Keywords

• e-Government (e-Gov); 
• Information and communication technology (ICT); 
• Adoption;
• Citizens; 
• Service maturity levels

1. Introduction

As a new and rapidly growing field, the concepts and theories of e-Government (e-Gov) are still in a premature stage. Researchers from different disciplines address this phenomenal theme from their respective speculations and conceptualize it in a scattered fashion (Heeks & Bailur, 2007 [8]). e-Gov has several aspects, including social, technical, economic, political, and public administrative. However, most dominating concepts of e-Gov arise from the technical perspective and a combination of the socio-economic and public administrative perspectives. Nevertheless, all the definitions are headed towards a single notion and encompass a generic and unique mission of e-Gov—presenting government systems using information and communication technology (ICT) to serve citizens better (Al-Mashari, 2007 [9], Evans & Yen, 2006 [10], Gil-Garcia & Martinez-Moyano, 2007 [11], Reddick, 2006 [12], Shareef et al., 2009 [13] and Sprecher, 2000 [14]).

Though different countries' e-Gov implementations extensively differ in setting common missions and objectives, all of them contain the similar fundamental essence of e-Gov value: it should be citizen focused. Therefore, it may be significant to observe that the most important tool for implementation of e-Gov is the willingness of citizens to adopt it (Evans & Yen, 2006 [10] and Shareef et al., 2009 [13]). While there is evidence for substantial growth, development, and diffusion of e-Gov universally, it is not clear whether citizens of all developed and developing countries are ready to embrace those services (Carter & Bélanger, 2005 [15]). The acceptance, diffusion, and success of e-Gov initiatives are contingent upon citizens' willingness to adopt these services.

Reviewing the existing literature on e-Gov adoption by citizens and business organizations (Al-Adawi et al., 2005 [16], Chen & Thurmaier, 2005 [17], Ebrahim & Irani, 2005 [18],Gilbert et al., 2004 [19], Klievink & Janssen, 2009 [20], Kumar et al., 2007 [21], Phang et al., 2005 [22],Reddick, 2004 [23], Sakowicz, 2007 [24], Schedler & Summermatter, 2007 [25], Shareef et al., 2009 [13],Tung & Rieck, 2005 [26] and Wang & Liao, 2008 [27]), we can infer that the adoption models offered so far in the academic literature are mainly conceptual. Extensive empirical studies among the actual users to validate and generalize the models are absent. Most of those who have attempted to validate their models did not rigorously review the literature and integrate discourses from technical, social, organizational, political, and cultural perspectives to develop their ontological and epistemological paradigms of model validation doctrine. As identified by Heeks and Bailur (2007) [8] through an extensive literature review of e-Gov, methodologically these models are not grounded on a strong theoretical framework. While developing those models of adoption, the generalization aspect is heavily ignored (Heeks & Bailur, 2007 [8]).

Despite the potentially significant impacts of e-Gov systems on public administrations, organizations, individuals, and society, so far only a few systematic and thorough studies have been undertaken on the subject to comprehensively integrate overall factors related to the successful implementation of e-Gov (Jaeger, 2003 [28] and Kraemer & King, 2003 [29]). However, citizens' behavior in terms of adopting a new technology-driven system is a very complex and robust subject. It is expected that extensive research will focus on criteria necessary for citizens to adopt technology that will enable successful implementation of e-Gov. Understanding and estimating the effect of citizens' adopting criteria, which leads to successful implementation of e-Gov, would have important managerial implications. Therefore, this research attempts to investigate the users' requirements for the adoption of e-Gov and sets the first objective:

1.

To identify and conceptualize the critical factors that affect citizens to adopt e-Government.

