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International
Public
Management
Journal
STAGES AND DETERMINANTS OF E-GOVERNMENT
DEVELOPMENT: A TWELVE-YEAR LONGITUDINAL
STUDY OF GLOBAL CITIES
ALEX INGRAMS
TILBURG UNIVERSITY
AROON MANOHARAN
UNIVERSITY OF MASSACHUSETTS BOSTON
LISA SCHMIDTHUBER
JOHANNES KEPLER UNIVERSITY LINZ
MARC HOLZER
SUFFOLK UNIVERSITY
ABSTRACT: Global e-government innovations are at the forefront of municipal efforts
to be better organized and more efficient in delivering services and improving outcomes
for the public. Scholars have argued that such innovations are embedded in institutional
and environmental factors, and municipal e-government growth evolves through stages
as a result of the effects of these factors. However, existing studies rarely model the
distinct success factors of the different stages. This article addresses that shortcoming
with data from the largest cities in the world’s top 100 “most wired”countries from
2003 to 2016. Cluster analysis addresses whether there are any consistent growth
trends, and finds that there are four clusters of e-government development. Regression
analysis tests whether stages may be driven by specific factors, and findings reveal that
e-government stages mostly have uniform drivers. Population size, GDP, and regional
#the Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-
NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use,
distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, trans-
formed, or built upon in any way.
International Public Management Journal, Vol 0(0), pages 1–39
DOI: 10.1080/10967494.2018.1467987 ISSN: 1096-7494 print/1559-3169 online
competition have a positive association across all stages. However, democracy level
appears to have a more ambiguous status, as it influences some higher stages in large
countries but has a negative association in small countries.
INTRODUCTION
E-government is the electronic delivery of information and services to citizens,
business, and public administration (Lee et al. 2008). Scholars of e-government have
learned how modern information and communication technologies (ICTs) spread
around the world through public and private sector innovation, and have become
relied upon by governments as tools of service provision and organization (Dunleavy
1994; Pratchett 1999). The stages model theories of e-government have emerged to
address e-government diffusion and evolution (e.g., Andersen and Henriksen 2006;
Lee, Chang, and Berry 2011; Layne and Lee 2001; Reddick 2004). In this theoretical
vein, e-government research can sometimes seem to imply a kind of technological
determinism, as if the maturity of e-governments through stages of development is
inevitable. In fact, the picture is more mixed. According to a large number of empir-
ical studies, determining factors do appear to be strongly influential in e-government
development. Chief among these determining factors are financial resources and
gross domestic product (GDP); socio-economic factors of the local citizen body, such
as education, wealth, and technology access; and political factors, such as the role of
elected officials (Gallego-!
Alvarez, Rodr!
ıguez-Dom!
ınguez, and Garc!
ıa-S!
anchez 2010;
Manoharan 2013b;Rodr
!
ıguez, S!
anchez, and !
Alvarez 2011). Additionally, determi-
nants of e-government maturity vary considerably within or between countries, levels
of government, or types of e-government (Bons!
on et al. 2012; Gallego-!
Alvarez et al.
2010; Holden, Norris, and Fletcher 2003;Moon2002; Norris and Moon 2005),
which means that e-government development is certainly neither inevitable
nor unilinear.
Despite evidence of variation, research has not yet tested analytical models that
explain antecedents of the e-government stages over time. E-government might
mature on multiple, interrelated fronts with different variables influencing each
stage, and not necessarily in a linear order. While a minimum set of economic, insti-
tutional, political, and social conditions must be present for the early stages, it may
be that more advanced levels of e-government success later rely on different sets of
factors rather than maturing uniformly (Ahn 2011). However, understanding of
such variations in determinants at different e-government stages has been limited by
a lack of multi-country longitudinal studies. Such studies can account for the fac-
tors of time and global variation to make stronger arguments about causality and
to explain what stages exist and how they come about. The authors of this article
suggest that there are two main reasons why such longitudinal and multi-country
analyses are vital for advancing the field. The first is that knowledge of human phe-
nomena relies upon theories that stand up to scrutiny in a range of country and cul-
tural contexts, with the possibility of accounting for trends over time (Cetina 2009;
Merton 1968). The second is that e-government is a phenomenon that lends itself to
2 International Public Management Journal Vol. 0, No. 0, 2018
global forms of policy innovation. E-government is based on the global network of
the Internet, which encourages global patterns of change. In this article, we explore
these global patterns in e-government development and test findings from earlier
studies on the determinants of e-government stages, with greater attention to spe-
cific dimensions and country contexts. Our research question is therefore two-fold:
(1) What evidence exists for global trends in e-government stages? (2) What factors
explain the development of the different e-government stages?
In order to address these questions, the article draws on cluster analysis and
panel analysis of the Global E-Governance Survey, which is a biennial evaluation
of official websites of the largest cities of the 100 “most wired”countries in the
world. The survey started in 2003 and there have been seven subsequent surveys,
the last covering 2016. The range of countries and the date ranges therefore make
the survey an ideal data source for investigating global patterns of e-government
development using a longitudinal perspective. The theoretical section of the article
will present stage model theories of how e-government evolves and how a gov-
ernment’s social, political, and economic context shapes success in different stages
of e-government. A systematic literature review that brings together research in
public administration, public management, and information science presents theor-
etical arguments and hypotheses for determining factors in e-government develop-
ment worldwide. The article then outlines the research methodology and describes
the data settings and the measures we are using for analyses. After the presentation
of the results, the final section covers discussion of the findings, presents best prac-
tice recommendations, refers to the contributions and implications of our study,
and points to directions for future research.
THEORETICAL FOUNDATIONS
E-Government Stages
Many e-government authors have investigated the development of e-government
and proposed different types of stage models. Layne and Lee (2001) were among
the first to describe e-government as “an evolutionary phenomenon,”and pro-
posed four stages of e-government. In the first stage, cataloguing, governments pro-
vide citizens with access to online information. The second stage, transaction,
involves adoption of interactive processes between government and citizens, which
results in higher efficiencies in service delivery. The third and fourth stages, vertical
integration and horizontal integration, cover the transformation and re-
conceptualization of government services; for example, such changes may occur
through a portal where citizens can access services from different governments, and
thus require the collaboration of governments from different levels.
Since Layne and Lee’s work, a civic dimension has been added to the online
information and service integration stages of e-government so as to create three
major classifications for e-government dimensions: e-information, e-services, and
e-participation (Lee, Chang, and Berry 2014; Manoharan 2013a; Nam 2014). For
example, Moon (2002) incorporated a civic stage that includes political participation
3STAGES AND DETERMINANTS OF E-GOVERNMENT
through e-government, and thus marks an important step in recognizing political
maturity alongside administrative maturity of e-government. In subsequent litera-
ture, the political and social elements of e-government maturity gained more prom-
inence. Andersen and Henriksen (2006) proposed an extension of the model by
Layne and Lee, involving a first phase of “cultivation”encompassing vertical and
horizontal integration within government. A second phase focuses on the import-
ance of a personalized web interface for customer processes. A third phase moves
towards an increased level of transparency and problem-solving mechanisms in
citizen exchange, and a fourth phase involves greater citizen-orientation through
systematic data mobility across organizations and transfer of data ownership.
Some authors have also analyzed the extent to which governments have progressed
through the stages thus far. Moon (2002) assessed the adoption of e-government in
1,471 municipal governments with populations over 10,000. He concluded that only
a few governments have moved into the participative stage, and fewer integrate
electronic service delivery across levels of government. Similarly, Reddick’s(2004)
study of American municipalities showed that the majority of cities have already
implemented services of Stage 1, such as online provision of news and information.
However, interactive services of Stage 2, such as the provision of online procurement
tools, have been marginally adopted by municipalities. Coursey and Norris (2008)
also found that, while the percentage of cities offering financial transactions (e.g., tax
payments) and nonfinancial transactions (e.g., service requests) increased between
2000 and 2004, advanced stages of e-government adoption remained low. Based
on these results from previous empirical studies, we expect that distinct stages of
e-government around the world evidence incrementally limited development of later
stages of e-government adoption. That is, the curve of e-government stages is
positively skewed, with the majority of world cities in lower stages of e-information
with limited integration of service levels or citizen participation.
H1: The e-government level of municipalities around the world shows
distinct stages of development, with the majority of municipalities
concentrated in information provision, with lesser levels of
progression to service and then participation stages.
Factors Influencing E-Government Development
Beyond assessing stages of digital maturity, we examine which factors influence
the three different dimensions of the e-government stages. Prior literature on
e-government offers a large range of factors that influence its adoption and develop-
ment, which are often referred to as “success factors”(Gil-Garcia and Pardo 2005;
Jacobson and Ok Choi 2008). These are shown in Table 1. A systematic review of
articles, book chapters, and conference papers from public administration, public
management, and information science was carried out using the Web of Science
electronic library, and the conference proceedings of specialist e-governance confer-
ences, the conference of the Digital Government Society, Electronic Government
4 International Public Management Journal Vol. 0, No. 0, 2018
TABLE 1
Summary of Findings on Antecedents of E-Government Success across Three Stages
E-government (general) E-information E-services E-participation
Environmental factors
Population
size (large)
Brudney and Selden, 1995;
Das, Singh, and Joseph,
2017; Gulati, Williams, and
Yates et al., 2014; Ho and
Ni, 2004; Holden, Norris,
and Fletcher, 2003;
Homburg and Dijkshoorn,
2012; Manoharan 2013b;
Moon, 2002; Norris and
Moon, 2005; Nelson and
Svara, 2012; Stier, 2015
Ahn, 2011; Feeney and
Brown, 2017; Gallego-
!
