HomeRecoletos Multidisciplinary Research Journalvol. 13 no. 2 (2025)

Determinants of Switching Intentions to E-Health Services among Healthcare Service Users: A Push–Pull–Mooring Perspective from Islamabad, Pakistan

Mariam Dilawar | Collins Dodzi Dzitse | Inaya Siddiqui

Discipline: healthcare science (non-specific)

 

Abstract:

Background: Technology has reshaped healthcare delivery, particularly during and after the COVID-19 pandemic, by providing faster, safer, and more accessible alternatives to in-person care. However, there is limited evidence on the factors and behavioral drivers of e-health adoption in Pakistan. This study investigates the factors influencing switching intentions among healthcare users in Islamabad, Pakistan. Methods: A quantitative cross-sectional survey was conducted to collect data from 930 healthcare service users in Islamabad, Pakistan. Structural Equation Modelling (SEM) was used to test the hypothesized relationships. Results: The findings revealed that the push factors of inconvenience (0.251) and perceived risk (-0.221) and the mooring factors of trust (0.442) and switching cost (− 0.211) were significantly associated with switching intentions (p< 0.05), whereas the pull factors of ubiquitous care and opportunity for alternatives had no significant relationship with the switching intentions of healthcare service users. Conclusion: The study concludes that inconvenience with traditional hospital services, risk perceptions, trust in e-services, and perceived switching costs are key drivers of e-health adoption in Islamabad. These findings underscore the need for user-centered digital health strategies that address both infrastructural and behavioral concerns to accelerate equitable healthcare transformation in the new era of technological development.



References:

