Organizational size and e-Government maturity
A scrutiny of the relationship between size and e-Government development in municipalities through a citizen/service-oriented maturity model
Master Thesis
Leiden University, the Netherlands
11th of June, 2019
Faculty of Governance and Global Affairs MSc Public Administration (Public Management)
Author: Michail Tsafantakis (s1946684) Supervisor: Dr. Joris van der Voet
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Abstract
This study shed light on the relationship between organizational size and e-Government maturity through the scrutiny of Greek local governments’ effort to deliver public value via IT innovation. Having taken into consideration the need for maturity models which demonstrate an explicit focus, the stage model themes Lee (2010) identified, and the remark by Andersen and Henriksen (2006) about the major importance of the customer perspective in e-Government strategic thinking, this study proposed a maturity model with an explicit focus on the citizen/service theme. The proposed maturity model consists of 7 developmental phases (Web presence, Two-way communication, Service & financial transaction, Participation,
Personalization, Collaboration, e-Governance). Through this model, this study initially
identified the e-Government maturity of Greek municipalities and subsequently, scrutinized its relationship with organizational size. Our findings revealed that higher number of permanent municipal employees is associated to higher possibility for the phases of “Service & financial transaction”, “Participation”, “Personalization” and “Collaboration” to be materialized. Furthermore, we concluded that an increase of 1 permanent municipal employee would increase the odds of materialization of “Two-way communication”, “Service & financial transaction”, “Participation” and “Collaboration” by 7.7%, 28%, 2.6% and 1.2% respectively.
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Table of contents
1. Introduction
... 41.1 General topic/background information ... 4
1.2 Introduction to topic ... 5
1.3 Literature gaps concerning the org. size – innovation adoption relationship. ... 8
1.4 Gaps concerning the e-Government maturity models. ... 8
1.5 Relevance/contribution of the study ... 9
1.6 Research Question ... 11
1.7 Order of information in the thesis ... 11
2. Theoretical framework
... 122.1 General information about innovation ... 12
2.2 Types of innovation ... 14
2.3 E-Government ... 15
2.4 Maturity models ... 17
2.5 Comments on the existing maturity models ... 18
2.6 The proposed maturity model... 19
2.7 Practical and theoretical utility of maturity models ... 22
2.8 Organizational size – innovation relationship ... 23
3. Methodology
... 283.1 Research aims/objectives ... 28
3.2 Research design ... 28
3.3 Case selection ... 29
3.4 Data collection methods ... 30
3.5 Operationalization of variables ... 30
3.6 Reliability and Validity ... 36
3.7 Analysis strategy ... 38
4. Data Analyses and presentation of results
... 394.1 Descriptive statistics ... 39
4.2 Bivariate correlations among the variables... 42
4.3 Binomial regression analyses ... 46
5. Discussion
... 525.1 Theoretical implications ... 54
3 5.3 Practical implications ... 60 5.4 Limitations ... 61 5.5 Future research ... 63
6. References
... 667. APPENDIX
... 737.1 E-Government maturity (Greek municipalities) ... 73
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1. Introduction
1.1 General topic/background information
Over the past decades a technological outburst has greatly impacted people’s everyday life (Dunleavy et al., 2006, p. 468; Moon, 2002; West, 2004). The internet has become an integral part of most people’s daily routine, and the use of information communication technologies (ICT) has risen prominently (Arduini & Zanfei, 2014; West, 2004, p. 148). This rapid development -World Wide Web initiated only in 1989- affected both the private and the public sector.As far as the public sector is concerned, the emergence of digital or electronic
government (e-Government or e-Gov) was a natural development which stemmed from the
numerous potentials ICT offers (Arduini & Zanfei, 2014, p. 476; Moon, 2002; West, 2004, p. 16).
A concise definition of e-Government is the one of Nam (2014), which proposed that it is “the use by government of information and communication technologies (ICTs) to
deliver information and services to citizens, businesses, and public agencies” (Nam, 2014,
p. 211). E-Government is a continuous and evolutionary phenomenon, which demonstrates a dynamic nature, and therefore, it is treated accordingly both by academics and practitioners (Das et al., 2017; Layne & Lee, 2001; Veljković et al., 2014). In order to describe “the state of a given level in a continuous process”, the concept of “maturity” is often used (Andersen & Henriksen, 2006, p. 239).
More specifically, based on the features that government websites demonstrate, the governmental online presence can be considered to have a certain degree of maturity, which operates as a depiction of the level up to which e-Government has evolved within a certain context. According to Das, Singh and Joseph, e-Government maturity can be defined as “the
extent to which a government has established an online presence” (Daset al., 2017, p. 416). There are several maturity models, which refer interchangeably to stages, phases or levels in order to express the development or maturity of e-Government (Das et al., 2017; Layne & Lee, 2001, West, 2004). These models demonstrate both a practical and a theoretical utility -we will elaborate further on this at the theoretical part of this study.
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1.2 Introduction to topic
The need to outline e-Government’s evolutionary nature through maturity models was largely generated by its rapid evolution within a relatively short time period, and the discrepancy it demonstrated between different contexts. As Liu and Yuan (2015) suggest, the role of ICT evolved apace, from supportive towards bureaucracy (e.g. information archiving, sharing, storage), to the integration of ICT throughout government operations (Liu & Yuan, 2015). In other words, ICT initially operated as a tool which facilitated administration, whereas consequently it transformed the character of governance as a result of its integration with the traditional structures through which governance takes place.
More specifically, despite the fact that the appearance of ICT in the public sector is placed in the 1970’s, it started to evolve and become widely adopted in the public sector of Western countries during the 1990’s (Liu & Yuan, 2015). In the beginning, ICT applications were limited to classification and indexing. However, once it was realized that ICT constituted a means able to enhance decision making, administrative control and operational performance, its role was upgraded in order to facilitate bureaucrats (Liu & Yuan, 2015).
This role has been continuously evolving ever since. In the trend of new public management (NPM), the importance of ICT kept rising, as it enhanced elements like efficiency, effectiveness, quality and control, which were highly valued within the new paradigm. As a result, new public managers’ dependence on ICT rose significantly (Liu & Yuan, 2015, p. 146). However, the evolution of ICT affected not only bureaucrats, but citizenry as well, since it provided them with the potential to further their interests both administratively and politically. Consequently, it “redefined relations among the government, the private sector, and the civil society” (Dunleavy et al., 2006, cited in Liu & Yuan, 2015, p. 146).