Alvarez et al., 2010;
Reddick and
Norris, 2013
Feeney and Brown, 2017;
Gallego-!
Alvarez et al.,
2010; Reddick, 2009;
Reddick and
Norris, 2013
Feeney and Brown, 2017;
Gallego-!
Alvarez et al.,
2010; Garc!
ıa-S!
anchez
et al., 2011; Guillamo!n
et al., 2016; Reddick
and Norris, 2013
Internet and ICT
infrastructure
Azad et al., 2010; Gulati et
al., 2014; Holzer and Kim,
2008; Holzer, You, and
Manoharan 2010; Holzer
and Manoharan, 2012;
Ifinedo, 2012; Singh, Das,
and Joseph, 2007; Stier,
2015; Titah and Barki, 2006
Gallego-!
Alvarez
et al., 2010
Bhatti, Olsen, and Holm
Pedersen, 2011;
Gallego-!
Alvarez
et al., 2010
Åstr€
om et al., 2012;
Gallego-!
Alvarez et al.,
2010; Gallego-S!
anchez
et al., 2011; Krishnan,
Teo, and Lymm, 2017
Socio-economic
characteristics
(Education)
Gulati et al. 2014; Holzer,
Zheng, and Manoharan,
2014;
Jun and Weare, 2010;
Manoharan, 2013b; Nelson
Reddick and
Norris, 2013
Reddick and
Norris, 2013.
Krishnan, Teo, and
Lymm, 2017; Reddick
and Norris, 2013
(continued)
5
TABLE 1
Continued
E-government (general) E-information E-services E-participation
and Svara, 2012; Pick and
Azari, 2008; Stier, 2015
Citizen demand Faniran and Olaniyan, 2009;
Ho and Ni, 2004; Lee,
Chang, and Berry, 2011;
Solvodelli et al., 2012; Zhao
et al., 2014
Ahn, 2011 Bhatti et al., 2011; Wang
and Feeney, 2016
Guillam!
on et al., 2016
Institutional factors
Political competition Lee et al. (2011) Ahn, 2011
Political support Ho and Ni, 2004; Puron-Cid,
2012; Schwester, 2009; Yun
and Opheim, 2010
Gallego-!
Alvarez
et al., 2011
Democratic institu-
tions and norms
Aladwani, 2016; Azad et al.,
2010; Basu, 2004; Berntzen
and Karamagioli, 2010;
Eom, 2013; Gulati et al.,
2014; Ho and Ni, 2004;
Ifinedo, 2012; Khalil, 2011;
Lee et al. 2011; Puron-Cid,
2012; Singh, Das, and
Joseph, 2007; Stier, 2015;
Zhao, Shen, and
Collier, 2014
Level of development Holzer and Kim, 2004; Holzer
and Kim, 2006; Holzer and
Kim, 2008; Holzer, You,
6
TABLE 1
Continued
E-government (general) E-information E-services E-participation
and Manoharan, 2010;
Holzer and Manoharan,
2012; Holzer, Zheng, and
Manoharan, 2014; Holzer
and Manoharan, 2016
Elected munici-
pal manager
Feeney and Brown, 2017 Feeney and Brown, 2017 Feeney and Brown, 2017;
Zheng, Schachter, and
Holzer, 2014
Appointed munici-
pal manager
Brudney and Selden, 1995;
Moon, 2002; Nelson and
Svara, 2012
Reddick and
Norris, 2013
Reddick, 2009
Internal capacity factors
Technical capacity Ifenido, 2012; Jun and Weare,
2010; Norris and Moon,
2005; Puron-Cid, 2012;
Schwester, 2009; Tolbert,
Mossberger, and
McNeal, 2008
Ahn, 2011 Reddick, 2004 Ma, 2014
Administrative
professionalism
Brudney and Selden, 1995;
Manoharan, 2013b; Norris
and Moon, 2005; Titah and
Barki, 2006; Van Veenstra
et al., 2010; Yun and
Opheim, 2010
Bhatti 2011; Wang and
Feeney, 2016
Lau et al., 2008;
Ma, 2014
(continued)
7
TABLE 1
Continued
E-government (general) E-information E-services E-participation
Wealth of public
organizations
Azad et al., 2010; Das, Singh,
and Joseph, 2017; Gulati et
al., 2014; Ifenido, 2012;
Pick and Azari, 2008;
Puron-Cid, 2012; Schwester,
2009; Singh, Das, and
Joseph, 2007; Tolbert,
Mossberger, and
McNeal, 2008
Ahn, 2011 Bhatti, et al. 2011;
Gallego-!
Alvarez
et al., 2010
Gallego-!
Alvarez
et al., 2010
Years of experience Jun and Weare, 2010;
Manoharan, 2013b; Yun
and Opheim, 2010
Reddick and
Norris, 2013
Reddick and
Norris, 2013
Reddick and
Norris, 2013
8
conference (EGOV-IFIP-CeDEM-ePart), and the United Nations E-Gov conference
(full bibliography in Appendix A). In our theoretical section, we have included as
many of the variables as possible that have been found in prior works. In some
cases, we have had to choose between two very similar variables from the literature
that are either worded almost identically or operationalized in similar ways. In
other cases where there are disagreements in the literature or where evidence is
sparse, we lean on other logical and empirical arguments to make decisions or to
theorize more explicitly about causal mechanisms in e-governance stages.
We have chosen to present our model using a three-part institutionalist frame-
work that sees government innovation as the result of internal and external charac-
teristics of public organizations (Frumkin and Galaskiewicz 2004; Manoharan and
Ingrams, 2018; Moon 2002). According to Eom (2013:880), this “institutional
approach helps e-government researchers not only to locate constraints on actors
in e-government policy processes and structural factors that influence the interac-
tions among them, but also to focus on mediators of the impact of information
technologies and various environmental factors.”Following the literature review,
we found a difference between internal technical factors, on the one hand, and
legal and political factors on the other. We therefore discuss internal and institu-
tional factors separately, in addition to the environmental factors. In our discus-
sion of the determinants, we specifically mention the variables that have an effect
on all e-government dimensions and those that are expected to have an effect on
just one or two specific dimensions.
Environmental factors
Population size is a major environmental factor that helps to encourage
municipalities to develop their e-government provisions (e.g., Ahn 2011;
Brudney and Selden 1995;Leeetal.2011;Rogers1995). This appears to hold
across e-information (e.g., Ahn 2011;Gallego-
!
Alvarez et al. 2010;Reddickand
Norris 2013), e-services (e.g., Gallego- !
Alvarez et al. 2010;Reddick2009;
Reddick and Norris 2013), and e-participation (e.g., Gallego- !
Alvarez et al.
2010;Garc
!
ıa-S!
anchez et al. 2011;Guillamo!netal.2016).Governmentplays
the role of a supplier and must meet the expectations of a densely populated
area with a large number of people by being more innovative (Rogers 1995;
Weare, Musso, and Hale 1999). Even across major global cities, population
size varies widely.
Another function of the responsiveness of government policymakers to public char-
acteristics concerns the characteristics that are often grouped under socio-economic
variables or “human capital,”such as education level. Education has been shown to
correlate with higher demand for public information, government services, and a will-
ingness to be more participative in the governance of public affairs (Putnam 1995).
According to Tolbert, Mossberger, and McNeal (2008), e-government innovation is
determined by innovation capacity, reflected largely in local citizen demographics such
as level of urbanization, wealth, and education. Similar to wealth and population size,
9STAGES AND DETERMINANTS OF E-GOVERNMENT
education is expected to influence all three e-government dimensions, which has been
confirmed in a prior study by Reddick and Norris (2013)oflocalgovernmentsinthe
United States.
H2: Population size is positively associated with higher e-information,
e-services, and e-participation development.
H3: Education level is positively associated with higher e-information,
e-services, and e-participation development.
Internal capacity factors
Perhaps one of the most intuitive and widely confirmed findings about the antece-
dents of e-government development is the relationship between e-government devel-
opment and the wealth and financial resources of public organizations, as measured
by GDP or taxable wealth (e.g., Bhatti et al. 2011; Gallego-!
Alvarez et al. 2010).
Regardless of environmental or leadership pressure to innovate, all e-government
dimensions require significant investment in technology. Therefore, wealthy govern-
ments with a healthy financial situation are more likely to adopt innovative e-gov-
ernment tools (Edmiston 2003;Ma2014; Tolbert et al. 2008).
In addition to the financial resources, e-government planning and implementa-
tion involve knowledge gained through learning and leadership (Lee et al. 2011). In
trying to explain why governments may evolve slowly through the e-government
stages, Ho (2002) suggests that there is a learning curve that is driven by the grad-
ual accumulation and application of new knowledge. Several studies have found
that governments with more years of experience working on e-government plat-
forms such as websites develop a better capacity and skill (e.g., Jun and Weare
2010; Manoharan 2013b; Yun and Opheim 2010). Layne and Lee’s(2001) original
model suggested that the stages are sequential because e-government capacities
build on prior stages; transactions depend on an organizational foundation of elec-
tronic information sharing and integration.
