Scholars have conducted extensive research on the issue of e-governance especially its relevance in modern society. Bwalya (2009, p. 89) notes that the world is changing very fast due to technological advancement, and this has made it necessary to find better management approaches other than the traditional ways of governance. The research by Alshehri and Drew (2010, p. 1053) has demonstrated that high-quality e-government services delivered most effectively and securely to citizens are a critical component in bringing benefits, such as improved efficiency, increased accountability and transparency, minimal corruption, increased citizen participation in the political processes, and cost reductions for both the government itself and the adopter of e-government services. According to Reddick (2005, p. 42), while a considerable body of academic literature exists on e-government services and their benefits, implying that much focus has been targeted at the supply side of e-government, surprisingly little is known about why and under what conditions citizens and organizations adopt them. As Doong et al (2010, p. 89) suggest, the demand side of e-government services particularly from the perspective of citizen adoption factors, remains comparatively unexplored. This section aims at reviewing the concept of e-government adoption, including the available definitions, differences between adoption and readiness factors, technology adoption models, as well as technology adoption factors.
E-government Adoption and Readiness Definitions
Elsheikh (2011, p. 16) says that there are diverse perspectives on the definition of the adoption and readiness of e-government services and applications. However, it is now clear that the success of e-government depends upon the citizens’ desire to adopt this innovation (Sharma et al 2012, p. 20). The table below shows various definitions of e-governance as given by various scholars.
Technology Readiness and Adoption: Understanding the Differences
The above table has given a detailed definition of technology adoption and technology readiness based on the definition of different scholars. Drawing from these definitions, it is evident that technology adoption entails the willingness to use the innovation (Elsheikh 2011, p. 16), or the desire to adopt the innovation (Sharma et al 2012, p. 20), while technology readiness is all about the propensity to embrace and use new technologies for realizing goals in home life and at work (Chang & Kannan 2006, p. 1). Since capability must be present for willingness to use a particular innovation to occur, it can be argued that technology readiness is a strong predictor of adoption. One study by Godoe and Johansen (2012, p. 45) reports that readiness factors, such as optimism and innovativeness, substantially affect known adoption factors, including perceived usefulness and perceived ease of use. In the e-government context, Sharma et al (2012) assert that citizens who are more optimistic and innovative about e-government services will find the services more useful and easier to use, hence the adoption process is much easier than in those who are less optimistic.
A strand of existing literature demonstrates that technology readiness and adoption are influenced by different factors. It is contended that some of the factors making a substantial contribution to technology readiness include the level of technology infrastructure, education, legal and regulatory framework on ICT use, supportive government policies, accessibility of ICT to the population at large, and culture (Dada 2006, p. 1; Kovacic 2005, p. 144). While technology adoption is affected by factors such as perceived usefulness, perceived ease of use, compatibility, relative advantage, perceived reliability, and perceived empathy (Carter & Belanger 2003; Ebrahim & Irani 2005; Kumar et al 2007). Technology readiness is defined by privacy, security, perceived trust, computer self-efficacy, and perceived uncertainty as further discussed below.
Technology Readiness Factors
This section attempts to outline and discuss the factors that are considered important in facilitating readiness for new technological innovations. According to Chang and Kannan (2006, p. 2), these are factors that would enhance the ability of one to be ready to use technology. These factors are discussed below.
Privacy
According to Alshehri and Drew (2010, p. 1053), people value privacy, and this has made it one of the most important factors in determining readiness to use innovation in governance. People would be ready to use technology in governance if they are assured that their security shall not be breached by the new system. If it is clear that the new system offers more privacy than the existing system, then they would be ready to embrace the new approach (Bwalya 2009, p. 4).
Perceived Trust
Trust is one of the most important factors that would define an individual’s readiness to use technology in governance. Lean (2008, p. 9) defines trust as the extent to which users believe the website is legal, ethical and credible, and can protect their privacy. This scholar further states that for an individual to be ready for a complete change, he or she must have trust in the new approach of governance being brought by technology. Alshawi and Alalwany (2009) assert that trust in the e-government context is associated with security and privacy in that the citizens’ trust not only requires maintaining security in handling of information, thus protecting their privacy but also it must assure them that their personal information will be treated with confidentiality.