This change became more evident with the proceeding mobile and wireless ICT adoption, which can be considered to be among the primary indicators of the following paradigm change toward new public governance (NPG) (Goodsell, 2006, cited in Liu & Yuan, 2015, p. 146). At that point, two game changers came up; social media and big data analytics. Thus, the concept of citizen-oriented service delivery gained popularity. Citizens gradually turned from passive information receivers to active information contributors, something which affected the power balance between citizenry and the government (Liu & Yuan, 2015, p. 147).
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Nevertheless, this was the situation which described the advance of ICT in the developed countries. As someone would probably assume, countries of the developing world deployed the “follow the leader” strategy. The interesting detail in this is the fact that they did not simply follow the path of technological development which had been used by the advanced countries (Liu & Yuan, 2015, p. 148). On the contrary, “they skipped some stages or even created their own individual path, which is different from their developed counterparts” (Liu & Yuan, 2015, p. 148).
This fact had both positive and negative implications. On the one hand, developing countries avoided heavy investments on IT innovation, as they were able to make targeted choices and even obtain previous technology systems from their developed counterparts (Liu & Yuan, 2015, p. 148). On the other hand, late-comers, having skipped some stages, they had to adjust to changes in a significantly faster pace. Hence, they faced “huge difficulties in developing critical technological capacity, allocating sufficient financial resources, and adjusting institutional contexts accordingly” (Heeks, 2010, cited in Liu & Yuan, 2015, p. 148).
This discrepancy between developed and developing countries is an indicative example which brings up several reasonable puzzles concerning both the drivers and the barriers which might affect IT innovation. Of course, the same discussion about IT innovation determinants holds not only for the national level, but also for the organizational one. Questions like “Why are some organizations more susceptible to IT innovation?”, “What is the secret to make an organization more innovative?”, and “What is the impact of certain organizational characteristics on IT innovation?” have been attracting substantial interest on behalf of scholars over time (Camisón-Zornoza et al., 2004; Del Aguila-Obra & Padilla-Meléndez, 2006; Hansen, 2011; Jakobsen & Thrane, 2016; Lee, & Xia, 2006; Li & Feeney, 2014; Moon, & Bretschneiber, 2002; Walker, 2007; 2014; Yao et al., 2003).
One such topic, which has attracted significant attention among scholars over the past four decades, is the relationship between organizational characteristics and IT innovation adoption (Del Aguila-Obra & Padilla-Meléndez, 2006; Hameed et al., 2012; Li & Feeney, 2014; Moon, & Bretschneiber, 2002; Walker, 2014). In particular, organizational size is an organizational feature which has been studied extensively as a determinant of IT innovation adoption over the years (Camisón-Zornoza et al., 2004; Lee, & Xia, 2006; Yao et al., 2003). Despite the plethora of studies on this topic, the research on the relationship between
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organizational size and IT innovation adoption has generated contradictory findings (Camisón-Zornoza et al., 2004, p. 332; Lee, & Xia, 2006, p. 975). As Camisón-Zornoza et al. characteristically frame it in their meta-analysis: “the most consistent result found in the organizational innovation literature is that its research results have been inconsistent” (Wolfe, 1994, cited in Camisón-Zornoza et al., 2004, p. 332).
Organizational size is an interesting variable to scrutinize. There are several reasons why organizational size attracts a large portion of scholars’ attention in the field of IT innovation adoption (Lee, & Xia, 2006, p. 976). To begin with, it is a variable which is relatively easy to handle in comparison to other organizational characteristics, because of its numeric nature. In addition, this numeric nature provides the notion of objectivity; something deeply appreciated by scholars. Moreover, the availability of data sets that concern size, which partially is a result of the aforementioned large attention, makes the specific variable even more tempting for scholars. Furthermore, on a practical level, organizational size is an organizational parameter which can be easily adjusted. Thus, practitioners can modify it in line with the suggestions provided by scholars, in order to attain desirable outcomes. Finally, size is often associated with particular organizational attributes like differentiation, formalization, decentralization, task specialization, complexity of communications, and availability of resources (Lee, & Xia, 2006, p. 976). These characteristics are able to exert significant influence on the innovation adoption processes (Lee, & Xia, 2006, p. 976).
As both Damanpour (1992) and Camisón-Zornoza et al. (2004) suggest, organizational size may affect IT innovation through more than one organizational aspect (Damanpour, 1992; Camisón-Zornoza et al., 2004). Consequently, the definition of the specific variable has been argued to be of major importance, since “different size measures can lead to different results” (Lee, & Xia, 2006, p. 977). In the context of this study, the main mechanism through which organizational size is postulated to affect the level of IT innovation adoption is the availability of IT-specific resources and capabilities. In short, the IT-specific resources and capabilities are essential both for the development and for the adoption of IT innovation. Therefore, the higher the availability of such resources and capabilities, the further IT innovation adoption is expected to take place.
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1.3 Literature gaps concerning the org. size – innovation adoption
relationship.
There are various factors which contribute to the inconsistency of the findings, in regard to the aforementioned relationship. Lee and Xia distinguish two of them as more important in comparison to others. The first of them is the inconsistent operationalization of
organizational size, which captures a different aspect/dimension from time to time (Lee, &
Xia, 2006, p. 975). The second factor which they recognized as crucial is the unsuccessful
recognition of important contextual variables (Lee, & Xia, 2006, p. 975). More specifically,
Lee and Xia stated that there might not be a uniform relationship between organizational size and IT innovation adoption and as a result of this, it is essential that context is appropriately taken into consideration at every occasion (Lee, & Xia, 2006, p. 975).
Context was also indicated by De Vries, Bekkers and Tummers as an important
element which needs to be further scrutinized, when it comes to innovation adoption in the public sector (De Vries et al., 2016). More specifically, after a literature review which included 181 articles and books, the authors recognized a gap of major importance in the state of the art understanding of innovation processes across different cultural contexts (De Vries et al., 2016, p. 163). The vast majority of relevant studies possess a UK/USA focus, which limits our understanding and creates ardent urgency for studies on different contexts (De Vries et al., 2016, p. 163).
De Vries et al. also underlined the need for quantitative studies to be increased, as the majority of the existing ones are of a qualitative nature (De Vries et al., 2016, p. 163). Moreover, the authors suggested a wider range of methods, which differ from the majority of the already deployed ones, in order for our understanding to be enhanced (De Vries et al., 2016, p. 163). Another gap identified by the same authors, is the lack of cross-national
studies (De Vries et al., 2016, p. 163).
