Global e-government development: besides the relative wealth of countries, do policies matter?

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Transforming Government: People, Process and Policy

Global e-government development: besides the relative wealth of countries, do policies matter?

Journal: Transforming Government: People, Process and Policy Manuscript ID TG-12-2019-0125.R2

Manuscript Type: Research Paper

Keywords: e-government, e-participation, benchmarking, developing countries, _{United Nations, EGDI}

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Transforming Government: People, Process and Policy

Global e-government development: besides the relative wealth of countries,

do policies matter?

Abstract

Purpose: This study investigates whether, discounting the effect of the relative wealth of countries, it is possible to observe the relevance of policies for e-government development. Methodology: The deviations of countries' results from what could be expected, considering their relative wealth is calculated by using the residuals of a linear regression using the Gross Domestic Product (GDP) per capita as the independent variable and the UN E-Government Development Index (EGDI) as the dependent variable. The countries that achieve better and worse results than expected are then identified and their cases are analyzed by resorting to secondary sources, namely published research referring to their cases. Those research documents were identified by successively searching the Scopus database, the Google Scholar database, and the Web.

Findings: The existence of formal e-government strategies and plans and the capacity to implement them can make a difference, allowing countries to achieve better results than expected or, in their absence, to perform worse than expected.

Research implications: The proposed methodology can be useful to e-government researchers, particularly as a basis for deeper and more detailed studies.

Practical implications: Countries should invest in well-developed and focused strategies and invest in continuity of public policies and their capacity to deliver results. For that purpose, political commitment and high-level coordination are key factors. For low-income countries, long lasting cooperation with external experienced partners is crucial. For high-income countries, innovative thinking is a key enabler.

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Originality: This study uses an innovative method to look beyond the effect of the relative

wealth of countries and investigate the relevance of public policies for e-government development.

Keywords: e-government; e-participation; benchmarking; developing countries; United Nations; EGDI.

Article Classification: Research paper. 1. Introduction

Since the Millennium Declaration (United Nations, 2000), the UN has been actively

promoting and measuring e-government development, namely because of its importance for promoting good governance (United Nations, 2003). Moreover, e-government can also give a relevant contribution to achieving the UN Sustainable Development Goals, in the scope of the 2030 Agenda for Sustainable Development (United Nations, 2015). Indeed, according to Ziemba (2018), ICT adoption by governments contributes to sustainable development by promoting ecological, economic, socio-cultural, and political sustainability.

Despite this relevance of e-government, there is no consensus among researchers of how to assess its development. Nevertheless, although some criticism exists (Potnis, 2010; Ayanso et al., 2011; Potnis and Pardo, 2011; Whitmore, 2012; Fong et al., 2015), the UN E-Government Development Index (EGDI) has become a de facto standard for assessing the “readiness and capacity of national institutions to use ICTs to deliver public services” (United Nations, 2018). Being published in a comparable basis since 2003, with bi-annual frequency since 2008, and for a maximum of 193 countries, the Index provides a basis for comparable and longitudinal studies of e-government development (see Figure 1). Based on the EGDI, the UN also publishes e-government development rankings.

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The EGDI is a composed index. It is calculated by equally weighting three Z-score

standardized sub-indices: The Online Service Index (OSI); the Telecommunication Infrastructure Index (TII), and the Human Capital Index (HCI). Thus, EGDI measures not only the achievements of countries to what concerns e-government development (namely by scoring their national websites), but also their readiness to implement e-government, namely by assessing the availability and use of communication technologies and the schooling related statistics.

Given its composition, it is not surprising that there is a high correlation between the EGDI and the Gross Domestic Product (GDP) per capita of the countries. In other words, results are, to a great extent, explained by the relative wealth of countries, which masks the effect that the quality of strategies and of the development plans might have on e-government development, namely in developing countries. Taking this into consideration, this paper tries to answer the following research question: do political commitment and the quality of public policies have an effect in actual e-government development despite the differences on the relative wealth of countries? To answer this question, overperforming and underperforming countries in the EDGI considering their GDP per capita are identified and studied. The rationale is to identify whether the existence of specific policies allows less developed countries to perform better than expected and if their absence or lack of quality prevent more developed countries from performing as good as expected.

