction amongst nurses. Journal of Advanced Nursing, 32(2), 454–464.
• Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
• Flinkman, M., Isopahkala-Bouret, U., & Salanterä, S. (2013). Young registered nurses’ intention to leave the profession and professional turnover in early career: A qualitative case study. ISRN Nursing, 916061, 1-12. https://doi.org/10.1155/2013/916061
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
• García-Madurga, M. Á., Gil-Lacruz, A. I., Saz-Gil, I., & Gil-Lacruz, M. (2024). The role of artificial intelligence in improving workplace well-being: A systematic review. Businesses, 4(3), 389-410.
• Giuntella, O., Konig, J., & Stella, L. (2025). Artificial intelligence and the wellbeing of workers. Scientific Reports, 15(1), 20087, 1- 13. https://doi.org/10.1038/s41598-025-98241-3
• Grand View Research. (2025). Artificial intelligence market size, share & trends analysis report by solution, by technology (deep learning, machine learning, NLP, machine vision, generative AI), by function, by end-use, by region, and segment forecasts, 2025–2030. Retrieved from https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market
• Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): A brief measure of general attitude toward artificial intelligence. Frontiers in psychology, 14, 1191628, 1-10. https://doi.org/10.3389/fpsyg.2023.1191628
• Gültekin, F., & Bayramoğlu, G. (2021). 2000-2020 yılları arasında Türkiye’de “Psikolojik iyi oluş” ile ilgili yazılan tezlerin içerik analizi. Journal of Management Theory and Practices Research, 2(2), 117-133.
• Gürbüz, S. (2025). Sosyal bilimlerde aracı ve düzenleyici etki analizleri: IBM SPSS PROCESS Macro uygulamaları ve örnek veri setleri (3. bs.). Seçkin Yayıncılık.
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate data analysis (8th ed.). Cengage Learning.
• Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.
• Hobfoll, S. E. (1989). Conservation of resources: a new attempt at conceptualizing stress. American Psychologist, 44(3), 513-524.
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
• Huang, D., & Zhou, H. (2024). Self-sacrificial leadership, thriving at work, workplace well-being, and work–family conflict during the COVID-19 crisis: The moderating role of self-leadership. BRQ Business Research Quarterly, 27(1), 10-25.
• Kleine, A. K., Rudolph, C. W., & Zacher, H. (2019). Thriving at work: A meta‐analysis. Journal of Organizational Behavior, 40(9-10), 973-999.
• Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
• Koçak, Ö. E. (2016). How to enable thriving at work through organizational trust. International Journal of Research in Business and Social Science, 5(4), 40-52.
• Leong, A. M. W., Bai, J. Y., Rasheed, M. I., Hameed, Z., & Okumus, F. (2025). AI disruption threat and employee outcomes: Role of technology insecurity, thriving at work, and trait self-esteem. International Journal of Hospitality Management, 126, 104064, 1-11. https://doi.org/10.1016/j.ijhm.2025.104064
• Li, Y., Chen, C., & Yuan, Y. (2025). Evolving the job demands-resources framework to JD-R 3.0: The impact of after-hours connectivity and organizational support on employee psychological distress. Acta Psychologica, 253, 1-11, 104710. https://doi.org/10.1016/j.actpsy.2025.104710
• Liu, D., Zhang, S., Wang, Y., & Yan, Y. (2021). The antecedents of thriving at work: A meta-analytic review. Frontiers in Psychology, 12, 1 – 19, 659072. https://doi.org/10.3389/fpsyg.2021.659072
• McKinsey & Company. (2025). The state of AI: How organizations are rewiring to capture value. Retrieved from https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
• Merkuž, A., Zupič, I., & Mihelič, K. K. (2024). Thriving at work: State of the art and looking towards to enhanced employee well-being in the future. Economic and Business Review, 26(3), 196–221.
• Novelli, C., Casolari, F., Rotolo, A., Taddeo, M., & Floridi, L. (2024). AI risk assessment: A scenario-based, proportional methodology for the AI act. Digital Society, 3(13), 1- 13, https://doi.org/10.1007/s44206-024-00095-1
• Okros, N., & Virga, D. (2023). Impact of workplace safety on well-being: The mediating role of thriving at work. Personnel Review, 52(7), 1861-1877.