Implementation and successive upgrading of the e-Gov system follow certain paths, levels of maturity, stages, or phases. Different countries implementing e-Gov in their ICT framework certainly have different missions and objectives; however, the gradual development of an e-Gov system in any country follows some unique levels of service maturity for evolution. Each of the service levels represents a different service pattern, different levels of technological sophistication, different stakeholder orientation, different types of interaction, different security requirements, and different reengineering processes (Holden et al., 2003 [30], Moon, 2002 [31] and Dorner, 2009 [32]). It can also be inferred that these levels describe the development of maturity of service in a sequential manner.

Based on the conceptualization of service development stages of e-Gov by different researchers (Accenture, 2003 [33], Andersen & Henriksen, 2006 [34], Evans & Yen, 2006 [10], Fang, 2002 [35], Klievink & Janssen, 2009 [20] and Layne & Lee, 2001 [36]), we define levels of service maturity of e-Gov as the pattern of service that a government develops, successively enhances interactivity, and delivers for stakeholders' acceptance and usage with upgrading of technological sophistication and functional characteristics. Since this research is engaged in developing adoption concepts of e-Gov by citizens at different levels of service maturity, we will put more attention into the first two functionally different levels of maturity of service development of e-Gov: the static stage and the interaction stage ( Bélanger & Carter, 2005 [37], Chandler & Emanuels, 2002 [38] and Howard, 2001 [39]). The reason behind this is that these two levels are widely developed in most of the countries. The third level is described as transaction stage. However, most of the countries are still struggling to attain this e-Gov service level, so this stage is not considered for developing any comprehensive model. The next stages of service maturity, such as vertical integration and horizontal integration, are also not very important for this research, as these stages are not fully achieved by most of the countries so far. Most countries have failed to realize horizontal stage of e-Gov universally across all public services in their countries.

From the end users' perspectives, the two stages of services have significant differences in characteristics and functionality (Gottschalk, 2009 [40]). In the publishing or static stage, stakeholders can only view and collect government information or download some forms and publications. This is one-way communication. Here the user cannot communicate with the government service system through this interface and the government authority does not respond to the user electronically (Accenture, 2005 [41]). In the next maturity level of service—the interaction phase—two-way communication is established. Through the government web page, at this stage, stakeholders can contact service providers to resolve any issues in different electronic ways, such as sending e-mails, using chat-room, etc. (Accenture, 2005 [41]).

However, differentiating and defining these two stages as gradual service maturity of e-Gov do not mean that citizens use or adopt these stages of e-Gov sequentially, i.e., first static level and then interact level. They can simply skip any beginning level and start adopting e-Gov from the next matured level. It can be predicted that the various development levels of e-Gov might differ in pursuing the intention to adopt e-Gov for its successful implementation. Static and interaction levels especially offer different modes of service with different levels of association of technology. As a result, adoption criteria for different stages by citizens might have significant implications. However, no literature so far has investigated these criteria while exploring adoption models for e-Gov. To investigate the users' requirements for the adoption of e-Gov at different levels of service maturity (not like Layne and Lee, 2001 [36] who look at organizational growth), this research paper sets its second objective as:

2.

Are these critical factors that affect citizens' adoption of e-Government different at different levels of service maturity?

2. Design perspective

While investigating and revealing the theoretical perspectives of independent variables of e-Gov adoption, this research explores two major fields as theoretical background. The first attempt is engaged in reviewing the surrounding areas of literature addressing e-Gov adoption, implementation, characteristics, and related issues. The second attempt is the extensive cultivation of theories related to technology adoption, public administration and organization, psychology, sociology, political science, culture, and marketing. Several researchers who have done a comprehensive literature synthesis on different e-Gov issues—including e-Gov adoption by end users—asserted that e-Gov implementation and adoption concepts have a significant lack of theoretical synopsis (Heeks & Bailur, 2007 [8], Titah & Barki, 2005 [42] and Wang & Liao, 2008 [27]).

