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Abstract

This study proposed and validated an extension of the unified model of electronic government adoption (UMEGA). The data analysis was conducted with a structural equation modeling technique using Smart PLS 3.0. The results have demonstrated contrary to expectations that performance expectancy, effort expectancy, and social influence do not predict the attitude toward the use of e-government services. Facilitating conditions, however, were found to significantly determine both the behavioral intention to use and effort expectancy of e-government services. Also, perceived service quality and trust in government were found to positively predict, respectively, the intention to use and recommend the adoption of e-government services. The implications of these and other result findings of this study are thoroughly interrogated.

Keywords

e-government e-government services UMEGA UTAUT

Introduction

The constant demand of quality standards of public service delivery and greater transparency of government by citizens, civil society, and other development partners have prompted governments to inculcate the right Information and Communication Technologies (ICTs) into the public administration systems to meet these ever-growing demands from citizens, businesses, and the general public. The use of appropriate ICTs, such as the internet to meet the demands of citizens, businesses, and the general public to enjoy quality public services from government and its agencies is known as electronic government (e-government). E-government is thus defined as the government’s application of ICTs to provide enhanced public services and strengthen the bond between the citizens and their government and encourage greater participation of citizens in the decision-making process (Mittal & Kaur, 2013; Singh et al., 2010). E-government attempts to promote government’s accountability and efficiency, providing faster and efficient services at minimum cost, and empower citizens through inclusive governance (W. A. Agangiba & Agangiba, 2013; M. Agangiba & Kabanda, 2016; Bal et al., 2015; Singh et al., 2010; Tolbert & Mossberger, 2006).

The implementation of e-government particularly in developing countries though promising, it is, however, confronted with some impediments, such as the absence of a well thought-out e-government strategy, lack of technological and IT infrastructure, proper policy and legal framework, organizational and cultural issues, and operational cost (Al-Rawahna et al., 2018). The absence of e-government strategy issues can be traced to an undefined and unclear vision and objective as well as a management strategy, lack of ownership, funding, and centralization of funding issues, and lack of proper implementation guidance (Ebrahim & Irani, 2005; Lam, 2005; Waller & Genius, 2015). In addition, the technology and IT infrastructure relate to matters that have to do with privacy and security concerns, inadequate reliable networks and low bandwidth, inadequate security of government hardware and software, and inaccessibility of open sources of software and standards (Eynon & Dutton, 2007; Savoldelli et al., 2014; Schwester, 2009; Waller & Genius, 2015). Also, the policy and legal framework concern issues, such as the sound legal policy, such as security roles, privacy policies, and laws and the lack of political commitment and support (Abu-Shanab et al., 2010; Al-Soud et al., 2014; Ebrahim & Irani, 2005; Lam, 2005; Salem, 2006; Savoldelli et al., 2014). Furthermore, the organizational and cultural barriers include the absence of trained in-house management with adequate IT skills and knowledge, challenges of retooling government processes and procedures, slower nature of government reforms, lack of agency readiness, improper cooperation and coordination between and among government sector agencies, and resistance to reforms and change (Abu-Shanab et al., 2010; Ebrahim & Irani, 2005; Eynon & Dutton, 2007; Lam, 2005; Savoldelli et al., 2014). Finally, the cost of installation, operation and maintenance of e-government system, high cost of training IT professionals, and consultancy and inadequate funding of most public sector agencies are some of the issues associated with the operational cost as a barrier to e-government implementation (Abu-Shanab et al., 2010; Ashaye & Irani, 2014; Eynon & Dutton, 2007; Savoldelli et al., 2014).

Although addressing these barriers discussed in the preceding section is crucial to the implementation of e-government projects, without the corresponding adoption of e-government services, it would be a huge drawback on the success of implementing e-government programs. E-government projects fail largely due to the non-factoring of the individual requirements and needs during the design and implementation of e-government projects (Ahmad et al., 2012). Therefore, understanding the individual user attitudes and behaviors toward the adoption of e-government services, particularly their desire to use such services, is a major contributing factor to the successful implementation of the e-government (AlAwadhi & Morris, 2008; Ibrahim & Zakaria, 2014; Shareef et al., 2011). In light of this, several studies have attempted to analyze and understand the factors influencing the adoption of e-government services. Some of these recent studies have indicated that perceived ease of use, perceived service quality, trust, and language were positive determinants of the willingness to adopt e-government services (Mensah et al., 2018). The language through which e-government services are delivered was also a predictor of perceived service quality, perceived ease of use, and perceived usefulness of e-government services (Mensah et al., 2018). Other studies have shown that performance expectancy, social influence, perceived risk, and computer self-efficacy were significant predictors of the attitudes toward the adoption of e-government services (Dwivedi et al., 2017; Verkijika & De Wet, 2018). Also, attitudes, facilitating conditions, the trust of government, and trust in the internet had a direct influence on the individual behavioral intention to use e-government services (Dwivedi et al., 2017; Verkijika & De Wet, 2018). Also, PE, social influence, effort expectancy, personal innovation, and enjoyment were found to significantly determine the continued intention to use e-government services (Sawalha et al., 2019). All these e-government adoption factors integrated models from the information system/technology (IS/IT) theories.