For people to be ready for a specific innovation such as e-government services, they must have trust in the Internet (measured by the degree of confidence of the citizens on the Internet), as well as trust in government organizations (measured by the level of security in handling of information and protecting the privacy of citizens). Mofleh & Wanous (2008) notes that trust also goes hand in hand with security, as users of new technology feel that the involved parties should provide a secure access point with the view to developing citizen trust. Kumar et al (2007, p. 71) assert that experience influences a user’s trust in using new technology, as users with experience, particularly if satisfied, would be more likely to be frequent users of the technology.
Security
Venkatesh et al (2012, p. 120) define security as the technical safety of the network against fraudulent access by others, including hackers. Users of innovation may fail to reinforce adoption and usage behavior until they feel secure and safe (Ebrahim & Irani 2005, p. 602; Reddick 2004 p. 65), thus it is argued that security is one of the critical factors in technology readiness (Sharma et al 2012, p. 20). In their study, Al-Ghaith et al (2010, p. 7) argue that users’ behavioral intention to use a technological innovation is substantially influenced by their perception about the level of security control that innovation has, and that perceived security is a much stronger determinant of readiness for a given innovation.
Perceived Uncertainty
Dimitrova and Chen (2006, p. 178) note that when people are uncertain of a given government approach, then they would try to defer its application until they are certain of its impact. This scholar states that uncertainty is one of the factors that make individuals reluctant to change. Once the issue of uncertainty is adequately addressed, people would always be ready for technological advancements in governance (Alshehri & Drew 2010, p. 1050).
Self-Efficacy
Dimitrova & Chen (2006, p. 177) notes that self-efficacy involves an individual’s ability to use new technological inventions. Higher confidence in one’s capacity to use new technology, according to these authors, is often associated with readiness for innovation. Shareef et al (2009, p. 19) assert that computer users’ self-efficacy and experience of the internet, ICT, and computers create a perception of security in their attitude towards using online systems and this affects their readiness to use new inventions.
Technology Adoption Models
Available literature demonstrates that two research streams have emerged to explain underlying issues concerned with technology adoption; while one stream is system-specific and focuses on how technology attributes affect an individual’s perception of a specific technology, which in turn affects the willingness to use the innovation, the other stream to a large extent focuses on latent personality dimensions to explain the use and acceptance of new technologies (Alshehri et al 2012; Godoe & Johansen 2012). Models such as the technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) follow the first stream, while others such as the technology readiness index (TRI) follow the second stream. This section specifically focuses on TAM as defined by Davis (1989), and UTAUT not only to understand how citizens adopt innovations such as e-government services but also to assess the models’ constructs and limitations.
The Technology Acceptance Model (TAM)
Adopted originally from Fishbein and Aizen’s theory of reasoned action (TRA) and designed by Davis (1989) specifically to explain computer adoption and usage behavior (Dimitrova & Chen 2006, p. 174; Godoe & Johansen 2012, p. 39), TAM provides promising theoretical foundations for investigating the factors contributing to the acceptance of innovations, and has been effectively applied in customer behavior, innovation acceptance and system use, as well as in a variety of instances of human behavior (Al-adawi et al 2005, p. 4; Lean 2008). This model hypothesizes that an individual’s acceptance of technology is essentially determined by his/her voluntary intentions to use that technology, with the intention being determined by two beliefs namely (1) the perceived usefulness (PU) of using the innovation and (2) the perceived ease of use (PEU) of the new innovation (Zafiropoulos et al 2012, p. 531). These authors assert that PU is the user’s subjective probability that using a particular application system will enhance his or her job performance within an organizational context, while PEU is the extent to which the user expects the target system to be free of effort (p. 531). TAM is embedded in the justification that enhancements in ease of use of a system contribute substantially to enhanced usefulness due to saved effort, and therefore theorizes that PEU actually influences PU (Godoe & Johansen 2012, p. 39).
Figure 1 not only demonstrates TAM as originally designed by Davis (1989), comprising six dimensions namely PU, PEU, attitude, behavioral intention as well as actual usage, but also shows the causal flow from beliefs, attitudes, and intentions to behaviors (Shyu & Huang 2011, p. 493). The flow demonstrates that user behavior is determined by behavioral intention, which is influenced by attitude and PU, and that PU and PEU of an innovation determine attitude. It is also demonstrated in the literature that external variables, depending on technology, context, and users, influence perceptions of usefulness and ease of use (Shyu & Huang 2011, p. 493). Following Al-Gahtani (2011, p. 54), it can be ascertained that the main building blocks of TAM are prominent beliefs, which are then employed to establish and consequently determine a user’s intentions and behavior. Overall, the practical utility of considering TAM in the present study is nested on the justification that e-government is profoundly technology-driven.