Organizational leadership also shapes the planning and capacity that leads to
adoption of innovations in e-government (Bhatti et al. 2011; Wang and Feeney
2016). Sophisticated innovations, such as micro-blogging and publishing financial
reports online, depend on a government leader who is internally motivated to
develop such programs to improve efficiency or transparency (Ho and Ni 2004;
Ma 2014). Eom (2013) found that e-government development resulted from having
officials in favorable positions of legal authority and technological expertise, and
Norris and Moon (2005) found that organizations with shortages of such expertise
by way of inadequate technology and web skills experience barriers to adopting
better e-government applications. These qualities of leadership are normally sub-
sumed under the responsibility of a chief information officer (CIO).
10 International Public Management Journal Vol. 0, No. 0, 2018
H4: Financial capacity (GDP per capita) is positively associated with
higher e-information, e-services, and e-participation development.
H5: Years of experience are positively associated with e-information,
e-services, and e-participation development.
H6: Technological leadership is positively associated with higher
e-information, e-services, and e-participation development.
Institutional factors
According to Gallego-!
Alvarez et al. (2010), different types of government organiza-
tions and politics play a role in shaping the e-government innovations. Prior work
suggests that competition among governments and pursuit of peer-to-peer legitim-
acy (Jun and Weare 2010) affect leadership responsiveness and the ability to adopt
new policies and programs (Zhang and Feiock 2009). Governments compete with
other neighboring governments to gain the respect of citizens and their satisfaction
with services (Ho and Ni 2004; Lee et al. 2011). Lee, Chang, and Berry’s study
considers the competition between neighboring country governments as a key
driver of e-government diffusion, but it makes sense that major cities of neighbor-
ing countries would also compete in this way. Neighboring cities compete by devel-
oping e-government infrastructure to become more economically attractive to
investors (Jun and Weare 2010). Governments are thus expected to be responsive
to other governments with which they share geographic proximity and to innovate
more in e-government if those neighbors are strong performers.
A substantial amount of theoretical and empirical research has also been under-
taken linking characteristics of democratic countries, such as “effective gov-
ernance,”and e-government (Azad et al. 2010; Eom 2013; Gulati et al., 2014; Stier
2015). Findings suggest that institutional arrangements can make the difference
between a highly successful e-services initiative and a failing initiative (Eom 2013;
Gulati et al. 2014). On the other hand, Gulati et al. (2014) argue that evidence of
the effect of democratic political structures is mixed, but further attention is needed
on the relationships between institutional arrangements and specific types of e-gov-
ernment. Despite extensive research on e-democracy and e-participation, there is
very little work on the connection between democratic characteristics and transac-
tional and interactional dimensions of e-government.
According to Stier (2015), certain democratic and legal prerequisites need to be
in place before e-government initiatives can be successful, but autocratic govern-
ments are also making progress. Some cities that score quite low on global meas-
ures of democracy, such as Singapore, attain high e-government levels (Calista and
Melitski 2007; Lee et al. 2011). Calista and Melitski (2007) suggest that autocratic
countries may eagerly embark on e-government initiatives, but have little interest
in the interactional dimensions. However, in empirical findings, the relationship
between democracy and higher stages of development remains unclear.
Historically, democratic countries have led innovation in e-government. So, while
11STAGES AND DETERMINANTS OF E-GOVERNMENT
evidence is mixed, it is likely, in the long term, that giving citizens more of a say
and facilitating open communication in government policies and programs drive
higher success in e-services and e-participation adoption.
H7: Regional competition is positively associated with e-information,
e-services, and e-participation development.
H8: Democracy is positively associated with e-services and e-participation
development.
The hypothesized determinants of the three stages of e-government development
are shown in Figure 1. Environmental and internal capacity factors are influential
at all stages of development. Institutional factors are shown at the bottom of the
diagram in two tracks. One consists of mimetic processes of competition and learn-
ing from other governments in the region. Like environmental and internal factors,
these are continuous throughout. The second institutional factor is the governance
factors, including democratic level and transparency norms. It is these institutional
factors which make a difference at the higher levels of e-government development,
e-services, and e-participation.
DATA AND METHODS
Setting and Data Collection
The survey instrument used in the research is based on the Rutgers E-
Governance Performance Index (Holzer and Manoharan 2016), one of the most
comprehensive instruments for e-government research, with 104 measures and five
distinct categorical areas. The survey assessed the official websites of the largest
cities in the world in five categories: two of the categories relate to website e-infor-
mation (information content and usability); two relate to website e-services
E-information
E-service
E-participation
Environmental factors
Population size (H2) and education level (H3) of citizens
Internal capacity factors
GDP (H4), years of experience (H5), and technological leadership (H6)
Institutional factors
Regional competition (H7)
Institutional factors
Democratic level (H8)
Figure 1. Conceptual model of e-government determinants.
12 International Public Management Journal Vol. 0, No. 0, 2018
(privacy and e-services); and one category relates to website e-participation (citizen
engagement). The five categories aim to capture the e-government levels of matur-
ity in the design of municipal websites as an interface for government-citizen infor-
mation and interaction (Layne and Lee 2001; Moon 2002; United Nations 2010).
Each category included 17–23 measures, which were coded into ordinal scales
where the high number shows full success in a website’s adoption of a feature such
as a privacy statement (in the privacy category) or a clear menu (in the usability
category). The municipalities evaluated were selected based on population size and
the percentage of individuals using the Internet. Using data from the International
Telecommunication Union (ITU), the top 100 wired nations were identified, and
the largest cities by population in each of those nations were selected for the study
as surrogates for their countries (shown in Appendix B). To ensure inter-rater reli-
ability, each municipal website was evaluated in the native language by two indi-
viduals, and websites with significant variation (more than 10%) were analyzed by
a third evaluator. Evaluators were also provided with sufficient examples to guide
how the variables needed to be measured and were given comprehensive written
instructions for assessing the websites.
Analytical Methods
Two methods are employed in the analysis: (1) cluster analysis and (2) time
series regression. To derive patterns of cities in e-government stages, the method
first involved conducting a hierarchical cluster analysis using five e-government
scores. Similar to other studies on e-government (e.g., Holzer, Manoharan, and
Van Ryzin 2010), cluster analysis is applied to group cities to construct types
which should reflect the level of e-government sophistication. The variables were
standardized by z-score transformation and used Euclidean distances with
Ward’smethodofclustering(AldenderferandBlashfield1984). To investigate
the groups of e-government across time, a cluster analysis was run using data
from 2003, 2009, and 2016.
Second, we conducted analyses on unbalanced panel data to identify the fac-
tors influencing e-government stages across municipalities. The time series ana-
lysis uses random effects estimates, taking cities as units of analysis across
biannual survey years (2003–2016). Random effects estimates are ideal for models
that may include unobserved heterogeneity in the units of analysis and when pre-
dictors remain constant over time. Further, a Hausman test supported the null
hypothesis that a random effects model is a better estimator than fixed effects.
Newey West robust standard errors are clustered at the city level to control for
unit fixed effects. Robust standard errors also provide wider confidence intervals,
which is a precaution here, given slightly high levels of multicollinearity in one
model with a 9.88 variance inflation factor (VIF). The other 17 models were in a
normal VIF zone of 0.85–4.80. A further reason for robust estimates is that
Breusch-Pagan tests in five of the models suggested that variance of the error
terms is not constant.
13STAGES AND DETERMINANTS OF E-GOVERNMENT
Measures
Dependent variables
Content. Content is the first dependent variable that measures the level of e-infor-
mation. It uses 25 items to assess five e-information aspects: contact information,
public documents, disability access, multimedia materials, and time-sensitive infor-
mation. The content for contact information included the hours of operation for
government offices, access to city codes and city charters, agency mission state-
ments, minutes of public meetings, and budget information. The website was eval-
uated for the availability of multilingual content, and how it addresses the
requirements of disabled users. The website was also evaluated for the presence of
audio or video files of public meetings or speeches, calendar of events, and to what
extent it integrated the emergency alert systems.
Usability. The category of usability is the second dependent variable for e-
information level. It determined the “user-friendliness”of websites’information
based on 19 indicators. These include specific features such as the use of hypertext
markup language (HTML), branding and structure (e.g., color consistency, font,
graphics, and page length), and if the website indicated the system hardware and
software requirements for the users. Related features, such as targeted audience
links, use of navigation bars, hyperlinks, and links to the homepage on every web
page, were also assessed during the evaluation. Finally, the website search tool was
checked in terms of ease of use and the capacity to sort the results as required by
the users.
Privacy and security. This category is the first e-services dependent variable. It
included 19 measures that focused on two particular aspects of user transactions:
the presence of privacy policies and the possibility of user authentication. The
evaluation assessed whether users who make an online transaction have access to
personal information to report inaccurate information, as well as administrative
steps taken by the government to address these privacy concerns, such as encryp-
tion, secure servers, and digital signatures. Finally, the survey assessed the inten-
tion of websites to monitor citizen activities through cookies and web beacons.