There are different models of IS/IT adoption theories which has been used in the context of e-government services adoption. Some of these popular models, which have been modified or extended with other constructs to understand individual adoption of technologies, are the unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al., 2003), technology acceptance model (TAM) (Davis, 1989), theory of planned behavior (TPB) (Ajzen, 1991), the decomposed TPB (DTPB) (Ajzen, 1991), etc. These models of IS/IT are not able to provide adequate background taking into consideration the complexities nature of e-government adoption, and therefore it was proposed that there is the need for scholars to build a theory that fits into the e-government complexities independently but based on the basic concepts of IS/IT theories (Dwivedi et al., 2012; Rana et al., 2017). Other scholars have also indicated the lack of a fundamental theoretical development in the context of e-government adoption research and consequently called for the development of an e-government-specific unified model to better analyze e-government adoption (Chan et al., 2011; Coursey & Norris, 2008; Dwivedi et al., 2017; Hardy & Williams, 2011; Heeks & Bailur, 2007; Norris & Lloyd, 2006). Based on this gap of lack of a specific and unique e-government adoption models for e-government adoption studies, Dwivedi et al. (2017) proposed and validated the unified model of electronic government adoption (UMEGA) which was based on the basic principle of the UTAUT (Venkatesh et al., 2003) model. According to Dwivedi et al. (2017), the proposed UMEGA which is an e-government-specific unified model is expected to outperform the other models, including the UTAUT.

The objective of this current study is to propose and validate an extension of the UMEGA proposed by Dwivedi et al. (2017) by integrating it with perceived service quality, trust in government, and intention to recommend the adoption of e-government services. These three variables were carefully selected to extend the UMEGA based on literature and because of the authors’ conviction that perceived service quality, trust in government, and intention to recommend are fundamentals and crucial to the development, growth, and diffusion of e-government services. The justifications are as follows: The whole idea of the introduction e-government is to improve the delivery of government services to citizens. So it is vital to explore how the expected improvement in service delivery from government and its agencies can influence the decision of citizens to use services delivered through e-government. The development of high-quality e-government information and services is an important factor and hence it is essential to understand the expectations and perceptions of citizens toward the nature of service quality of e-government services (Papadomichelaki & Mentzas, 2012). Efficiency, reliability, citizen support, ease of use, content, and appearance of information are some dimensions of e-government service quality (Papadomichelaki & Mentzas, 2009, 2012). Addressing the quality expectations of citizens toward e-government will lead to the provision of e-government services to meet citizens’ requirements and satisfaction (Papadomichelaki & Mentzas, 2009). Second, trust is the fundamental of any established relationship. As such, citizens have a legal and social contract or relationship with government entities. The nature of this relationship is based on the trust and confidence of citizens toward the government concerning their capacity to behave in a certain manner and to deliver enhanced services through e-government. Understanding the nature of such a trust relationship in government on the development and adoption of e-government is an imperative inquest. This view is supported by Horsburgh et al., 2011, who elaborated that trust in government has implications for “e-government.” This is buttressed by other scholars who have also indicated that trust in government is beneficial to the government when it comes to the implementation of government policy directives and programs and tax collection because citizens will have confidence in government if they perceived government as working for their ultimate interest (Fjeldstad, 2004; Yang & Holzer, 2006). However, a government that is confronted with mistrust and suspicion may result in citizens ignoring and resisting its policy directives and programs (Goldfinch et al., 2009).

And finally, the intention to recommend variable is a central ingredient for the wider diffusion of e-government services. The recommendation from citizens to their fellow citizens to use and access services through e-government platform is a major way toward the faster diffusion and use of e-government services. Also, it is a further step toward the judicious utilization of government scarce resources on e-government development. Since it will be a waste of government resources if the huge investment is made on the development of an e-government platform but citizens fail to adopt it. The recommendations from friends, relatives, and highly respected individuals are powerful mechanisms for achieving greater recognition, promotion, and success in technology adoption (Oliveira et al., 2016), such as e-government. Although the intention to recommend has been explored in terms of mobile payment adoption (Oliveira et al., 2016; Verkijika, 2020), there are limited studies on the intention to recommend when it comes to e-government services adoption. In other words, most studies on e-government have not experimented on this variable and thus make it imperative and important to examine it in the context of e-government. These reasons formed the basis for selecting these three variables to extend the UMEGA. This is to provide a unique insight on the impact of these three constructs on the adoption of e-government services under the ambit of UMEGA. The extension is also based on the fundamental principle of the UTAUT model upon which the UMEGA was also based. This proposed extension is expected to provide a richer understanding of the factors influencing the adoption of e-government services as compared to the original UMEGA. The extension is also important because no single proposed model of e-government adoption is capable of unearthing fully all the characteristics or factors determining the adoption of e-government services. The extension of the UMEGA would seek to contribute to the e-government adoption literature.

The remainder of this research paper is organized as follows: Discussion of the research framework and research hypotheses development, research model, research methodology, results and data analysis, discussion of the results, conclusion, and limitations.

Research Framework and Research Hypotheses Development

UTAUT

The UTAUT was devised by Venkatesh et al., 2003. It was developed to provide a better explanation of the individual user behavior toward the adoption of computer- and technology-related applications. The UTUAT is made up of four main determinants of the intention to use and actual use of technology, such as performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2003). These four constructs in the UTAUT are moderated by age, gender, experience, and voluntariness (Venkatesh et al., 2003). Since the development of the UTAUT, it has been applied in various studies and across different fields that utilize or integrate IT. This extensive application, testing, and validating are vividly visible in areas, such as e-government (Al-Swidi & Faaeq, 2019; Naranjo-Zolotov et al., 2018; Sawalha et al., 2019), e-commerce (Li et al., 2018; Pobee & Opoku, 2018; Shaw & Sergueeva, 2019; Soomro, 2019), e-health/mobile health (Alam et al., 2018; Bawack & Kamdjoug, 2018; Quaosar et al., 2018), e-tourism (Gupta et al., 2018; Lama et al., 2019), and mobile banking (Rahi et al., 2018; Raza et al., 2019).