Available literature demonstrates that TAM has not only received substantial support over the years, especially in business and IS research, but has also been validated over a broad range of systems, with PU and PEU proving to be reliable and valid cognitive dimensions of the model (Godoe & Johansen 2012, p. 39). However, TAM has some criticisms and challenges. Legris et al (2003), cited in Alshawi & Alalwany (2009, p. 197), claim that although the model is useful in studying user acceptance of technology, it often provides inconsistent results and suffers from the absence of important factors, including considering both human and social change processes. This view is corroborated by Shareef et al (2009, p. 19), who suggest that TAM cannot capture and stipulate the comprehensive essence of the e-government adoption behavior of citizens. Davis (1989, p. 78) notes that the adoption models so far discussed in scholarly works are essentially conceptual because comprehensive empirical studies among the actual users to validate and generalize the models are lacking. Furthermore, as reported by Shareef et al (2009, p. 20) some scholars such as Carter & Belanger (2004) have failed to identify any meaningful correlation between the adoption of new technology and perceived usefulness. Lastly, it is generally felt that while the model is largely applicable in multiple contexts involving the adoption of new technologies, it nevertheless fails to provide adequate information concerning the opinion of users about novel systems (Shyu & Huang 2011, p. 494).
Unified Theory of Acceptance and Use of Technology (UTAUT)
A product of Venkatesh et al (2003), the UTAUT model was designed by combining constructs from existing technology adoption models ( theory of reasoned action, TAM, the motivational model, theory of planned behavior, a model incorporating TAM with the theory of planned behavior, the model of PC utilization, innovation diffusion theory, and social cognitive theory) with the view of overcoming their limitations (Al-Shafi & Weerakkody 2010, p. 5-6; Lean 2008, p. 19), and that it is theorized in four constructs, namely performance expectancy, effort expectancy, social influence, and facilitating conditions Ovais et al (2013, p. 226). The four dimensions, according to these authors, play a substantially important function as express determinants of user acceptance and adoption of technological innovation and services, including in e-government contexts. Venkatesh et al (2003), cited in Ovais et al (2013, p. 230), argue that “UTAUT provides 70 percent of the variance in intention to use technology, which is more effective than previously known models.” The model was tested by its designers in four different organizational settings and its constructs were found to demonstrate significant predicts intention (Al-Shafi & Weerakkody 2010, p. 6).
Ovais et al (2013, p. 230-231) say, “Performance expectancy is the degree to which one believes that using the system will help him or her to achieve gains in job performance, while effort expectancy is the degree of ease associated with the use of the system.” Figure 2 below represents a simplified version of UTAUT adopted from Ovais et al (2013, p. 231). From the figure, a conclusion can be drawn that the relationship between facilitating conditions and behavioral intention is not specified by the UTAUT (Rana et al 2012, p. 40) and that the most important component of the model is the relationships between use intention and two independent variables, namely performance expectancy and effort expectancy (Al-Shafi & Weerakkody 2010; Ovais et al 2013).
Although UTAUT is considered an improved model with parsimonious and vigorous characteristics that could be employed to explain the factors influencing an individual’s intention and usage (Lean 2008, p. 19), it is nevertheless criticized in some quarters, as nearly all the scholars who apply this model in studies related to e-government fail to take into consideration the fundamental importance of citizen’s readiness in the usage process. Venkatesh et al (2012, p. 78) say, “Those who developed the model emphasized that an important difference between a consumer use setting and the organization use setting, where UTAUT was developed, is that consumers always bear the monetary cost of such use while employees do not.” By their own admission, the designers of UTAUT also acknowledge that performance expectancy cannot be the only driver in evaluating consumer technology acceptance and use, hence the need to include other drivers such as hedonic motivation and price value (Tung & Rieck 2005, p. 419).
Technology Adoption Factors
This section attempts to outline and discuss the factors that are considered important for the successful adoption and use of new technological innovations, especially among individuals. These are factors that would motivate the adoption of technology in governance. Adoption factors, according to Ziemba et al (2013, p. 89), are areas, constructs, and operations which should be focused on principally, with the view of achieving the most satisfying results in technology adoption. Below are the technology adoption factors.