Different researchers (Al-Shehry et al., 2006, Sept. 11 [43], Chen & Thurmaier, 2005 [17] and Kumar et al., 2007 [21]) emphasized that e-Gov adoption is more than a technological matter as it is influenced by many factors, including organizational, human, economic, social, and cultural issues. These perspectives provide important speculations for analyzing the e-Gov structure that reflects government nature and its responsibility in society (Carter & Bélanger, 2004 [44] and Moon & Norris, 2005 [45]). In addition, the adoption of e-Gov systems requires analyzing the changes in social values over time (Ebrahim & Irani, 2005 [18]). Steyaert (2004) [46] adopted a marketing perspective to analyze e-Gov performance. He proposed an e-Commerce (EC)-based performance model to evaluate e-Gov performance in terms of citizen satisfaction. Parent et al., 2005 [47] and Warkentin et al., 2002 [48] investigated the effect of trust on the adoption of e-Gov.Gilbert et al. (2004) [19] proposed the integration of the service quality, technology, and behavioral aspects of the e-Gov adoption framework. Shareef et al., 2007 [49] and Shareef et al., 2009 [13] investigated technological, behavioral, economic, and service quality aspects of e-Gov adoption criteria in formulating and validating a framework of e-Gov adoption.Titah & Barki, 2005 [42] and Phang et al., 2005 [22] reviewed the adoption literature of e-Gov extensively and they suggested that technological, organizational, social, cultural, behavioral, and economic aspects should be considered in a comprehensive framework of e-Gov adoption. Therefore, from our literature review, we perceive that technological, behavioral, social, cultural, organizational, economic, political, and marketing aspects might provide important insights while investigating explanatory variables for e-Gov adoption.

To delineate the theoretical paradigms, this research looks at the core characteristics of e-Gov. We find that e-Gov is affected by several issues like, organizational reformation, cultural revolution, habitual change, technology adoption, information and service modification, speed of service, accessibility, availability, more participation, transparency, cost effectiveness, democratization, and globalization (Evans & Yen, 2006 [10], Kim et al., 2009 [50], Robin et al., 2009 [51], Titah & Barki, 2005 [42], Turner & Desloges, 2002 [52] and Wang & Liao, 2008 [27]). Since e-Gov is a revolutionary reformation of organizational structure and characteristics, its adoption might have close ties with organizational attributes. e-Gov offers enormous benefits to its end users, which include economic incentives and service improvement. Therefore, marketing and economic behavior reflects citizens' preferences in adopting e-Gov. Transaction cost analysis (TCA) also sheds light on these perspectives of e-Gov adoption criteria. From the core principle of TCA, the motivation for behavioral intention to interact with different organizational structures is significantly influenced by economic parameters (Shelanski & Klein, 1995 [53]). According to the theory of planned behavior (TPB) (Ajzen, 1991 [54]) and the theory of reasoned action (TRA) (Ajzen & Fishbein, 1980 [55]), social and cultural values affect beliefs and attitudes and the adoption of e-Gov operated through ICT. Beliefs and attitudes about e-Gov lead to formation of behavioral intention to learn, accept, and use e-Gov systems. Therefore, behavioral or attitudinal aspects of citizens are very important in stimulating an adoption framework of e-Gov. If we translate the core doctrine of socio-technical theory, which explains the effect of social and technological aspects on a system, we get thorough insights into integrating the social, organizational, and technological aspects of the e-Gov adoption (Damodaran, Nicholls, & Henney, 2005 [56]).

However, these perspectives, where we have concentrated our investigation of critical factors for the adoption framework of e-Gov, are not mutually exclusive phenomena. These are interrelated issues. Adoption perspectives of e-Gov by different stakeholders at different levels of service maturity of e-Gov are intertwined with different explanatory variables. Therefore, our investigation for identifying critical factors of e-Gov will not track those perspectives of e-Gov adoption factors separately; rather, we will look for interdependent and comprehensive effects. We will connect technological, behavioral, social, cultural, organizational, economic, political, and marketing aspects of consumers to develop a comprehensive e-Gov adoption model.