UMEGA

The UMEGA was developed based on the UTAUT proposed by Venkatesh et al. (2003). The UMEGA was proposed and validated by Dwivedi et al. (2017) as an attempt to provide a specific model or theory for the context of e-government adoption. Its development was based on the primary reason that the current e-government adoption studies have largely dependent on applied theories and models of an IS/IT adoption. Some of these IS/IT theories or models of adoption are theory of reason action (TRA) (Fishbein & Ajzen, 1975), TAM (Davis, 1989), social cognitive theory (SCT) (Compeau et al., 1999; Compeau & Higgins, 1995), innovation diffusion theory (IDT) (Rogers, 2003), diffusion of innovation (DOI) (Rogers, 2003), DTPB (Taylor & Todd, 1995), TPB (Ajzen, 1991; Fishbein & Ajzen, 1975), and UTAUT by Venkatesh et al. (2003).

The UMEGA links construct toward the behavioral intention to use through attitudes toward usage. The variables that consist of the UMEGA are PE, effort expectancy, social influence, facilitating conditions, perceived risk, attitude, and behavioral intention to use (Dwivedi et al., 2017). As per the UMEGA PE, effort expectancy perceived risk and social influence were expected to have a direct impact on the attitudes toward the adoption of e-government, whereas attitude is to have a positive impact on the behavioral intention to use. In addition, the facilitating condition is projected to influence both behavioral intention and effort expectancy. The validated UMEGA performed better as compared to other models, including the UTAUT, due to the proper selection of better-suited measures for the UTAUT variables within the context of e-government rather than depending on its original measures which were based on technology adoption in the organizational context (Dwivedi et al., 2017). Also, the inclusion of perceived risk into the UMEGA is to provide an e-government-specific construct into the UMEGA and has thus strengthened the performance of the validated model (Dwivedi et al., 2017). The explanatory power of the UMEGA is 80% as compared with the UTAUT model (variance in the behavioral intention of 69%) (Dwivedi et al., 2017).

The UMEGA which was developed by Dwivedi et al. (2017) based on the UTAUT Venkatesh et al. (2003) is shown in Figure 1.

Figure 1.

The unified model of electronic government adoption (UMEGA) (Dwivedi et al., 2017).

The UMEGA was further extended in another study to include constructs, such as computer self-efficacy, trust in the internet, and trust in government (Verkijika & De Wet, 2018). The extension which was based on the premise that computer self-efficacy and trust (in government and internet) is important and valuable in e-government adoption (Verkijika & De Wet, 2018). Through the addition of these new factors, it showed that the explanatory power of attitudes increased by 3.2% and behavioral intention 4.5% (Verkijika & De Wet, 2018). These increments are an improvement of the prediction potential of attitudes as compared with the original UMEGA.

Performance Expectancy

PE is the degree to which an individual user believes that the use of a new technology system would aid in improving his or her work performance (Venkatesh et al., 2003). PE has been found to have a significant impact on the attitudes and the intention to use e-government services in previous studies (Bhuasiri et al., 2016; Dwivedi et al., 2017; Kurfalı et al., 2017; Verkijika & De Wet, 2018; Williams et al., 2016). Hence H1 was proposed.

Hypothesis 1 (H1): Performance expectancy is positively related to the attitudes toward e-government adoption.

Effort Expectancy

Effort expectancy is considered as the extent to which the individual perceives that the use of new technologies to be simple and easy to use (Venkatesh et al., 2003). Studies have shown that the easier a technology is the more attracted users would be to adopt it. And this is corroborated by previous scholars who have demonstrated that effort expectancy has a direct significant effect on the attitude and the intention to use e-government services (Dwivedi et al., 2017; Verkijika & De Wet, 2018; Williams et al., 2016). Consequently, H2 was proposed.

Hypothesis 2 (H2): Effort expectancy is positively related to the attitudes toward e-government services adoption.

Social Influence

Social influence is the extent to which the decision of the individual user to adopt new technologies is influenced by the close relations, friends, and important persons they have respect for (Venkatesh et al., 2003). The research findings of social influence on technology adoption are mixed. Although other studies have demonstrated that social influence is positively related to the technology adoption (Oliveira et al., 2016), others have shown otherwise that social influence does not affect technology adoption (Lallmahomed et al., 2017). These mixed and varying findings may be a result of the theoretical understanding that social influence is related to behavioral intention through the mediating role of the individual citizens’ attitudes toward the technology adoption (Lin et al., 2011). Social influence concerning the adoption of e-government services is significant in determining the user attitude and intention to adopt e-government services (Bhuasiri et al., 2016; Dwivedi et al., 2017; Kurfalı et al., 2017; Verkijika & De Wet, 2018; Williams et al., 2016). Accordingly, H3 was proposed.

Hypothesis 3 (H3): Social influence is positively related to the attitudes toward e-government services adoption.

Facilitating Conditions

Facilitating conditions is the individual perception that there is available the right technical and organizational capacity and infrastructure to enable him or her to use new technologies successfully (Venkatesh et al., 2003). In the context of e-government, facilitating conditions depicts the degree to which citizens believe that there are available enough resources to facilitate and encourage them to use and have access to e-government services (Verkijika & De Wet, 2018). The significant impact of facilitating conditions on the behavioral intention to use e-government services and effort expectancy has been demonstrated by previous studies (Dwivedi et al., 2017; Kurfalı et al., 2017; Verkijika & De Wet, 2018; Williams et al., 2016). It thus follows that when citizens are sure that there are available the requisite facilitation conditions, it will influence their intention to use and improve their understanding of the effort expectancy of e-government services. Accordingly, H4 and H5 were proposed.

Hypothesis 4 (H4): Facilitating condition is positively related to the intention to use e-government services.

Hypothesis 5 (H5): Facilitation condition is positively related to the effort expectancy of e-government services.