The Perceived Usefulness
According to Dimitrova and Chen (2006, p. 176), unless people perceive the personal need to utilize e-government services, it is not likely that they may change their behavior. Citing Davis (1989), Kumar et al (2007, p. 70) argue that PU can be operationalized not only as of the extent to which the new technology assists users to do the work more quickly but also as an improvement in job performance with the view to increasing productivity and effectiveness.
Perceived Ease of Use
In the IS literature, perceived ease of use (PEU), also referred to as usability, is described as the extent to which using an innovation would be free of effort (Venkatesh et al 2012, p. 119). As demonstrated by these authors, PEU is a key characteristic of online services and a critical driver of technology adoption and use, in large part because it is the most important factor in which users assess innovation and the most essential determinant of service quality and user satisfaction. TAM proposes that the higher the perceived usefulness of the new technology, the more likely it is to be adopted by the consumer (Dimitrova & Chen 2006, p. 174-175). However, Kumar et al (2007, p. 70), citing Gefan (2000), notes that PEU is a predominant factor if people use the new technology only to get information, but if the innovation is used for transaction intentions then the PEU might not be able to influence the rate of user adoption. In the e-government context, a transactional e-government service necessitating multifaceted transactions to operate may likely be viewed by users as being complex and, consequently, limit adoption and use of the service due to poor usability.
Performance Expectancy
This is the degree to which individuals believe that using the system will help them improve their job performance and contain five variables: performance expectancy, extrinsic motivation, job-fit, relative advantage, and outcome expectations (Al-Shafi & Weerakkody 2010, p. 6). Available literature demonstrates that many individuals would be willing to adopt and use a new innovation if they are guaranteed that it will help them improve performance (Susanto & Goodwin 2010, p. 64), hence making performance expectancy a strong predictor of individual behavior (Chang & Kannan 2006). From the e-government perspective, this construct facilitates citizens to access the required information with speed and at a time and place of their expediency.
Effort Expectancy
This is the degree of ease associated with the use of the system; effort expectancy is made up of perceived ease of use, complexity, and actual use (Al-Shafi & Weerakkody 2010, p. 6). In the e-government context, this construct to a large extent influences the citizen’s adoption behavior and attitude towards usage by demonstrating if e-government services are easy to use or not, how the citizen interrelates with the service, and if the innovation is cost-effective or not (Lai & Pires 2010, p. 36).
Social Influence
Al-Shafi and Weerakkody (2010, p. 6) describe that as the level to which peers may influence the use of a system either positively or negatively. Available literature demonstrates that, In UTAUT, this factor is developed from concepts of other existing models such as social influence, social factors and image, and plays a fundamental role in determining the acceptance, adoption, and usage behavior of new users of technological innovations (Mahadeo 2009, p. 394; Suki & Ramayah 2010, p. 398).
Facilitating Conditions
Al-Shafi and Weerakkody (2010, p. 6-7) describe this as the level to which people believe that technical infrastructure exists to support a system. It is evident from the existing technology adoption literature that facilitating conditions consist of three fundamental constructs, namely perceived behavioral control, facilitating conditions, and compatibility (Mahedeo 2009, p. 395-396), and that facilitating conditions are a direct predictor of the actual usage of the technology as they provide the required environment, equipment, and assistance (Rehman et al 2012, p. 260).
Behavioral Intention
Behavioral intention, which is a central component in the TAM, is defined as a person’s subjective probability that s/he will perform some behavior (Al-adawi et al 2005, p. 4). Research studies following TAM such as Carter & Belanger (2003) dependably demonstrate a positive relationship between intentions to use a particular technology and actual use. Venkatesh et al (2012, p. 120) assert that this construct not only captures the motivational aspects that influence behavior towards innovation but also signifies how hard individuals are willing to try and how much effort they are willing to apply with the view to performing the behavior.
Availability of Resources
This adoption factor reflects the level to which users of new technology need to expend effort on obtaining the prerequisite resources to make use of the innovation (Venkatesh et al 2012, p. 119). Shareef et al (2009, p. 20) argue that a country must avail all the necessary skills and resources required for using new technology to its citizens if it expects the same propensity from citizens to adopt and use that innovation. Venkatesh et al (2012, p. 119) argue that computer resources, including software and hardware, are essential if users are to have the capacity to adapt and use innovation. As demonstrated by these authors, the ease of securing software and hardware upgrades is bound to facilitate the adoption and use of technology, while the absence of facilitating resources represents barriers to adopting and using the innovation.