Services. The services category is the second e-services measure. It is composed of
20 items that gauge the extent to which official websites facilitated the provision of
public services to citizens, which include those that allow the users to communicate
and interact with the city and those that allow users to register online for public
events. The initial types of questions focused on the interactivity of websites in terms
of reporting crimes or violations, accessing private information (such as medical
records or education background), and web portal customization. The questions per-
taining to online registration included permits, licenses, online procurement, and the
ability to access and bid requests for proposals online. Finally, the availability of
transactional services, such as online payments of bills, parking tickets, fees and
fines, and online tax payment systems, was determined.
Citizen and social engagement. This category is the single measure used in the
research for municipal e-participation level. It employs 17 items to evaluate users’
14 International Public Management Journal Vol. 0, No. 0, 2018
ability to provide feedback to public administrators and elected officials, along
with the presence of discussion boards, blogs, and policy forums. The category
also included questions that pertain to the provision of current and accurate infor-
mation by governments through online newsletters, listservs, and social media
channels. Surveys are another important avenue for gathering citizen feedback on
government policy and implementation process, and these were also determined on
the official websites.
Overall score. Finally, one dependent variable is an overall e-government score
that is calculated by adding the five categories together.
Independent variables
Each of the seven independent variables is derived from a different secondary data
source. Measurement items are shown in Table 2, and descriptive statistics are shown
in Table 3. Measurements were taken every other year in seven surveys between
2003 and 2016. Due to slight changes in the 100 cities evaluated in each survey year,
there are 118 cities that have been evaluated, but only 82 of these have been consist-
ently part of at least five of the seven surveys. Therefore, in order to minimize the
estimation problems of missing data, we chose to focus only on these 82 cities in the
panel regression.
Environmental variables. Among the environmental variables, education level was
operationalized using data from the QS Global University Rankings to establish
the number of universities from the Global E-Governance Survey cities that feature
in the top 500 universities in the world. In order to weight the data for better-
quality universities as opposed to the quantity, two points were given for a univer-
sity in the top 100 of the ranking and one point was given for a ranking below 100.
Each city’s score was therefore an additive score of the number of quality univer-
sities in the city. Finally, population size was measured using World Bank statistics
on city population size.
Internal capacity variables. GDP per capita is used as a proxy for the financial
capacity of the local government and comes from the Brookings Global Metro
Monitor. The Brookings data are incomplete for years prior to 2013, and so figures
from earlier years are extrapolated using the average annual rate of GDP growth
between the years 2000 and 2014 according to the Global Metro Monitor. The
years of experience variable was operationalized using the age of the city’s website.
Older websites indicate longer experience of creating e-government programs
through websites. The online tool “Wayback Machine”was used to identify the
year when the website was launched. The data for whether the city has a CIO were
obtained from organizational charts publicly available on the city website.
Occasionally, organizational charts could not be found, and thus 73 observations
are not included.
Institutional variables. Among the institutional variables, regional competition is
operationalized by the mean e-governance index score of the geographic regions
where the cities are located. This measure relied on the regional average scores
15STAGES AND DETERMINANTS OF E-GOVERNMENT
TABLE 2
Descriptive of Data Operationalization and Sources
Measure Source
Dependent variables
Total score Combined score of other scores (privacy,
usability, content, services,
and engagement)
Global E-governance survey
Privacy score Score on website privacy features Global E-governance survey
Usability score Score on website usability features Global E-governance survey
Content score Score on website content features Global E-governance survey
Services score Score on website services features Global E-governance survey
Engagement score Score on website participation features Global E-governance survey
Independent
variables
Population size
(natural log)
Number of people living in the municipality City population size data from the World Bank
Education level Continuous variable measuring the number
and quality of higher education institutions
in the city
Own scale using the QS Global
University Rankings
Years of experience Continuous variable measuring age of website The Wayback Machine
GDP per capita
(natural log)
Extrapolated from year 2014 using average
annual growth rate for 2000–2014
Brookings Global Metro Monitor
Chief information
officer (CIO)
Binary variable of whether the city govern-
ment has a chief information officer
City website organizational chart
16
TABLE 2
Continued
Measure Source
Regional
competition
Average e-government score for the geo-
graphic region of the city
United Nations E-government Index
Democratic level Categorical variable measuring country dem-
ocracy with three levels (Free, Partially
free, and Not free)
Freedom House measure of “Freedom in
the World”
Control variables
Development status Dichotomous variable measuring country
stage of development (Developed
or Developing)
United Nations classification of developed and
developing countries
Region Seven world regions: Africa; Central Asia;
Western Europe; Eastern Europe; Middle
East; Oceania; North America; South and
Central America; South and East Asia
(North America is the reference category)
United Nations classification of major
world regions
17
from the UN E-government index for 10 regions according to the UN classifica-
tion. The higher the average score of the region, the higher the level of regional
competition. Democratic level was operationalized using the Freedom House index
of democracy (“Free,”“Partly free,”or “Not free”) that assesses seven items on
political and civil liberties, such as electoral process, the rule of law, and political
participation.
Control variables. Two controls were used: first, the geographic region of the
country where the city is located, and second, the development status of the city’s
country. The regional control aimed to account for variation in e-government
development effects at the regional level. Development status controlled the role of
unobserved factors resulting from economic and political status that influence e-
government level. Another important area of sample variance comes from the
range of sizes in the countries. To distinguish country size effects, all of the varia-
bles are estimated in three separate models: (1) cities in small countries; (2) cities in
large countries; and (3) all cities together. In the absence of official definitions of
“large”and “small”countries, we used modal figures in our data and geographic
knowledge to adopt our own classification where a large country has a population
of at least 50 million and a land area of at least 200,000 square kilometers accord-
ing to the CIA World Factbook. This resulted in sample sizes of 39 observations
for cities in small countries, and 43 cities in large countries.
Table 3 shows that the number of observations varies between 534 and 356 for
the predictor variables due to missing data at the city level. To address this prob-
lem, the study included a statistical method of imputing missing data using the
data matrix’s observed values. The statistical package missForest with R tests
imputed values by averaging random forests (multiple “trees”of variables in a data
matrix) with non-parametric assumptions, and has been shown to outperform
other methods such as maximum likelihood and multiple imputation (Overton
2016; Stekhoven and B€
uhlmann 2012). MissForest then computes an out-of-bag
(OOB) estimate for the true imputation error. Error estimates approaching 0 show
a good level of imputation, while estimates approaching 1 are considered poor.
OOB estimates for models estimated in this research ranged from 0.001044 to
0.001236, indicating an excellent OOB score.
RESULTS
Cluster Analysis
The cluster analysis on the five e-government categories estimates the clustering
of each city in relation to one of four cluster groups. Based on inspection of the
dendrograms, a four-cluster solution was selected as most meaningful. Table 4
illustrates the distribution of cities across the clusters using 2003, 2009, and 2016
data. In order to identify the characteristics of all cluster groups, we estimate the
mean score for each cluster in relation to each of the five e-government categories.
For each e-government category, we use one-way analysis of variance (ANOVA)
to test for significant differences in means across the cluster groups. Table 4 further
18 International Public Management Journal Vol. 0, No. 0, 2018
reports the statistically significant differences between the cluster groups as deter-
mined by one-way ANOVA.
The results of all cluster analysis suggest four levels of world cities. The profiles
are shown in Figure 2. Similar to Holzer et al. (2010), the cluster profiles indicate
that groups differ regarding scores but are similar in terms of shapes. All cluster
TABLE 3
Summary Statistics
Variable NMean Std. Dev. Min Max
Dependent variables
Total score 534 33.68 15.70 3.73 87.74
Privacy score 534 4.66 4.78 0 18.80
Usability score 534 12.06 3.13 2.82 19.38
Content score 534 7.61 3.84 0.16 18.80
Services score 534 5.84 3.88 0 19.83
Engagement score 534 3.53 3.10 0 18.75
Independent variables
Population size (natural log) 511 0.65 0.48 0 1
Education level 457 3.40 3.15 0 15
GDP (natural log) 356 10.23 1.49 8.57 11.15
Experience 498 15.2 5.09 2 21
CIO 461 0.09 0.29 0 1
Regional competition 534 0.55 0.14 0.17 0.89
Democratic level 534 2.46 0.13 1 3
Control variables
Development level
Developed 309
Developing 225
Total 534
Region
Africa 29
Latin America 93
North America 16
Central Asia 16
East Asia 27
South East Asia 49
Eastern Europe 110
Western Europe 115
Middle East 65
Oceania 14
Total 534
19STAGES AND DETERMINANTS OF E-GOVERNMENT
groups show high means for usability and indicate low degrees for service and citi-
zen engagement and privacy. The first group of “digitally mature cities”shows
relatively high mean scores for all five categories. The second group of “digitally
moderate cities”suggests decreased mean scores for the e-government categories,
especially for service and citizen engagement. In terms of 2009 data, the second
cluster group indicates higher scores for privacy than the first one. However, it per-
forms poorly with regard to service and citizen participation. The third group of
“digitally minimal cities”shows a similar mean value for usability as the second
group. The 2009 data further point to higher means for usability and content.