By identifying the most successful and unsuccessful country cases and discussing their strategies, plans and results, the study may prove useful for practitioners and policy makers responsible for developing e-government at the national level.

By proposing a new way of measuring the countries' success based on relative performance rather than absolute performance, given their GDP per capita, it may also be useful for e-government researchers, particularly as a basis for deeper and more detailed studies.

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The remaining of this paper is organized as follows: section 2 addresses the background;

section 3 introduces the methods used in the study; section 4 presents and discusses the results; and section 5 addresses the main conclusions, its implications, and the future work. 2. Background

Many studies worldwide have addressed the determinants of e-government development, including at the local level (e.g. Moon, 2002; Dias and Costa, 2013; Wang and Feeney, 2016); at the agency level (Ziemba et al., 2016), at the state level (West, 2000), and at the country level (e.g. Weerakkody et al., 2011; Franke and Eckhardt, 2014). Although some international perspectives on the subject address local e-government development (e.g. Wohlers, 2009; Maciel et al., 2016; Ingrams et al., 2018), the vast majority consider the country level as the basis for analysis. Among the latter, common approaches include the use of EGDI for performing comparative analysis in regional contexts (Aniscenko et al., 2017; Torres-Porras and Duarte-Amaya, 2018; Brimkulov and Baryktabasov, 2017) or between specific countries (e.g. Kurdi et al., 2016; Ulman et al., 2016), and to identify global trends. In this section, we review the studies that adopt the latter approach, as it is the same adopted in this study.

By resorting to EGDI in order to find global trends of e-government development, Imran and Gregor (2005) found that, “at a base level, access by individuals and organizations to ICT tools and IT-related education is necessary for e-government to be feasible” and that “strategies were observed to be linked to progress with e-government across a number of developing countries”, namely those related to “leadership and willingness to initiate change within the government sector” and “incremental, step-by-step approach to development”. The relevance of public policies was also identified by Warf (2014) when analyzing Asian

countries. He found that “the most successful implementation of e-government has occurred

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puts considerable pressure on bureaucrats to conform”. The examples of South Korea, Japan,

and Singapore are given. Thus, considering these contributions, it makes sense to study the impact of political commitment and the quality of public policies in e-government

development, as was intended with the study presented in this article.

Investigating national cultures, Zhao (2011) found correlations between e-government development and individualism, power distance and long-term orientation. A few years later, he also contributed to identify that the digital divide had significant impact on e-government development (Zhao et al., 2014). Both studies point to the relevance of the context in which the results are obtained. More recently, Nielsen and Martins (2019) have presented a

preliminary longitudinal analysis of the UN e-government surveys showing that regional analyses and analyses within homogeneous groups of countries (landlock developing countries, small island developing states, least developed countries, and low, lower middle, upper middle and high income states) help to reveal different development profiles. Taking these contributions into consideration, this study considers structural characteristics of countries such as income profile, type of state (microstate or fragile state) and region. Another contextual factor that was found determinant by several studies is the political regime. Stier (2015), for example, found that “while the innovation-friendly environment of democracies was the primary political source of e-government development, autocracies are catching up in order to enhance pro-regime activism on the internet and legitimize their rule by improving economic performance”. This was confirmed by other studies that found that authoritarian regimes are using e-government as a form of legitimization (Gulati et al., 2012; Gulati et. al., 2014; Maerz, 2016). Taking this into consideration, the political regimes of the studied countries are also analyzed.

Finally, Muñoz and Bolíver (2017) reviewed studies by other authors and systematize a set of key factors for e-government success in developing countries: creating clear strategic plans

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and defining coordination entities; dealing with technological key issues, such as integration

and quality in the early stages of the process; implementing “a common infrastructure whose services may be used by whole of governments”; training human resources and cooperating with international organizations; dispelling resistance and negative attitudes by providing encouragement, training and incentives to middle management and staff; assuring support from major public leaders; developing the telecommunications infrastructure and promoting citizens education and literacy; removing any legal and policy barriers; and assuring privacy and security in order to promote trust.