Website Design
In the e-government context, website design influences both PU and PEU, which in turn influences the user’s behavioral intention and use behavior (Bwalya 2009, p. 10). Kumar et al (2007, p. 70) assert that designing a website that will encourage adoption requires consideration of elements such as ease of navigation, aesthetics, content, accessibility, and features such as personalization, customization, customer self-care, and communities. As posited by these authors, these components in combination directly manipulate the user’s experience with the website and, eventually, their gratification, adoption, and usage.
Conclusion
This section has successfully dealt with technology adoption and, in most instances, located the discussion in the context of e-government adoption. The section has provided several definitions of e-government adoption, and also analyzed the differences between readiness and adoption. Additionally, the section has comprehensively illuminated the most widely used technology adoption models (TAM and UTAUT), including their constructs and weaknesses. Finally, technology adoption factors have been carefully illuminated in line with existing technology adoption literature.
Reference List
Ahmad, MO, Markkula, J & Oivo, M 2013, ‘Factors affecting e-government adoption in Pakistan: A Citizen’s perspective’, Transforming Government: People, Processes and Policy, vol. 7 no. 2, pp. 225-239.
Al-adawi, Z, Yousafzai, S & Pallister, J 2005, ‘Conceptual model of citizen adoption of e-government’, The Second International Conference on Innovations in Information Technology.
Al-eryani, A & Rashed, A 2012, ‘The impact of culture on the e-readiness for e-government in developing countries (Yemen)’, The 13th International Arab Conference on Information Technology. Web.
Al-Gahtani, SS 2011, ‘Modeling the electronic transactions acceptance using an extended technology acceptance model’, Applied Computing and Informatics, vol. 9, pp 47-77.
Al-Ghaith, W, Sanzogni, L & Sandhu, K 2010, ‘Factors influencing the adoption and usage of online services in Saudi Arabia’, The Electronic Journal on Information Systems in Developing Countries’, vol. 40 no. 1, pp. 1-32.
Alghamdi, IA, Goodwin, R & Rampersad, G 2011, ‘E-government readiness assessment for government organizations in developing countries’, Computer and Information Science, vol. 4 no. 3, pp. 3-17.
Al-Shafi, S & Weerakkody, V 2010, ‘Factors affecting e-government adoption in the state of Qatar’, The European and Mediterranean Conference on Information Systems, Abu Dhabi, UAE, 2009. Web.
AlShawi, S & Alalwany, H 2009, ‘E-government evaluation: Citizen’s perspective in developing countries’, Information Technology for Development, vol. 15 no. 3, pp. 193-208.
Alshehri, M & Drew, S 2010, ‘Challenges of e-government services adoption in Saudi Arabia from an e-ready citizen perspective’, World Academy of Science, Engineering and Technology, vol. 66, pp. 1053-1059.
Alshehri, M, Drew, S & Alfarraj, O 2012, ‘A comprehensive analysis of e-government services adoption in Saudi Arabia: Obstacles and challenges’, International Journal of Advanced Computer Science and Applications, vol. 3 no. 2, pp. 1-6.
Al-Shehry, A, Rogerson, S, Fairweather, NB & Prior, M 2006, The motivations of change towards e-government adoption: Case studies from Saudi Arabia. Web.
Bwalya, KJ 2009, ‘Factors affecting adoption of e-government in Zambia’, The Electronic Journal on Information Systems in Developing Countries’, vol. 38 no. 4, pp. 1-13.
Carter, L & Belangar, F 2003, ‘The influence of perceived characteristics on innovating on e-government adoption’, Electronic Journal of E-government, vol. 2 no. 1, pp. 11-20.
Chang, AM & Kannan, PK 2006, ‘Employee technology readiness and adoption in wireless technology and services’, Proceedings of the 39th Hawaii International Conference on System Sciences. Web.
Dada, D 2006, ‘E-readiness for developing countries: Moving the focus from the environment to users’, The Electronic Journal on Information Systems in Developing Countries, vol. 27 no. 6, pp. 1-14.
Davis, F 1989, Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, vol. 13, pp. 983-1003.
Dimitrova, DV & Chen, YC 2006, ‘Profiling the adopters of e-government information and services: The influence of psychological characteristics, civic mindedness, and information channels’, Social Science Computer Review, vol. 24 no. 2, pp. 172-182.
Doong, HS, Wang, HC & Foxall, GR 2010, ‘Psychological traits and loyalty intentions towards e-government services’, International Journal of Information Management, vol. 30, pp. 457-464.
