The Dual Role of ERMS in Digital Transformation and Mental Health Outcomes: An Empirical Study of Polytechnic Staff in Nigeria

This study examines the dual role of Electronic Records Management Systems (ERMS) as both a job resource and a potential job demand in the context of workplace digital transformation. Drawing on an integrated TOE and Job Demands-Resources (JD-R) framework, the study surveyed 400 academic and non-academic staff across six Nigerian polytechnics using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results revealed that ERMS practices significantly enhance workplace digitalization outcomes (β = 0.67, p < .001), which in turn positively predict work engagement and job satisfaction (β = 0.58 and 0.53, p < .001) and negatively predict stress and emotional exhaustion (β = −0.42 and −0.39, p < .05). ERMS demonstrated both a significant indirect effect (β = 0.38, 95% CI [0.25, 0.52]) and a direct effect (β = 0.21, p = .045) on employee mental health. The findings confirm the dual nature of ERMS in digital transformation, functioning primarily as a valuable job resource while potentially becoming a demand when implementation support is inadequate. This study provides important empirical evidence for human-centric digitalization strategies in resource-constrained higher education institutions in developing countries.

Electronic Records Management Systems, ERMS

1. Abdulkareem, A. K., Ishola, A. A., Bello, M. L., & Adejumo, A. (2024). The dark side of digitalization: Examining the impact of digital overload on job autonomy and job satisfaction. Journal of Information, Communication and Ethics in Society, 22(3), 354–371. https://doi.org/10.1108/JICES-07-2023-0091 [Google Scholar] [Crossref]

2. 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 [Google Scholar] [Crossref]

3. Bech, P., Olsen, L. R., Kjoller, M., & Rasmussen, N. K. (2003). Measuring well-being rather than the absence of distress symptoms: A comparison of the SF-36 Mental Health subscale and the WHO-5 Well-Being Scale. International Journal of Methods in Psychiatric Research, 12(2), 85–91. https://doi.org/10.1002/mpr.145 [Google Scholar] [Crossref]

4. Borle, P., Boerner-Zobel, F., Voelter-Mahlknecht, S., Hasselhorn, H. M., & Ebener, M. (2021). The social and health implications of digital work intensification: Associations between exposure to information and communication technologies, health and work ability in different socio-economic strata. International Archives of Occupational and Environmental Health, 94(3), 377–390. https://doi.org/10.1007/s00420-020-01588-5 [Google Scholar] [Crossref]

5. Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385–396. https://doi.org/10.2307/2136404 [Google Scholar] [Crossref]

6. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. [Google Scholar] [Crossref]

7. DePietro, R., Wiarda, G., & Fleischer, M. (1990). The context for change: Organization, technology and environment. In R. A. Goodman & L. M. Atay (Eds.), Organizational change and information technology (pp. 59–74). AFCEA International Press. [Google Scholar] [Crossref]

8. Dragano, N., & Lunau, T. (2020). Technostress at work and mental health: Concepts and research results. Current Opinion in Psychiatry, 33(4), 407–413. https://doi.org/10.1097/YCO.0000000000000613 [Google Scholar] [Crossref]

9. Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11 [Google Scholar] [Crossref]

10. Federal Ministry of Communications and Digital Economy. (2021). Nigeria e-Government master plan (2021–2025). https://fmst.gov.ng/wp-content/uploads/2021/09/Nigeria-eGov-Master-Plan-2021.pdf [Google Scholar] [Crossref]

11. Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data? MIS Quarterly, 36(3), 981–1001. https://doi.org/10.2307/41703484 [Google Scholar] [Crossref]

12. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications. [Google Scholar] [Crossref]

13. Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis (3rd ed.). Guilford Press. [Google Scholar] [Crossref]

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

15. Israel, G. D. (2013). Determining sample size. University of Florida IFAS Extension. https://edis.ifas.ufl.edu/publication/PD004 [Google Scholar] [Crossref]

16. Kelley, K. (2023). pwr2ppl: Power and sample size for the people (R package version 0.4.0) [Computer software]. https://CRAN.R-project.org/package=pwr2ppl [Google Scholar] [Crossref]

17. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308 [Google Scholar] [Crossref]

18. Lohr, S. L. (2022). Sampling: Design and analysis (3rd ed.). Chapman & Hall/CRC. [Google Scholar] [Crossref]

19. Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). Maslach Burnout Inventory manual (3rd ed.). Consulting Psychologists Press. [Google Scholar] [Crossref]

20. National Board for Technical Education. (2024). Statistical digest of Nigerian polytechnics: 2024 edition.https://www.nbte.gov.ng/wpcontent/uploads/2025/01/NBTE_Statistical_Digest_2024.pdf [Google Scholar] [Crossref]

21. National Information Technology Development Agency. (2021). National Digital Literacy Framework & Infrastructure Assessment Template. https://nitda.gov.ng/wp-content/uploads/2021/11/National-Digital-Literacy-Framework.pdf [Google Scholar] [Crossref]

22. Nigerian Communications Commission. (2023). ICT sectoral report: Internet and digital infrastructure access (Q3 2023) (Report No. NCC/STAT/ICT/2023/09). https://www.ncc.gov.ng/images/Statistics/ICT_Sectoral_Report_Q3_2023.pdf [Google Scholar] [Crossref]

23. Office of the Secretary to the Government of the Federation. (2021). National policy on regional development and equity (Gazette No. FSG/B/841/1/Vol.1/92). https://policies.gov.ng/wp-content/uploads/2021/03/National-Policy-on-Regional-Development-and-Equity.pdf [Google Scholar] [Crossref]

24. Owolabi, R. O., Banjo, B. Y., Dada, R. O., Olaseigbe, Y. F., Ladele, M. F., & Aroyewun, A. (2025). The influence of work culture and digital information resources use on knowledge sharing practices of health professionals in state-owned hospitals in Oyo State, Nigeria. Seybold Report Journal, 20(5), 165–188. https://doi.org/10.5281/zenodo.15473320 [Google Scholar] [Crossref]

25. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

26. Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). SAGE Publications. [Google Scholar] [Crossref]

27. Santomauro, D. F., Mantilla Herrera, A. M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D. M., … Ferrari, A. J. (2021). Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet, 398(10312), 1700–1712. https://doi.org/10.1016/S0140-6736(21)02143-7 [Google Scholar] [Crossref]

28. Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2021). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 29(3), 302–327. https://doi.org/10.1016/j.ausmj.2019.06.001 [Google Scholar] [Crossref]

29. Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire. Educational and Psychological Measurement, 66(4), 701–716. https://doi.org/10.1177/0013164405282471 [Google Scholar] [Crossref]

30. Schoemann, A. M., Boulton, A. J., & Short, S. D. (2017). Determining power and sample size for simple and complex mediation models. Social Psychological and Personality Science, 8(4), 379–386. https://doi.org/10.1177/1948550617715068 [Google Scholar] [Crossref]

31. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson. [Google Scholar] [Crossref]

32. Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books. [Google Scholar] [Crossref]

33. Udoro, H. O., & Osakwe, S. O. (2024). Digital electronic record systems and administrative efficiency of the civil service in Delta State, Nigeria. Greekline Journal of Management and Administrative Studies, 1(2), 45–62. [Google Scholar] [Crossref]

34. Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond “p < 0.05”. The American Statistician, 73(Suppl. 1), 1–19. https://doi.org/10.1080/00031305.2019.1583913 [Google Scholar] [Crossref]

35. World Health Organization. (2022). World mental health report: Transforming mental health for all. https://www.who.int/publications/i/item/9789240049338 [Google Scholar] [Crossref]

• Tribal Child Nutrition and Health in District of Sundargarh: A Public Health Review of ICDS Intervention
• Knowledge, Attitudes and Practices Towards Prostate Cancer Screening Amongst Men Aged 40-60 Years in The Buea Health District: A Cross-Sectional Study
• Compliance with JCI Protocols: A Focus on Employee Safety
• Influence and Involvement of Teachers in Menstrual Hygiene Management of Female Secondary School Students in Kogi State, Nigeria
• A Critical Evaluation of Ayushman Bharat Pradhan Mantri Jan Arogya Yojana in Bihar