Determinants of E-Government Portal Adoption in Pakistan: Evidence from Diffusion of Innovations Theory
DOI:
https://doi.org/10.62019/8mejmg23Keywords:
E-government, e-government portals, e-government portals adoption, Diffusion of Innovation Theory, Behavioral intentions to use e-government.Abstract
Electronic government is the use of the internet to provide government facilities and services, at any time and from anywhere. Successful e-government implementation through portals necessitates a thorough grasp of the system as well as the resolution of technical and non-technical difficulties from the standpoint of citizens. Several countries, notably developing countries, are implementing e-government systems; yet, the utilization pattern remains unclear. The ultimate goal of this study was to discover the determining factors for the adoption of e-government portals in Pakistan from citizens' viewpoints, using the Diffusion of Innovation Theory. To collect user perception, the study used a quantitative cross-sectional technique, with a Likert scale questionnaire survey. The survey included 345 residents from the urban areas of Sindh, Pakistan. Participants were chosen based on their familiarity with e-government portals, relevance, and convenience. The study used path analysis with AMOS software and structural equation modeling to examine the complex relationships between variables. The results show that behavioral intentions to use e-government portals are positively and significantly impacted by the Trialability, Observability, and Relative Advantage variables. The Compatibility and Complexity variables did not, however, appear to have a positive impact on behavioral intentions for the adoption of e-government sites. The study suggests that in order to boost e-government portal adoption, the government should make successful e-government portal usage more visible, offer opportunities for users to test out such services, and clearly communicate the perceived advantages of e-government portals over traditional methods.
References
Abu-Shanab, E. (2014). Antecedents of trust in e-government services: an empirical test in Jordan. Transforming Government: People, Process and Policy, 8(4), 480–499. https://doi.org/10.1108/TG-08-2013-0027
Alzahrani, L., Al-Karaghouli, W., & Weerakkody, V. (2017). Analysing the critical factors influencing trust in e-government adoption from citizens’ perspective: A systematic review and a conceptual framework. International Business Review, 26(1), 164–175. https://doi.org/10.1016/j.ibusrev.2016.06.004
Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4(3), 205–225. https://doi.org/10.1016/S1467-0895(03)00026-5
Carter, L., & Bélanger, F. (2005). The utilization of e‐government services: citizen trust, innovation and acceptance factors*. Information Systems Journal, 15(1), 5–25. https://doi.org/10.1111/j.1365-2575.2005.00183.x
Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Routledge. https://doi.org/10.4324/9780203771587
Colesca, S. E., & Dobrica, L. (2008). Adoption and use of E-Government services: The case of Romania. Journal of Applied Research and Technology, 6(03). https://doi.org/10.22201/icat.16656423.2008.6.03.526
Dearing, J. W. (2009). Applying Diffusion of Innovation Theory to Intervention Development. Research on Social Work Practice, 19(5), 503–518. https://doi.org/10.1177/1049731509335569
Dillman, D. A., & Smyth, J. D. (2007). Design Effects in the Transition to Web-Based Surveys. American Journal of Preventive Medicine, 32(5), S90–S96. https://doi.org/10.1016/j.amepre.2007.03.008
Dingfelder, H. E., & Mandell, D. S. (2011). Bridging the Research-to-Practice Gap in Autism Intervention: An Application of Diffusion of Innovation Theory. Journal of Autism and Developmental Disorders, 41(5), 597–609. https://doi.org/10.1007/s10803-010-1081-0
GREENHALGH, T., ROBERT, G., MACFARLANE, F., BATE, P., & KYRIAKIDOU, O. (2004). Diffusion of Innovations in Service Organizations: Systematic Review and Recommendations. The Milbank Quarterly, 82(4), 581–629. https://doi.org/10.1111/j.0887-378X.2004.00325.x
Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research. Management Science, 52(12), 1865–1883. https://doi.org/10.1287/mnsc.1060.0597
McDonald, T., & Siegall, M. (1992). The Effects of Technological Self-Efficacy and Job Focus on Job Performance, Attitudes, and Withdrawal Behaviors. The Journal of Psychology, 126(5), 465–475. https://doi.org/10.1080/00223980.1992.10543380
Mustonen‐Ollila, E., & Lyytinen, K. (2003). Why organizations adopt information system process innovations: a longitudinal study using Diffusion of Innovation theory. Information Systems Journal, 13(3), 275–297. https://doi.org/10.1046/j.1365-2575.2003.00141.x
Rana, N. P., Dwivedi, Y. K., Lal, B., Williams, M. D., & Clement, M. (2017). Citizens’ adoption of an electronic government system: towards a unified view. Information Systems Frontiers, 19(3), 549–568. https://doi.org/10.1007/s10796-015-9613-y
References: A-priori Sample Size for Structural Equation Models. (n.d.). Free Statistics Calculators. Retrieved April 22, 2026, from https://www.danielsoper.com/statcalc/references.aspx?id=89
Rahman, M. (2023). Identifying Evidence-Based Strategies to Strengthen the Ability of Social Enterprises to Scale Health Impact in Low-and Middle-Income Countries (Doctoral dissertation, Doctoral dissertation, Duke University) (Doctoral dissertation, Doctoral dissertation, Duke University).
Rahman, M. (2025). Persistent Environmental Pollutants and Cancer Outcomes: Evidences from Community Cohort Studies. Indus Journal of Bioscience Research, 3(8), 561-568.
Rahman, M. (2024). Molecular Epidemiology of Uranium Exposure: Omics Approaches in Cancer Research. Indus Journal of Bioscience Research, 2(1), 25-31.
Robertson, T. S. (1967). The Process of Innovation and the Diffusion of Innovation. Journal of Marketing, 31(1), 14. https://doi.org/10.2307/1249295
Rahman, M. (2024). Molecular Epidemiology of Uranium Exposure: Omics Approaches in Cancer Research. Indus Journal of Bioscience Research, 2(1), 25-31.
Sanson‐Fisher, R. W. (2004). Diffusion of innovation theory for clinical change. Medical Journal of Australia, 180(S6). https://doi.org/10.5694/j.1326-5377.2004.tb05947.x
Sim, J., & Lewis, M. (2012). The size of a pilot study for a clinical trial should be calculated in relation to considerations of precision and efficiency. Journal of Clinical Epidemiology, 65(3), 301–308. https://doi.org/10.1016/j.jclinepi.2011.07.011
Wang, H.-J., & Lo, J. (2016). Adoption of open government data among government agencies. Government Information Quarterly, 33(1), 80–88. https://doi.org/10.1016/j.giq.2015.11.004
Ahmad, N. R. (n.d.). International Journal of Business and Management Sciences. BigBio Researchers Publishers.
Ahmad, N. R. (n.d.). AI-enabled public governance in developing states: Service delivery gains, accountability risks, and a practical risk-based regulatory model. https://doi.org/10.52152/wja5db40
Ahmad, N. R. (n.d.). The impact of fintech startups on financial innovation and stability in Pakistan’s evolving financial landscape. Punjab Model Bazaars Management Company, Lahore.
Ahmad, N. R. (n.d.). Sustainable business strategies for achieving competitive advantage in Pakistan’s developing economy. https://doi.org/10.63878/qrjs361
Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. Free Press.
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—Applications and challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103
Doz, Y., & Kosonen, M. (2010). Embedding strategic agility: A leadership agenda for accelerating business model renewal. Long Range Planning, 43(2–3), 370–382. https://doi.org/10.1016/j.lrp.2009.07.006
Lee, I., & Shin, Y. J. (2018). Fintech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46. https://doi.org/10.1016/j.bushor.2017.09.003
Hart, S. L., & Dowell, G. (2011). A natural-resource-based view of the firm: Fifteen years after. Journal of Management, 37(5), 1464–1479. https://doi.org/10.1177/0149206310390219
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