Abstract
The desired e-government potentials and its shortcomings in reality are key reasons why e-government has become a major topic of interest to academics and practitioners, leading to an extensive body of knowledge. However, the literature still demands further quantitative empirical research to substantiate theory development. This situation calls for a specific review of the literature that arranges relevant knowledge and provides a solid foundation for future research. However, available meta-analyses do not deliver the particular insights to appropriately address the shortage of quantitative empirical e-government research. Therefore, this study explicitly focuses on this specific field to systematically uncover areas requiring further exploration, and defines promising research directions for a solid foundation for future investigations. Key findings of the meta-analysis are: the existence of a systematic divide of existing quantitative empirical e-government studies into 12 research subtopics, which are assessed according to different classification criteria for scientific research gap-spotting; the identification of emerging subtopics that carry innovative research potential; and that e-government is expected to be an ongoing, open-ended research environment that still provides manifold investigative opportunities. Based on these findings, straightforward suggestions for future research are provided.
Points for practitioners
Beyond providing insights into the current state of quantitative empirical research for scientific researchers, this article also delivers value for professionals working in public management and administration. First, the study provides a comprehensive overview of e-government-related meta-analyses, which allows us to quickly identify the literature in order to tackle particular e-government management issues. Second, the article classifies existing quantitative empirical studies, defines specific subject areas and arranges relevant knowledge, which eases the processes of confining and labelling e-government activities. Last, since these deliverables are based on empirical studies that draw their conclusions from perceptions of reality, the summaries and classifications are thus regarded to be of special importance for public managers.
Introduction
E-government can be a powerful instrument to enhance citizen–government interaction (Im et al., 2012), to advance public administration (United Nations, 2014) as well as public service provision (Welch et al., 2005), and to improve internal government efficiency (Parent et al., 2005). Moreover, it is supposed to embellish public service quality and to be a concept for adequately reflecting citizens’ demands for transparency and accountability (United Nations, 2014). Against this background, e-government, which refers to ‘the use of information technology to enable and improve the efficiency with which government services are provided to citizens, employees, business and agencies’ (Carter and Bélanger, 2005: 5), has become a major topic of interest to academics and practitioners (Bélanger and Carter, 2012; Wirtz and Daiser, 2015). Thus, up to now, an extensive, complex and interdisciplinary body of knowledge has been accumulated (Arduini and Zanfei, 2014).
Despite this wide-ranging, multifaceted theoretical body of science, the academic literature postulates the necessity of further quantitative empirical research since a shortcoming of potent approaches prevails (Morgeson and Mithas, 2009; Piehler et al., 2014; Rana et al., 2013). However, closing a research gap in an area showing a plethora of research calls for a sound review of prior, relevant literature that arranges existing knowledge and provides a firm foundation for future research to systematically uncover areas requiring further investigation and define promising research directions (Webster and Watson, 2002). Given this research issue, we examined e-government-related literature reviews that have dealt with this topic in the past years (e.g. Andersen et al., 2010; Arduini and Zanfei, 2014; Bélanger and Carter, 2012; Hu et al., 2010; Hui et al., 2014; Irani et al., 2012; Kromidha and Cordoba-Pachon, 2014; Rana et al., 2013; Savoldelli et al., 2014).
On the whole, the investigated meta-analyses aim at covering the entire range of conceptual and empirical articles with regard to specific core issues, such as e-government adoption, diffusion or service quality, delivering highly aggregated analyses of distinct e-government subtopics. Even though these analyses clearly advance scientific knowledge by giving a detailed synopsis of their respective topic, their specialized content in combination with their broad scope of research approaches, covering conceptual, qualitative and quantitative work, does not provide a sufficient research perspective that delivers the particular insights required to appropriately address the shortfall of quantitative empirical e-government research.