• Peters, S. E., Sorensen, G., Katz, J. N., Gundersen, D. A., & Wagner, G. R. (2021). Thriving from work: Conceptualization and measurement. International Journal of Environmental Research and Public Health, 18(13), 7196, 1-20. https://doi.org/10.3390/ijerph18137196
• Porath, C., Spreitzer, G., Gibson, C., & Garnett, F. G. (2012). Thriving at work: Toward its measurement, construct validation, and theoretical refinement. Journal of Organizational Behavior, 33(2), 250–275.
• Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior research methods, 40(3), 879-891.
• Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57(6), 1069–1081.
• Schaufeli, W. B., & Taris, T. W. (2014). A critical review of the Job Demands-Resources Model: Implications for improving work and health. In G. F. Bauer & O. Hämmig (Eds.), Bridging occupational, organizational and public health: A transdisciplinary approach (pp. 43–68). Springer.
• Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014, 1-13. https://doi.org/10.1016/j.chbr.2020.100014
• Sharma, A., & Sharma, H. (2024). Job autonomy and employee psychological well-being: The mediating effect of employee voice. South Asian Journal of Human Resources Management, 11(1), 1–24. https://doi.org/10.1177/23220937241257279
• Spreitzer, G., Sutcliffe, K., Dutton, J., Sonenshein, S., & Grant, A. M. (2005). A socially embedded model of thriving at work. Organization Science, 16(5), 537-549.
• Stansfeld, S., Smuk, M., Onwumere, J., Clark, C., Pike, C., McManus, S., & Bebbington, P. (2013). Stressors and common mental disorder in the English national survey of psychiatric morbidity. Social Psychiatry and Psychiatric Epidemiology, 48(10), 1651–1661.
• Streiner, D. L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80(1), 99–103.
• Suh, W., & Ahn, S. (2022). Development and validation of a scale measuring student attitudes toward artificial intelligence. Sage Open, 12(2). 1- 12. https://doi.org/10.1177/21582440221100463
• Tara, N., & Iqbal, S. M. J. (2023). Examining the impact of job demands, resources and technostress on psychological wellbeing of gig workers: A theoretical model. Qlantic Journal of Social Sciences, 4(4), 369-378.
• Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
• Telef, B. B. (2013). Psikolojik iyi oluş ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 28(3), 374-384.
• Topaz, M., & Ronquillo, C. (2023). Artificial intelligence in nursing: Priorities and opportunities. Journal of Nursing Scholarship, 55(3), 259–266. https://doi.org/10.1111/jnu.12864
• Türk, N., Batuk, B., Kaya, A., & Yıldırım, O. (2025). What makes university students accept generative artificial intelligence? A moderated mediation model. BMC psychology, 13(1), 1-13.
• Valtonen, A., Saunila, M., Ukko, J., Treves, L., & Ritala, P. (2025). AI and employee wellbeing in the workplace: An empirical study. Journal of Business Research, 199, 115584, 1- 16. https://doi.org/10.1016/j.jbusres.2025.115584
• Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634.
• Wei, S., Ding, H., & Sun, H. (2025). Interplay between teachers’ affective well-being and thriving at work: A cross-lagged study. Journal of Happiness Studies, 26(1), 12–23.
• Xiaomei, Z., Sen, W., & Qin, H. (2021). Impact of skill requirements on employees’ thriving at work: From the perspective of artificial intelligence embedding. Foreign Economics & Management, 43(11), 15-25.
• Yousaf, K., Abid, G., Butt, T. H., Ilyas, S., & Ahmed, S. (2019). Impact of ethical leadership and thriving at work on psychological well-being of employees: Mediating role of voice behaviour. Business, Management and Economics Engineering, 17(2), 194-217.
• Yu, L., Zhu, X., & Ren, H. (2025). Navigating the digital frontier: Thriving in remote work through AI and human connection. Journal of Business and Management, 30(1), 4-25.