3. Theoretical framework

As we mentioned in the Introduction [57] section, the existing literature on e-Gov has failed to present a comprehensive framework of e-Gov adoption and performance at different phases of service maturity of e-Gov implementation. Therefore, although, we are attempting to identify all of the constructs from our detailed literature review in conjunction with the insight from different theories related to technology adoption, diffusion, and behavioral, social, and cultural characteristics, the adoption behavior of e-Gov is in a very premature stage (Heeks & Bailur, 2007 [8]). Consequently, this study has potentially an exploratory nature. It means, we are conducting this research not to test any specified theory of e-Gov adoption, rather we are conducting this research in the hope of developing a theory of the adoption of e-Gov at different service maturity levels. So, as we are advancing from theory development to statistical analysis, we should continue to refine our exogenous variables and also hypotheses to develop our final paradigms of adopting e-Gov at different service maturity levels. For an exploratory study, this refinement of variables and hypotheses is typical and also a part of the theory development process (Stevens, 1996 [58]).

3.1. Explanatory variables

According to information management principles for open government adoption, a prime factor for adoption is creating awareness among the stakeholders. This means informing the citizens about the transformation of public administration, implementation of innovation, basic paradigms of the new system, application of ICT, objectives and mission of e-Gov development, comprehensive information about relative advantages and disadvantages of e-Gov, and the overall credibility of the system. A long history of government service shows that citizens and business organizations are traditionally habituated to use brick and mortar government services for information collection, interaction, and all types of transactions that are basically operated offline. The history of e-Gov evolution is very new. Basically, most of the countries are just at the beginning efforts of implementing e-Gov. Therefore, stakeholders are still not very aware of this new innovation of the government system. As we learned from the TPB and TRA, beliefs about a system turn to the attitude of using the system. However, awareness of the system is important at the beginning to develop beliefs (Limayem, Hirt, & Cheung, 2007 [59]).

Before developing an attitude to adopt e-Gov, stakeholders need to be aware of its complete characteristics, including the background of the system, functional behavior, strategic benefits, the safety and legal environment, etc. Awareness of e-Gov has several different aspects: political, marketing, behavioral, and social. When citizens are aware of the political agenda of e-Gov, social values related to the strategic implementation of e-Gov, service quality, and the competitive advantages of e-Gov—i.e., the marketing paradigm and attitudinal or behavioral motivation of the EG system—they might then have an intention to adopt the e-Gov system. Several researchers asserted awareness as the significant independent variable to create the attitude to use an e-Gov system (Eggers, 2004 [60] and Parent et al., 2005 [47]). So we categorize this predicted variable for adoption as the “Attitude” to use, since awareness is the primary stimulus of creating attitude. Depending on the previous arguments, this research proposes:

From TPB, diffusion of innovation theory (DOI), and TCA, a user will not arrive at an intention to use an EG system, which requires computer knowledge to get a competitive advantage, unless the user has competence from experience in the use of modern ICT. From technological, behavioral, economic, and organizational perspectives, it is anticipated that failing to get hands-on experience of technology will not create in the user an attitude favorable to adopting the system. Also, in the absence of computer knowledge, a user cannot perceive the economic advantages of e-Gov. The organizational structure of e-Gov is computer- and internet-based, while from the viewpoint of end users, traditional government services do not require computer knowledge. Therefore, from organizational perspectives, computer self-efficacy is an important predictor of whether a user will adopt an e-Gov system instead of using traditional government services.

Several researchers exploring the barriers of adoption of e-Commerce and e-Gov revealed that users' computer self-efficacy and experience of the internet, ICT, and computers create a perception of security in the users' attitude toward using online systems (Moon & Norris, 2005 [45], Tung & Rieck, 2005 [26] and Van Dijk et al., 2008 [61]) that affects their intention to use. Wang (2002) [62] investigated the relation of technology availability and computer self-efficacy with behavioral intention to adopt an online tax filing system and observed a positive relation. Computer self-efficacy is concerned not with the skills one has but with judgments of what one can do with whatever skills one possesses. Therefore, we categorize this explanatory variable of e-Gov adoption as the “Attitude” to use.