Against this background and in light of the compelling interest in e-government research, this article tries to advance the knowledge regarding the state and the direction of quantitative empirical e-government research with the aims to identify potential investigation backlogs and to derive implications for future research. For that reason, we examined existing scientific quantitative empirical e-government research by investigating its density within different thematic clusters, identifying applied statistical and data collection methods, and evaluating its research tendency. Therefore, the present review is specifically focused on quantitative empirical e-government research and thus presents a focused, fine-grained complement to recent reviews of e-government literature.
To achieve the aforementioned aims, we scrutinized 129 peer-reviewed articles covering quantitative empirical e-government research work. This particular data set and the resulting quantitative-based research overview are supposed to help in identifying research challenges and opportunities since an adequate review of existing knowledge should create a solid foundation for uncovering research gaps and providing inspiration for future research (Webster and Watson, 2002). For this purpose, the article is outlined as follows: in the next section, we briefly illustrate the origin and development of e-government, which is followed by an overview of current e-government meta-analyses portraying the extant literature review gap. The following section delineates the application of the empirical meta-analysis and presents the findings of the quantitative-based research approach. The article concludes by summarizing the findings in the discussion and conclusions part and by providing implications for future quantitative empirical e-government research.
Origin and development
The beginning of e-government was set by the implementation of modern information and communication technologies (ICTs) into the practice of government and public authorities to electronically provide information and services to citizens and businesses (Dawes, 2009). Research concerning the potential and political implications of e-government has been further actuated by the growing interest of the public sector in ICTs (Welch et al., 2005; Yang and Rho, 2007), which originates from the perspective that e-government is regarded as possessing considerable potential as a new interface for enhancing public service delivery, while, at the same time, increasing productivity in the public sector. In combination with the continuing advancement of technology, the field of e-government constantly offered interesting research questions and new challenges to both academics and practitioners.
Although e-government tended to be rather practitioner-oriented in the beginning, this topic quickly gained massive importance among academics (Reece, 2006). The e-government research in the 1990s was characterized by descriptive, practical-oriented studies rather than explanatory research. This development is mainly attributed to the situation that e-government did not reach a point of critical mass with regards to usage by politicians and governments before then and thus did not allow the emergence of more rigorous studies before this point in time. It was not until the mid-2000s that e-government theory development became increasingly supported by quantitative empirical investigations, providing scholars and public managers with further insights into e-government systems and procedures, as well as the nexus between e-government and the embracing public management and political science literature (Reece, 2006).
Considering this ongoing development, an extensive body of knowledge has now been accumulated (Arduini and Zanfei, 2014). However, the literature still mentions a shortcoming of potent quantitative empirical approaches (Morgeson and Mithas, 2009; Piehler et al., 2014; Rana et al., 2013). In this context, Figure 1 illustrates the number of quantitative empirical research studies in the course of time. Here, it should be noted that 62.0% of the identified studies were published in the past five years. If we extend this period to 10 years, this already represents 94.6%, indicating that quantitative empirical e-government research is of ongoing interest and still a relatively young field.
Number of quantitative empirical research studies.
In summary, it can be stated that the multifaceted development of e-government research – starting from a practitioner’s point of view, followed by descriptive conceptual investigations and finally quantitative empirical approaches – in combination with the ongoing advancement of ICTs and continuing difficulties in implementation, has led to an open-ended research environment. Apart from the generally persisting lack of quantitative empirical approaches, the described situation aggravates sustainable theory development and validation and causes a constant need for qualitative and quantitative research in the field of e-government.
State of meta-analyses
Overview of e-government-related meta-analyses over the past 10 years.