Perceived Risk

Consumers of technology-related innovations have often exhibited the tendencies of risk toward the adoption of such technologies. Perceived risk is considered as the individual user perception or subjective judgment that he or she will incur some degree of loss or damage while experimenting or interacting with new forms of technology or innovation (Gefen et al., 2003; Warkentin et al., 2002). Perceived risk when it comes to e-government is the belief of citizens that they will suffer some form of loss when adopting e-government services, particularly when e-government services have to be accessed through the internet system which has its associated risks and challenges (Verkijika & De Wet, 2018). This fear has the potential to limit the nature of the interaction of citizens with e-government services (Verkijika & De Wet, 2018). It thus can be illustrated that when citizens feel that the risk of accessing e-government services is high, they will be discouraged or have negative attitudes toward the adoption of e-government services. Empirical studies have shown that perceived risk is negatively related to both the attitudes and the intention to use e-government services (Bélanger & Carter, 2008; Dwivedi et al., 2017; Veeramootoo et al., 2018; Verkijika & De Wet, 2018). Therefore, H6 was proposed.

Hypothesis 6 (H6): Perceive risk is negatively related to the attitudes toward the adoption of e-government services.

Attitude

Technology acceptance models, such as TRA and TAM, have emphasized the role the individual attitudes play in the adoption of new technologies (Davis, 1989; Fishbein & Ajzen, 1975). Attitude toward the adoption of technologies is the extent to which the individual user expresses either positive or negative assessment of engaging or interaction with such technologies, such as e-government services. When it comes to e-government, citizens with a positive attitude or appraisal of e-government services will have a high intention of using such a system (Verkijika & De Wet, 2018). Previous studies have established the direct impact of attitudes on the behavioral intention to use e-government services (Dwivedi et al., 2017; Verkijika & De Wet, 2018). Accordingly, H7 was proposed.

Hypothesis 7 (H7): Attitude is positively related to the behavioral intention to use e-government services.

Perceived Service Quality

Improvement in the public service delivered to the citizens is one of the key factors for the introduction of e-government as a tool to reform and enhance the delivery of public services. Service quality is considered as the provision of services that meets or exceeds the expectations of services required (Parasuraman et al., 1988). Perceived service quality is defined as the difference between the expected services and perceptions of the actual service provided (Parasuraman et al., 1988). The significant impact of service quality on the behavioral intention to use e-government services has been demonstrated by previous studies (Mensah et al., 2018; Sung et al., 2009; Veeramootoo et al., 2018). The extent to which citizens feel that services delivered through e-government are of good quality can also tend to entice them to recommend the adoption of e-government services to others. Consequently, H8 and H9 were proposed.

Hypothesis 8 (H8): Perceived service quality is positively related to the behavioral intention to use e-government services.

Hypothesis 9 (H9): Perceived service quality is positively related to the intention of citizens to recommend the use of e-government services.

Trust in Government

Trust in government is considered as the individual citizens’ perception or understanding that the government and its related agencies have the ability, integrity, and capacity to deliver quality public services through e-government. Trust in government is among one of the major factors determining the adoption of new technologies, such as e-government services (Bélanger & Carter, 2008; Gefen et al., 2005). According to Bélanger and Carter (2008), before citizens can have full confidence in e-government initiatives, they must be convinced that government agencies have the perspicacity, managerial, and technical resources required for the successful implementation of e-government projects. Studies have shown that trust in government is significant in influencing the behavioral intention to use e-government services (Bélanger & Carter, 2008; Karavasilis et al., 2016; Mensah et al., 2017). Also, trust in government can influence the extent to which citizens would be willing to recommend the adoption of new technologies, such as e-government services. Consequently, H10 and H11 were proposed.

Hypothesis 10 (H10): Trust in government is positively related to the behavioral intention to use e-government services.

Hypothesis 11 (H11): Trust in government is positively related to the intention of citizens to recommend the adoption of e-government services.

Behavioral Intention to Use

Both the TAM and UTAUT have shown that the antecedent of behavioral intention to use, such as perceived usefulness, perceived ease of use, PE, effort expectancy, social influence, and facilitation conditions, are important factors determining the behavioral intention to use (Davis, 1989; Venkatesh et al., 2003). Behavioral intention to use is also posited to affect the actual use of new technologies (Davis, 1989; Venkatesh et al., 2003). It is also possible that users who have the intention to use can also have the tendency to recommend its adoption. Studies have shown that the behavioral intention to use has a positive effect on the intention to recommend (Oliveira et al., 2016; Verkijika, 2020). Consequently, H12 was proposed.

Hypothesis 12 (H12): Behavioral intention to use e-government services is positively related to the citizens’ intention to recommend the adoption of e-government services.

Research Model

The research model based on the extension of the UMEGA is shown in Figure 2. Perceived service quality, trust in government, and intention to recommend are the additional constructs that have been added to the original UMEGA (shown in Figure 1).

Figure 2.

Research model—extension of the UMEGA.