3. Methods

In order to perform the study, two statistical analyses were conducted. First, a correlation study was used in order to assess to what extend the 2018 measurement of the EGDI is associated with the logarithm of the GDP per capita of the countries. Then, outliers in the EGDI were identified by inspecting the residuals of a linear regression having the 2018 measurement of the EGDI as dependent variable and the logarithm of the GDP per capita as independent variable. The assumption behind these analyses is that the logarithm of the GDP per capita is a good a predictor for EGDI results and, consequently, its residuals are good indicators of overperforming and underperforming countries in EGDI, given their GDP per capita.

The GDP per capita of the countries was linearized using a logarithmic transformation since it originally exhibited an exponential distribution. This transformation is essential in order to allow the use of parametric correlations and linear regressions. The statistical analyses were performed using IBM SPSS Statistics 25.

Table 1 presents the results of the Pearson's correlation between the EGDI and the logarithm of the GPD per capita. As can be observed, the correlation is high, direct, and significant,

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meaning that the logarithm of the GDP per capita is a good candidate for predicting country’s

results in EGDI. In the linear regression model (see Table 2), the logarithm of the GDP per capita is significant and explains 74% of the EGDI variability, which further validates the previous assumption. The coefficients of the model are presented in Table 3. As can be seen in Figure 2, the resulting standardized residuals follow a normal distribution.

In order to answer the research question, the cases of countries with standardized residuals above the 90th_{percentile (which exhibit the greatest improvements in EGDI relative to the} expected value) and below the 10th_{percentile (which exhibit the lowest results in the EGDI} relative to the expected value) were selected for further analysis. To perform this analysis, a secondary research approach was used: indexed research documents and, on their absence, other documents were used as a source for identifying evidences. The following multistep method was used:

 In the first step, the Scopus database was searched for research documents relating to e-government development policies for each of the selected countries. With this purpose, the occurrence of the expressions ‘e-government’ or ‘digital government’ simultaneously with the name of the country were used as search keywords. The Scopus databased was used in detriment of other databases, such as Web of Science, because it provides a better coverage of e-government sources (Dias, 2016). In the second step of the process, the abstracts of the identified documents were analyzed in order to discharge false positives. The full text of the remaining documents was then analyzed in order to find and register any references to policy factors associated with the e-government development for the countries in question.

 Whenever relevant documents were not found in Scopus, the Google Scholar database and, subsequently, the whole Web were searched for research documents by using the same keywords.

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4. Results and discussion

4.1 Overall results

Figure 3 shows a density map representing the value of the residuals of the linear regression presented in the previous section. The interpretation is as follows: countries represented with higher color density are those that surpass their expected EGDI value considering their GDP per capita, whereas the countries represented with lower color density correspond to those that are below that expected value.

As can be verified, there are significant differences between the maps shown in Figure 1 (EGDI values) and Figure 3 (deviations from the expected EGDI values). Indeed, in the second map, the EUA, Western Europe, Central Europe, and Australia (typically more developed countries) are less prominent, while some less developed countries in Eastern Europe, Eastern Africa and Asia become more prominent.

Table 4 shows the EGDI score, the income profile, the political regime (The Economist Intelligence Unit, 2019), type of state (microstate or fragile state) and the region of the countries having residuals above the 90th percentile (which exhibit the greatest

improvements in EGDI relative to the expected value). Four types of countries are listed: two ranked countries in global EGDI, with high income profile, and that constitute well-known success cases (Estonia and South Korea); ten countries classified in the upper half of the EGDI table, with middle income profile, having authoritarian or hybrid regimes, and located in Eastern Europe and Asia (Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, Russian Federation, Ukraine, Uzbekistan, and Vietnam); four democratic countries classified in the upper half of the EGDI table, with middle income profile, and located in Eastern Europe, Asia and Africa (India, Philippines, Serbia and Tunisia); and three fragile states (Organisation for Economic Co-operation and Development, 2018) classified in

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the lower half of the EGDI table, with low income profile, and located in Asia and Africa

(Nepal, Rwanda and Uganda).