1.4 Gaps concerning the e-Government maturity models.
As regards the models which are able to depict the development of e-Government, things are rather fuzzy. Despite the fact that there is an abundance of maturity models, which have been generated by international organizations, private or public stakeholders, and
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individual researchers, models look as if they are incongruent with each other (Lee, 2010, p. 220). Lee (2010) recognizes the different perspectives based on which each maturity model has been constructed and the use of different metaphors, as the main factors of the incongruence (Lee, 2010, p. 220).
According to our point of view, an extremely significant factor which inevitably leads to the aforementioned incongruence is the mixed foci of the vast majority of the existing
models. More specifically, as Lee (2010) very successfully recognized through his qualitative
meta-synthesis, two underlying themes characterize the development models. The first one refers to citizens/service and the other one to operation/technology (Lee, 2010, p. 220). This dual focus of the existing maturity models is inevitably creating an extra burden for both scholars and practitioners. Scholars on the one hand face difficulties to connect e-Government research to each other, and practitioners on the other struggle with both foci at once, when in most cases they use maturity models for particular purposes –solely connected either to the citizen/service or to the operation/technology theme.
1.5 Relevance/contribution of the study
This study is designed as a response to the gaps which were previously identified in the literature. To begin with, organizational size will be explicitly operationalized in
accordance to the purposes of this study and to the recommendations of Lee and Xia (2006),
in order to capture the desirable dimensions; the type of size measure is considered to be an important determinant for the organizational size – IT adoption relationship (Lee & Xia, 2006). Moreover, this study intends to shed light on the relationship between organizational
size and IT adoption, which is considered to be a blurry one, since contradictory findings
have come up among various studies (Hameed et al., 2012, p. 222). As a matter of fact, this contradiction is depicted in the competing character that our hypotheses demonstrate.
Furthermore, context will have an important role in this study, and hence, it will be paid the respective amount of attention. More specifically, the context that will be scrutinized is the context of Greek municipalities. Following the recommendations of De Vries et al. (2016), we chose a context different from the UK/USA one, despite the fact that they are much easier to be scrutinized, because of the availability of pre-existing studies and statistics.
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Concerning the particular choice of context, we expect the Greek perspective to be proven valuable, since it will offer insights from a totally unexplored context on the research
field of public sector innovation. In addition, we consider Greece as a fascinating case for
scholars, because of its unique financial condition over the recent past. Greece suffered the longest recession of any advanced capitalist economy to date, during which a series of strict austerity measures have been implemented (Oxenford & Chryssogelos, 2018). On the other hand, e-Government has been considered to enhance both the efficiency, and the productivity of the public sector (Parent et al., 2005; Wirtz & Daiser, 2018). As Linders (2012) put it, the advent of ICT provided hope to budget-strapped governments in their effort to deliver public value (Linders, 2012, p. 446). Hence, the investigation of the Greek context demonstrates a distinct character, since it provides the opportunity to investigate e-Government’s
development in organizations which have operated under critical fiscal circumstances over
a long period of time.
In line with the suggestions of De Vries et al. (2016), another contribution of this study is generated by its quantitative character. Specifically, the proposed hypotheses will be tested through a large-N design, something which will increase the number of quantitative studies. Moreover, the method which will be used is website content analysis. This will contribute to the need for wider variety of deployed methods in the specific research field. These two choices were deliberately made, in order to provide the study with the ability to be easily replicated in various contexts, while at the same time it is characterized by a rather high degree of objectivity and validity. This leads us to another important element of this study, the
ability, which it aims to provide, for easy cross-national replication. At this point, it must be
made clear that this study does not constitute a cross-national study; nevertheless, it aims to create a framework which would facilitate cross-national studies.
However, perhaps the most important contribution of this study lies with the
framework which is suggested for the inference of e-Government’s maturity. The attributes
of this framework aim to provide a solid model which is able to advance the state of the art
in the respective research field. Furthermore, it can be used as a basis for further research,
and help towards the creation of analogous models as well. In addition, it is able to enhance
the understanding around maturity models in general, and in this way to decrease the fragmentation of research. Finally, it will significantly facilitate the communication among scholars and enhance the practitioners’ ability to reach the most beneficial decisions about e-Government.
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The framework, which will be used for the inference of e-Government’s maturity, is based on Lee’s (2010) remark that two underlying themes characterize e-Government’s maturity models; namely the citizen/service and the operation/technology one (Lee, 2010). More specifically, utilizing the citizen/service theme, a citizen/service-oriented maturity
model for E-Government is proposed, whose distinct stages refer explicitly to the
aforementioned theme. In this way, the focus of the stage model is explicit, something which has several important implications both for this study and for the literature in general. We will elaborate on the proposed maturity model further in the subsequent chapter of this study.
1.6 Research Question
Utilizing the previously described citizen/service-oriented maturity stage modelfor e-Government, this study aims to shed some light on the relationship between organizational size and e-Government’s development. Specifically, it aims to provide an answer to the question: “What is the relationship between organizational size and e-Government
maturity?”. In order for this to be accomplished, we will investigate a totally unexplored
context on the specific research field, namely the Greek municipalities.
1.7 Order of information in the thesis
Subsequently, Chapter 2 will provide the theoretical basis of this study. Firstly, its central concepts will be presented and then, in accordance with the theory, our maturity model will be proposed. Lastly, the relationship between organizational size and IT innovation will be portrayed, something which will lead to our hypotheses. Chapter 3 will present the research objectives, the research design, the data collection methods and the operationalization of the variables. It will also outline our analysis strategy. Then, the 4th chapter will present and elaborate on the results of the analyses. In Chapter 5, the results will be discussed alongside with their theoretical, methodological and practical implications. Finally, the limitations of this study will be underlined and our proposals for further research will be provided.
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2. Theoretical framework
This chapter aims to provide the theoretical basis of this study. First, readers will become familiar with the concept of innovation, and the variety of innovation types will be portrayed. Subsequently, e-Government and its evolutionary nature will be outlined. Then, three indicative maturity models will be presented, as tools for monitoring e-Government’s development, and a discussion about the respective literature will take place. Afterwards, a maturity model will be proposed, with the intention to cover the gaps identified in the literature, and the utility of maturity models will be stressed. Lastly, the relationship between IT innovation and organizational size will be presented, something which will naturally lead to our hypotheses.