Research Methodology

To validate the proposed extension of the UMEGA, a research questionnaire was developed to collect data from Ghanaian citizens. The questionnaire was administered within the Ministry enclave and its environs in Accra, Ghana. This place was chosen because it is the busy and most vibrant business hub within the capital, Accra, and thus home to several public sector agencies and ministries and private sector organizations. It was therefore easy and convenient to reach the targeted population for this study. A non-probability sample technique approach was therefore used as the sampling technique for the study. The variables for this study were adopted from previous studies but were modified to reflect the content of this current study. They were adopted as follows: performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention to use (Dwivedi et al., 2017; Venkatesh et al., 2003). Perceived risk and attitudes (Bélanger & Carter, 2008; Colesca, 2009; Davis, 1989; Dwivedi et al., 2017), perceived service quality (Janita & Miranda, 2018), trust in government (Bélanger & Carter, 2008; Carter & Bélanger, 2005), and intention to recommend (Al-Ansi et al., 2019; Oliveira et al., 2016). The questionnaire items used for the study are attached as Appendix. The content of the research questionnaire was divided into two sections. The first section contained the research constructs examined in this study, and the second section contained basic information about the demographic profile of the respondents. The constructs in the questionnaires were measured on a five-point Likert-type scale which ranged from 1 = Strongly Disagree (SD) to 5 = Strongly Agree (SA). People were recruited and tasked to administer the questionnaire manually to the targeted population of 500 citizens within the ministries and its environs. The administration of the research questionnaire was completed within a month. After the questionnaires were administered, 345 of the questionnaires were recovered which represents 69% and 56 of them were discarded because of incomplete information. Thus 289 (57.8%) of the questionnaires were found suitable and were captured and used for the data analysis. The data capture and analysis were conducted with SPSS and SmartPLS using the structural equation modeling technique. The dataset was also tested for common method bias to ensure that it was free from any common method bias. The common method of bias was conducted using Harman’s single factor test (Harman, 1976; Podsakoff et al., 2003). No significant common bias was found in the dataset because the variance accounted for was less than the cut-off value of 50% of variance as recommended by Podsakoff et al., 2003, 2012.

Results and Data Analysis

Profile Statistics

The profile statistics of the respondents are shown in Table 1. The male (60.9%) respondents were more than their female counterparts (39.1%). Also, the majority of the respondents were between the age groups of 36 to 40 years (31.1%). In terms of education, most of them hold bachelor’s degrees (49.8%) and the majority also worked in the public sector (44.3%).

Table 1.

Profile Statistics of Respondents.

Items	Characteristics	Frequency	Percentage
Gender	Male	176	60.9
Gender	Female	113	39.1
Age distribution (years)	18–28	23	8.0
	26–30	47	16.3
	31–35	70	24.2
	36–40	90	31.1
	>40	59	20.4
Educational level	Bachelors	144	49.8
	Masters	96	33.2
	Others	49	17.0
Job category	Public sector	128	44.3
	Private sector	95	32.9
	Self-employed	18	6.2
	Unemployed	48	16.6

Measurement Model

The composite reliability, average variance extracted (AVE), Cronbach’s alpha, and factor loadings were used as the quality benchmarks for testing the validity and reliability of the constructs employed in this study. The results of the measurement model are shown in Table 2. The acceptable factor loadings and Cronbach’s alpha values should be above .70 (Hair et al., 2013; Henseler et al., 2009; Nunnally & Bernstein, 1994). Composite reliability values are recommended to be above .80, whereas the AVE should have values above .50 (Fornell & Larcker, 1981; Henseler et al., 2009). In addition, the discriminant validity was also tested by applying the Fornell–Larcker criterion. The Fornell–Larcker principle stipulates that a variable is perceived to have a discriminate validity if the square root of the AVE is greater than the paired intercorrelation between the latent constructs (Fornell & Larcker, 1981). The Fornell–Larcker principle has been met which is indicative that there is discriminate validity of the constructs examined. The results of the discriminate validity are shown in Table 3.

Table 2.

Measurement Model.

Construct	Item	AVE	Composite reliability	Cronbach’s alpha	Loading
Performance expectancy	PE1PE2PE3PE4PE5	.887	.987	.967	.985.958.846.962.954
Social influence	SI1SI2SI3SI4SI5	.931	.986	.981	.974.930.956.978.986
Perceived risk	PR1PR2PR3PR4PR5	.942	.987	.984	.987.958.985.950.974
Effort expectancy	EE1EE2EE3EE4EE5	.909	.980	.975	.983.963.966.982.927
Facilitation conditions	FC1FC2FC3FC4FC5	.891	.976	.969	.968.962.930.923.939
Attitude	ATT1ATT2ATT3ATT4ATT5	.925	.984	.979	.972.932.982.951.971
Perceived service quality	PSQ1PSQ2PSQ3PSQ4PSQ5	.877	.972	.964	.903.903.955.973.948
Trust in government	TIG1TIG2TIG3TIG4TIG5	.916	.982	.977	.975.971.911.965.965
Intention to use	IU1IU2IU3IU4IU5	.909	.981	.975	.946.925.977.951.969
Intention to recommend	ITR1ITR2ITR3ITR4ITR5	.945	.989	.985	.962.975.986.970.970

Note. AVE = average variance extracted.

Table 3.

Discriminant Validity.

Item	PE	SI	PR	EE	FC	ATT	PSQ	TIG	IU	ITR
PE	.942
SI	.673	.965
PR	.579	.580	.970
EE	.587	.658	.561	.953
FC	.585	.776	.743	.568	.943
ATT	.578	.433	.622	.533	.604	.961
PSQ	.676	.526	.585	.526	.732	.548	.936
TIG	.481	.560	.567	.432	.579	.537	.435	.957
IU	.562	.509	.646	.673	.693	.611	.546	.685	.953
ITR	.566	.535	.513	.589	.635	.536	.574	.639	.548	.972

Note. Correlations of constructs and square roots of AVE (Bold). PE = performance expectancy; SI = social influence; PR = perceived risk; EE = effort expectancy; FC = facilitation conditions; ATT = attitude; PSQ = perceived service quality; TIG = trust in government; IU = intention to use; ITR = intention to recommend; AVE = average variance extracted.