However, there is a large gap between the mean values of privacy in contrast to
digitally moderate cities. Finally, the last group of “digitally marginal cities”sug-
gests low values for all categories, with the exception of usability.
Further results on the performance of countries according to the e-government
dimensions are shown in the appendix. Appendix C sheds light on the top perform-
ing cities in e-government based on the 2016 overall e-government score. It further
TABLE 4
Comparison of Clusters and Analysis of Variance
NPrivacy Usability Content Services Engagement
2003 80 2.53 (3.52) 11.45 (3.59) 6.43 (3.60) 4.82 (3.48) 3.26 (3.04)
Cluster 1 7 10.51 (2.81) 16.48 (1.82) 12.52 (1.89) 13.01 (1.58) 9.16 (3.42)
Cluster 2 27 3.12 (3.21) 13.66 (2.16) 8.91 (2.33) 6.24 (1.60) 5.16 (1.93)
Cluster 3 16 2.19 (1.67) 12.01 (1.66) 5.98 (1.69) 4.13 (1.74) 1.38 (.75)
Cluster 4 30 .31 (.70) 7.98 (2.36) 3.01 (1.16) 2.01 (1.44) 1.19 (.98)
F (3,76) 41.17!!! 49.82!!! 80.69!!! 104.42!!! 65.67!!!
2009 87 5.57 (5.09) 11.96 (3.09) 8.21 (3.78) 6.68 (3.94) 3.50 (3.11)
Cluster 1 21 9.68 (4.75) 14.23 (2.34) 12.20 (2.46) 11.09 (3.55) 7.46 (3.30)
Cluster 2 13 12.46 (1.22) 12.99 (2.50) 8.71 (2.33) 7.21 (2.29) 2.84 (1.73)
Cluster 3 16 2.35 (2.08) 13.60 (2.43) 10.10 (2.68) 6.99 (2.47) 3.49 (1.67)
Cluster 4 37 2.22 (1.94) 9.61 (2.25) 4.96 (2.06) 3.87 (2.48) 1.49 (1.21)
F (3,83) 64.13!!! 22.39!!! 48.80!!! 31.09!!! 38.38!!!
2016 97 5.55 (4.65) 12.38 (2.84) 8.22 (3.85) 6.82 (4.00) 3.87 (3.24)
Cluster 1 12 10.74 (4.05) 16.88 (.93) 13.88 (1.84) 12.46 (2.46) 9.71 (3.14)
Cluster 2 34 8.87 (2.81) 13.05 (1.90) 10.34 (2.77) 9.33 (2.31) 5.01 (1.72)
Cluster 3 20 2.41 (1.88) 12.50 (1.95) 7.20 (1.86) 5.61 (2.10) 2.82 (1.82)
Cluster 4 31 1.92 (2.94) 9.84 (1.97) 4.36 (1.27) 2.67 (1.28) 1.04 (.71)
F (3,93) 52.65!!! 44.86!!! 78.01!!! 95.61!!! 78.77!!!
Notes: Please note that the sample size varies among 2003, 2009, and 2016 data. Significance levels
are indicated as follows: !!!p<.001.
20 International Public Management Journal Vol. 0, No. 0, 2018
0
2
4
6
8
10
12
14
16
18
Privacy Usability Content Services Engagement
Cluster 1 Cluster 2 Cluster 3 Cluster 4
0
2
4
6
8
10
12
14
16
18
Privacy Usability Content Services Engagement
Cluster 1 Cluster 2 Cluster 3 Cluster 4
0
2
4
6
8
10
12
14
16
18
Privacy Usability Content Services Engagement
Cluster 1 Cluster 2 Cluster 3 Cluster 4
2003
2009
2016
Figure 2. Cluster profiles for 2003, 2009, and 2016.
21STAGES AND DETERMINANTS OF E-GOVERNMENT
outlines the values of each of the five categories of municipal e-governance for
these 20 cities.
Multivariate Results
The results of the time series regression are shown in Tables 5 and 6.Table 5
shows the overall score across e-government categories, while Table 6 shows esti-
mates for each of the five categories. There is some notable variation in antece-
dents across the e-government categories, as well as between cities in small and
large countries. In Table 5, for example, GDP is a consistent predictor across cities
in small and large countries. In contrast, regional competition and population size
only make a difference in the large countries. But these results are much more
nuanced when we look into the different models for each of the e-government cate-
gories. In Table 6, interpretation of the models for small countries should be taken
cautiously, as three of the models have insignificant F-statistics and are therefore
not significant overall. However, the Wald tests suggest that the models are still
better than a constant-only model, except for the “participation”model. GDP has
a significant effect on e-information and e-services stages in small countries, as
does regional competition in e-services stages. Small countries also evidence a sur-
prising finding regarding democratic level, which is that the latter is negatively
associated with e-services components of privacy and services.
Large countries are notably different from small countries. In large countries,
population size is positively associated with the e-services and e-participation
stages, while GDP is only influential at the higher stages. Regional competition is
significant across all stages. Finally, democratic level is significant, but only for the
privacy component in large countries. Considering the results for small and large
countries, as well as the coefficients for all countries, both H2, for the effect of
population size on e-information, e-services, and e-participation development, and
H4, for the effect of GDP, are broadly supported, although there is substantial
variation between small and large countries in terms of which stages are affected.
H3, for education level, is not supported. In fact, in the case of e-services in large
countries, the effect is significant and negative. The effects of other internal
capacity factors, years of experience (H5) and technology leadership (H6), on e-
information, e-services, and e-participation development are also not supported.
H7, for the effect of regional competition on e-information, e-services, and e-
participation development, is widely supported, and as expected, the effect is
constant across all e-government stages. Also, like GDP and population, regional
competition influences small and large countries in different ways. The positive
association between regional competition and e-participation stages is only found
in large countries. Finally, H8, for the variable of democracy level and its effect on
the development of higher e-government levels of e-services, and e-participation, is
not statistically supported.
22 International Public Management Journal Vol. 0, No. 0, 2018
Best Practices in Global Municipal E-Governance
The following presents some of the best practices from the 2016 Global E-
Governance Survey—Seoul, Helsinki, Madrid, Hong Kong, and Prague. The city
of Seoul ranked first in the survey, repeating its performance in previous surveys,
along with high performance in content, service delivery, and citizen and social
engagement. The website of Seoul is user-friendly, clearly formatted, and the home-
page is within the appropriate length. These, along with relevant search tools,
enable users to locate any required information and encourage them to post their
TABLE 5
Regression Results for Overall E-government Level (Robust Standard Errors in
Parentheses)
Small countries Large countries All countries
Population size –01.39 (6.23) 5.75 (3.05) 5.26!! (1.93)
Education –0.63 (2.81) –0.33 (0.51) –0.41 (0.49)
GDP 24.66!!! (6.79) 9.80!(4.52) 17.16!!! (3.84)
Experience –0.40 (0.39) –0.49 (0.40) 0.17 (0.30)
CIO –3.99 (6.43) 0.96 (7.73) 4.01 (4.41)
Regional competition 22.23 (14.24) 52.34!!! (11.80) 27.51!! (9.63)
Democracy –13.86 (7.53) 1.39 (1.92) 1.49 (2.38)
Development level:
Developed 4.87 (4.83) 0.96 (4.22) –0.48 (3.96)
Region:
Western Europe 1.96 (9.07) 2.13 (8.38) –2.89 (5.41)
Eastern Europe 4.30 (8.50) –5.79 (8.61) –4.56 (5.98)
Middle East 5.08 (10.52) 8.15 (12.23) –6.44 (6.69)
East Asia 2.60 (9.74) –4.73 (7.95) –5.38 (7.21)
Oceania –5.26 (9.98) 5.41 (4.90) 2.20 (4.12)
South America –3.54 (8.05) 2.40 (7.99) 1.75 (6.93)
Central America 6.22 (3.85) –20.03!(7.99) –3.60 (6.95)
Southeast Asia 1.56 (5.12) –2.56 (8.15) –0.26 (6.69)
Africa –1.32 (3.11) –7.02 (9.01) –4.82 (8.31)
Central Asia –7.34 (8.77) 10.12 (9.74)
Constant –171.39 (133.86) –180.41!(83.23) –240.00!!! (59.91)
R-square 0.296 0.452 0.237
F-score 3.294!!! 6.966!!! 4.749!!!
Wald Test 45.05!!! 154.87!!! 88.177!!!
Breusch-Pagan Test 17.37 43.71!!! 19.01
Panels 17 24 41
N116 161 277
Notes: Significance levels are indicated as follows: !p<.05; !!p<.01; !!!p<.001.
Referent region is North America. No Central Asian countries included in the small coun-
try sample.
23STAGES AND DETERMINANTS OF E-GOVERNMENT
TABLE 6
Regression Results for Five E-government Components (Robust Standard Errors in Parentheses)
Information Services Participation Information Services Participation Information Services Participation
Cont Use Priv Serv Part Cont Use Priv Serv Part Cont Use Priv Serv Part
Population size "0.17
(1.58)
"2.83
(1.51)
2.57
(3.21)
"0.01
(1.54)
–0.14
(1.69)
1.12
(0.83)
0.16
(1.00)
–0.37
(1.43)
3.36!!