2.1 General information about innovation
The term “innovation” has traditionally been attributed a positive connotation. It entails the enthusiasm of a discovery, the satisfaction of a well-fulfilled need and an underlying hope for a superior future. However, considering the unfruitful attempts on the road for innovativeness, is this notion of optimism justified? Where does it stem from? One plausible explanation is that, in order for an innovation to alter the status quo and become established, it must provide a certain amount of additional value, large enough to compensate at least for the cost of the transition from the initial state of order to the following one. This implicit amount of additional value, which innovations are expected to offer, is probably what constitutes the term “innovation” as something positive and desirable for the majority.
According to Rogers (2003) innovation is “an idea, practice, or object that is
perceived as new by an individual or other unit of adoption” (Rogers, 2003, p. 12, cited in De
Vries et al., 2016, p. 152). The particular definition makes evident that the very existence of an innovation is a matter of perception. Something that is perceived as innovative in one case might be considered as typical in another. This fact reveals two important implications. Firstly, what constitutes an innovation entails a certain degree of subjectivity, since it is a matter of perception. In addition, the fundamental importance of the context becomes apparent. As Hartley (2005) put it, “innovation may include reinvention or adaptation to another context, location or time period” (Hartley, 2005, p. 27). Consequently, in order for
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people to innovate they do not have to “discover America”. An existing idea, practice or object adapted to a different context can still be innovative.
Another important element of innovation, which has to be emphasized, is the discontinuity with the past state of order. The particular attribute of innovation is considered to be of major importance, as it differentiates innovation from incremental or “continuous” change (De Vries et al., 2016, p. 152). There are various scholars, for instance Osborne and Brown (2013), who argue that in case of this element’s absence, innovation could be considered as a particular form of discontinuous change (Osborne & Brown, 2013, p. 3, cited in De Vries et al., 2016, p. 152).
Like in the case of innovation’s definition by Rogers (2003), the context, similarly, maintains a key role in the relationship between innovation and performance. According to the meta-analysis conducted by Rosenbusch, Brinckmann and Bausch (2011) in small and medium-sized enterprises (SMEs), the innovation-performance relationship is context dependent (Rosenbusch et al., 2011). More specifically, it is argued that various factors may have an influence on the relationship between innovation and performance. Some of them are the age of the firm, the type of the innovation and the cultural context in which the innovation takes place (Rosenbusch et al., 2011, p. 453). The fact that context demonstrates a differentiated impact in various cases, reveals the necessity for scholars to pay the appropriate attention to it in every occasion.
When it comes to the relationship between organizational size and innovation, context must, once again, be paid a respective amount of attention. In case we refer to innovation on an abstract level, someone would intuitively assume that the more people, the easier would be for them to innovate. This is probably because innovation is closely linked with creativity. In some cases they are even postulated to be integral parts of the same process (Anderson et al., 2014). Creativity refers to idea generation, whereas innovation refers to the implementation of these ideas toward better procedures, practices or products (Anderson et al., 2014, p. 1298). Moreover, creativity is generated by the human factor. As Joo, McLean and Yang (2013) put it, “creativity does not magically come from an invisible hand; it comes from people” (Joo et al., 2013, p. 391). Having that into consideration, someone would expect that since creativity is favored by people, the same would hold for innovation as well. But is it really so? Or to put it better, is it so in every context?
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2.2 Types of innovation
The distinction among the various types of innovation has been argued to facilitate the understanding of organizational innovativeness, as each type demonstrates unique characteristics which affect innovation adoption in a different manner (Walker, 2006; 2007). It has to be noted that, the term “dimensions” is often used interchangeably to “types” of innovation, and someone may come across the particular term especially in the private sector literature (Damanpour, 1991, cited in De Vries et al., 2016, p. 152).
According to the systematic review conducted by De Vries et al. (2016), public innovation can be divided in four main categories (De Vries et al., 2016, p. 152). However, this categorization holds mostly on a theoretical level. In practice, these types are often intertwined, as a particular case of innovation might belong in more than one of those categories, demonstrating a hybrid nature (De Vries et al., 2016, p. 152). The innovation types which are proposed by De Vries et al. (2016) are: “Product or Service innovation”, “Governance innovation”, “Conceptual innovation”, and “Process innovation”. Moreover, process innovation can be further decomposed into two subsets. These subsets are: “administrative process innovation” and “technological process innovation”(De Vries et al., 2016, p. 153).
In order to facilitate the understanding of the aforementioned types of innovation, an indicative definition will be provided for each one of them. To begin with, “Product or
Service innovation” refers to the type of innovation which focuses on the “creation of new
public services or products” (De Vries et al., 2016, p. 153). “Governance innovation” concerns the kind of innovation which aims to develop “new forms and processes to address specific societal problems” (De Vries et al., 2016, p. 153). Innovative re-organizations which may affect the decision making, the financing and the production systems belong to this type of innovation (Moore & Hartley, 2008). “Conceptual innovation” entails the introduction of new concepts, that operate as frames of reference or new paradigms which facilitate (re)framing the nature of specific problems alongside with their potential solutions (De Vries et al., 2016, p. 153). Finally, there is “Process innovation”. This innovation type includes
innovations which aim to enhance the quality and efficiency of internal and external organizational processes (De Vries et al., 2016, p. 153).
In case we refer to a public context, “process innovations pertain to how a service is rendered” (Walker, 2014, p. 23). Additionally, they are able to affect management and
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organization, since they can modify intra-organizational relationships, rules, roles, procedures, structures and exchanges which demonstrate either an intra- or an inter-organizational character (Abernathy & Utterback, 1978; Damanpour & Gopalakrishnan, 2001, cited in Walker, 2014, p. 23). Moreover, as it was previously mentioned, process innovation can be further analyzed into “administrative process innovation” and “technological process innovation”.
“Administrative process innovation” refers to the type of innovation that is focused
on establishing new organizational forms, on instituting new management methods and
techniques, and novel working methods (De Vries et al., 2016, p. 153). Some practical instances of administrative process innovations are the improvements in organizational practices, the introduction of a novel motivation or reward scheme, or even “new methods of purchasing, delivering services, and generating revenue” (Walker, 2014, p. 24).
The second subset of “Process innovation” is the “Technological process
innovation”. The particular innovation type intends to develop or use new technologies,
which are introduced in an organization so as to render services to users and citizens (De
Vries et al., 2016, p. 153). Moreover, technological process innovations transform organizational processes and systems in such a way that efficiency and/or effectiveness are enhanced (Walker, 2014, p. 24). Due to the character that the advent of technology maintains over the past decades, these innovations are typically associated with information technology (IT) (Walker, 2014, p. 24).