Structural model

Structural model for the extended UMEGA

The results of the structural model for the extended UMEGA test are shown in Table 4. The findings have shown that PE (β = .053, p > .05), effort expectancy (β = .054, p > .05), and social influence (β = .002, p > .05) had no significant impact on the attitudes. Hence H1, H2, and H3 were not supported. However, it was found that facilitating conditions were significant in predicting both the intention to use (β = .198, p < .05) and effort expectancy (β = .688, p < .05). H4 and H5 were therefore supported. In addition, perceived risk had negative and significant impact on attitude (β = −.783, p < .05). H6 was therefore supported. Also, attitude showed a positive and significant impact on the intention to use (β = .314, p < .05). Hence, H7 was also supported. Furthermore, our analysis revealed that perceived service quality was significant in predicting both the intention to use (β = .492, p < .05) and recommendation intentions (β = .234, p < .05). H8 and H9 were subsequently supported. Trust in government was also found to positively and significantly determine both the intention to use (β = .387, p < .05) and recommendation intentions (β = .263, p < .05). H10 and H11 were therefore supported. Finally, the intention to use was found to significantly determine the intention to recommend the adoption of e-government services (β = .501, p < .05). H12 was accordingly supported. The graphical depiction of the validated structural model is illustrated in Figure 3.

Table 4.

Results of Structural Model (Hypotheses).

Hypotheses	Path	β	T-value	Sign	Supported
H1	PE→ATT	.053	1.658	.098*	No
H2	EE→ATT	.054	1.141	.255	No
H3	SI→ATT	.002	.091	.927	No
H4	FC→IU	.198	3.714	.000***	Yes
H5	FC→EE	.688	70.036	.000***	Yes
H6	PR→ATT	−.783	22.437	.000***	Yes
H7	ATT→IU	.314	7.298	.000***	Yes
H8	PSQ→IU	.492	8.400	.000***	Yes
H9	PSQ→ITR	.234	4.956	.000***	Yes
H10	TIG→IU	.387	6.154	.000***	Yes
H11	TIG→ITR	.263	8.001	.000***	Yes
H12	IU→ITR	.501	11.671	.000***	Yes

Note. PE = performance expectancy; ATT = attitude; EE = effort expectancy; SI = social influence; FC = facilitation conditions; IU = intention to use; PR = perceived risk; PSQ = perceived service quality; ITR = intention to recommend; TIG = trust in government.

***

p < 0.01. *p < 0.1.

Figure 3.

Validated researched model (extended UMEGA).

Comparison With Other UMEGA Models

Original UMEGA

The original UMEGA which was empirically validated by Dwivedi et al., 2017 is shown in Figure 4. This can be compared with the validated extended UMEGA of this current study illustrated in Figure 3. The original UMEGA (Figure 4) indicates that all the hypothesized relationships tested were all positively significant with attitudes and facilitating conditions explaining 80% of the variance in the behavioral intention to use. Also, facilitating conditions explained about 77% of the variance in effort expectancy, whereas perceived risk, PE, effort expectancy, and social influence accounted for about 49% of the variance in attitude.

Figure 4.

Original UMEGA (Dwivedi et al., 2017).

In comparison to our current extended UMEGA (Figure 3), perceived risk, effort expectancy, PE, and social influence accounted for 62.6% of the variance toward attitudes. This variance is higher and improved than in Figure 4 which recorded 49%. In terms of the percentage of variance explained by facilitating conditions on effort expectancy, the original UMEGA seems to have performed better with a variance of 77% (Figure 4) which is less than the variance explained by 68.8% in the extended UMEGA (Figure 3). About the behavioral intention to use, the original UMEGA (Figure 4) showed higher variance (80%) as compared to the extended model which recorded 77.8% (Figure 3). The major difference and addition to the original model (UMEGA) is the intention to recommend. Perceived service quality, trust in government, and behavioral intention to use accounted for 76.4% of the variance toward the intention to recommend the adoption of e-government services.

Modified UMEGA

We present a comparison with our model (Figure 3) to the previous extension of UMEGA by Verkijika & De Wet, 2018 as shown in Figure 5. Our extended model (Figure 3) seems to have provided better variances than the modified UMEGA (Figure 5). The variances for Figure 5 are as follows: Behavioral intention to use (69.2%), Attitude (21.3%), effort expectancy (36.7%) as compared with the variance explained in Figure 3, such as behavioral intention to use (77.8%), attitude (62.6%), and effort expectancy (65.3%). This indicates a clear improvement of the UMEGA model in our current study (Figure 3) as compared to the UMEGA extended (Figure 4) by Verkijika & De Wet, 2018.

Figure 5.

Modified UMEGA (Verkijika & De Wet, 2018).

Discussion

This study extended the UMEGA by integrating it with perceived service quality, trust in government, and intention to recommend the adoption of e-government services. This extension and validation of the proposed model are grounded on the premise of providing a better explanatory power to the adoption of e-government services. Our extended model of UMEGA was compared with other models/studies that also attempted the extension of the UMEGA. Our results have indicated that PE, effort expectancy, and social influence do not have a positive impact on the attitude. These findings are a departure and different from studies that demonstrated that PE and effort expectancy are both significant predictors of the attitudes toward the adoption of e-government services (Dwivedi et al., 2017; Hammad et al., 2019). Although our findings are inconsistent with the findings of Verkijika & De Wet, 2018 that PE predicts attitude, it is, however, consistent with the same study that showed that effort expectancy does do not predict the attitudes toward the adoption of e-government services. Also, the non-significant impact of social influence on attitudes is different from the findings of Dwivedi et al., 2017; Verkijika & De Wet, 2018 that obtained contrary results of the positive impact of social influence on the attitude toward the adoption of e-government services.

In addition, our extended model indicated that facilitating conditions predict significantly both the intention to use and effort expectancy. These findings are in agreement with studies that illustrated that facilitating conditions was significant in determining the behavioral intention to use and the effort expectancy of e-government services (Dwivedi et al., 2017; Verkijika & De Wet, 2018). The perceived risk was also found to be significant in predicting the attitudes of citizens toward the adoption of e-government services. This finding supports findings that suggested that perceived risk is a determinant of attitude (Dwivedi et al., 2017; Verkijika & De Wet, 2018). It was again discovered that attitude was a predictor of the behavioral intention to use e-government services and this finding supports the outcome of the results (Dwivedi et al., 2017; Verkijika & De Wet, 2018) that attitude is a positive predictor of intention to use.