(1.09)
1.87!!
(0.59)
0.91
(0.49)
–0.32
(0.47)
1.73!!
(0.57)
1.72!!
(0.52)
0.87!!
(0.41)
Education 0.01
(0.72)
1.12
(0.67)
–2.31
(1.40)
0.05
(0.71)
0.98
(0.79)
–0.04
(0.14)
0.11
(0.17)
–0.03
(0.23)
–0.45!
(0.18)
–0.12
(0.09)
–0.09
(0.11)
0.12
(0.11)
–0.14
(0.16)
–0.21
(0.12)
–0.11
(0.43)
GDP 3.98!
(0.01)
2.68
(1.63)
12.02!!!
(3.16)
7.29!!!
(1.17)
3.31
(1.87)
1.08
(1.23)
0.38
(1.54)
1.49
(2.04)
6.31!!
(1.93)
2.85!!
(0.86)
2.93!!
(1.01)
1.97!
(0.88)
5.02!!!
(1.44)
5.39!!!
(1.06)
3.02!!!
(0.89)
Experience 0.01
(0.09)
0.06
(0.09)
–0.45!!
(0.15)
–0.01
(0.11)
0.07
(0.11)
–0.12
(011)
–0.13
(0.14)
0.01
(0.18)
–0.03
(0.12)
–0.01
(0.06)
0.01
(0.07)
–0.03
(0.07)
–0.05
(0.10)
–0.09
(0.07)
0.00
(0.06)
CIO –0.49
(1.63)
–2.26
(1.56)
1.72
(3.75)
–0.50
(1.73)
–3.35
(1.74)
0.55
(2.11)
1.83
(2.65)
1.81
(3.50)
–1.07
(3.35)
–2.35
(1.66)
1.77
(1.02)
1.28
(0.98)
1.32
(1.45)
1.80
(1.07)
–0.39
(0.89)
Regional
competition
4.47
(3.73)
–7.58!
(3.32)
13.63!!
(6.89)
9.16!
(3.95)
0.21
(4.19)
13.73!!!
(3.16)
6.64
(3.43)
15.84!!
(4.83)
10.15!!
(3.48)
5.17!
(2.40)
6.34!!
(2.42)
–0.41
(2.29)
12.27!!!
(3.58)
5.44!
(2.55)
2.20
(2.28)
Democracy –2.17
(0.26)
–0.81
(1.63)
–6.65!
(4.12)
–2.67!
(1.32)
0.18
(2.04)
0.46
(0.52)
–0.71
(0.65)
0.52
(0.87)
0.79
(0.57)
0.20
(0.38)
0.21
(0.53)
0.16
(0.50)
0.39
(0.76)
0.52
(0.55)
0.07
(0.47)
Development
level:
Developed 0.38
(0.93)
0.57
(0.80)
5.48!!
(1.91)
–0.27
(0.95)
0.55
(1.06)
–0.51
(1.14)
–0.67
(1.15)
1.29
(1.81)
0.01
(1.24)
0.63
(0.83)
1.44
(1.73)
1.28
(1.10)
–0.30
(1.73)
–0.12
(1.23)
0.00
(1.12)
Region:
Western Europe –1.06
(1.72)
–1.56
(1.45)
–3.21
(3.65)
–0.15
(1.73)
–1.41
(2.06)
0.61
(3.94)
1.11
(3.32)
4.64
(6.13)
–6.12
(4.67)
0.46
(2.68)
0.75
(2.68)
–0.91
(2.56)
1.45
(3.87)
1.39
(2.82)
–2.27
(2.43)
Eastern Europe 1.21
(1.12)
1.86
(0.95)
–0.57
(2.31)
1.60
(1.13)
2.22
(1.34)
–2.56
(2.90)
–2.10
(2.34)
–4.94
(4.70)
–4.17
(3.55)
–2.31
(1.86)
1.18
(2.53)
2.04
(2.39)
–5.79
(3.73)
–0.98
(2.82)
–1.88
(2.38)
Central Asia –2.10
(3.78)
–2.44
(2.96)
–1.27
(6.21)
–1.64
(4.69)
0.34
(2.28)
–0.98
(2.10)
–2.24
(2.00)
–4.24
(3.02)
2.03
(2.21)
–2.94
(1.89)
Middle East 0.12
(2.08)
0.56
(1.75)
0.03
(4.54)
–1.46
(2.08)
1.38
(2.52)
–0.37
(3.47)
0.90
(2.88)
–0.12
(5.50)
–7.23
(4.17)
–0.03
(2.32)
0.43
(1.94)
1.83
(1.84)
–1.80
(2.85)
–0.59
(2.04)
–0.89
(1.59)
24
East Asia 2.04
(2.34)
–0.95
(2.00)
1.99
(4.54)
4.06
(2.37)
4.57
(2.72)
–0.54
(2.98)
1.29
(2.36)
0.41
(4.84)
–2.11
(3.66)
–1.41
(1.84)
–0.29
(2.01)
2.49
(1.92)
–4.74
(2.90)
0.37
(2.12)
–1.94
(1.82)
Oceania 0.05
(2.17)
0.28
(1.82)
–4.28
(4.74)
–0.95
(2.17)
–1.87
(2.62)
–0.62
(3.08)
0.23
(2.42)
–1.94
(5.06)
–7.36
(3.82)
–0.52
(1.87)
2.25
(2.70)
4.01
(2.57)
–2.33
(2.41)
–0.21
(2.84)
–3.22
(2.44)
Southeast Asia 1.49
(1.71)
–0.26
(1.44)
–2.02
(3.57)
3.47!
(1.71)
3.76
(2.03)
–3.18
(3.30)
–2.65
(2.63)
–1.29
(5.36)
–6.02
(4.05)
–3.50
(2.06)
0.04
(1.66)
0.68
(1.54)
–2.55
(3.89)
0.47
(1.75)
–1.07
(1.51)
South America 2.48
(3.21)
2.69
(2.71)
4.65
(6.79)
1.41
(3.21)
0.33
(3.85)
–1.50
(3.00)
0.56
(2.42)
–2.36
(4.82)
–6.09
(3.65)
–0.69
(1.92)
1.99
(1.98)
2.50
(1.87)
–0.95
(2.90)
2.44
(2.08)
–1.37
(1.84)
Constant –27.95
(37.31)
28.47
(31.74)
–62.17
(32.31)
–70.21!!
(21.54)
–32.83
(43.96)
–30.02!
(22.23)
3.35
(27.67)
–14.35
(36.05)
–80.36!!
(24.68)
–54.97!!
(16.65)
–40.06!!!
(15.72)
4.27
(15.05)
–76.29!!!
(22.45)
–76.64!!!
(16.53)
–39.38!!
(13.95)
R–square 0.170 0.148 0.286 0.562 0.147 0.386 0.125 0.255 0.349 0.358 0.194 0.083 0.202 0.231 0.125
F-score 1.610 1.364 3.150!!! 10.107!!! 1.367 5.302!!! 1.21 2.891!!! 4.510!!! 4.785!!! 3.271!!! 1.227 3.427!!! 4.071!!! 1.930!
Wald Test 23.72!20.96!29.87!18.109!!! 18.11 95.19!!! 22.95 51.237!!! 53.304!!! 86.147!!! 69.73!!! 29.10 67.28!!! 82.133!!! 38.54!!
Breusch-Pagan
Test
3.29 13.63 28.26!! 8.14 27.19!37.59!! 16.299 13.47 31.72!31.91!29.04 24.95 40.76!! 28.13 36.896!!
Panels 17 17 17 17 17 24 24 24 24 24 41 41 41 41 41
N116 116 116 116 116 161 161 161 161 161 277 277 277 277 277
Notes: Significance levels are indicated as follows: !p<.05; !!p<.01; !!!p<.001.
Referent region is North America. No Central Asian countries included in the small country sample.
25
questions and feedback. The website provides a simple “easy reads”section for citi-
zens to quickly review important news and updates in the city. Seoul’s website is
also a recognized model for enhancing privacy protection and Internet security, as
well as offering a high level of usability. Helsinki ranked second and improved sig-
nificantly from its sixteenth position in the previous 2014 survey, particularly due
to its performance in privacy and citizen and social engagement. The website pro-
vides adequate content on its homepage in a clear and understandable manner,
especially its privacy statement, which includes information on the use of cookies,
and the collection and use of data. The website excels in providing opportunities
for citizen participation online with interactive tools, discussion forums, and feed-
back forms, all of which helped elevate Helsinki to first place in the category of
citizen and social engagement.
Madrid ranks third owing to high scores in the categories of content, services,
and citizen and social engagement. The website enables citizens to utilize a wide
range of services online, such as payment of taxes, license and permit applications,
parking fees, fines, environmental and car control services, and other related social
services. The license and permit application system is particularly well-integrated
with citizens’online municipal accounts, and citizens can both apply and track
their applications online. The website of Madrid also offers a “Debates”section to
provide opportunities for interactions between citizens and public administrators.
