Employee Concerns Related to Technological Changes in Future Work: A Network Analysis of Organizational Work Factors in Norway
References
- Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022). Artificial intelligence and jobs: Evidence from online vacancies. Journal of Labor Economics, 40(S1), 293–340. 10.1086/718327
- Alekseeva, L., José, A., Mireia, G., & Sampsa, S. (2024). AI Adoption and the Demand for Managerial Expertise. S&P Global Market Intelligence Research Paper Series. 10.2139/ssrn.4986379
- Alekseeva, L., José, A., Mireia, G., Sampsa, S., & Bledi, T. (2021). The demand for AI skills in the labor market. Labour Economics, 71. 10.1016/j.labeco.2021.102002
- Bai, S., Zhang, X., Yu, D., & Yao, J. (2024). Assist me or replace me? Uncovering the influence of AI awareness on employees’ counterproductive work behaviors. Frontiers in Public Health, 12, 1449561. 10.3389/fpubh.2024.1449561
- Bailey, D., Faraj, S., Hinds, P., Leonardi, P., & Von Krogh, G. (2022). We are all theorists of technology now: A relational perspective on emerging technology and organizing. Organization Science, 33(1), 1–18. 10.1287/orsc.2021.1562
- Barley, S. (2020). Work and technological change. Oxford University Press. 10.1093/oso/9780198795209.003.0002
- Bessen, J. E. (2019).
AI and jobs: The role of demand . In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda. University of Chicago Press. 10.3386/w24235 - Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121. 10.1146/annurev-clinpsy-050212-185608
- Browne, M. W., & Cudeck, R. (1993).
Alternative ways of assessing model fit . In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage. - Caselli, M., Fracasso, A., & Marcolin, A. (2024). Technological innovations and workers’ job insecurity: The moderating role of human resource strategies. Journal of Industrial and Business Economics, 52, 153–176. 10.1007/s40812-024-00329-w
- Christensen, J. O., Vleeshouwersa, J., Finne, L. B., Sørensen, K., Rasmussen, L., & Kristiansen, J. (2021). Future of work – Work environment and occupational health. A modified Delphi survey of work environment experts in Norway and Denmark. STAMI-rapport/ISSN nr.1502-0932.
- Coupé, T. (2019). Automation, job characteristics and job insecurity. International Journal of Manpower, 40(7), 1288–1304. 10.1108/ijm-12-2018-0418
- 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. 10.1207/S15327965PLI1104_01
- Derks, D., & Bakker, A. B. (2014). Smartphone use, work-home interference, and burnout: A diary study on the role of recovery. Applied Psychology, 63(3), 411–440. 10.1111/j.1464-0597.2012.00530.x
- Dølvik, J. E., & Steen, J. R. (2018).
The nordic future of work – drivers, institutions, and politics. Report TemaNord 2018. The Nordic Council of Ministers. 10.6027/TN2018-555 - Elliot, M., & Arthur, R. (2020).
Organizational justice: Does “IT” matter? Empirical analysis of the influence of information technology on employee justice perceptions . In I. Nunes (Ed.), Advances in human factors and systems interaction (Vol. 1207, pp. 83–89). Springer. 10.1007/978-3-030-51369-6_12 - Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior research methods, 50(1), 195–212. 10.3758/s13428-017-0862-1
- Epskamp, S., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(8), 1–18. 10.18637/jss.v048.i04
- Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617–634. 10.1037/met0000167
- Frank, M., Autor, D., Bessen, J., Brynjolfsson, E., Cebrián, M., Deming, D., Feldman, M., Groh, M., Lobo, J., Moro, E., Wang, D., Youn, H., & Rahwan, I. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences of the United States of America, 116, 6531–6539. 10.1073/pnas.1900949116
- Friedman, J., Hastie, T., & Tibshirani, R. (2019). glasso: Graphical lasso—Estimation of Gaussian graphical models (R package version 1.11).
https://CRAN.R-project.org/package=glasso - Gusti, M., Satrianto, A. C., Juniardi, E., & Fitra, H. (2024). Artificial intelligence for employee engagement and productivity. Problems and Perspectives in Management, 22(3), 174–184. 10.21511/ppm.22(3).2024.14
- Harayama, Y., Milano, M., Baldwin, R., Antonin, C., Berg, J., Karvar, A., & Wyckoff, A. (2021).
Artificial intelligence and the future of work . In B. Braunschweig & M. Ghallab (Eds.), Reflections on artificial intelligence for humanity. Lecture Notes in Computer Science (Vol. 12600, pp. 53–67). Springer. 10.1007/978-3-030-69128-8_4 - Harris, K., Harris, R., Carlson, J., & Carlson, D. (2015). Resource loss from technology overload and its impact on work-family conflict: Can leaders help? Computers in Human Behavior, 50, 411–417. 10.1016/j.chb.2015.04.023
- Hesselbarth, Y., Alfes, K., & Festing, M. (2024). Understanding technology-driven work arrangements from a complexity perspective: A systematic literature review and an agenda for future research. The International Journal of Human Resource Management, 35, 964–1006. 10.1080/09585192.2023.2298244
- Hornung, S. (2024). Faces of Power in Organizations: Foundations, Applications, and Extension. International Journal of Behavior Studies in Organizations, 12, 1–22. 10.32038/jbso.2024.12.01
- Hu, D., Oh, I.-S., & Agolli, A. (2025). A meta-analysis of employee HR attributions and their relationships with employee-perceived high-performance work systems and employee outcomes. Human Resource Management, 64, 1281–1303. 10.1002/hrm.22309
- 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. 10.1080/10705519909540118
- ICHW. (2025).
