Balancing AI’s Promise and Pressure: A Dual-Process Model of AI Adoption Psychological States, and Employee Performance in Indonesia

Asmini Asmini

Abstract


This study investigates how artificial intelligence (AI) adoption affects employee performance through two competing psychological mechanisms: a positive pathway involving technological self-efficacy and job engagement, and a negative pathway involving perceived job insecurity and AI-induced stress. Drawing on data from 280 Indonesian employees across diverse sectors, the study employs structural equation modeling using SmartPLS to examine the hypothesized relationships. The findings confirm the dual-process model, showing that AI adoption positively influences performance by enhancing confidence in technology use and fostering engagement. However, the results also reveal that AI adoption can trigger job insecurity and psychological stress, which in turn negatively impact employee performance. Both sequential mediations positive and negative are statistically significant, emphasizing the coexistence of opportunity and risk in AI-driven transformation. These results contribute to theory by integrating cognitive-motivational and emotional-threat perspectives within the same framework. Practically, the study highlights the importance of organizational strategies that simultaneously empower employees through digital skill development while mitigating fears and emotional strain associated with technological disruption. The findings are particularly relevant to emerging economies like Indonesia, where rapid AI diffusion is not always matched by institutional protections or workforce readiness. Recommendations for inclusive, human-centered AI implementation are discussed

Full Text:

PDF

References


Aldabbas, H., Pinnington, A., & Lahrech, A. (2023). The influence of perceived organizational support on employee creativity: The mediating role of work engagement. Current Psychology, 42(8), 6501–6515. https://doi.org/10.1007/s12144-021-01992-1

Ali, M., Khan, T. I., & Şener, İ. (2025). Transforming hospitality: The dynamics of AI integration, customer satisfaction, and organizational readiness in enhancing firm performance. Journal of Hospitality and Tourism Technology. https://doi.org/10.1108/JHTT-04-2024-0261

Ariño-Mateo, E., Venegas, M. A., Mora-Luis, C., & Pérez-Jorge, D. (2024). The level of conscientiousness trait and technostress: A moderated mediation model. Humanities and Social Sciences Communications, 11(1), 302. https://doi.org/10.1057/s41599-024-02766-3

Aryanti, I., & Perkasa, D. H. (2024). The Effect of Leadership Compensation and Work Discipline on Employee Performance (Study at PT Panca Putra Solusindo Jakarta). Review: Journal of Multidisciplinary in Social Sciences, 1(04), Article 04. https://doi.org/10.59422/rjmss.v1i04.302

Bakker, A. B., & Demerouti, E. (2024). Job demands–resources theory: Frequently asked questions. Journal of Occupational Health Psychology, 29(3), 188–200. https://doi.org/10.1037/ocp0000376

Boccoli, G., Gastaldi, L., & Corso, M. (2024). Transformational leadership and work engagement in remote work settings: The moderating role of the supervisor’s digital communication skills. Leadership & Organization Development Journal, 45(7), 1240–1257. https://doi.org/10.1108/LODJ-09-2023-0490

Burhan, Q.-A. (2024). Unraveling the AI enigma: How perceptions of artificial intelligence forge career adaptability through the crucible of career insecurity and skill development. Management Research Review, 48(3), 470–488. https://doi.org/10.1108/MRR-01-2024-0022

Chen, L., & Zeng, S. (2021). The Relationship Between Intolerance of Uncertainty and Employment Anxiety of Graduates During COVID-19: The Moderating Role of Career Planning. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.694785

Cheung, S. F., & Cheung, S.-H. (2024). manymome: An R package for computing the indirect effects, conditional effects, and conditional indirect effects, standardized or unstandardized, and their bootstrap confidence intervals, in many (though not all) models. Behavior Research Methods, 56(5), 4862–4882. https://doi.org/10.3758/s13428-023-02224-z

Chughtai, M. S., Syed, F., Naseer, S., & Chinchilla, N. (2024). Role of adaptive leadership in learning organizations to boost organizational innovations with change self-efficacy. Current Psychology, 43(33), 27262–27281. https://doi.org/10.1007/s12144-023-04669-z

Compeau, D. R., & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688

Ersanlı, C. Y., Çelik, F., Barjesteh, H., Duran, V., & Manoochehrzadeh, M. (2025). A review of global reskilling and upskilling initiatives in the age of AI. AI and Ethics. https://doi.org/10.1007/s43681-025-00767-9

Eseye, E., & Debebe, E. (2024). Effect of Employee Engagement on Job Performance Case of Tibebe Ghion Specialized Hospital. International Journal of Management Research and Emerging Sciences, 14(4), Article 4. https://doi.org/10.56536/ijmres.v14i4.673

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

George, D. A. S. (2024a). Artificial Intelligence and the Future of Work: Job Shifting Not Job Loss. Partners Universal Innovative Research Publication, 2(2), Article 2. https://doi.org/10.5281/zenodo.10936490