Hong Kong ranks fourth overall and ranks highly in usability and service delivery,
particularly due to its “one stop platform”that enables citizens to avail themselves
of crucial services related to business, administration, and citizenship. The website
provides well-organized content highlighted with important information related to
budget and policy. The city takes strides to protect users’privacy and meet security
concerns, and also addresses the needs of non-citizens and expatriates, who form a
sizable portion of Hong Kong’s residents. Prague took the fifth position, and also
was highly ranked in privacy and content categories. The privacy statement
addresses important questions related to users’privacy and security, and the web-
site enables users to locate relevant information in the content category, such as
meeting minutes, mission statements, budget reports, and performance
information.
DISCUSSION AND IMPLICATIONS
This article contributes to the stream of literature on e-government development
through the following contributions, covered only to a marginal extent thus far.
First, the article adds to the studies examining the development and current stages
of e-government by extending the sample analyzed. Whereas Reddick (2004)
and Coursey and Norris (2008) focused on American cities, this study analyzes e-
government and e-governance from an international perspective, and provides evi-
dence for the current stage of digital governance from 80 global cities. By applying
cluster analysis, it provides an empirical classification of e-government types,
reflecting previous studies on a staged adoption of e-government in terms of world
26 International Public Management Journal Vol. 0, No. 0, 2018
cities. This typology provides a basis for comparing e-government stages and deter-
minants across countries. Second, the study uses city-level data which have not
been used before to analyze the stages of municipal digital governments. The muni-
cipal e-governance survey provides data for analysis of cities’e-government devel-
opment in three stages—e-information, e-services, and e-participation—and thus
adds to the empirical literature that focuses on the earlier stages of e-government
(e.g., Moon 2002; Reddick 2004; Coursey and Norris 2008).
The findings of the regression analysis in the article corroborate earlier find-
ings about the different institutional antecedents associated with different dimen-
sions or practice areas of e-government (e.g., Ahn 2011; Chen and Hsieh 2009;
Lee et al. 2011; Wang and Feeney 2016), and generate stronger research insights
by systematically drawing on information science literature, in addition to public
administration and public management. In addressing these associations across
three different stages of e-government across time, the results provide more
granular evidence of the associations, and explain why some governments may
move faster than others and in specific stages. Cross-dimensional approaches
such as these are important for understanding broad e-government trends.
Furthermore, as the cluster analysis has shown, the dimensions of e-government
develop simultaneously; regardless of the level of digital maturity evidenced by a
municipal government, the dimensions of e-government are derived in the same
order, with usability in the lead and citizen and social engagement following
behind. This means that digitally mature cities are ahead on all dimensions,
rather than being ahead on some, but not others. The regression analysis sup-
ports this view by showing that many success factors, especially GDP, popula-
tion size, and regional competition, are associated with all three stages. These
support earlier findings on GDP per capita (e.g., Edmiston 2003;Ma2014) and
regional competition (e.g., Lee et al. 2011).
However, there are also some specific factors that can contribute uniquely to
individual stages. The strongest effects of the antecedents on the higher levels of e-
government development occur in larger countries, which echoes earlier views that
complexity is a driving factor of higher e-government innovation (Gallego- !
Alvarez
et al. 2010; Norris and Moon, 2005). We add to this theory of e-government innov-
ation by testing the variable of regional competition, which points to a further
driver of e-government development, especially in larger countries in the higher e-
government stages. Cities in larger countries face more complex tasks demograph-
ically and geographically, and globalization forces of migration and international
labor supplies lend themselves to increasing competition (Shipan and Volden 2012;
Wang 2001; Weare et al. 1999). As we hypothesized, institutional factors such as
democratic level also matter for the higher stages of e-government, but the rela-
tionships among these variables are more complex than we expected. Notably,
democratic level, which does influence the e-services stage in one component (priv-
acy and security), only seems to make a difference in large countries. In small
countries, democratic level is inversely related to the e-services stage. This finding
might suggest that small, democratic countries are better at providing traditional
27STAGES AND DETERMINANTS OF E-GOVERNMENT
services than online services. While prior research does suggest that democratic
countries tend to have better capacity and political support for e-government to be
more fully developed, such research is mixed, or finds that differences depend on
how democratic level is measured (e.g., Lee et al. 2011; West 2005). Small, non-
democratic governments also have an advantage when it comes to building online
services quickly across central agencies (Gulati et al. 2014), and may do so to
enhance support and legitimacy among citizens and among international peers
(Stier 2015). Large democratic countries, in contrast, may have a comparative dis-
advantage, as well as less incentive, in driving forward fast gains in
e-government development. However, many of these interesting nuances of demo-
cratic variables are largely conjecture here, and further institutional analysis is
needed to piece together the puzzle.
Other results from the research were surprising in that variables were not
found to be supported, despite having support in earlier research. Notably
among these are the internal variables relating to technical expertise and leader-
ship, as well as education level. Each of these variables had evidence supporting
them, but other studies also have failed to confirm them. Education, in particu-
lar, has been found to be interdependent on inclusion of wealth and GDP in
models (Tolbert 2008; West, 2005), and the latter was a strongly significant vari-
able in this study. Further, while internal variables are important, technology
transformation and innovation theory tends to emphasize political and institu-
tional factors over internal administrative or technical capacity (Jun and Weare
2010). Institutional and environmental variables tend to be more fixed compared
to internal ones, and over the long term they may play the bigger role needed
to embed technologies in processes of innovation (Fountain 2004; West 2005;
Yang 2003).
Limitations and Directions for Future Research
This study is based on a purposive sample of cities that are in the most Internet-
advanced countries in the world. However, the advantage of this is that, by select-
ing the largest cities in the most wired countries, we are comparing similar entities
with a certain level of technological sophistication, and by selecting the largest city
in each such country, we further underscore that attribute. Thus, by controlling for
“most wired”and largest cities, we are making it possible to look for factors affect-
ing e-government that are not simply a function of the available technology. The
study has drawn conclusions from statistical analysis, but there is much more that
can be learned from these cities and the determinants of e-government level
through an explanatory case study approach. While the present study finds that
statistical predictors are important, a case study approach can reveal what the spe-
cific mechanisms are, as well as detailing and identifying the common characteris-
tics or distinguishing factors among groups of countries. Case study approaches
may also address a limitation in this article that some of the variables identified in
prior literature, such as citizen demand, political support, and political competi-
tion, could not be measured because of a lack of data for city-level characteristics.
28 International Public Management Journal Vol. 0, No. 0, 2018
Cases focusing on small groups of cities could assess these factors in qualitative
ways or through surveys to determine whether they also play important roles in
e-government success.
As this study’s results suggest that the determinants of e-government level vary
across the different dimensions of e-government, future research could also address
the interdependency of these causal relationships. As the dimensions are not inde-
pendent of one another, there must be interactions among e-governance predictors
and outcomes. Case-based approaches are suitable for these types of questions, but
so too are quantitative approaches that concentrate on the relationships among a
limited number of moderators and outcome variables. There may be more specific
associations or combinations of factors associated with specific dimensions. Citizen
engagement is among the more critical areas to address in this respect, as the pre-
sent work is not the first to suggest that government efforts in this area are fre-
quently hampered.
This article offers a global perspective on e-governance development over
time. The results of the cluster analysis revealed that there are four global clus-
ters of e-government development relating to mature, moderate, minimal, and
marginal stages. The results of the regression analysis show support for the find-
ings of earlier empirical studies on the predictors of the level of e-government
provision. However, the original contribution of this study is to show, at a glo-
bal level, that these predictors are associated with the stages of e-government in
different ways. Specific institutional and environmental factors may help to fos-
ter the growth of e-government, but this fostering does not occur equally across
the spectrum of e-government. Rather, e-government performance responds to
specific conditions.
Finally, by revealing the patterns of e-government performance across three
stages of e-government, the article provides several specific points for intervention
by e-government practitioners and policymakers in government. Such interventions
may wish to focus on improving specific areas of e-government performance that
are lacking. While governments can provide a conducive environment by focusing
on a broad range of institutional and environmental factors, they may wish to
improve in the important dimension-specific areas suggested in this article’s
research. The knowledge of the factors and barriers of e-government success can
help administrators to devise specific strategies to implement e-government projects
that will meet their stakeholder demands, remain cost-effective, and reduce the pos-
sibility of IT project failure.
The phenomenon of e-government has multiple implications at all levels of gov-
ernment, particularly the local government level, where citizens tend to have
greater interaction with public officials. This research examines the trends and best
practices in municipal e-government and, more importantly, sets the tone for the
transformation from e-governance to smart governance among global municipal-
ities. The dimensions discussed in the article can contribute to crucial steps in the
progress towards becoming a smart city. Future studies in municipal e-governance
need to examine how governments are building smart cities, and to what extent
29STAGES AND DETERMINANTS OF E-GOVERNMENT
they involve citizens and stakeholders in the process. What are the various stages
of development of smart cities, and what are the critical success factors and bar-
riers affecting these initiatives? More importantly, future research needs to focus
on how to measure the performance of smart cities and analyze them from a global
comparative perspective.
