A meta-analysis of empirical e-government research and its future research implications
Abstract
The desired e-government potentials and its shortcomings in reality are key reasons why e-government has become a major topic of interest to academics and practitioners, leading to an extensive body of knowledge. However, the literature still demands further quantitative empirical research to substantiate theory development. This situation calls for a specific review of the literature that arranges relevant knowledge and provides a solid foundation for future research. However, available meta-analyses do not deliver the particular insights to appropriately address the shortage of quantitative empirical e-government research. Therefore, this study explicitly focuses on this specific field to systematically uncover areas requiring further exploration, and defines promising research directions for a solid foundation for future investigations. Key findings of the meta-analysis are: the existence of a systematic divide of existing quantitative empirical e-government studies into 12 research subtopics, which are assessed according to different classification criteria for scientific research gap-spotting; the identification of emerging subtopics that carry innovative research potential; and that e-government is expected to be an ongoing, open-ended research environment that still provides manifold investigative opportunities. Based on these findings, straightforward suggestions for future research are provided. Points for practitioners: Beyond providing insights into the current state of quantitative empirical research for scientific researchers, this article also delivers value for professionals working in public management and administration. First, the study provides a comprehensive overview of e-government-related meta-analyses, which allows us to quickly identify the literature in order to tackle particular e-government management issues. Second, the article classifies existing quantitative empirical studies, defines specific subject areas and arranges relevant knowledge, which eases the processes of confining and labelling e-government activities. Last, since these deliverables are based on empirical studies that draw their conclusions from perceptions of reality, the summaries and classifications are thus regarded to be of special importance for public managers.
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There is a lack of studies that consider both user and provider perspectives in empirical e-government research.
“It should also be noted that, in general, there is a lack of studies that consider both user and provider perspectives in empirical e- government research (Wirtz and Daiser, 2018).”
From Recommendations for digital inclusion in the use of European digital public services (2025) · cites this paper as Wirtz and Daiser, 2018
Cites
Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,888 · see citing works
Research Design: Qualitative, Quantitative, and Mixed Methods Approaches
Sloane-Seale, A. (2009) · Canadian Journal of University Continuing Education · doi:10.21225/d54s3d · Cited by 14,328 · see citing works
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298 · see citing works
Multivariate data analysis : a global perspective
Hair, J. F. (2010) · Cited by 12,258 · see citing works
The Logic of Scientific Discovery
Bhaskar, R. (2013) · Routledge eBooks · doi:10.4324/9780203090732-9 · Cited by 7,765 · see citing works
Qualitative Case Study Methodology: Study Design and Implementation for Novice Researchers
Baxter, P., & Jack, S. (2015) · The Qualitative Report · doi:10.46743/2160-3715/2008.1573 · Cited by 7,594 · see citing works
Analyzing the Past to Prepare for the Future: Writing a Literature Review
Webster, J., & Watson, R. T. (2002) · MIS Quarterly · doi:10.2307/4132319 · Cited by 7,449 · see citing works
Watts, J. H., & Roberson, C. (2013) · Routledge eBooks · doi:10.1201/b16015-6 · Cited by 6,516 · see citing works
Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size.
Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.391 · Cited by 3,833 · see citing works
Sreejesh, S., Mohapatra, S., & Anusree, M. R. (2013) · Springer eBooks · doi:10.1007/978-3-319-00539-3 · Cited by 2,957 · see citing works
The utilization of e-government services: citizen trust, innovation and acceptance factors
Carter, L., & Bélanger, F. (2005) · Information Systems Journal · doi:10.1111/j.1365-2575.2005.00183.x · Cited by 2,207 · see citing works
Statistics without maths for psychology
Dancey, C. P., & Reidy, J. G. (1999) · Cited by 1,261 · see citing works
Linking citizen satisfaction with e-government and trust in government
Welch, E. W. (2004) · Journal of Public Administration Research and Theory · doi:10.1093/jopart/mui021 · Cited by 1,222 · see citing works
Introduction to Structural Equation Modeling: Issues and Practical Considerations
Lei, P.-W., & Wu, Q. (2007) · Educational Measurement Issues and Practice · doi:10.1111/j.1745-3992.2007.00099.x · Cited by 1,084 · see citing works
The Reviewer's Guide to Quantitative Methods in the Social Sciences
(2010) · doi:10.4324/9780203861554 · Cited by 942 · cited 1× · see citing works
“The research goal classifies the studies into confirmatory (hypothesis-testing) and exploratory (finding structures) research approaches (cf. Hancock and Mueller, 2010; Lei and Wu, 2007).”