These articles may be clustered into seven overarching topics: (1) adoption; (2) impact; (3) maturity/stage models; (4) policy development; (5) portal/service quality; (6) research design/models; and (7) general overview. The adoption-related meta-analyses provide a systematic review of implementation-related subtopics, such as success factors, barriers, diffusion and usage (cf. Dixon, 2010; Hui et al., 2014; Rana et al., 2013; Savoldelli et al., 2014; Titah and Barki, 2006). Andersen et al. (2010) analysed the impacts of e-government according to 55 empirical peer-reviewed journal publications in order to evaluate the ICT impact on public sector domains. Kromidha and Cordoba-Pachon (2014) conducted a discourse analysis to create a better understanding of emerging concepts in e-government publications and their relation to public policy development. The studies of Kohlborn (2014) and Halaris et al. (2007) target the assessment of provided e-government portal service quality. Irani et al. (2012) and Yildiz (2012) pursued a different approach by analysing the research designs/models. In this context, the work of Yildiz (2012) stands out by postulating that e-government research needs to ask big, critical questions, such as if e-government can enhance government operations or democracy (Yildiz 2012).
Apart from these special content meta-analyses, there are also general overviews that provide a historical assessment of e-government research. While Hu, Pan and Wang (2010) elaborate a widely shared conception of e-government and a consensual definition of the field, and Reece (2006) illustrates the general state of e-government at this time, Norris and Lloyd (2006) focused on empirical research. Arduini and Zanfei (2014) contributed to scientific knowledge creation by assessing the delivery, diffusion, adoption and impact of public e-services. In this regard, they elaborated a general overview on e-government, e-education, e-health, infomobility and e-procurement. Bélanger and Carter (2012) conducted a literature review on e-government research specifically tailored to the field of information systems. All in all, the meta-analyses cover a broad range of e-government-related topics; however, none of them shows a particular quantitative empirical focus providing a solid foundation for quantitative empirical e-government research.
Empirical analysis
The meta-analysis was restricted to publications in scientific academic journals since these sources reflect high-quality, up-to-date research (Norris and Lloyd, 2006; Webster and Watson, 2002) and this approach is becoming increasingly pursued in the academic literature (Arduini and Zanfei, 2014; Braadbaart and Yusnandarshah, 2008). The investigated articles were identified in a systematic four-step approach, taking into account the recommendations of Webster and Watson (2002) to ensure a relatively comprehensive census of the existing scientific literature.
(1) Initially, we conducted an EBSCOhost database query using Academic Search Complete, Business Source Complete and EconLit, with limitation to peer-reviewed academic English journals, and using ‘e-government’, ‘egovernment’, ‘electronic government’, ‘online government’ and ‘digital government’ as title and abstract search terms. The search results were refined by applying full-text search criteria, which were elaborated based on the denomination of statistical methods according to the scientific literature (cf. Dancey and Reidy, 2014; Gravetter and Wallnau, 2013; McClave and Sincich, 2013; Rencher and Christensen, 2012; Tabachnick and Fidell, 2013), to identify studies of a quantitative empirical nature. 2 (2) In a second step, we cross-checked the search results with the Web of Science database and (3), if relevant, high-impact journals were included. In the next step, the resulting article database – with the exception of three articles, which were not available through the library system – was scrutinized for relevance to quantitative empirical e-government research.
This proceeding led to a final set of 129 relevant quantitative empirical articles. Concerning the identification process of these publications, it is possible that further articles that may treat quantitative empirical e-government research without mentioning any of the search terms may not have appeared during our analysis and may thus have escaped our scrutiny. However, since we followed a systematic and proper proceedure, as suggested in the scientific literature (Webster and Watson, 2002), using online databases and relevant academic journals, we have reason to believe that the developed database can be regarded as acceptable. Therefore, we are confident that this particular data set provides a solid foundation for advancing knowledge on the current state of quantitative empirical e-government literature, as well as determining reasonable future research indications.
Quantitative database analysis
Final classification criteria.
ANOVA = Analysis of Variance; MANOVA = Multivariate Analysis of Variance; SEM = Structural Equation Modeling.
Key topics of the investigated quantitative empirical studies.