Table 5 shows the EGDI score, the income profile, the political regime, type of state (microstate or fragile state) and the region of the countries having residuals below the 10th percentile (which exhibit the lowest results in the EGDI relative to the expected value). Three types of countries are listed: six very small states (microstates) from Western and Central Europe and Oceania (Liechtenstein, Micronesia, Monaco, Nauru, Palau and San Marino); ten fragile states classified in the lower half of the EGDI table, with low or middle income profiles (Angola, Chad, Djibouti, Equatorial Guinea, Iraq, Libya, Papua New Guinea, Somalia, South Sudan, and Sudan); and three other countries classified in the lower half of the EGDI table and with low or middle income profiles (Cuba, Botswana and Turkmenistan). The 38 countries listed in Tables 4 and 5 are also highlighted in Figure 4, which plots all countries according to their GDP per capita and EGDI. The linear trend, represented by the dashed line, shows that there is an approximately linear relation between both variables (as was demonstrated by the correlation study). The highlighted countries are those that are further from the linear trend line. The residuals of the linear regression, which were used to select these countries, are a function of this distance. Thus, the further a country is from the line, the higher is its residual and the better is its relative result, if above the line; and the lower is its residual and the worse is its relative result, if below the line.

The cases of the selected countries are further discussed in the next two subsections. Countries are categorized as overperforming or underperforming and, subsequently, as overperforming high income countries, overperforming non-democratic countries, overperforming fragile countries, and other overperforming countries; and as

underperforming microstates, underperforming fragile states, and other underperforming countries.

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4.2 Overperforming countries

4.2.1 High income countries

South Korea (Korea Rep.) and Estonia are well-known success cases to what concerns e-government achievements. They are both ranked in the upper half of the EGDI table (3rd_and 16th_{, respectively). In both cases it is evident the impact that strong political leadership,} strategic focus, innovation, and continuous investments have had for achieving results.

In the case of South Korea, e-government was assumed as a strategic enabler for government innovation in 2002, after an impressive development of the country’s communication

infrastructure. According to Chung (2015), the success factors of the country can be summarized as “a strong political leadership, clear vision and policy objective, project’s strategic priority and human & financial resource distribution”. Between 2003 and 2018, South Korea rose from the 13th_{to the 3}rd_{place in the UN EDGI ranking.}

In the case of Estonia, the e-government initiative began in 2000, when Estonia introduced a public system for electronic tax filing, followed by the introduction of a universal electronic identification card with digital signatures, in 2002. Today the country is well-known for being “the first country in the world to enable all its citizens to vote over the Internet in political elections” (Ernsdorff and Berbec, 2006). This successful progress is due to innovation and “a strong commitment to Information Technology (IT) on the part of the prime minister and other senior government officials, stemming from the country’s

independence from the Soviet Union in 1991” (Anthes, 2015). According to Ernsdorff and Berbec (2006), the success factors were mainly related to “the strategic thinking within the government to implement e-democracy, good attention to detail and a positive attitude towards ICT policy, innovative thinking and the development of a legal framework, and the economic growth and the macroeconomic stability of the country”. In 2018, being below the

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average of the European Union (EU) in terms of relative wealth, Estonia obtained the 16th

place in the EGDI ranking (the 9th_{in the EU).}

4.2.2 Non-democratic countries

Out of the 10 overperforming middle-income and non-democratic countries, nine were once part of the Soviet Union (USSR - Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz

Republic, Moldova, Russian Federation, Ukraine, and Uzbekistan). In the case of these countries, the development of e-government seems to have been associated to the

construction of the new states after the collapse of the USSR (as was also the case of Estonia, described in the previous subsection and historically also part of the USSR). However, one fundamental difference is that these countries are not democracies, being considered by The Economist Intelligence Unit (2019) as either hybrid or authoritarian regimes. This fact is relevant because several authors associate the development of digital government in these countries with efforts to legitimize the regimes (Johnson and Kolko, 2010; Grönlund, 2011; Stier, 2015; Maerz, 2016), including by promoting economic development (Goderdzishvili and Gvenetadze, 2014; Stier, 2015; Kassen, 2016), gaining “external legitimacy” (Kassen, 2016; Maerz, 2016), and fighting against corruption (Kassen, 2016; Alkanova, 2019; Knox and Janenova, 2019). There is also evidence that countries’ results are generally associated with specific political and managerial factors, such as a strong political commitment

(Goderdzishvili and Gvenetadze, 2014; Kassen, 2016; Agenția Guvernare Electronică, n.d.) and a top-down administrative command (Marushka and Ablameyko, 2011; Goderdzishvili and Gvenetadze, 2014; Kassen, 2016; Knox and Janenova, 2019; Agenția Guvernare Electronică, n.d).

Addressing post-Sovietic countries, Knox and Janenova (2019) refer to the existence of an “e-government paradox” characterized by: “an emphasis on technological development”; deployment of faster “transactional services” that, however, displace “attention from core

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public services”; reduction of “petty corruption”, although “grand corruption remains”;

“pressure from international organisations to use e-government as a mechanism to improve state–citizen relations and movement towards democratisation”; and the limitation of “greater participation by citizens”. A similar phenomenon has partially been observed in the case of Vietnam, whose government “implemented a ‘two-faces’ policy through which it promotes the effectiveness of e-government and public services on the one hand, and control and restrict the political e-participation” on the other (Long et al., 2017). In the case of Vietnam, the centralized approach to e-government development (Van et al., 2019) and the relevance of e-government to help “international integration” (Long et al., 2017), “enhance

competitiveness in the global environment” (Shepherdson et al., 2009), and fight against corruption (Vu and Hartley, 2018) have also been reported.

4.2.3 fragile countries

The only low-income countries that achieved significant better results in EGDI than could be expected are Nepal, Rwanda, and Uganda. All three are fragile (Organisation for Economic Co-operation and Development, 2018), low income countries that are ranked in the lower half of the EGDI table. Their cases are especially interesting in order to identify to what extend public policies have contribute to overcome their base difficulties for the development of e-government.

In the case of Rwanda, the success seems to be the result of the combination of three main factors: the development of focused plans such as the National Information Communication Infrastructure plans (2000-2015), the ICT Sector Strategic Plan (2013-2018), and the Smart Rwanda Master Plan (2016-2020); a strong and lasting partnership with a private company (Twizeyimana, 2017); and a clear choice of a one-stop government model (Bakunzibake et al., 2019).

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In Uganda, although some criticisms and limitations exist (Kigwana et al., 2017; Kanagwa et

al., 2018; Nakakawa and Namagembe, 2019), it seems evident that the relative success is also the consequence of public policies, including the commitment to become a leading

e-government country in Africa in the scope of the 2012 Uganda e-Government Master Plan (National Information Technology Authority Uganda, 2012), which was developed with the technical support of the National IT Promotion Agency of Korea. The situation is similar in the case of Nepal, whose first master plan was also developed with Korean technical support (Elets News Network, 2006). The Nepalese plan has also been the subject of some criticism (Rupakhetee and Heshmati, 2013).

4.2.4 Other overperforming countries

The remaining overperforming countries (India, Philippines, Serbia, and Tunisia) are all middle-income countries and considered flawed democracies (The Economist Intelligence Unit, 2019).

In India, “most of the state governments have taken initiatives in e-governance, which have resulted in varying degrees of success” (Kalsi and Kiran, 2013). According to the same authors, “most of the basic factors, like infrastructure, political commitment and will, bureaucratic leadership, continuity, positive attitude, change management, administrative ownership, technology, training of government staff, self-sustainability model” were in place in the ten major states of India, although “with varying degree of emphasis”. At the central level, it is worth mentioning the “holistic view of e-governance initiatives across the country” that was allowed by the 2006 National e-Government Plan (Kumar et al., 2018). This plan gave origin of the Digital India initiative, which was launched by the Indian Prime Minister in 2014. The high-level political commitment to this initiative is evidenced by the existence of a monitoring committee under the direct leadership of Prime Minister (Saxena, 2018).