2.3 E-Government
Taking into consideration that the very existence of e-Government has been materialized as a consequence of the advent of the ICT, and based on the fact that by definition e-Government involves the delivery of information and services, e-Government can be categorized into technological process innovations. That being said, it has to be noted that e-Government can be argued to demonstrate characteristics of other innovation types as well, due to its dynamic nature. For instance, in case that e-Government is considered as a mean able to provide innovative ways of decision making, it could be argued that it belongs to governance innovations as well. However, despite the fact that we should keep it in mind, this should not be a matter of concern, since these multiple categorizations hold mostly in the
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latter stage of e-Government, where it converges with e-Democracy. Hence, not only it is difficult for someone to come across them in practice, but even then, this does not alter the fact that e-Government remains a technological process innovation as well.
As it was mentioned in the introduction, e-Government is a continuous and evolutionary phenomenon, which demonstrates a dynamic nature (Das et al., 2017; Layne & Lee, 2001; Veljković et al., 2014). To begin with, the term “continuous” reveals e-Government’s character as a reform; it cannot be implemented at once, and it rather follows the pattern of incrementalism (Burnes, 2004; Kuipers et al., 2014; Todnem By, 2005). The term “dynamic nature” is attributed to it due to several reasons. Firstly, as a result of governments’ structural transformations, through which, electronically-enabled government will come up (Layne & Lee, 2001, p. 123). Furthermore, fundamental alterations in the form of government may occur, because of the integration of the novel Internet-based government models with traditional public administration (Layne & Lee, 2001, p. 123). Lastly, e-Government’s focus evolves over time, as initially a focus on internal processes can be noticed, whereas later on external developments gain ground (e.g. citizen inclusion in government) (Veljković et al., 2014). Finally, the term “evolutionary” is there to depict the development of e-Government through incremental changes. In addition, these changes are noticeable and they can be argued to demonstrate an escalating degree of complexity, since latter changes build on those which have already taken place.
Terms like “evolution”, “development”, “adoption” and “implementation” are often used interchangeably so as to describe the materialization of e-Government. In other words, these terms are used by scholars in order to express the provision of information and services by government through the use of ICTs. The degree up to which this effort has been accomplishedis described by the concept of maturity. Maturity can “be defined as the extent
to which a government has established an online presence” (Das et al., 2017, p. 416). The
notion of maturity has been useful over time in order for a state of a given level in a continuous process to be portrayed (Andersen & Henriksen, 2006). As a consequence, scholars have come up with a broad variety of maturity models that monitor the level up to which e-Government has been developed. Some indicative examples of the maturity models available in the literature will follow.
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2.4 Maturity models
Layne and Lee stages growth model
Layne and Lee (2001) in their effort to depict the different stages of e-Government development, they provided a popular “stages of growth” model, considering its acceptance by scholars (Andersen & Henriksen, 2006, p. 237). Their model consists of 4 stages which outline the multi-perspective transformation government structures and functions undergo as they proceed towards a digitalized environment (Layne & Lee, 2001). The four stages which compose the proposed stage model are: cataloguing, transaction, vertical integration and
horizontal integration (Layne & Lee, 2001). Cataloguing refers to the establishment of online
presence on behalf of the government, which entails the dissemination of government information on the web (Layne & Lee, 2001). The transaction stage aims at allowing citizen-government electronic transactions (Layne & Lee, 2001). The stage of vertical integration seeks to connect governments of various levels in order to cooperatively provide different functions or services of government (Layne & Lee, 2001). Finally, horizontal integration refers to integration across various functions and services (Layne & Lee, 2001).
Andersen and Henriksen (PPR) maturity model
Andersen and Henriksen (2006), building on Layne and Lee (2001), proposed the Public Sector Process Rebuilding (PPR) maturity model (Andersen & Henriksen, 2006). PPR maturity model constitutes an extension of the growth model proposed by Layne and Lee (2001). A central element of the particular article is the view that there should be a reorientation of e-Government strategic thinking towards the customer perspective (Andersen & Henriksen, 2006, p. 237). Consequently, in an effort to adopt an activity and customer centric approach, rather than the technological capability, the authors propose a maturity model of four phases. These phases could be better outlined as discrete points in a continuous development processinstead of distinct stages, as the growth model of Layne and Lee (2001) suggested (Andersen & Henriksen, 2006, p. 239). The four phases are cultivation, extension,
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Moon’s maturity model
Having examined e-Government at the municipal level in the US, through data from the 2000 e-Government survey by International City/County Management Association and Public Technologies Inc., Moon (2002) proposed a five stage maturity model for e-Government (Moon, 2002). The stages of this maturity model are: one-way communication,
two-way communication, service and financial transactions, horizontal and vertical integration, and political participation (Moon, 2002). One-way communication refers to
simple information dissemination. Two-way communication entails the requests on behalf of the citizenry alongside with the organizational responds to them. In the “service and financial
transactions” stage civil servants are replaced by “web-based self-services” through which
service and financial transactions take place. The “horizontal and vertical integration” stage corresponds to the relevant stages in the Layne and Lee (2001) maturity model. Finally, the stage of political participation provides the ability for direct and enhanced interaction with the citizenry through Web-based political activities.
2.5 Comments on the existing maturity models
The aforementioned models constitute just an indicative example of the existing maturity models, as there is a large number of analogous models in the literature (Alhomod et al., 2012; Almazan & Gil-García, 2008; Hiller & Bélanger, 2001; Howard, 2001; Kim & Grant, 2010; Lee & Kwak, 2012; Shahkooh et al., 2008; Siau & Long, 2005; West, 2004, cited in Fath-Allah et al., 2014). However, a detailed reference to all of them would not serve the purpose of this study.
An important implication about the existing maturity models is the incongruence which they demonstrate to each other, as a result of the different perspectives they adopt and the dissimilar foci they have (Fath-Allah et al., 2014; Lee, 2010). Lee (2010) in his retrospect on e-Government stage models identified two underlying themes: the citizen/service one and the operation/technology (Lee, 2010). Based on this categorization, it could be postulated that the vast majority of maturity models demonstrate mixed foci not only to each other, but even themselves, as their stages, phases or levels belong to different themes. This turbidity incommodes both scholars and practitioners, since they face difficulties both in the
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interpretation of the research results and in planning future actions for e-Government (Lee, 2010).
