AI-Related Technostress, Psychological Needs, and Work Engagement in Digital Counseling

Yanifa Anya; Zenis Fadhilah

doi:10.5281/zenodo.20835642

Authors

Yanifa Anya Department of Psychology, Faculty of Psychology, Universitas 45 Surabaya, Surabaya, Indonesia
Zenis Fadhilah Department of Psychology, Faculty of Psychology, Universitas 45 Surabaya, Surabaya 60256, Indonesia

DOI:

https://doi.org/10.5281/zenodo.20835642

Keywords:

artificial intelligence, technostress, digital counseling, psychological need satisfaction, Work Engagement

Abstract

Purpose – This study investigates how AI-related technostress, digital counseling self-efficacy, psychological empowerment in AI-augmented practice, and perceived trust in AI-assisted psychological support influence work engagement in digital counseling. Drawing on the Job Demands–Resources Model and Self-Determination Theory, the study positions basic psychological need satisfaction at work as a central motivational mechanism linking AI-related psychosocial demands and resources to professional engagement.

Design/methodology/approach – A quantitative, cross-sectional survey design was employed using a structured self-administered questionnaire. Data were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS. The model examined direct and indirect relationships among six latent constructs: AI-related technostress, digital counseling self-efficacy, psychological empowerment in AI-augmented practice, perceived trust in AI-assisted psychological support, basic psychological need satisfaction at work, and work engagement.

Findings – The findings indicate that AI-related technostress weakens basic psychological need satisfaction and work engagement, suggesting that AI can become a psychosocial demand when it creates uncertainty, role ambiguity, and pressure to adapt. In contrast, digital counseling self-efficacy, psychological empowerment, and perceived trust in AI-assisted psychological support strengthen autonomy, competence, relatedness, and engagement. Basic psychological need satisfaction emerges as a key motivational pathway through which AI-related work experiences shape professional engagement.

Originality/value – This study contributes to digital counseling and cyberpsychology literature by explaining AI integration as a psychosocial and motivational process rather than merely a technological readiness issue. It extends the Job Demands–Resources Model and Self-Determination Theory into AI-assisted counseling by showing that sustainable AI use depends on reducing technostress while preserving professional agency, ethical judgment, psychological need satisfaction, and human connection.

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Author Biographies

Yanifa Anya, Department of Psychology, Faculty of Psychology, Universitas 45 Surabaya, Surabaya, Indonesia

Yanifa Anya is affiliated with the Department of Psychology, Faculty of Psychology, Universitas 45 Surabaya, Surabaya, Indonesia. Her research interests focus on digital counseling, cyberpsychology, artificial intelligence in psychological support, technostress, and professional well-being. She is particularly interested in how AI-assisted technologies shape psychological needs, ethical judgment, and work engagement in human-centered counseling practice.
Zenis Fadhilah, Department of Psychology, Faculty of Psychology, Universitas 45 Surabaya, Surabaya 60256, Indonesia

Zenis Fadhilah is affiliated with the Department of Psychology, Faculty of Psychology, Universitas 45 Surabaya, Surabaya, Indonesia. Her academic interests include counseling psychology, digital mental health, AI-assisted psychological services, work engagement, and psychosocial resources in technology-mediated professional practice. Her work emphasizes the importance of maintaining professional agency, trust, and human connection in digital counseling environments.

References

Abd-Alrazaq, A. A., Rababeh, A., Alajlani, M., Bewick, B. M., & Househ, M. (2020). Effectiveness and safety of using chatbots to improve mental health: Systematic review and meta-analysis. In Journal of Medical Internet Research (Vol. 22, Number 7). https://doi.org/10.2196/16021

Adams, C., Pente, P., Lemermeyer, G., & Rockwell, G. (2023). Ethical principles for artificial intelligence in K-12 education. Computers and Education: Artificial Intelligence, 4. https://doi.org/10.1016/j.caeai.2023.100131

Albannai, N. A. A., & Raziq, M. M. (2025). Navigating ethical, human-centric leadership in AI-driven organizations: a thematic literature review. Service Industries Journal. https://doi.org/10.1080/02642069.2025.2534360

Autin, K. L., Herdt, M. E., Garcia, R. G., & Ezema, G. N. (2022). Basic Psychological Need Satisfaction, Autonomous Motivation, and Meaningful Work: A Self-Determination Theory Perspective. Journal of Career Assessment, 30(1). https://doi.org/10.1177/10690727211018647

Bakker, A. B., & de Vries, J. D. (2021). Job Demands–Resources theory and self-regulation: new explanations and remedies for job burnout. Anxiety, Stress and Coping, 34(1). https://doi.org/10.1080/10615806.2020.1797695

Baltar, F., & Brunet, I. (2012). Social research 2.0: Virtual snowball sampling method using Facebook. Internet Research, 22(1). https://doi.org/10.1108/10662241211199960