BackgroundCrossroads—Organizational Performance as a Dependent Variable
March, J. G., & Sutton, R. I. (1997) · Organization Science · doi:10.1287/orsc.8.6.698 · Cited by 825 · see citing works
Methods of Multivariate Analysis
Rencher, A. C., & Christensen, W. F. (2012) · Wiley series in probability and statistics · doi:10.1002/9781118391686 · Cited by 767 · see citing works
The Reviewer’s Guide to Quantitative Methods in the Social Sciences
(2018) · Routledge eBooks · doi:10.4324/9781315755649 · Cited by 758 · see citing works
Ordinal Measures in Multiple Indicator Models: A Simulation Study of Categorization Error
Johnson, D. R., & Creech, J. C. (1983) · American Sociological Review · doi:10.2307/2095231 · Cited by 550 · see citing works
Pearson's R and Coarsely Categorized Measures
Bollen, K. A., & Barb, K. H. (1981) · American Sociological Review · doi:10.2307/2094981 · Cited by 342 · see citing works
10year retrospect on stage models of e-Government: A qualitative meta-synthesis
Lee, J. (2010) · Government Information Quarterly · doi:10.1016/j.giq.2009.12.009 · Cited by 340 · see citing works
Savoldelli, A., Codagnone, C., & Misuraca, G. C. (2014) · Government Information Quarterly · doi:10.1016/j.giq.2014.01.008 · Cited by 312 · see citing works
Building Citizen Trust Through E-government
Parent, M., Vandebeek, C. A., & Gemino, A. (2005) · Government Information Quarterly · doi:10.1016/j.giq.2005.10.001 · Cited by 303 · see citing works
What Drives Key Informant Accuracy?
Homburg, C., Klarmann, M., Reimann, M., & Schilke, O. (2012) · Journal of Marketing Research · doi:10.1509/jmr.09.0174 · Cited by 276 · see citing works
Governance in the digital age: A research and action framework for an uncertain future
Dawes, S. S. (2009) · Government Information Quarterly · doi:10.1016/j.giq.2008.12.003 · Cited by 263 · see citing works
Internet, trust in government, and citizen compliance
Im, T., Cho, W., Porumbescu, G., & Park, J. (2014) · Journal of Public Administration Research and Theory · doi:10.1093/jopart/mus037 · Cited by 245 · see citing works
A meta-analysis of existing research on citizen adoption of e-government
Rana, N. P., Dwivedi, Y. K., & Williams, M. D. (2013) · Information Systems Frontiers · doi:10.1007/s10796-013-9431-z · Cited by 238 · see citing works
Residential Satisfaction in Housing Estates in European Cities: A Multi-level Research Approach
Dekker, K., de Vos, S., Musterd, S., & van Kempen, R. (2011) · Housing Studies · doi:10.1080/02673037.2011.559751 · Cited by 200 · see citing works
E-Government Adoption and Acceptance
Titah, R., & Barki, H. (2006) · International Journal of Electronic Government Research · doi:10.4018/jegr.2006070102 · Cited by 173 · see citing works
Diffusion of e-government: A literature review and directions for future directions
Zhang, H., Xu, X., & Xiao, J. (2014) · Government Information Quarterly · doi:10.1016/j.giq.2013.10.013 · Cited by 152 · see citing works
Tech, V., Bélanger, F., & Carter, L. D. (2012) · Journal of the Association for Information Systems · doi:10.17705/1jais.00295 · Cited by 141 · see citing works
Classification and synthesis of quality approaches in e‐government services
Halaris, C., Magoutas, B., Papadomichelaki, X., et al. (2007) · Internet Research · doi:10.1108/10662240710828058 · Cited by 141 · see citing works
Fads and Facts of E-Government: A Review of Impacts of E-government (2003–2009)
Andersen, K. N., Henriksen, H., Medaglia, R., Danziger, J. Ν., Sannarnes, M. K., & Enemærke, M. (2010) · International Journal of Public Administration · doi:10.1080/01900692.2010.517724 · Cited by 140 · see citing works
E-Government for Better Performance: Promises, Realities, and Challenges
Yang, K., & Rho, S.-Y. (2007) · International Journal of Public Administration · doi:10.1080/01900690701225556 · Cited by 140 · see citing works
Morgeson, F. V., & Mithas, S. (2009) · Public Administration Review · doi:10.1111/j.1540-6210.2009.02021.x · Cited by 125 · see citing works