Ebrahim, Z & Irani, Z 2005, ‘E-government adoption: Architecture and barriers’, Business Process Management, vol. 11 no. 5, pp. 589-611.
Elsheikh, Y 2011, A model for the adoption and implementation of web-based government services and applications, Unpublished PhD Thesis, University of Bradford.
Farooquie, JA 2011, ‘A review of e-government readiness in India and UAE’, International Journal of Humanities and Social Sciences, vol. 1 no. 1, pp. 6-13.
Godoe, P & Johansen, TS 2012, ‘Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept’, Journal of European Psychology Students, vol. 3, pp. 38-52.
Kovacic, Z.J 2005, ‘The impact of national culture on worldwide eGovernment readiness’, Informing Science Journal, vol. 8, pp. 143-158.
Kumar, V, Murkerji, B, Both, I, & Persuad, A 2007, ‘Factors for successful e-government adoption: A conceptual framework’, The Electronic Journal of E-government, vol. 5 no. 1, pp. 63-76.
Lai, CS & Pires, G 2010, ‘Testing of a model evaluating e-government portal acceptance and satisfaction’, The Electronic Journal Information Systems Evaluation, vol. 13 no. 1, pp. 35-46.
Lean, OK 2008, Factors influencing intention to use e-government services among citizens in Malaysia, Unpublished Masters Thesis, University of Malaysia.
Mahadeo, J 2009, ‘Towards an understanding of the factors influencing the acceptance and diffusion of e-government services’, Electronic Journal of e-Government, vol. 7 no. 4, pp. 391-402.
Mofleh, SI & Wanous, SM 2008, Understanding factors influencing citizen’s adoption of e-government services in the developing world: Jordan as a case study. Web.
Rana, NP, Williams, MD, Dwivedi, YK & Williams, J 2012, ‘Theories and theoretical models for examining the adoption of e-government services’, e-Service Journal, vol. 8 no. 2, pp. 26-56.
Reddick, CG 2004, ‘Empirical models of e-government growth in local governments’, e-Service Journal, vol. 3 no. 2, pp. 59-84.
Reddick, CG 2005, ‘Citizen Interaction with e-government: From the street to servers’, Government Information Quarterly, vol. 22, pp. 38-57.
Rehman, M, Esichaikul, V & Kamal, M 2012, ‘Factors influencing e-government adoption in Pakistan’, Transforming Government: People, Processes and Policy, vol. 6 no. 3, pp. 258-282.
Shareef, MA, Kumar, V, Kumar, U & Dwivedi, YK 2011, ‘E-government adoption model (GAM): Differing service maturity levels’, Government Information Quarterly, vol. 28, pp. 17-35.
Sharma, G, Bao, X & Qian, W 2012, ‘Empirical investigation on adoption of e-governance services in developing countries and ethical issues’, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, no. 12, pp. 19-27.
Shyu, SHP & Huang, JH 2011, ‘Elucidating usage of e-government learning: A perspective of the extended technology acceptance model’, Government Information Quarterly, vol. 28, pp. 491-502.
Suki, NM & Ramayah, T 2010, ‘User acceptance of the e-government services in Malaysia: Structural equation modeling approach. Interdisciplinary Journal of Information, Knowledge, and Management, vol. 5, pp. 395-413.
Susanto, TD & Goodwin, R 2010, ‘Factors influencing citizen adoption of SMS-based e-government services’, Electronic Journal of E-government, vol. 8 no. 1, pp. 55-71.
Tung, LL & Rieck, O 2005, ‘Adoption of electronic government services among business organizations in Singapore’, Journal of Strategic Information Systems, vol. 14, pp. 417-440.
Venkatesh, V, Thong, JYL & Xu, X 2012, ‘Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology’, MIS Quarterly, vol. 36 no. 1, pp. 157-178.
Wangpipatwong, S, Chutimaskul, W & Papasratorn, B 2008, ‘Understanding citizens’ continuance intention to use e-government website: A composite view of technology acceptance model and computer self-efficacy’, The Electronic Journal of e-Government, vol. 6 no. 1, pp. 55-64.
Zafiropoulos, K, Karavasilis, I & Vranna, V 2012, ‘Assessing the adoption of e-government services by teachers in Greece’, Future Internet, vol. 4, pp. 528-544.
Ziemba, E, Papaj, T & Zelazny, R 2013, ‘A model of success factors for e-government adoption – The case of Poland’, Issues in Information Systems, vol. 14 no. 2, pp. 87-100.