Including others, the most important and dominating barriers for adopting e-Gov, particularly in developing countries are scarcity of electricity, telephones, computers, the internet, and related accessories, and government supports like call-center, resource-center, and cyber-cafés. If we examine the capability theory (Nussbaum & Sen, 1993 [63]), it highlights that citizens' use of e-Gov systems is limited unless they have the freedom of utility required to use modern ICT-based e-Gov. If a country cannot make the skills and resources required for using e-Gov available to all citizens equally, the country cannot expect the same capability from all citizens to adopt that system. Therefore, without reducing the digital divide, promoting equality in resources of using e-Gov and making available all components of an e-Gov system and knowledge, the adoption of e-Gov will not be successful. The availability of resources required for the use of e-Gov has behavioral, economic, cultural, social, and technological aspects. Generally, where computers, internet, and modern ICT are not available, the citizens are economically poor, less educated, unaware of modern technology, socially and culturally unfamiliar with modern technology, and lack the necessary skills to use technology. As a result, they also do not believe that they will receive benefits by using an e-Gov system. Therefore, there is an obvious relation between availability of resources and the adoption of e-Gov (van Dijk et al., 2008 [61]). We argue that AOR creates a belief in using an e-Gov system operated through ICT, which, in turn, creates an attitude to use e-Gov. Therefore, we categorize the construct AOR as the “Attitude” to use e-Gov. Drawing a conclusion from the above arguments, we propose here:

The technology acceptance model (TAM) proposes that perceived ease of use (PEOU) and perceived usefulness (PU) determine the attitude toward adoption of ICT. This behavioral attitude, in turn, leads to the intention to use ICT and the final acceptance of the system (Bhattacherjee, 2001 [64], Davis et al., 1989 [65], Lucas & Spitler, 1999 [66], Moon, 2002 [31] and Venkatesh, 2000 [67]). e-Gov fails if the users do not have the ability to use the technology to access useful information and services, and eventually do not perceive e-Gov as useful. This would lead to a non-acceptance of the system by citizens (Shareef et al., 2007 [49]). Numerous scholarly articles (Evans & Yen, 2006 [10], Gil-Garcia & Martinez-Moyano, 2007 [11] and Shareef et al., 2009 [13]) revealed that PEOU and PU are potential indicators of user acceptance, adoption, and motivation to use web services. However, some researchers (Carter & Bélanger, 2004 [44]) did not find any significant relation between the adoption of e-Gov and PU. A plausible explanation lies in the logic that inclusion of relative advantage as a predicted variable in the adoption model explains enough of PU in the adoption construct. So, at this stage, we will provide further insight into PU and relative advantage for inclusion in the adoption model as the predictor variables.

According to the DOI theory (Rogers, 1995 [68]), the rate of diffusion is affected by an innovation's relative advantage, complexity, compatibility, trialability, and observability. Literature reviews suggest that among those five constructs, relative advantage, compatibility, and complexity are the most relevant constructs to determine the adoption characteristics of technology innovation (Gilbert et al., 2004 [19], Moore & Benbasat, 1991 [69],Rogers, 1995 [68] and Tornatzky & Klein, 1982 [70]). These authors, especially Tornatzky and Klein (1982) [70] in their meta-analysis of research on the adoption of innovations, argue that trialability and observability are not related constructs for technology adoption. Therefore, in this study we are not considering these two constructs.