The research goal classifies the studies into confirmatory (hypothesis-testing) and exploratory (finding structures) research approaches (cf. Hancock and Mueller, 2010; Lei and Wu, 2007). The research statement was elaborated based on the recommendations of Baxter and Jack (2008). Thus, studies are classified as descriptive if they describe a phenomenon, explanatory if they explain causal links and instrumental if they test or develop tools. The criteria statistical method arranges the articles according to the applied quantitative statistical method. Since some articles used more than one method, for example, descriptive statistics to present the data and multiple regression to confirm the underlying model, the respective study was classified according to the statistical method that was used to achieve the key study outcomes. ‘Data collection’ refers to the process of information gathering that was used to obtain the analysed data, and ‘research perspective’ indicates the object of the study, meaning if the study is, for example, user- or provider-oriented.
Of the 129 investigated studies, 39 deal with adoption/driver-related topics, as well as issues concerning the development of e-government implementation. Here, we found mainly exploratory research, showing a potential need for confirmatory quantitative empirical approaches. Concerning the identified 33 studies showing an acceptance, use and/or satisfaction orientation, the finding is vice versa. In this case, the majority of investigations are of a confirmatory nature. This suggests that this subtopic instead shows well-studied concepts or theories. Apart from that, the analysis indicates that there prevails limited confirmatory quantitative knowledge approaches concerning studies with a socio-economic, management, information/data, usability/complexity, cultural, HR skills/development, intergovernmental issues and autonomy/outsourcing context. The same seems to be the case with regards to exploratory quantitative research concerning the key topics of HR skills/development, intergovernmental issues and autonomy/outsourcing. Against this background, the claim for further quantitative empirical research within the field of e-government appears reasonable since various aspects of the subject indicate a lack of quantitative empirical proof.
Number of studies according to key topics and research perspective.
Concerning the evolution of the key topics over time, we could not identify any specific trend up to now. All key topics were generally represented according to their overall number over the last 10 years. However, publications with a socio-economic, usability/complexity and intergovernmental issues context have only come up in the last five years, suggesting that these topics represent a rather young field within quantitative empirical e-government research.
The majority of publications apply structural equation modelling and confirmatory factor analysis, mainly targeting at a confirmatory research aim, as well as simple and multiple regression, generally following an exploratory research approach, which, taken together, make up 72.1% of the investigated studies (see Figure 2). Against this background, these quantitative statistical methods seem to be the favoured approaches.
Number of studies according to applied statistical method. Method-related evaluation of quantitative empirical e-government articles.
Of the 129 articles, 83 used questionnaires for data collection, 42 thereof for confirmatory work, and 33 studies employed secondary data, 29 thereof with an exploratory character. Nine articles based their quantitative empirical analyses on field studies and four on interviews. These findings suggest that data collection through questionnaires is the preferred method for quantitative confirmatory work, even though it also allows substantial explanatory investigations. Secondary data were often applied, too. However, their field of application served exploratory research instead. Of the investigated quantitative empirical studies, 65 were of an explanatory and 60 of a descriptive nature. Only four articles dealt with research instruments. This indicates that e-government research tends to adapt measurement instruments from related disciplines instead of developing new ones.
Quantitative empirical research started to gain momentum in 2007. Since that time, there have been 14.3 peer-reviewed publications on average per year. This finding supports the statements of the literature review that e-government research became increasingly supported by quantitative investigations starting in the mid-2000s (Reece, 2006). While confirmatory quantitative empirical approaches just picked up steam after 2006, the first quantitative empirical studies applied simple and multiple regression. A similar development was found regarding confirmatory and exploratory approaches, showing that confirmatory work, after a couple of years with only a few publications, has increased in quantity after 2007. Although the publications decreased from 2013 to 2014, the last year was interesting since confirmatory work (10) outweighed exploratory work (5) for the first time. This could be an indicator of a beginning shift from inductive towards deductive research, substantiating previous theory development.