Taking into consideration the need for maturity models which demonstrate an explicit focus, the themes Lee (2010) identified, and the remark by Andersen and Henriksen (2006) about the major importance of the customer perspective in e-Government strategic thinking, this study proposes a maturity model with an explicit focus on the citizen/service theme.
2.6 The proposed maturity model
The proposed citizen/service-oriented stage model is composed of 7 developmental stages (Graph 1). These stages are: i) Web presence (One-way communication), ii) Two-way
communication, iii) Service and financial transaction, iv) Personalization (Individual consideration), v) Participation, vi) Collaboration vii) e-Governance. At this point, it has to
be noted that e-Government’s development may proceed despite the fact that a particular stage might be partially developed. However, in practice, just the stage of “personalization” may be present ahead of its order within the model, since the rest of the stages have some essential prerequisites in order to be initiated.
Subsequently, the developmental stages of our maturity model are outlined:
The stage of “Web presence” refers to the existence of a website through which information are communicated to the public. The term “presence” implies the “static” nature of this stage, which is regarded as a state of inertia, since it involves no action apart from information dissemination. The alternative term which has been chosen to describe this stage is “One-way communication”. It can be used alternatively, as it signifies the flow of information, which can be paralleled to an one-way road; from the organization to the public. It corresponds to the stage of publishing/cataloguing/presence that pre-existing maturity models entail (Lee, 2010, p. 225).
The second stage of e-Government’s development is “Two-way communication”. It concerns an interactive state between the government and the public, in which they can operate either as transmitter or as receiver of information. This stage has been named after the fact that information is able to flow to both directions. In other maturity models, someone may come across this stage named as “interaction” (Lee, 2010, p. 225).
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“Service and financial transaction” refers to the allowance of online services and
financial transactions. During this stage of e-Government’s development, “web-based self-services” replace civil servants in the duties they previously performed (Moon, 2002). The term “Service & financial transaction” was consciously preferred over the “transaction” and the “portal” terms which other stage models use for their corresponding stages. Plain “transaction” was avoided because, subconsciously, people primarily think of financial transactions, whereas our intention was to signify every kind of transaction. Furthermore, the metaphor of “portal” was avoided because, as Lee (2010) notes, it may connote the integration of operations (Lee, 2010, p. 225). This would correspond to the operation/ technology theme, and hence, the “portal” metaphor would contradict the citizen-oriented character of our model.
The next stage of our maturity model is the stage of “Personalization”. It concerns e-Government’s adjustment to the personal needs, abilities or preferences of the user. The explanatory term “Individual consideration” which has been chosen, outlines the fact that this stage, practically, turns the public from “users” into “individuals”. From this stage onwards, the individual contribution of the user becomes significant, as it acquires the ability to make a difference. Considering the citizen-oriented character of our model, this stage is regarded as of fundamental importance. Surprisingly, despite the large number of maturity models which were reviewed for this study, only the maturity model by Deloitte and Touche (2000) included a stage of personalization (Deloitte & Touche, 2000, cited in Fath-Allah et al., 2014, p. 75). However, this case differed from the suggestion of our model, since it concerned personalization of the portal, which is a narrower concept in comparison to ours. To make it more comprehensible, Nam (2014) identified 5 types of e-Government’s use. Namely, he identified service use, general info use, policy research, participation and
co-production (Nam, 2014). The portal personalization by Deloitte and Touche (2000) refers to the first two types of e-Government use, whereas our suggestion concerns all the aforementioned types.
“Participation” is the fifth stage of our model, which refers to the participation of the
citizenry in web-based political activities. More specifically, it concerns the utilization of public input for the enhancement of policy decisions and government services (Veljković et al., 2014). There are two discrete forms of participation; Citizen engagement, which is about collecting opinions for agenda setting and decision-making processes, and citizen sourcing, which concerns collecting useful ideas and solutions for public/government problems
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(Linders & Wilson, 2011, p. 267). An important attribute of participation is that, during this stage, the government maintains full decision-making powers (Linders & Wilson, 2011). A corresponding stage can be spotted in Hiller and Belanger (2001) and in other maturity models as “Political participation” or “Open participation” (Almazan & Gil-Garcia, 2008; Hiller & Belanger, 2001; Lee & Kwak, 2012; Moon, 2002). However, the term “participation” was preferred for our model since, as it was previously described, the public input may be utilized not only for political purposes, but also for problem solving and service enhancement.
The sixth stage of our model is called “Collaboration”, and it refers to cooperation between the government and the citizenry. In contrast to “participation”, “collaboration’ is aimed at more responsive decision making based on the public input, as active involvement in government operations is enabled (Veljković et al., 2014). The main attribute of “collaboration” is that, contrary to “participation”, it requires significant (if not equal) power sharing (partnering) in terms of decision making (Linders & Wilson, 2011, p. 268). Apart from the political cooperation, the public may also participate as a co-producer of public services -e.g. Linders (2012), “Government as platform” (Linders, 2012). Despite the fact that some maturity models imply or include the notion of collaboration, the maturity model by Lee and Kwak (2012) was the only among those reviewed, to devote a stage to “open collaboration” (Lee & Kwak, 2012).
The final stage of our model is “e-Governance” and refers to the realization of all the abilities that e-Government is able to provide. At this point, the power sharing is clearly in favor of the public through web-based direct Democracy, and the government merely demonstrates a facilitative character. The particular stage is characterized by “Do it Yourself or Citizen to Citizen Government” (Linders, 2012). Consequently, the public is in charge of the decision making process and is also actively involved in service provision, either through self-service or through forms of co-production, in which it is in charge of. Considering that this stage is regarded as the “end-point” of e-Government’s development, it would be rare for someone to come across. Nevertheless, demonstrating the boundaries of what is practically feasible is exactly what constitutes a model useful. The maturity models which were reviewed had remained chary toward e-Government’s final stage, by choosing to depict what seemed feasible according to the present situation, instead of up to what point e-Government is able to be developed. For instance, West (2004) proposed “interactive democracy”, Shahkooh, Saghafi and Abdollahi (2008) suggested “digital democracy” and Siau and Long (2005)
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proposed “e-democracy” (Shahkooh et al., 2008; Siau & Long, 2005; West, 2004). However, they refer to personalization, online voting, public forums and opinion surveys, leaving little space for participation and citizen engagement, considering the fact that they refer to e-Government’s final stage (Shahkooh et al., 2008; Siau & Long, 2005; West, 2004). Contrary to them, our maturity model proposes that despite the difficulties there might be for such an endeavor to be materialized in practice, when e-Government is developed in full, the winner of the power sharing will be the public.