George, Dr. A. S. (2024b). Sleep Disrupted: The Evolving Challenge of Technology on Human Sleep Patterns Over Two Centuries. https://doi.org/10.5281/ZENODO.11179796

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hasan, M. R., Ray, R. K., & Chowdhury, F. R. (2024). Employee Performance Prediction: An Integrated Approach of Business Analytics and Machine Learning. Journal of Business and Management Studies, 6(1), 215–219. https://doi.org/10.32996/jbms.2024.6.1.14

Hemanth Kumar Tummalapalli, Addada Narasimha Rao, Gangula Kamal, Naga Kumari, & Swarup Kumar. (2024). Exploring AI-Driven Management: Impact on Organizational Performance, Decision Making, Efficiency, and Employee Engagement. Journal of Advanced Research in Applied Sciences and Engineering Technology, 148–163. https://doi.org/10.37934/araset.52.2.148163

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Hou, Y., & Fan, L. (2024). Working with AI: The Effect of Job Stress on Hotel Employees’ Work Engagement. Behavioral Sciences, 14(11), Article 11. https://doi.org/10.3390/bs14111076

Idris, H. (2024). The Effects of FOMO on Investment Behavior in the Stock Market. Golden Ratio of Data in Summary, 4(2), 879–887. https://doi.org/10.52970/grdis.v4i2.757

Kartikasari, E. D. K. E. D. (2025). Economic Growth Strategies and Poverty in Rural Indonesia: Subjective Experiencesof Rural Communities in Indonesia’s Rural Areas in Facing Economic Development Challenges. Journal of Economic and Financial Studies, 1(1), Article 1.

Kaushik, S., & Tiwari, P. K. (2023). Job Engagement: A Theoretical Foundation for Enhanced Perspective. 9(1).

Kim, B.-J., Kim, M.-J., & Lee, J. (2024). The impact of an unstable job on mental health: The critical role of self-efficacy in artificial intelligence use. Current Psychology, 43(18), 16445–16462. https://doi.org/10.1007/s12144-023-05595-w

Kim, B.-J., & Lee, J. (2025). The Dark Sides of Artificial Intelligence Implementation: Examining How Corporate Social Responsibility Buffers the Impact of Artificial Intelligence-Induced Job Insecurity on Pro-Environmental Behavior Through Meaningfulness of Work. Sustainable Development, 33(3), 4732–4756. https://doi.org/10.1002/sd.3376

Koen, J., & van Bezouw, M. J. (2021). Acting Proactively to Manage Job Insecurity: How Worrying About the Future of One’s Job May Obstruct Future-Focused Thinking and Behavior. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.727363

Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Understanding Psychosocial Barriers to Healthcare Technology Adoption: A Review of TAM Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology and UTAUT Frameworks. Healthcare, 13(3), Article 3. https://doi.org/10.3390/healthcare13030250

Mahapatra, M., & Ford, D. P. (2024). Technostress and disengagement from knowledge sharing: Insights from pre-pandemic and mid-pandemic data sets. Journal of Knowledge Management, 28(9), 2689–2711. https://doi.org/10.1108/JKM-08-2023-0711

Meng, K., Xiao, X., Wei, W., Chen, G., Nashalian, A., Shen, S., Xiao, X., & Chen, J. (2022). Wearable Pressure Sensors for Pulse Wave Monitoring. Advanced Materials, 34(21), 2109357. https://doi.org/10.1002/adma.202109357

Nawaz, A., & Shabir, G. (2024). Transforming Work Performance: The Role of Artificial Intelligence in Job Enhancement. Unpublished. https://doi.org/10.13140/RG.2.2.11699.85287

Nawaz, A., Soomro, S. A., & Mansoor Kundi, Y. (2023). Linking engagement for innovation with innovative performance: The role of discretionary efforts and knowledge-sharing behaviour. International Journal of Innovation Management, 27(06), 2350027. https://doi.org/10.1142/S1363919623500275

Prayag, G., & Dassanayake, D. M. C. (2023). Tourism employee resilience, organizational resilience and financial performance: The role of creative self-efficacy. Journal of Sustainable Tourism, 31(10), 2312–2336. https://doi.org/10.1080/09669582.2022.2108040

Pykett, J., & Paterson, M. (2022). Stressing the ‘body electric’: History and psychology of the techno-ecologies of work stress. History of the Human Sciences, 35(5), 185–212. https://doi.org/10.1177/09526951221081754

Qudus, L. (2025). Leveraging Artificial Intelligence to Enhance Process Control and Improve Efficiency in Manufacturing Industries. International Journal of Computer Applications Technology and Research. https://doi.org/10.7753/ijcatr1402.1002

Radic, A., Singh, S., Singh, N., Ariza-Montes, A., Calder, G., & Han, H. (2024). The good shepherd: Linking artificial intelligence (AI)-driven servant leadership (SEL) and job demands-resources (JD-R) theory in tourism and hospitality. Journal of Hospitality and Tourism Insights, 8(4), 1494–1521. https://doi.org/10.1108/JHTI-06-2024-0628