Discussion and conclusions
The starting point of this study was that available meta-analyses did not provide an adequate focus on quantitative empirical e-government research delivering the particular insights required to appropriately address the investigative shortage on this respective topic. Therefore, this literature review explicitly focuses on quantitative empirical e-government research, sorting prior, relevant literature to systematically uncover areas that require further exploration, to provide a solid foundation for future investigations and to define promising research directions. Against this background, the present meta-analysis contributes to scientific knowledge creation and complements recent e-government literature reviews.
One of our first findings was that adoption-, acceptance-, satisfaction-, success-, performance- and attitude-related studies made up 82.2% (106 of the 129 studies investigated) of quantitative empirical e-government research. Since 56 of the 106 studies were questionnaire-based, user-oriented studies and 24 secondary data provider-oriented studies, it may be expected that easy respondent and data access strongly contributed to the rather high amount of available publications in comparison to the other key topics. However, the findings indicate that there exists relatively little knowledge from a provider perspective, especially concerning the key topic of acceptance/use/satisfaction.
Regarding the topic evolution over time, the findings did not show a general trend. On the whole, the e-government subtopics were represented according to their overall number, indicating that there did not exist a specific focus with regards to the ongoing development of e-government. Nevertheless, we spotted fresh subtopics providing a socio-economic, complexity or intergovernmental perspective that did not show many quantitative empirical publications yet, but are suggestive of being innovative since these came up within quantitative empirical research during the past five years. This discovery supports the previously mentioned assumption that e-government reflects an ongoing, open-ended research environment that generally requires further quantitative empirical analyses to substantiate existing theories and approaches. This is further backed up by the identified increasing number of quantitative empirical publications since 2007, revealing that quantitative empirical e-government research has gained presence.
However, the shift from exploratory quantitative empirical work (five publications) to confirmatory quantitative empirical work (10 publications) that occurred for the first time in 2014 is interesting. This situation could be portrayed as an indicator of a beginning trend from inductive towards deductive research with the aim to further substantiate previous theory development. At this point in time, we cannot reliably gauge in which direction quantitative empirical e-government research is heading, but given the call for quantitative empirical research in this field, we aspire to see a continuing rise of quantitative empirical research with the target to support and validate firm theory development. In particular, given one of the central methodological ideas of critical rationalism that knowledge should be rationally tested and criticized (Popper, 2002), investigation designs of established research fields should be of a scrutinizing, confirmatory nature.
Structural equation modelling and confirmatory factor analysis, as well as simple and multiple regression, seemed to be the favoured statistical methods. Considering the potential trend towards confirmatory quantitative empirical work, structural equation modelling, being an appropriate technique for hypotheses-testing since it allows for the estimation of separate relationships for sets of dependent variables and for the measurement of multiple relationships between dependent and independent variables (Hair et al., 2010), is a suitable choice. If the researcher follows an exploratory approach with the aim to predict a single metric variable that is dependent from other independent variables (Hair et al., 2010), multiple regression should be the chosen method. Although factor analysis and discriminant analysis, as well as logistic regression, were used to a lesser degree, this does not confine their raison d’être and should thus be kept in mind and applied where suitable.
With the exception of three studies that showed an instrumental research statement character, all other studies were of an explanatory or descriptive nature. These findings suggest that the development and validation of measurement tools and research instruments are only of minor importance. Apparently, quantitative empirical e-government research adapts measures and measurement models from related disciplines rather than establishing its own. Furthermore, the relatively high amount of questionnaire-based data collection, especially among similar key topics, indicates that existing conceptualization and operationalization approaches were applied.
Even though this study shows manifold findings and draws correspondent conclusions for quantitative empirical e-government research, it also carries limitations. Given the broad range of e-government-related publications and the eclectic nature of the approach to the literature, this study cannot assure that each relevant article treating quantitative empirical e-government research could be identified. However, in view of the systematic approach, taking into consideration the recommendations of the scientific literature, we are nevertheless confident that the final set of studies provides an adequate fund of papers.