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In the case of Philippines, e-government development was framed, successively, by the

National Information Technology Plan, adopted in 1997, the Government Information Systems Plan, adopted in 2000, the Strategic ICT Roadmap 2006‒2010, and the Philippine Digital Strategy 2011‒2016 (Magno, 2018). According to Villanueva (2018), the latter was characterized by strong “political will”, mostly motivated by the fight against corruption. The focus on e-government interoperability and the creation of a single regulatory agency” were key for the country’s results (Magno, 2018; Villanueva, 2018).

Although Serbia “had a late start in e-government”, with the first dedicated strategy

introduced only in 2009, “considerable efforts have been made in order to offset the gap in e-government development compared to the rest of Europe” (Stoimenov et al., 2010). As happens with other overperforming countries, the existence of a “strong political

commitment” (Šereš and Horvat, 2011) and of a “central government body, aimed at creating a simpler everyday existence for private individuals and businesses, and more efficient public administration” (Stefanovic et al., 2016) were recognized as key enablers.

In the case of Tunisia, according to Nabil and Abdelhakim (2014), ”the digital strategy has been drawn up by the (…) government to reach well defined objectives in a predetermined timeframe” and its ”relative success lies in the fact that the strategy sits on a clearly defined set of easily accessible measurables”. Currently, e-government development is part of the Digital Tunisia initiative (République Tunisienne, n.d). Although some limitations exist (Sta, 2018), it has been reported that the creation of the Electronic Administration Unit, in 2005, “greatly facilitated the quick implementation” of Tunisian e-government program “by ensuring a high level of coordination between the different stakeholders and working on alleviating any technical or legal hurdle“ (Nabil and Abdelhakim, 2014).

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4.3 Underperforming countries

4.3.1 Microstates

Six out of the 19 underperforming countries are microstates: Liechtenstein, Micronesia, Monaco, Nauru, Palau, and San Marino. Those include both high-income and middle-income countries. In the case of these countries, underperformance seems to be associated with their very small dimension.

On one hand, microstates typically suffer from a set of constraints, such “heavy reliance on a few key produces and industries”, “limited human and financial resources”, and “cultural and psychological dependence” that can impact e-government development (Nielsen, 2016). Despite this, some case studies show that “political and financial commitment” (Nielsen, 2016), “political will and determination” (Sağsan and Yildiz, 2010), and the existence of a “champion and leader” (Commonwealth Secretariat, 2008) can be relevant to overcome difficulties in such states. Evidence of those have not been identified in the cases of the six underperforming microstates under analysis, although it must be taken into consideration that research documents referring the existence of official e-government strategies were only identified in the cases of Liechtenstein (Lentner and Parycek, 2016) and San Marino (Petroni and Tagliente, 2005).

On the other hand, some of the identified microstates are among the countries with higher GDP per capita in their regional contexts, as are the cases of Liechtenstein and Monaco in Europe (the highest and the second highest GDP per capita in the region, respectively) and of Palau and Nauru in Oceania (the third highest and fourth highest GPD per capita in the region, after Australia and New Zealand). Thus, poor relative results might be more the consequence of above the average GDP per capita, fueled by some specific characteristics of

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these countries (e.g. tax benefits for residents or businesses), than of any specific difficulty in

developing e-government policies. 4.3.2 Fragile states

In the group of the 13 remaining countries, ten are states of fragility (Angola, Chad, Djibouti, Equatorial Guinea, Iraq, Libya, Papua New Guinea, Somalia, South Sudan, and Sudan), i.e., countries that exhibit a combination of “risks and coping capacities in economic,

environmental, political, security and societal dimensions” (Organisation for Economic Co-operation and Development, 2018). Contrary to what was observed in the cases of Rwanda, Uganda, and Nepal, also fragile states, the countries in this group seem to have been unable to overcome their difficulties to what concerns e-government development. In fact, it is natural that the fragility of the state constitutes a handicap for the development of programs that depend on the policies of the same state. In this sense, Rwanda, Uganda, and Nepal are the exception and, thus, the most relevant cases for the analysis.