Furthermore, our current study has demonstrated that perceived service quality is a significant predictor of both the behavioral intention to use and intention to recommend the adoption of e-government services. The significant impact of perceived service quality on the intention to use corroborates the results finding that indicated that perceived service quality is a positive determinant of the intention to use (Mensah et al., 2017; Veeramootoo et al., 2018). However, the significant impact of perceived service quality on the intention to recommend appears to be special and a new addition to the e-government adoption literature particularly the extended UMEGA. Again, we also revealed that trust in government showed a significant impact on both the intention to use and recommend that the adoption of e-government services. The significant impact of trust in government on the intention to use e-government services is in line with previous studies that established the positive significant impact of trust in government on the intention to use e-government services (Kurfalı et al., 2017; Verkijika & De Wet, 2018). However, the significant impact of trust in government on the intention to use is inconsistent with studies that showed that trust in government was not a significant predictor of intention to use (Mensah, 2018, 2019). The finding on the positive impact of trust in government on the intention to recommend also appears to be a new finding and an addition to the e-government adoption literature especially the extended UMEGA. Finally, our validated extended UMEGA has shown that the behavioral intention to use e-government services is a positive significant predictor of the intention to recommend the adoption of e-government services. This finding is in line with studies that showed that intention to use is positive and significantly related to the intention to recommend (Oliveira et al., 2016; Verkijika, 2020).

Implications for Practice

The first implication is that facilitating conditions are important drivers to the adoption of e-government services because the empirical validation of the impact of facilitation conditions on both the intention to use and effort expectancy of e-government services has been supported by this study. E-government is dependent on the huge investment in the right ICTs to enable its success. Government and related agencies must provide the right facilitating conditions, such as investment in the procurement and development of the best technology infrastructure and ICT tools, such as the internet, broadband, 4G, and 5G networks for the implementation of e-government. Also, the provision of cheap data and internet access, constant uninterrupted power supply, and improving the economic well-being of people will contribute to creating the enabling environment for the adoption of e-government services. These enabling environment or facilitating conditions will specifically attract and encourage citizens and the general public to harbor the intention to use e-government services. Also, the provision of these facilitating conditions can be a catalyst to reduce the perceived challenges that users may have toward the adoption of e-government services. In other words, these right facilitating conditions can reduce the degree of effort expectancy associated with the use of e-government services. This was also echoed by Dwivedi et al. (2017) who indicated that putting in place training programs, organizational and technological infrastructure can facilitate the adoption of e-government services.

Furthermore, the significant impact of perceived risk on attitude toward the adoption of e-government services was also empirically validated in this study. This finding implies the negative effect of perceived risk on the attitudes of users toward e-government. The virtual environment, in which IT-related technologies operate, such as e-government, is surrounded by many levels of uncertainty to the users of such technologies. Some of the risks which may be associated with e-government services are financial risk, social risk, psychological risk, physical risk, security risk, and performance risk. Financial risk is the fear or concern that users may have concerning the exchange of financial information through transactional e-government. For example, users may harbor some levels of fear of losing payment made through insecure e-government portals and disclosure of transaction data, information, and passwords to unauthorized people because of failure to build a watertight, reliable, and secure e-government payment infrastructure. These risks have the potential to negatively affect the attitude toward the adoption of e-government services. To prevent the negative impact of these risks on the attitudes of citizens toward the adoption of e-government services, the government must develop and implement an e-government system that can provide the highest form of protection against these perceived risks associated with e-government. Once these risks are addressed, it will reduce the perceived risk and thus will have a positive impact on the attitudes of citizens toward e-government.

In addition, attitude toward e-government was found to positively determine the behavioral intention to use e-government services. It means that putting in measures to enable the development of positive attitudes toward e-government is critical to the adoption of e-government services. Measures, such as education and training programs, have the potential to influence positively the attitudes toward e-government. When people are trained on the usage of e-government systems, they happen to have a better understanding of such a system and hence will cultivate a positive attitude toward it. The policymakers and implementers of e-government projects must, therefore, develop quality training and educational programs which include exposure to the technical access to e-government services to users or intended users of e-government services. This training will enable them to develop a positive attitude toward using e-government services which will, in turn, influence their intention to use e-government services.

Also, perceived service quality was found to determine both the intention to use and recommend the adoption of e-government services. One of the ultimate goals for the implementation of e-government is to improve the nature of service provided and any e-government service that can guarantee the provision of quality public services will have a direct impact on the users’ behavioral intention to use and recommend its adoption as well. It is thus imperative that government agencies in charge of e-government projects should focus on delivering quality public services through e-government to meet the quality expectations of citizens/users. Any deviation from these expectations of the quality of public services will have a negative impact on the behavioral intention to use and recommending e-government services.

In addition, trust in government was found to be significant predictors of both the intention to use and the recommending of e-government services adoption. These findings are an important indication of the role the nature and level of trust in government can have on the adoption of e-government services. It is again imperative for the government to engage in activities and actions that will stimulate high levels of trust toward the government. The government can attract high levels of trust from citizens by being open and transparent about its activities and programs. Particularly when it comes to budgets and expenditure for major policy and development programs that have the potential to impact on the lives of individual citizens. A citizen may be consulted to provide input into policy programs and initiatives before they are implemented or passed into law. These actions have the potential to create a congenial environment between the government and its citizens thereby developing a good level of trust toward government. Once citizens hold the view that government has nothing to hide and thus open and that policies implemented will serve citizen’s interest, they will develop higher levels of trust in government. This will, in turn, culminate in their behavioral intention to use and recommend the adoption of e-government services.

