The Internet, Policy & Politics Conferences

Oxford Internet Institute, University of Oxford

Nele Leosk: Patterns of Change. A Study of the Relation between the Development of Electronic Governance, Actors, and Institutions

Nele Leosk, European University Institute

1. Introduction and research question

This paper examines the development of e-governance, that stands for the use of the Internet and information and communication technologies (ICTs) by governments to i) provide services, and ii) involve public in the policy making process (adopted Dawes 2008). The main goal of the paper is to understand, basing on an extensive statistical analysis, how e-governance has developed over the past years throughout established democracies; and what has accounted for the changing patterns of e-governance. More specifically, the question of this paper is: To what extent can changes in institutional arrangements, organisational structures and actors’preferences account for the changes in the development patterns of e-governance across time and countries?

2. Theoretical considerations

Recent institutional perspectives on this subject complement the earlier works of technodeterminism, technology adaptation, and innovation diffusion theory; explaining the development of e-governance through path dependency, critical junctures, thresholds/tipping points, and positive/negative feedbacks. These works add both formal institutions such as laws, regulations, and processes; and informal institutions such as routines, norms, and culture to the list of the factors influencing the development of e-governance (Fountain 2004; Fountain 2015; Gil Garcia 2012; Margetts 1999; Margetts 2011). Lately, the role of actors, namely the role of politicians, public officials, businesses, and experts has been discussed in the e-governance literature (Dunleavy et al. 2006; Fountain and Eom 2013); yet, their embeddedness in the institutional environment as well as the interaction between these different groups of actors has been largely ignored. Moreover, little attention has been paid to the long term development of e-governance and the variation we can observe over time.

3. Analytical framework and hypotheses

I derive my theoretical arguments and hypotheses from the technology enactment framework proposed by Jane Fountain in ‘Building the Virtual State. Information Technology and Institutional Change’ (2004), who suggests that objective information technologies are in some way modified by organisational and institutional factors as well as actors to become enacted. I develop the framework further; first, by looking at the potential role of three power level of actors in the development of e-governance and; second, by ascertaining the institutional arrangements and organisational structures that potentially favour (or not) the development of e-governance. Thus, I posit:

Hypothesis 1 ‘actors: It is the users of e-governance (citizens, associations, and businesses) ‘mediated by institutional arrangements ‘that affect the development of e-governance. The higher the social capital (skills, trust, networks) of the users, the higher the development level of e-governance;

Hypothesis 2 ‘organisational structures and processes: The higher the level of vertical and horizontal power diffusion in and between the three groups of actors, the greater the collaboration; and thus, the higher the development level of e-governance;

Hypothesis 3 ‘institutional arrangements: The higher the formal possibilities to participate in e-governance planning, implementation, and monitoring process, i.e., the higher the institutionalisation of the policy making process, the higher the development of e-governance.

4. Methodological set-up

In order to find whether the changes in institutional arrangements, organisational structures, and actors’preferences have an effect on the changing levels of e-governance, this study employs a comparative statistical method. More specifically, I use a time-series cross-sectional (TSCS) data analysis. I employ fixed-effects with vector decomposition (fevd) model (Pl№mper and Troeger 2007).

5. Data and operationalization

The data-set has 41 country-cases (OECD and EU member states) and 14 time-points (years that represent the period between 2001 and 2014). Theoretically, there are 574 cases in the data-set; however, this maximum is never the actual number of the cases included in the analyses, because data on the institutions, organisational structure, and actors is not available for all years and sometimes not for all 41 countries.

The dependent variable the development of e-governance is a country’s overall score in the e-government development index of United Nations (UN) e-Government Survey. EGDI is a weighted average of three normalized scores on important dimensions of e-government, namely, scope and quality of online services, development status of telecommunication infrastructure, and inherent human capital. Each of these sets of indices is itself a composite measure that can be extracted and analyzed independently. I am using the online services index (including e-participation index that can also be extracted and analysed independently) for the proxy of my dependent variable. The independent variable actors refers to the three power-levels of actors: i) decision-makers (politicians, higher public officials, presidents) ii) implementers (public officials: top managers, middle managers, lower civil servants), and iii) users of e-governance (businesses, associations, citizens), and their social capital (trust, skills, networks). The independent variable organizational structures is divided into: i) openness and globalization, ii) vertical and horizontal power diffusion and iii) budgeting and IT spending. The independent variable institutional arrangements uses indicators such as i) institutionalization of policy making process and ii) legislation enabling the development of e-governance. I also introduce other potential variables explaining the development of e-governance such as technological and economic development.

6. Preliminary results

In this study, I do not find any conclusive results concerning the institutional factors that might predict the variance in the level of development of e-governance, suggesting that it is the actors, their preferences and social capital that is driving e-governance development. However, this finding also indicates that the formal institutional environment enabling the participation of the users in the planning, implementation, and monitoring of e-governance (and also enabling collaboration between the actors), does not seem to explain the variance. As the preliminary results show that the higher power diffusion levels lead to higher levels of e-governance (also enabling collaboration between different actors), it can be concluded that the collaboration between the three groups of actors contributes to the higher development of e-governance, yet, it is not determined by the formal institutional environment.


Dawes, S. S. (2008). The evolution and continuing challenges of e’governance. Public Administration Review, 68(s1), S86-S102.

Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). Digital era governance: IT corporations, the state, and e-government. Oxford University Press. Fountain, J. E. (2004). Building the virtual state: Information technology and institutional change. Brookings Institution Press.

Fountain, J. (2015). Institutionalizing Big Data in the Federal Government: Key Issues. In 2015 Fall Conference: The Golden Age of Evidence-Based Policy. Appam.

Fountain, J. E., & Eom, S. J. (2013). Enhancing Information Services through Public-Private Partnerships: Information Technology Knowledge Transfer underlying Structures to Develop Shared Services in the US and Korea.

Gil-Garcia, J. R. (2012). Enacting electronic government success: An integrative study of government-wide websites, organizational capabilities, and institutions (Vol. 31). Springer Science & Business Media.

Margetts, H. (1999). Information technology in government: Britain and America. London: Routledge. Margetts, H., John, P., Escher, T., & Reissfelder, S. (2011). Social information and political participation on the internet: an experiment. European Political Science Review, 3(03), 321-344.

Pl№mper, T., & Troeger, V. E. (2007). Efficient estimation of time-invariant and rarely changing variables in finite sample panel analyses with unit fixed effects. Political Analysis, 15(2), 124-139.

Nele Leosk