Corroborating this, examples of the previously mentioned inhibiting factors have been mentioned as constraining e-government development in some of the fragile states under analysis: “Low populations, large disparities in income and access to on-line services of any sort and low educational levels” in the case of Djibouti (Dolan, 2014); non-stable “civil and political conditions” (Abd et al., 2019) and “resistance of some official and nonofficial organizations, groups, and individuals”, “delay voting on e-government project that has been submitted to parliament”, and “less concern or support shown by politicians and high level government” (Salman, 2019), in the case of Iraq; “technical, infrastructure, cultural and social barriers” (Ahmed et al., 2013) and “lack of studies and researches”, “low trust”, and “security and privacy concerns” (Murah, and Ali, 2018), in the case of Libya; and cultural and

educational factors, income level, lack of trust, and “insufficient number of computers and

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lack of training packages for employees”, in the case of Sudan (Elamin and Abushama,

2016).

4.3.3 Other underperforming countries

The last three underperforming countries are Cuba, Botswana, and Turkmenistan. All three are middle-income countries. Turkmenistan and Cuba have Authoritarian regimes while Botswana is a flawed democracy (The Economist Intelligence Unit, 2019). In the case of Cuba, the EGDI underperformance is explained by the absence of a structured e-government development plan, which is “most likely determined by the country's particular political situation” (Dias, 2019). In Turkmenistan, the first step towards the development of government was only taken in 2011, simply aiming at improving workflow within e-government agencies, namely by adopting an interagency document management system (Orazbayev, 2012). Botswana, although having a national e-government strategy (Republic of Botswana, 2011), seems to have been unable to take advantage of it to achieve concrete results. This might be related to the lack of capacity to successfully implement the plan. To this respect, it is worth mentioning that Jain and Akakandelwa (2016) classified the country in the group of African countries that “have not taken any initiative or are very slow in realizing its full take off”.

4.4 Discussion of findings

The main political explanations for the results of the overperforming and underperforming countries are resumed in the Table 6. As can be observed, political commitment constitutes one of the explanations for the results. On the one hand, it is present in all groups of

overperforming countries: strong leadership in the case of democratic and high income countries; strong political will, in the case middle income countries (both democratic and non-democratic); and, indirectly, through the active search for strong partnerships, in the case

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of fragile and low-income countries. On the other hand, it is generally absent in the case of

underperforming countries or, when it exists, it does not translate into concrete effects or does not allow results compatible with what could be expected.

This calls attention to the other relevant factor: the quality of public policies. All

overperforming countries have e-government development plans and exhibit some capacity to implement them. Concrete evidences include: strategic focus, innovative thinking and

continuous investment, in the case of high income countries; high level coordination or monitorization, in the case middle-income democracies; top-down administrative control, in the case of middle-income non-democratic countries; and the capacity to benefit from strong partnerships with more developed countries or private companies, in the case of low-income fragile states. On the other hand, the capacity to implement public policies is generically absent from underperforming countries: because dimension acts as an inhibitor, in the case of some microstates; because fragility of the state acts as an inhibitor, in the case of fragile states; or because of specific difficulties, in the case of middle income countries.

Thus, political commitment and quality of public policies do help to explain why some countries perform better than expected while others perform worse than expected, given their GDP per capita. These findings are consistent with results previously obtained by other authors that, using different methodologies, identified factors such as “decisive leadership” (Warf, 2014), “willingness to initiate change” (Imran and Gregor, 2005), support from major public leaders (Muñoz and Bolíver, 2017), clear strategic plans, existence of coordination entities, and cooperation with international organizations (Muñoz and Bolíver, 2017). The added value of this study is that, contrary to the studies mentioned above, it uses an objective measure of relative success in order to identify the analyzed countries and it considers both successful and unsuccessful cases in that analysis.