Apart from that, we are aware of the loss of information that is created through aggregating information, as well as potential overlappings of study content and clustering criteria, which may lead to partial indistinct allocations. Since we were conscious of this constraint while allocating, aggregating and filtering the base of articles, the degree of risk associated with this limitation should be reduced to an acceptable level. Furthermore, the study focuses explicitly on quantitative empirical e-government research and, thus, only aims at supporting and advancing the associated knowledge. For this reason, the scope of the present meta-analysis does not allow us to summarize the general main research streams and directions of e-government in its entirety or present the key overarching e-government arguments and their development over time, but, rather, to provide systematic guidance for researchers with a quantitative orientation.
Concerning the study guidance on quantitative empirical research and the call for additional work in this field, researchers should keep its special character in mind, though, since the quantification of phenomena is not all that distinguishes quantitative from qualitative research. Quantitative research generally shows a deductive orientation based on epistemological positivism and ontological objectivism, while qualitative research comes from an inductive, interpretivist, constructivist position (Bryman, 2012). The associated advantages of quantitative research are that it generally suits theory testing, as well as the generalization or replication of findings (Creswell, 2014), and is less susceptible to subjectivity as it possesses higher intersubjective certifiability compared to qualitative research (Zikmund et al., 2013).
Since quantifying social phenomena may involve complex conceptualization and operationalization processes and may require sophisticated statistical methods, quantitative approaches also tend to show potential disadvantages. Common examples are: misspecified sample sizes that do not allow result generalization (Hu and Bentler, 1998; Marsh et al., 1988); common method bias, meaning that the respondents provide information on dependent and independent variables (Homburg et al., 2012; March and Sutton, 1997); endogeneity, reflecting that variables are influenced by other factors that are not measured (Greene, 2012); or a scaling of data that may – due to the quantitative measurement of variables – lead to distortions (Bollen and Barb, 1981; Johnson and Creech, 1983). Here, the researcher should strictly adhere to methodical standard procedures to reduce potential distortions to a level that do not significantly influence the validity and reliability of the analysis.
Summing up, quantitative empirical e-government research shows a solid foundation in a couple of subtopics. Nevertheless, there prevail manifold research opportunities. Thus, the call for additional quantitative empirical research seems legitimate. In light of this, implications for quantitative empirical research are discussed in the next section.
Implications for future quantitative empirical research
Considering the findings, conclusions and limitations of this meta-analysis, quantitative empirical e-government research still reflects a growing, open-ended field that provides manifold opportunities for investigation. However, researchers in this area should take into account the following recommendations. First, considering the methodological idea of critical rationalism that knowledge should be rationally tested (Popper, 2002) and the amount of exploratory quantitative empirical research, investigation designs should be of a scrutinizing, confirmatory nature instead of further exploratory work to substantiate existing theories.
When looking at the limited availability of quantitative empirical studies from a provider’s point of view, especially concerning acceptance-, use- and satisfaction-related publications, more focus should be put on this perspective. Apart from that, there are basically no studies available that examine the subject from a mixed viewpoint, taking into account the user as well as the provider. Hypothetically, this would allow direct comparisons of e-government attitudes, services, quality issues and so on in the form of a multilevel analysis (cf. Dekker et al., 2011; Homburg et al., 2010; Roberts, 2004).
Similarly, the findings indicate that there exists only little quantitative empirical research on management-related e-government topics, as well as on socio-economic, complexity or intergovernmental aspects. Here, quantitative empirical investigations from a user as well as provider perspective seem to carry plenty of quantitative empirical research potential. From a content perspective, since available literature reviews do not provide an overarching perspective on the main research streams and directions of e-government, a study on the development of key e-government arguments seems helpful as a step towards merging the still diffuse e-government research landscape. Finally, it is worth pointing out that the findings imply that quantitative empirical e-government research adapts measures from former studies and related disciplines rather than establishing its own since only a few instrumental studies exist. Thus, future research may also come back to available scales to conceptualize and operationalize empirical data.
Footnotes
Funding
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Notes
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