The Work Futures Initiative: Unfiltered voices. Part 1: The first 10 sandboxes . (Vol.https://healthyworkplaces.berkeley.edu/work-futures-initiative ). University of California, Berkeley: Interdisciplinary Center for Healthy Workplaces (ICHW). - Ivanov, S., Kuyumdzhiev, M., & Webster, C. (2020). Automation fears: Drivers and solutions. Technology in Society, 63, 101431. 10.1016/j.techsoc.2020.101431
- Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. 10.1016/j.bushor.2018.03.007
- Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. 10.5465/annals.2018.0174
- Liu, J., Carminati, L., & Wilderom, C. (2025). Work alienation through the dialectical lens. Applied Psychology, 74(1),
e12600 . 10.1111/apps.12600 - Liu, N., Wang, Y., & Lin, Y. (2024). Employees’ adaptation to technology uncertainty in the digital era: An exploration through the lens of job demands–resources theory. IEEE Transactions on Engineering Management, 71, 7286–7297. 10.1109/TEM.2023.3264293
- Lloyd, C., & Payne, J. (2019). Rethinking country effects: Robotics, AI and work futures in Norway and the UK. New Technology, Work and Employment, 34(3), 208–225. 10.1111/ntwe.12149
- Matthijs Bal, P. et al. (2024).
The Psychology of Automation and Artificial Intelligence at Work: Exploring Four Fantasies and Their Leadership Implications . In A. Akande (Eds.), Power, Politics and Influence. Springer Studies on Populism, Identity Politics and Social Justice, Springer, Cham. 10.1007/978-3-031-69610-7_22 - Morandini, S., Fraboni, F., Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The Impact of Artificial Intelligence on Workers’ Skills: Upskilling and Reskilling in Organisations. Informing Science: International Journal of an Emerging Transdiscipline, 26, 39–68. 10.28945/5078
- Norman, D. (2017). Design, business models, and human technology teamwork. Research-Technology Management, 60, 26–30. 10.1080/08956308.2017.1255051
- Olaniyi, O., Ezeugwa, F., Okatta, C., Arigbabu, A., & Joeaneke, P. (2024). Dynamics of the digital workforce: Assessing the interplay and impact of AI, automation, and employment policies. Archives of Current Research International, 24(5), 124–139. 10.9734/acri/2024/v24i5690
- Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 100838. 10.1016/j.hrmr.2021.100838
- 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), 1171–1204. 10.1111/apps.12241
- R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
https://www.R-project.org/ - Raisch, S., & Fomina, K. (2024). Combining human and artificial intelligence: Hybrid problem-solving in organizations. Academy of Management Review, 50(2). 10.5465/amr.2021.0421
- Revelle, W. (2022). psych: Procedures for Psychological, Psychometric, and Personality Research. In
https://CRAN.R-project.org/package=psych . - Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology, 125(6), 747–757. 10.1037/abn0000181
- Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. 10.18637/jss.v048.i02
- Rožman, M., Oreški, D., & Tominc, P. (2022). Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprises. Frontiers in Psychology, 13. 10.3389/fpsyg.2022.1014434
- Sandoval-Reyes, J., Acosta-Prado, J., & Sanchís-Pedregosa, C. (2019). Relationship amongst technology use, work overload, and psychological detachment from work. International Journal of Environmental Research and Public Health, 16(23), 4602. 10.3390/ijerph16234602
- Santoni de Sio, F., Almeida, T., & van den Hoven, J. (2021). The future of work: Freedom, justice and capital in the age of artificial intelligence. Critical Review of International Social and Political Philosophy, 27(5), 659–683. 10.1080/13698230.2021.2008204
- Tabachnick, B., & Fidell, L. (Eds.). (2014). Using multivariate statistics (6th ed.). Pearson Education.
- Teufer, B., Lang, G., Affengruber, L., & Grillich, L. (2020). Challenges and opportunities of digitalization for health and well-being at work. European Journal of Public Health, 30, ckaa165.029. 10.1093/eurpub/ckaa165.029
- Tommasi, F., Degen, J. L., Orhan, M. A., & Sartori, R. (2025). Exploring Work and Organizational Psychology in the Metaverse: A Critical Research Agenda. Scandinavian Journal of Work and Organizational Psychology, 10(1): 1, 1–12. 10.16993/sjwop.266
- Van Der Aalst, W. M. P. (2021). Hybrid Intelligence: To automate or not to automate, that is the question. International Journal of Information Systems and Project Management, 9(2), 5–20. 10.12821/IJISPM090201
- Yam, K., Tang, P., Jackson, J., Su, R., & Gray, K. (2022). The rise of robots increases job insecurity and maladaptive workplace behaviors: Multimethod evidence. Journal of Applied Psychology, 108(5), 850–870. 10.1037/apl0001045
DOI: https://doi.org/10.16993/sjwop.395 | Journal eISSN: 2002-2867
Language: English
Page range: 4 - 4
Submitted on: Mar 15, 2025
Accepted on: Nov 13, 2025
Published on: Feb 17, 2026
Published by: Stockholm University Press
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© 2026 Emmanuel Aboagye, Frederick Anyan, Siw Tone Innstrand, published by Stockholm University Press
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