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4.5 Limitations

This study aimed at finding to what extent the political commitment and the quality of public policies have effect in actual e-government development, despite the differences in the relative wealth of countries. To pursue this objective, the countries to be analyzed were selected by inspecting the residuals of a linear regression. The main limitations of the approach taken are that only a sample of countries was studied and that, for this sample, the evidence was based on studies carried out by other authors that do not always use comparable methodologies and that exist in greater numbers for some countries than for others.

Two alternatives could have been followed: to directly analyze the official policy documents and progress reports of the selected countries; and to find an indirect measure of political commitment and quality of policies, such as investment in e-government, and include that variable in the regression study. However, both alternative approaches present drawbacks: dependence on the availability of relevant documents, translating them from several different languages, and defining a common framework of analysis, in the first case; and dependence on availability of sufficient budgetary information, need to harmonized criteria for what can be considered an e-government investment, and need to find a common basis for comparison, such as purchasing power parity, in the second case. Because of these drawbacks, these approaches were not followed in this study. In any case, it is our conviction that the evidence collected through reviewing published research supports the study's conclusions adequately. 5. Conclusions, implications, and further work

The relative wealth of countries, as measured by the logarithm of the GDP per capita, explains 74% of EGDI variability, which is a very significative proportion. Regarding the remaining variability, the political commitment and the quality of public policies provide plausible explanations for the results. Indeed, it is evident that the existence of formal

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government strategies and plans and the capacity to implement the associated policies can

make a difference, allowing countries to achieve better results than expected or, in their absence, to perform worse than expected. Thus, besides the relevance of the relative wealth of countries, policies matter. This is the main conclusion of this study.

The main implication of this study is that in order to successfully develop e-government, countries should invest in well-developed and focused strategies, in continuity of public policies, and in the capacity to deliver results. For that purpose, political commitment and high-level coordination are key factors, either obtained by central coordination and

monitorization, in the case of democracies, or by means of top-down administrative control, in the case of autocracies. For less developed countries, including low-income countries, middle income microstates and fragile states, long lasting cooperation with external experienced partners (such as other countries, international organizations, or private companies) is crucial. Finally, for high-income countries, innovative thinking is also a key enabler. Taking this into consideration, Table 7 provides practical guidelines for

overperforming and underperforming countries.

For researchers, this study proposes a new way to classify countries that, instead of focusing on absolute achievements in e-government development, measures how well or how poorly countries’ performances are compared to what could be expected, given their socio-economic development. This approach allows the identification of interesting cases that otherwise could be unnoticed. Those cases can now be further studied by using qualitative methods. The approach can also be followed to perform complementary quantitative studies, namely studies seeking to find factors that, in addition to GDP, contribute to explain the EGDI variability. From this point of view, the use of hierarchical regressions can be a valid methodology. This study could also be continued by further deepening the analysis of some particular

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ones that constitute the exceptions to the dominant pattern: overperforming high income

countries, overperforming fragile countries, and non-fragile and non-microstates

underperforming countries. Future work will follow this path. Other alternative courses of action include the study of e-government development in microstates and in overperforming non-democratic countries.

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Table 1: Results of the correlation analysis.

Ln(GPDpc) Pearson's correlation ,860**

Sig. (2 extremities) 0,000

EGDI

N 192

**. The correlation is significant at the 0.01 level (2 extremities).

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Table 2: Summary of the regression model.

Model R R Square Adjusted R Square Std. Error of the Estimate EGDI ,860a _0,740 _0,739 _0,1079033 a. Predictors: (Constant), Ln(GDPpc) Page 34 of 43 Transforming Government: People, Process and Policy

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Table 3: Coefficients of the regression model.

Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. (Constant) -0,532 0,047 -11,261 0,000 EGDI Ln(GDPpc) 0,289 0,012 0,860 23,249 0,000