  1. Adjekum, A., Blasimme, A., & Vayena, E. (2018). Elements of trust in digital health systems: Scoping review. Journal of Medical Internet Research, 20(12), e11254. https://doi.org/10.2196/11254
  2. Ahmed, A., & Ahmed, M. (2018). The telemedicine landscape in Pakistan: Why are we falling behind? Journal of the Pakistan Medical Association, 68(12), 1820–1822. https://pubmed.ncbi.nlm.nih.gov/30504948/
  3. Alamri, H. M., & Alshagrawi, S. (2024). Factors influencing telehealth adoption in managing healthcare in Saudi Arabia: A systematic review. Journal of Multidisciplinary Healthcare, 17, 5225–5235. https://doi.org/10.2147/JMDH.S498125
  4. Bansal, H. S. (2005). “Migrating” to new service providers: Toward a unifying framework of consumers’ switching behaviors. Journal of the Academy of Marketing Science, 33(1), 96–115. https://doi.org/10.1177/0092070304267928
  5. Barony Sanchez, R. H., Bergeron-Drolet, L.-A., Sasseville, M., & Gagnon, M.-P. (2022). Engaging patients and citizens in digital health technology development through the virtual space. Frontiers in Medical Technology, 4, 958571. https://doi.org/10.3389/fmedt.2022.958571
  6. Benoit, S., Klose, S., & Ettinger, A. (2017). Linking service convenience to satisfaction: Dimensions and key moderators. Journal of Services Marketing, 31(6), 527–538. https://doi.org/10.1108/JSM-10-2016-0353
  7. Bokolo, A. J. (2021). Exploring the adoption of telemedicine and virtual software for care of outpatients during and after COVID-19 pandemic. Irish Journal of Medical Science, 190, 1–10. https://doi.org/10.1007/s11845-020-02299-z
  8. Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge.
  9. Dahl, A. J., Peltier, J. W., & Swan, E. L. (2023). Anticipatory value-in-use in early-stage digital health service transformations: How consumers assess value propositions before and after abrupt, exogenous shocks. Journal of Business Research, 163, 113910. https://doi.org/10.1016/j.jbusres.2023.113910
  10. Dogra, N., Bakshi, S., & Gupta, A. (2023). Exploring the switching intention of patients to e-health consultation platforms: Blending inertia with push–pull–mooring framework. Journal of Asia Business Studies, 17(1), 15–37. https://doi.org/10.1108/JABS-02-2021-0066
  11. Elhadi, M., Elhadi, A., Bouhuwaish, A., Bin Alshiteewi, F., Elmabrouk, A., Alsuyihili, A., Alhashimi, A., Khel, S., Elgherwi, A., Alsoufi, A., Albakoush, A., & Abdulmalik, A. (2021). Telemedicine awareness, knowledge, attitude, and skills of health care workers in a low-resource country during the COVID-19 pandemic: Cross-sectional study. Journal of Medical Internet Research, 23(3), e20812. https://doi.org/10.2196/20812
  12. Fornell, C., & Larcker, D. (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
  13. Frishammar, J., Essén, A., Bergström, F., & Ekman, T. (2023). Digital health platforms for the elderly? Key adoption and usage barriers and ways to address them. Technological Forecasting and Social Change, 189, 122319. https://doi.org/10.1016/j.techfore.2023.122319
  14. Galavi, Z., Pourasad, M. H., Norouzi, S., Jahani, Y., & Khajouei, R. (2023). Public usage, perceived usefulness, and satisfaction with e-health services in COVID-19 pandemic. Journal of Clinical Research in Paramedical Sciences, 11(2), e133719. https://doi.org/10.5812/jcrps-133719
  15. Gong, S., Zhang, L., & Zhao, X. (2025). Association between e-health usage and consideration for clinical trial participation: An exploratory study on the mediating role of cancer-related self-efficacy and patient-centered communication. Digital Health, 11, 1–14. https://doi.org/10.1177/20552076251328598
  16. Grand View Research. (n.d.). Digital health market size and share industry report, 2025–2030. https://www.grandviewresearch.com/industry-analysis/digital-health-market
  17. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
  18. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
  19. Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press
  20. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. https://doi.org/10.1177/001316446002000116
  21. Khan, S. J., Asif, M., Aslam, S., Khan, W. J., & Hamza, S. A. (2023). Pakistan’s healthcare system: A review of major challenges and the first comprehensive universal health coverage initiative. Cureus, 15(5), e44641. https://doi.org/10.7759/cureus.44641
  22. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
  23. Krishnan, G., & Raghuram, J. N. V. (2023). Exploring factors and contextual applications of the push-pull-mooring (PPM) framework in switching intention: A systematic literature review. Multidisciplinary Reviews, 7(1), e2024003. https://doi.org/10.31893/multirev.2024003
  24. Liu, F., Li, Y., & Ju, X. (2019). Exploring patients’ consultation behaviors in the online health community: The role of disease risk. Telemedicine and e-Health, 25(3), 213–220. https://doi.org/10.1089/tmj.2018.0033
  25. Liu, C., Wang, J., Chen, R., & Zhou, W. (2024). Exploring the influence of Chinese online patient trust on telemedicine behavior: insights into perceived risk and behavior intention. Frontiers in Public Health, 12, 1415889. https://doi.org/10.3389/fpubh.2024.1415889
  26. Longino, C. F., Jr. (1992). The forest and the trees: Micro-level considerations in the study of geographic mobility in old age. In A. Rogers (Ed.), Elderly migration and population redistribution (pp. 23–34). Routledge.
  27. Marx, T. (2025). The push-pull-mooring model of consumer service switching: A meta-analytical review to guide future research. Journal of Service Theory and Practice, 35(7), 1–29. https://doi.org/10.1108/JSTP-06-2024-0201
  28. 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, 879–891. https://doi.org/10.3758/BRM.40.3.879
  29. Reddy, A., Arthur, J., Dalal, S., Hui, D., Subbiah, I., Wu, J., Anderson, A. E., Castro, D., Joy, M., Nweke, C., Gogineni, M., Maddi, R., de Moraes, A. R., Shelal, Z., & Bruera, E. (2021). Rapid transition to virtual care during the COVID-19 epidemic: Experience of a supportive care clinic at a tertiary care cancer center. Journal of Palliative Medicine, 24(10), 1467–1473. https://doi.org/10.1089/jpm.2020.0737
  30. Snoswell, C. L., Chelberg, G., De Guzman, K. R., Haydon, H. M., Thomas, E. E., Caffery, L. J., & Smith, A. C. (2021). The clinical effectiveness of telehealth: A systematic review of meta-analyses from 2010 to 2019. Journal of Telemedicine and Telecare, 29(9), 669–684. https://doi.org/10.1177/1357633X211022907
  31. Statista. (2024). Digital health – Statistics and facts. https://www.statista.com/topics/2409/digital-health/
  32. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
  33. Vimalananda, V. G., Orlander, J. D., Afable, M. K., Fincke, B. G., Solch, A. K., Rinne, S. T., Kim, E. J., Cutrona, S. L., Thomas, D. D., & Strymish, J. L. (2020). Electronic consultations (e-consults) and their outcomes: A systematic review. Journal of the American Medical Informatics Association, 27(3), 471–479. https://doi.org/10.1093/jamia/ocz185
  34. Wu, H., & Deng, Z. (2019). Knowledge collaboration among physicians in online health communities: A transactive memory perspective. International Journal of Information Management, 49, 13–33. https://doi.org/10.1016/j.ijinfomgt.2019.01.003
  35. Zhang, Q., Zhang, R., Lu, X., & Zhang, X. (2023). What drives the adoption of online health communities? An empirical study from patient-centric perspective. BMC Health Services Research, 23(1), 1-15. https://doi.org/10.1186/s12913-023-09469-6
  36. Zhang, Y., & Wu, P. (2024). Continuous adoption of online healthcare platforms: An extension to the expectation confirmation model and network externalities. BMC Public Health, 24, 1-20. https://doi.org/10.1186/s12889-024-20072-0