Baumeister, V. M., Kuen, L. P., Bruckes, M., & Schewe, G. (2021). The Relationship of Work-Related ICT Use With Well-being, Incorporating the Role of Resources and Demands: A Meta-Analysis. SAGE Open, 11(4). https://doi.org/10.1177/21582440211061560

Boucher, E. M., Harake, N. R., Ward, H. E., Stoeckl, S. E., Vargas, J., Minkel, J., Parks, A. C., & Zilca, R. (2021). Artificially intelligent chatbots in digital mental health interventions: a review. Expert Review of Medical Devices, 18(sup1). https://doi.org/10.1080/17434440.2021.2013200

Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138. https://doi.org/10.1016/j.chb.2022.107468

Chiu, T. K. F., Ahmad, Z., & Çoban, M. (2025). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5). https://doi.org/10.1007/s10639-024-13094-z

Coxen, L., van der Vaart, L., Van den Broeck, A., & Rothmann, S. (2021). Basic Psychological Needs in the Work Context: A Systematic Literature Review of Diary Studies. In Frontiers in Psychology (Vol. 12). https://doi.org/10.3389/fpsyg.2021.698526

Dragano, N., & Lunau, T. (2020). Technostress at work and mental health: concepts and research results. In Current Opinion in Psychiatry (Vol. 33, Number 4). https://doi.org/10.1097/YCO.0000000000000613

Evans, J. R., & Mathur, A. (2005). Internet Research"Social research 2.0: virtual snowball sampling method using Facebook" The value of online surveys. Internet Research Intelligence & Planning Internet Research, 15(1).

Ezeugwa, B., Talukder, M. F., Amin, M. R., Hossain, S. I., & Arslan, F. (2022). Minimum Sample Size estimation in SEM: Contrasting results for Models Using Composites and Factors. Data Analysis Perspectives Journal, 3(1).

Glikson, E., & Woolley, A. W. (2020a). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2). https://doi.org/10.5465/annals.2018.0057

Glikson, E., & Woolley, A. W. (2020b). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals (in press). Academy of Management Annals, 14(2).

Gong, Y., Wu, Y., Huang, P., Yan, X., & Luo, Z. (2020). Psychological Empowerment and Work Engagement as Mediating Roles Between Trait Emotional Intelligence and Job Satisfaction. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.00232

Gutierrez, G., Stephenson, C., Eadie, J., Asadpour, K., & Alavi, N. (2024). Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review. In Frontiers in Psychiatry (Vol. 15). https://doi.org/10.3389/fpsyt.2024.1356773

Hair, J. F., Tomas, H. G., Ringle, C. M., & Marko, S. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2).

Henseler, J., Lee, N., Roemer, E., Kemény, I., Dirsehan, T., & Cadogan, J. W. (2024). Beware of the Woozle effect and belief perseverance in the PLS-SEM literature! Electronic Commerce Research, 24(2). https://doi.org/10.1007/s10660-024-09849-y

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in Education: Towards a Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32(3). https://doi.org/10.1007/s40593-021-00239-1

Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1). https://doi.org/10.1111/isj.12131

Llorente-Alonso, M., García-Ael, C., & Topa, G. (2024). A meta-analysis of psychological empowerment: Antecedents, organizational outcomes, and moderating variables. Current Psychology, 43(2). https://doi.org/10.1007/s12144-023-04369-8

Lucas, M., Zhang, Y., Bem-haja, P., & Vicente, P. N. (2024). The interplay between teachers’ trust in artificial intelligence and digital competence. Education and Information Technologies, 29(17). https://doi.org/10.1007/s10639-024-12772-2

Mazzetti, G., Robledo, E., Vignoli, M., Topa, G., Guglielmi, D., & Schaufeli, W. B. (2023). Work Engagement: A meta-Analysis Using the Job Demands-Resources Model. In Psychological Reports (Vol. 126, Number 3). https://doi.org/10.1177/00332941211051988

Nazir, T. (2026). Therapists and Technology: A Qualitative Study on AI′s Role in Counseling. Human Behavior and Emerging Technologies, 2026(1), 3616773. https://doi.org/https://doi.org/10.1155/hbe2/3616773

Nisafani, A. S., Kiely, G., & Mahony, C. (2020). Workers’ technostress: a review of its causes, strains, inhibitors, and impacts. Journal of Decision Systems, 29(sup1). https://doi.org/10.1080/12460125.2020.1796286

Pan, X. (2020). Technology Acceptance, Technological Self-Efficacy, and Attitude Toward Technology-Based Self-Directed Learning: Learning Motivation as a Mediator. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.564294

Parker, S. K., & Grote, G. (2022). Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World. Applied Psychology, 71(4). https://doi.org/10.1111/apps.12241

Safaei, M. (2021). Identifying and ranking the factors affecting the establishment of mobile payment systems (A case study of Iran). Journal of FinTech and Artificial Intelligence.

Yan, Y., Liu, H., Zhang, H., Chau, T., & Li, J. (2025). Designing a Generalist Education AI Framework for Multimodal Learning and Ethical Data Governance. Applied Sciences (Switzerland), 15(14). https://doi.org/10.3390/app15147758