Logic of Scientific Discovery, The
Barker, P. (2022) · The SAGE Encyclopedia of Research Design · doi:10.4135/9781071812082.n309 · Cited by 92 · see citing works
The Scholarly Literature on E-Government
Norris, D. F., & Lloyd, B. A. (2006) · International Journal of Electronic Government Research · doi:10.4018/jegr.2006100103 · Cited by 90 · see citing works
Towards E-Government 2.0: An Assessment of Where E-Government 2.0 Is and Where It Is Headed
Dixon, B. E. (2010) · Purdue University Indianapolis (Indiana University) · Cited by 90 · see citing works
Homburg, C., Fürst, A., & Koschate, N. (2009) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-009-0172-y · Cited by 82 · see citing works
An analysis of methodologies utilised in e‐government research
Irani, Z., Weerakkody, V., Kamal, M. M., Hindi, N. M., Osman, I. H., Anouze, A. L., El‐Haddadeh, R., Lee, H., Osmani, M., & Al‐Ayoubi, B. (2012) · Journal of Enterprise Information Management · doi:10.1108/17410391211224417 · Cited by 67 · see citing works
Adolescents with Disabilities in High School Setting: Student Characteristics and Setting Dynamics.
Deshler, D. D., Lenz, B. K., Bulgren, J. A., et al. (2004) · Learning disabilities · Cited by 49 · see citing works
E-Government Literature Review
Reece, B. (2006) · Journal of E-Government · doi:10.1300/j399v03n01_05 · Cited by 42 · see citing works
An Analysis of Continuity Intentions of eGovernment Portal Users
Piehler, R., Wirtz, B. W., & Daiser, P. (2014) · Public Management Review · doi:10.1080/14719037.2014.965270 · Cited by 42 · see citing works
Self-citationBig questions of e-government research
Yıldız, M. (2012) · Information Polity · doi:10.3233/ip-2012-000284 · Cited by 41 · see citing works
Public sector benchmarking: a survey of scientific articles, 1990—2005
Braadbaart, O., & Yusnandarshah, B. (2008) · International Review of Administrative Sciences · doi:10.1177/0020852308095311 · Cited by 35 · see citing works
Quality assessment of service bundles for governmental one-stop portals: A literature review
Kohlborn, T. (2014) · Government Information Quarterly · doi:10.1016/j.giq.2013.10.006 · Cited by 22 · see citing works
The distinctive lexicon and consensual conception of e-Government: an exploratory perspective
Hu, G., Pan, W., & Wang, J. (2010) · International Review of Administrative Sciences · doi:10.1177/0020852310372447 · Cited by 10 · see citing works
Bridging the gaps between e-government practice and research
Kromidha, E., & Córdoba‐Pachón, J. (2014) · International Journal of Public Sector Management · doi:10.1108/ijpsm-05-2012-0064 · Cited by 6 · see citing works
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Tassabehji, R., Hackney, R., & Popovič, A. (2016) · Government Information Quarterly · doi:10.1016/j.giq.2016.04.003 · Cited by 103 · see citing works
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- Authors
- Wirtz, B. W., & Daiser, P.
- Affiliations
- German University of Administrative Sciences (DE)
- Corresponding author
- Wirtz, Bernd W.
- Venue
- International Review of Administrative Sciences
- Publisher
- SAGE Publications
- Published
- 2016-07-09
- Volume
- 84
- Issue
- 1
- Pages
- 144-163
- ISSN
- 0020-8523 (ISSN-L)
- 1461-7226
- OpenAlex
- W2460398570
- Cited by
- 112
- References
- 48
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 16 · 2023: 13 · 2022: 17 · 2021: 16 · 2020: 8 · 2019: 4 · 2018: 3 · 2017: 2 · 2016: 4
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Review of Administrative Sciences (publishedVersion)
- Topics
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Public Administration › Public Policy and Administration Research
- Social Sciences › Political Science and International Relations › Public Administration and Political Analysis
- Keywords
- e-government, research gaps, research directions, emerging research areas, public sector management, e-government management
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