Finally, the behavioral intention to use was a significant determinant of the intention to recommend the adoption of e-government services. This is quite an important finding which implies citizens who harbor the intention to use e-government services, also have the potential to recommend its adoption to friends, colleagues, and other important social network friends. The government thus must come up with good measures and initiatives about e-government which will attract citizens to develop the intention to use services emanating from e-government. This will, in turn, influence positively the citizens’ intention to recommend e-government services to people within their close circles or networks. The spreading through recommending of e-government services is important to the success of e-government projects because more people will be attracted to use e-government services through personal networks.

It is important to highlight once more that the intention to recommend validated in our extended UMEGA has demonstrated how citizens’ recommendations can impact the development and diffusion of e-government services. Government and local government agencies spend so much resource in creating the much-needed awareness about government public policy and programs of which e-government is part to reach a wider usage and acceptance among the populace. So it thus follows that, if citizens can recommend the adoption of e-government services to people within their immediate and extended environment, it can save the government more resources particularly when it comes to budgeting for e-government publicity and awareness drive. The saving made can then be channeled to other judicious use.

Implications for Theory

The modification and extension of the original UMEGA (Dwivedi et al., 2017) which was based on the UTAUT model have contributed to further the understanding of the variance predicting the adoption of e-government services. Since the first UMEGA (UMEGA 1) was proposed and validated by Dwivedi et al., 2017, it was only extended (UMEGA 2) a second time by Verkijika & De Wet, 2018 whose extension outperformed the first (original) UMEGA. Our current study is thus the second modification (UMEGA 3) and extension of the original UMEGA as far as the literature is concerned. Our second extension of the UMEGA has shown that the explanatory power of attitudes toward use increased by 13.6% and 41.3% as compared, respectively, to both the UMEGA 1 and UMEGA 2. Also, in terms of the explanatory power of intention to use, it showed an increase in 8.6% and effort expectancy by 4.3% as compared to the UMEGA 2 (Verkijika & De Wet, 2018). These variances (predictive power) are among the major contribution of this study because these variances have outperformed both the UMEGA 1 and UMEGA 2.

Also, despite the addition of perceived service quality and trust in government, its contribution to the variance explained in the behavioral intention to use in the newly validated UMEGA was lower (77.84%). As compared to variance in explained in the behavioral intention in the original UMEGA (80%). This means that when it comes to the variance accounted for the behavioral intention to use, the original UMEGA outperformed the newly validated model in this study. In addition, the introduction of intention to recommend provided an explanatory power of 76.4% which is different from the UMEGA 1 and UMEGA 2 because they both did not experiment on this construct (intention to recommend) and perceived service quality. The inclusion of the intention to recommend to this model in the e-government context is an important theoretical contribution because this construct is underexplored in the vast body of e-government studies. Hence the adding of intention to recommend to the UMEGA provides the theoretical foundation for researchers and scholars to refine and test this extended UMEGA to improve on its predictive capacity and thereby getting the UMEGA well grounded. These contributions are unique to our study and thus have enriched the e-government adoption literature particularly when it comes to validating the UMEGA.

Conclusion

The UMEGA is the major e-government adoption model that has been proposed and validated by two previous studies. The current study is the second of such extensions and validation of the UMEGA. These validations provided an empirical basis for the UMEGA to be applied, tested, and extended by researchers/scholars to enhance the determination of the factors predicting the adoption of e-government services within different contexts and settings. The UMEGA can, therefore, be instrumental in finding out the factors influencing the demand side of e-government. The UMEGA according to Dwivedi et al., 2017 can be applied in e-government research because it provides e-government-specific contextualization and hence can be used by researchers (e-government) as the foundation and substitute for alternative theoretical models, such as TRA, TAM, TPB, DOI, etc.

Our current extension of the UMEGA has shown that from the perspectives of the people sampled for this study, the provision of e-government facilitating conditions is important in determining the intention to use and effort expectancy of e-government services. Also, the extent of risk associated with e-government services can influence the citizens’ attitudes toward e-government which will, in turn, impact the intention to use. The nature of perceived service quality and trust in government is fundamental in predicting the intention to use and recommend e-government adoption. In addition, citizens’ intention to use e-government may lead to its recommendation to other people as well. These validated factors have provided empirical evidence for policymakers and government agencies to factor them into the design and implementation of e-government projects. This will ultimately lead to providing quality and uninterrupted public services to citizens and the general public.

Limitations and Future Research

First, the validated extended UMEGA may be applied in other contexts or countries but the results may be inconsistent with this current study. Second, the sample size may not be representative and hence caution should be exercised in the generalization and interpretation of the results of the study. Third, not all the factors which may influence the adoption of e-government services were included in the UMEGA model, and hence future study will attempt to improve the UMEGA by inculcating it with other factors, such as e-government performance, government capacity, etc., to improve the explanatory power of the model. Also, a future study will attempt to examine the indirect effects in the research model particularly the indirect effects of other factors on the recommendation intentions.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Isaac Kofi Mensah

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E-Government Services Adoption: An Extension of the Unified Model of Electronic Government Adoption

Abstract

Keywords

Introduction

Research Framework and Research Hypotheses Development

UTAUT

UMEGA

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions

Perceived Risk

Attitude

Perceived Service Quality

Trust in Government

Behavioral Intention to Use

Research Model

Research Methodology

Results and Data Analysis

Profile Statistics

Measurement Model

Structural model

Structural model for the extended UMEGA

Comparison With Other UMEGA Models

Original UMEGA

Modified UMEGA

Discussion

Implications for Practice

Implications for Theory

Conclusion

Limitations and Future Research

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

References