Graph 1. Proposed e-Government stage model (Initial).
2.7 Practical and theoretical utility of maturity models
In regard to their practical utility, maturity models can be used as a communication tool, as they are ideal in order to effectively explain e-Government to third parties and facilitate citizens to understand its trajectory (Kim & Grant, 2010, cited in Das et al., 2017, p.416). At the same time, they operate as a valuable guide to practitioners, who can benchmark a variety of features (Das et al., 2017, p.416; West, 2004, p. 17). For instance, they offer the aptitude for direct comparison among various cases (contexts), based on which, administrators are able to deploy the “follow the leader” strategy. Another example would be
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the ability they offer for effective monitoring of e-Government’s development, in order to verify the outcomes of particular policies, or the success or failure of administrators’ actions. Finally, they offer the ability to monitor e-Governments’ development at the same context in different time points.
Regarding to the theoretical utility of the maturity models, they constitute a valuable tool for e-Government research. To begin with, they offer a unified framework of reference, which facilitates the communication among scholars. Furthermore, they are able to operate as guidelines for further research. Having outlined the various maturity levels explicitly, facilitates the advance of research, as the features of each stage are further processed in order to become more realistic and useful. In addition, maturity models are an effective way, through which the trajectory of e-Government’s development can be easily comprehended and followed (Kim & Grant, 2010, cited in Das et al., 2017, p.416). Finally, they can be used in order to compare and contrast various contexts. In other words, they operate as benchmarks that enable the direct comparison of cases, which otherwise would not be comparable.
This latter potential they offer is rather important for research. Being able to effectively monitor e-Government’s development provides researchers the ability to draw rather important conclusions. For instance, innovation diffusion and adoption, e-Government’s antecedents, its barriers, civil servants’ and citizens’ reactions are only a few elements which could be studied with the assistance of maturity models. Considering the abundance of zealous proponents of technology, factors affecting new technology adoption (Del Aguila-Obra & Padilla-Meléndez, 2006; Jakobsen & Thrane, 2016; Li & Feeney, 2014; Moon, & Bretschneiber, 2002; Walker, 2007), innovation diffusion (Hameed et al., 2012; Moon & Bretschneider, 1997; Zhang & Xiao, 2014), innovation’s antecedents (Hansen, 2011; Walker, 2014), moderators (Damanpour, 1992) and predictors of innovation (Camisón-Zornoza et al., 2004; Lee, & Xia, 2006; Yao et al., 2003) attract a large proportion of interest.
2.8 Organizational size – innovation relationship
The introduction provided a brief overview of the evolution of information and communication technology in public administration worldwide over the past decades. As it was noted, the rate of successful adoption and operation of IT varies from country to country, and the discrepancies among them can be discerned (Liu & Yuan, 2015). In accordance with
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these discrepancies, which hold on a national level, discrepancies of IT adoption can be observed on an organizational level as well. These discrepancies of IT adoption come as a natural outcome of the differences which organizations demonstrate in terms of internal and external characteristics (Del Aguila-Obra & Padilla-Meléndez, 2006; Hameed et al., 2012; Li & Feeney, 2014; Moon, & Bretschneiber, 2002; Walker, 2014).
The variety of factors which are able to affect IT organizational adoption is very broad, since technological, organizational, environmental, and individual characteristics may contribute towards this direction (Hameed et al., 2012). Some indicative examples of technological factors are cost, complexity and compatibility. In terms of organizational characteristics, the organizational size, existing resources, centralization, formalization and IT expertise constitute indicative examples. Competitors’ pressure, customers’ demands, environmental uncertainty and political correlations affecting organizational operation are some relevant environmental factors. Finally, two factors which fall under the “individual” category are CEOs’ knowledge of and attitude towards IT (Hameed et al., 2012, p. 218).
Among these categories of factors, the organizational ones attract a significant amount of scholarly attention. In particular, size, including both organizational and IT department size, is one of the most frequently studied determinants of IT innovation adoption (Lee & Xia, 2006, p. 976). In spite of this, empirical results on the relationship between them have been inconsistent and sometimes even contradictory (Hameed et al., 2012, p. 218; Lee & Xia, 2006, p. 975). This very fact operates as an indication of the urgency for further research concerning this relationship to take place.
In general terms, organizational size has been found to demonstrate a positive effect on the adoption of process innovation (Walker, 2014). This result can be expanded to IT innovation adoption as well, since IT innovation belongs to technological process innovations (Walker, 2014). Indeed, both the meta-analysis conducted by Lee and Xia (2006) and the study by Yao, Liu, Xu and Lu (2003) confirmed the positive effect that organizational size has on IT innovation adoption (Lee & Xia, 2006; Yao et al., 2003). These results are in line with other studies which revealed a positive relationship between organizational size and innovation (Camisón-Zornoza et al., 2004; Damanpour, 1992).
Going one step further, according to Camisón-Zornoza et al. (2004) several studies argue that ‘organizational size is the best predictor of innovation’ (Aiken & Hage, 1971; Damanpour, 1992; Dewar & Dutton, 1986; Ettlie et al., 1983; Kimberly & Evanisko, 1981;
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Moch & Morse, 1977; Sullivan & Kang, 1999, cited in Camisón-Zornoza et al., 2004, p. 332). In order for this positive relationship to be explained, quite a few arguments have been used. For instance, larger organizations possess more complex and diversified resources and capacities (Nord & Tucker, 1987, cited in Camisón-Zornoza et al., 2004, p. 339). Moreover, they have the ability to take on greater risks (Damanpour, 1992; Hitt et al., 1990, cited in Camisón-Zornoza et al., 2004, p. 339). This provides them both with the “safe space” which is necessary in order to think out of the box, and the ability to benefit from learning through failures. In contrast to them, small organizations may demonstrate tight IT budgets (as a result of resource poverty) or a lack of IT personnel and expertise, which may constitute a failure disastrous (Lee & Xia, 2006, p. 976).
Furthermore, according to Hartley, Sørensen and Torfing (2013) there is growing evidence that collaboration is able to stimulate innovation (Hartley et al., 2013, p. 825). The theories of collaborative innovation in the public sector have their roots in various kinds of theories. For instance, theories of network governance suggest that networks which are in close collaboration constitute an effective way of coming up with innovative solutions to problems demonstrating a high degree of complexity (Hartley et al., 2013, p. 825). Furthermore, several commonalities can be observed with management theories which refer to private sector innovation. There, central concepts like “social innovation” (Phills, Diegelmeier & Miller, 2008), “co-creation” (Prahalad & Ramaswamy, 2004), and “open innovation” (Chesbrough, 2003) refer to innovation which is generated as a result of different types of interaction from a variety of actors (Hartley et al., 2013).