The best practices discussed in the article provide guidance and lessons for cities
that have recently adopted e-government initiatives. Moreover, the comparison of
the top-performing cities over the years shows that many cities are not able to
repeat their success over consequent surveys. This trend suggests that administra-
tors need to ensure that their e-government applications are not only effective and
efficient, but also sustainable for long-term success by addressing specific environ-
mental and institutional barriers to innovation. Perhaps the most important impli-
cation for cities adopting e-government is to begin to conceptualize e-government
in terms of multiple dimensions, rather than an overall phenomenon. This will
enable cities to implement e-government through incremental stages that will allow
for citizens to provide their opinions, and for administrators to respond to them in
a timely manner, as they implement the new technologies.
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ABOUT THE AUTHORS
Alex Ingrams (a.ingrams@uvt.nl) is an assistant professor in the Institute of
Governance at Tilburg University. His research interests are in digital government
reform, and particularly impacts on transparency and accountability. He also
researches other topics focused around the intersection of technology change and
governance reform, such as open government, open data, and big data.
Aroon Manoharan (aroon.manoharan@umb.edu) is an associate professor in the
McCormack Graduate School of Policy and Global Studies at the University of
Massachusetts, Boston. He received his Ph.D. from the School of Public Affairs
and Administration, Rutgers University-Newark. His research interests include e-
government, performance measurement, public reporting, citizen participation,
strategic planning, and comparative public administration. His recent research
focuses on the role of academic programs in strengthening the IT capacity of cities
and local governments.
Lisa Schmidthuber (lisa.schmidthuber@jku.at) is a doctoral researcher in the
Institute for Public and Nonprofit Management at Johannes Kepler University
Linz, Austria. Her research interests include innovation management in the public
sector in general, and focus on value creation by integrating citizens in innovation
processes in particular.
Marc Holzer (marcholzer1@gmail.com) is a distinguished professor of public
administration at the Sawyer Business School, Suffolk University. He is a Past
President of the American Society for Public Administration (ASPA) and the for-
mer founding dean of the School of Public Affairs and Administration at Rutgers
University. He has published numerous articles, books, and monographs in public
administration research and theory. He currently chairs the ASPA Endowment
supporting education in strong, effective, and ethical public governance.
33STAGES AND DETERMINANTS OF E-GOVERNMENT
APPENDIX A
List of Works Cited in Table 1
Ahn, Michael. J. 2011. “Adoption of E-communication Applications in US Municipalities:
The Role of Political Environment, Bureaucratic Structure, and the Nature of
Applications.”The American Review of Public Administration 41(4):428–452.
Aladwani, Adel. M. 2016. “Corruption as a Source of E-government Projects Failure in
Developing Countries: A Theoretical Exposition.”International Journal of Information
Management 36(1):105–112.
Åstr€
om, J., Karlsson, M., Linde, J., and Pirannejad, A. 2012. “Understanding the rise of e-
participation in non-democracies: Domestic and international factors.”Government
Information Quarterly 29(2):142–150.
Azad, Bijan, Samer Faraj, Jie Mein Goh, and Tony Feghali. 2010. “What Shapes Global
Diffusion of E-government: Comparing the Influence of National Governance
Institutions.”Journal of Global Information Management 18(2):85–104.
Basu, Subhajit. 2004. “E-government and Developing Countries: An Overview. International
Review of Law, Computers & Technology 18(1):109–132.
Berntzen, Lasse, and Evika Karamagioli. 2010, February. “Regulatory Measures to Support
eDemocracy.”In Digital Society, 2010. ICDS'10. Fourth International Conference (311-
316), IEEE.
Bhatti, Yosef, Asmus L. Olsen, and Lene Holm Pedersen. 2011. “Administrative
Professionals and the Diffusion of Innovations: The Case of Citizen Service Centres.”
Public Administration 89(2):577–594.
Brudney, Jeffrey L., and Sally Coleman Selden. 1995. “The Adoption of Innovation by
Smaller Local Governments: The Case of Computer Technology.”The American
Review of Public Administration 25(1):71–86.
Das, Amit, Harminder Singh, and Damien Joseph. 2017. “A Longitudinal Study of E-gov-
ernment Maturity.”Information & Management 54(4):415–426.
Eom, Seok-Jin. 2013. “Institutional Dimensions of E-government Development:
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APPENDIX B
100 Cities Selected by Continent (2016)
Africa (7)
Accra (Ghana)
Cairo (Egypt)
Casablanca (Morocco)
Johannesburg (South Africa)
Lagos (Nigeria)
Nairobi (Kenya)
Tunis (Tunisia)
Asia (30)
Almaty (Kazakhstan)
Amman (Jordan)
Baghdad (Iraq)
Bangkok (Thailand)
Colombo (Sri Lanka)
Dhaka (Bangladesh)
Dubai (United Arab Emirates)
Ho Chi Minh City (Vietnam)
Hong Kong (Hong Kong, China)
Jakarta (Indonesia)
Jerusalem (Israel)
Karachi (Pakistan)
Kuwait City (Kuwait)
Macao (China)
Manama (Bahrain)
Manila (Philippines)
Mumbai (India)
Muscat (Oman)
Riyadh (Saudi Arabia)
Seoul (Republic of Korea)
Shanghai (China)
Singapore (Singapore)
Tashkent (Uzbekistan)
Tbilisi (Georgia)
Tehran (Iran)
(continued)
37STAGES AND DETERMINANTS OF E-GOVERNMENT
Kathmandu (Nepal)
Kuala Lumpur (Malaysia)
Tokyo (Japan)
Yerevan (Armenia)
Europe (40)
Amsterdam (Netherlands)
Athens (Greece)
Belgrade (Serbia and Montenegro)
Berlin (Germany)
Bratislava (Slovak Republic)
Brussels (Belgium)
Bucharest (Romania)
Budapest (Hungary)
Chisinau (Moldova)
Copenhagen (Denmark)
Dublin (Ireland)
Helsinki (Finland)
Istanbul (Turkey)
Kiev (Ukraine)
Lisbon (Portugal)
Ljubljana (Slovenia)
London (United Kingdom)
Luxembourg City (Luxembourg)
Madrid (Spain)
Minsk (Belarus)
Moscow (Russian)
Nicosia (Cyprus)
Oslo (Norway)
Paris (France)
Prague (Czech Republic)
Riga (Latvia)
Rome (Italy)
San Marino (San Marino)
Sarajevo (Bosnia and Herzegovina)
Schaan (Liechtenstein)
Sofia (Bulgaria)
Stockholm (Sweden)
Tallinn (Estonia)
Tirane (Albania)
Valletta (Malta)
Vienna (Austria)
Vilnius (Lithuania)
Warsaw (Poland)
Zagreb (Croatia)
Zurich (Switzerland)
North America (11)
Castries (St. Lucia)
Guatemala City (Guatemala)
Hamilton (Bermuda)
Mexico City (Mexico)
New York (United States)
Panama City (Panama)
Saint Joseph (Costa Rica)
San Juan (Puerto Rico)
San Salvador (El Salvador)
Santo Domingo (Dominican Republic)
Toronto (Canada)
South America (10)
Asuncion (Paraguay)
Bogota (Colombia)
Buenos Aires (Argentina)
Caracas (Venezuela)
Guayaquil (Ecuador)
Lima (Peru)
Montevideo (Uruguay)
Santa Cruz de la Sierra (Bolivia)
Santiago (Chile)
Sao Paulo (Brazil)
Oceania (2)
Auckland (New Zealand) Sydney (Australia)
Source: Rutgers eGovernance survey, 2016; N¼97.
38 International Public Management Journal Vol. 0, No. 0, 2018
APPENDIX C
Top 20 Cities in Digital Governance (2016)
Ranking City
Total
score
Five Dimensions of Municipal E-Governance
Privacy
score
Usability
score
Content
score
Services
score
Engagement
score
1 Seoul 79.92 13.33 15.94 17.30 16.89 16.46
2 Helsinki 69.84 14.44 17.50 13.17 11.80 12.92
3 Madrid 69.24 12.22 16.56 15.56 13.44 11.46
4 Hong Kong 67.56 12.59 17.81 13.65 14.75 8.75
5 Prague 66.48 14.44 15.31 15.08 11.64 10.00
6 Tallin 62.10 8.52 17.50 14.13 15.08 6.88
7 New York City 62.02 12.59 14.06 15.71 13.61 6.04
8 Bratislava 60.34 11.85 17.19 13.97 7.54 9.79
9 Yerevan 59.61 3.70 17.81 14.92 12.13 11.04
10 Vilnius 59.12 14.44 15.63 12.22 10.16 6.67
11 Buenos Aires 57.88 11.85 16.25 10.00 10.82 8.96
12 Tokyo 57.04 8.89 18.13 12.54 13.11 4.38
13 Singapore 56.03 9.63 14.38 10.16 13.11 8.75
14 Moscow 54.73 2.59 16.88 13.97 12.13 9.17
15 Oslo 54.37 14.07 10.94 14.44 10.33 4.58
16 Amsterdam 54.36 10.37 14.38 12.86 11.97 4.79
17 Auckland 54.27 8.89 14.06 12.22 11.80 7.29
18 London 52.54 12.22 15.00 10.00 11.15 4.17
19 Lisbon 51.68 9.26 12.50 11.90 8.85 9.17
20 Sydney 50.08 8.15 15.94 10.16 10.00 5.83
39STAGES AND DETERMINANTS OF E-GOVERNMENT