Rane, N. L., Paramesha, M., Choudhary, S. P., & Rane, J. (2024). Artificial Intelligence, Machine Learning, and Deep Learning for Advanced Business Strategies: A Review. Partners Universal International Innovation Journal, 2(3), Article 3. https://doi.org/10.5281/zenodo.12208298

Ruth, A. O., Meddour, H., & Majid, A. H. A. (2024). Unleashing work engagement: Sighting the influence of technology self-efficacy and the mediating role of ICT adoption. Multidisciplinary Science Journal, 6(9), 2024089–2024089. https://doi.org/10.31893/multiscience.2024089

Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The Measurement of Work Engagement With a Short Questionnaire: A Cross-National Study. Educational and Psychological Measurement, 66(4), 701–716. https://doi.org/10.1177/0013164405282471

Seidl, B. (2025). Legitimizing and Contesting Lethal Autonomous Weapons Systems in Japan: A Multilayered Analysis of Public Discourse. https://bristoluniversitypressdigital.com/edcollchap-oa/book/9781529237191/ch014.xml

Selenko, E., Bankins, S., Shoss, M., Warburton, J., & Restubog, S. L. D. (2022). Artificial Intelligence and the Future of Work: A Functional-Identity Perspective. Current Directions in Psychological Science, 31(3), 272–279. https://doi.org/10.1177/09637214221091823

Setiadi, & Muharam, H. (2024). Logistics Innovation in Developing Economies: Integrating Digital Solutions in E-Commerce Supply Chains. Sinergi International Journal of Logistics, 2(4), Article 4. https://doi.org/10.61194/sijl.v2i4.733

Shao, C., Nah, S., Makady, H., & McNealy, J. (2025). Understanding User Attitudes Towards AI-Enabled Technologies: An Integrated Model of Self-Efficacy, TAM, and AI Ethics. International Journal of Human–Computer Interaction, 41(5), 3053–3065. https://doi.org/10.1080/10447318.2024.2331858

Singh, Y., & Phoolka, S. (2024). Unleashing the creative spark: The mediating role of employee work engagement on the relationship between employee training and creativity. International Journal of Educational Management, 38(2), 429–446. https://doi.org/10.1108/IJEM-07-2023-0342

Suseno, Y., Chang, C., Hudik, M., & Fang, E. S. (2023). Beliefs, anxiety and change readiness for artificial intelligence adoption among human resource managers: The moderating role of high- performance work systems. In Artificial Intelligence and International HRM. Routledge.

Tan, K.-L., Hofman, P. S., Noor, N., Tan, S.-R., Hii, I. S. H., & Cham, T.-H. (2024). Does artificial intelligence improve hospitality employees’ individual competitive productivity? A time-lagged moderated-mediation model involving job crafting and meaningful work. Current Issues in Tourism, 0(0), 1–18. https://doi.org/10.1080/13683500.2024.2391114

Tarafdar, M., Cooper, C. L., & Stich, J.-F. (2019). The technostress trifecta - techno eustress, techno distress and design: Theoretical directions and an agenda for research. Information Systems Journal, 29(1), 6–42. https://doi.org/10.1111/isj.12169

Uche Ojika, F., Oseremen Owobu, W., Anthony Abieba, O., Janet Esan, O., Chibunna Ubamadu, B., & Ifesinachi Daraojimba, A. (2024). The Role of Artificial Intelligence in Business Process Automation: A Model for Reducing Operational Costs and Enhancing Efficiency. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 1449–1462. https://doi.org/10.62225/2583049x.2024.4.6.4046

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Wadhwa, S. N., Bhardwaj, G., Srivastava, A. P., & Malik, R. (2025a). AI-driven job insecurity and work performance: Unveiling the mediating role of psychological well-being. International Journal of Information Technology. https://doi.org/10.1007/s41870-025-02602-0

Wadhwa, S. N., Bhardwaj, G., Srivastava, A. P., & Malik, R. (2025b). AI-driven job insecurity and work performance: Unveiling the mediating role of psychological well-being. International Journal of Information Technology. https://doi.org/10.1007/s41870-025-02602-0

Yang, L., & Zhao, S. (2024). AI-induced emotions in L2 education: Exploring EFL students’ perceived emotions and regulation strategies. Computers in Human Behavior, 159, 108337. https://doi.org/10.1016/j.chb.2024.108337

Yu, X., Xu, S., & Ashton, M. (2022). Antecedents and outcomes of artificial intelligence adoption and application in the workplace: The socio-technical system theory perspective. Information Technology & People, 36(1), 454–474. https://doi.org/10.1108/ITP-04-2021-0254




DOI: http://dx.doi.org/10.14203/STIPM.2025.429

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 STI Policy and Management Journal

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright of Journal of STI (Science Technology Innovation) Policy and Management Journal (e-ISSN 2502-5996 p-ISSN 2540-9786). Powered by OJS.