In general, communication and collaboration constitute platforms through which a variety of actors who possess different innovation assets can contribute to the innovation process (Bommert, 2010, cited in Hartley et al., 2013, p. 826). Indeed, the positive effects that both internal and external communication and collaboration demonstrate on innovation were validated by Damanpour’s (1991) meta-analysis (Damanpour, 1991, cited in Hartley et al., 2013, p. 825). Consequently, since innovation is enhanced by the interaction of actors and their contribution to the overall process, a reasonable postulation would be that, the higher the number of the actors who interact, the better the chances for innovation to take place. This postulation would intuitively lead us to the assumption that organizational size is positively related to innovation.
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Consequently, in line with the previous arguments, the first hypothesis of this study will be:
H1: “The larger the size of a municipality is, the higher the degree of e-Government maturity it will demonstrate”.
On the other hand, there are some studies which question the aforementioned relationship between organizational size and IT innovation adoption. For instance, Hameed, Counsell and Swift (2012), in their meta-analysis, concluded that the impact of organizational size on IT adoption has produced mixed results (Hameed et al., 2012, p. 222). The authors postulated that there is a controversy among studies which found this impact to be a significant attribute, and others which concluded that it is not (Hameed et al., 2012, p. 222). However, despite the controversy they came across in the studies they scrutinized, they found a weak significance in the organizational size - IT adoption relationship (Hameed et al., 2012, p. 221).
Someone may also come across scholars who suggest that the aforementioned relationship is a negative one (Aldrich & Auster, 1986; Hage, 1980; Wade, 1996, cited in Camisón-Zornoza et al., 2004, p. 332). They also provide a series of convincing arguments. Smaller organizations demonstrate a higher degree of flexibility and adaptability (Lee & Xia, 2006, p. 976). For instance, the fewer the people who are involved, the fewer the potential objections at a debate about an innovative idea, and the easier it is for them to reach a decision, since the potential concessions are minimized. Hence, the smaller the organizational size, the faster the organization will adapt to new circumstances which might come up at its environment; something which facilitates innovation.
In addition, communication is considered to be more effective within smaller organizations. The fewer the times a message is transmitted, the more likely it is for the last receiver to receive it intact. Effective communication is of vital importance for management, since the goal of management is to organize people in such a way that they are able to achieve particular results. This is impossible without effective communication. Hence, members of smaller organizations are able to collaborate and be coordinated in a more efficient way, which accommodates innovation (Lee & Xia, 2006, p. 976). Lastly, the inferior degree of formalization, which smaller organizations exhibit, promotes innovation (Lee & Xia, 2006, p. 976). Formalization aims to ensure that organizational members act in a particular
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determined way. Consequently, absence of formalization is most likely to be beneficial for innovation.
As a result of these, an alternative hypothesis will be:
H2: “The smaller the size of a municipality is, the higher the degree of e-Government maturity it will demonstrate”.
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3. Methodology
3.1 Research aims/objectives
The goal of this research is predictive. Specifically, this study intends to shed light on the relationship between size, as an organizational characteristic, and the maturity of e-Government –which can be considered as a particular type of technological process innovation- within the context of municipalities (Walker, 2014). Furthermore, since our study is predictive, by definition, it retains a prospective character. Finally, the research will be X-Y
focused, since both the organizational size and the stage up to which e-Government has been
developed (maturity) need to be estimated.
3.2 Research design
This research demonstrates an observational and quantitative character. Specifically, a large-N design was chosen in order to test our hypotheses. We reached this decision due to various reasons. Firstly, according to Toshkov (2016) “large-N designs are better suited to uncovering general relationships between variables” (Toshkov, 2016, p. 256). This is in line with our intention to investigate the relationship between organizational size and e-Government maturity. Furthermore, large-N research is more effective when it comes to providing answers in prospective causal questions, something that fits with this study’s prospective character (Toshkov, 2016, p. 256). Moreover, the purpose of this study is to test assumptions which are based on existing theories, and large-N is deemed to be considerably good on this (Toshkov, 2016, p. 255). Furthermore, the supremacy of large-N research over comparative and single-case designs, in terms of identifying and estimating weak and heterogeneous causal relationships, operated decisively for our choice (Toshkov, 2016, p. 200). Finally, we believe that the particular research design will promote the state of the art in the research field of public sector innovation, due to the urgency for quantitative studies and theory testing (De Vries et al., 2016). It will shed light on a blurry relationship (organizational size - IT innovation) and hence, it will contribute towards a more comprehensive research body.
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3.3 Case selection
The Greek municipalities were chosen to serve as units of analysis due to several factors. The reasoning behind this choice is very specific. Initially, as it was previously described, we reached the conclusion that the most suitable research design to scrutinize the relationship between our variables is a large-N design. As a result of this, there was a necessity of a large number of observations. Thereafter, considering the definition of e-Government, our research concerns public organizations, and thus a public context which would facilitate our purpose had to be found. Bearing in mind the abetment of De Vries et al. (2016) for studies on different contexts from the UK/USA one, we sought for an interesting context to scrutinize (De Vries et al., 2016, p. 163).
As it was thoroughly described in the introduction, the Greek context was considered to be of significant value, since it provides insights about organizations that have operated under critical fiscal circumstances over a long time period, due to the recent financial crisis. Apart from the attention that the case of Greece inevitably draws because of its financial condition, Greece consists of 325 municipalities; a number which enables large-N type of research. At the same time, the number of the municipalities is small enough to enable the scrutiny of all the available municipalities, something which would practically minimize randomness.
In addition to these,the characteristics that Greek municipalities demonstrate facilitate this study. More specifically, they are organizations which belong to the same sector, same hierarchical level, they have identical mission and they perform similar tasks. This homogeneity, which municipalities are characterized of, is valuable for our study, since it enables the consideration of the whole population of municipalities as a target population. On the other hand, the heterogeneity they demonstrate, both in terms of size and e-Government maturity, allows the necessary variability of our variables, which constitutes this study meaningful. Finally, another crucial reason for our choice is the availability and accessibility of information concerning the aforementioned variables.