Complexity, comparable to TAM's PEOU construct, captures the perception of some pre-use complexities that seem to have very close relation to the perceptions of complexities in using modern ICT, internet, and computers. Therefore, we comprehend the generic essence of the construct complexity with PEOU and introduce perceived ability to use (PATU) as the predictor of adoption to reflect other aspects of e-Gov above and beyond technology. The construct PATU has technological and organizational perspectives. Due to revolutionary reengineering of the traditional government system, the perception of online organizational structure, which is apparently new, is an important aspect of the perceived ability to use the system. Technology is a very predictable aspect to get insight into PATU. We argue that PATU reflects the ability of citizens to use an e-Gov system and categorize it as the “Ability” to use for e-Gov adoption.

Compatibility construct has cultural, behavioral, and social aspects. It is dependent both on individual characteristics such as avoiding personal interaction, and social influence. Several researchers indicated that specific characteristic of e-Gov that allow citizens to avoid personal interaction might create the perception of compatibility among citizens to adopt an e-Gov system (Gilbert et al., 2004 [19]). Shedding light on the TPB, TRA, and capability theory, the compatibility of an e-Gov system with adopters' beliefs, values, and attitudes reflects the behavioral aspect. From the socio-technical and complementary theories, beliefs and attitudes of adopters of a new technology system also have social and cultural aspects. Several researchers use this construct as a significant predictor of EG adoption (Carter & Bélanger, 2004 [44], Chen & Thurmaier, 2005 [17] and Shareef et al., 2007 [49]). This research argues that PC creates citizens' attitudes to use an e-Gov system and thus we categorize this construct as the “Attitude” to use.

Relative advantage captures the gain from receiving services and information through e-Gov systems in comparison with that from traditional government offices. This definition expands the limited concept of PU of the TAM, which captures only the absolute benefits from job performance. However, by adopting e-Gov systems, a user can gain a lot of relative and absolute benefits ranging from effectiveness, efficiency, availability, accessibility from anywhere, comfort in use, time savings, cost savings, and convenience. The combined effects of the two constructs, therefore, basically capture the essence of absolute and comparable functional benefits of the e-Gov system. If we integrate both views of PU and relative advantage, we can introduce perceived functional benefit (PFB) as the predicted variable of e-Gov adoption. This construct has economic, organizational, marketing, behavioral, and social perspectives. If we look at the TCA, we can understand that PFB also captures the essence of time efficiency and price savings. Several researchers assumed that this time constraint characteristic is also typical in EG because it is rational to assume that citizens might adopt e-Gov systems as they save time to perform tasks relative to the functions of a traditional paper-based government office (Carter & Bélanger, 2005 [15], Gilbert et al., 2004 [19] and Wagner et al., 2003 [71]). Another construct, price savings, which is a measure of e-Gov efficiency in terms of reduction in service rendering cost, is also an overlapping concept of PFB (Tung & Rieck, 2005 [26]). This analysis shows that PFB captures the essences of the behavioral, economic, and marketing aspects of adoption. It also has an organizational aspect, because institutional theory asserts that actors will accept organizational change as long as they perceive it to be beneficial to them (Lawrence & Suddaby, 2006 [72]). We argue that PFB imparts reasons to use an e-Gov system, instead of using a traditional government system, and, thus, it is categorized in this research as the “Adherence” to adopt the use of e-Gov. We investigate the three dimensions PATU, PC, and PFB here since we assume that these might affect EG adoption. Therefore, we propose:

In addition, we also include a new construct, Image, as proposed by Moore and Benbasat (1991) [69]. According to the DOI, Image influences the acceptance and use of an innovation. Image refers to citizens' perceptions that adopting e-Gov makes them superior to others in the society. Interaction with e-Gov systems, instead of using traditional government offices, is perceived to give these citizens superior status. Several researchers have, therefore, included this construct in their proposed model of EG adoption (Gilbert et al., 2004 [19], Phang et al., 2005 [22] and Tung & Rieck, 2005 [26]). Since the adoption of e-Gov might reflect the adopters familiarity with modern technology, higher level of education, competence in using computers and the internet, and perception of modernism, these phenomena impart some degree of social values and prestige to adopters. Therefore, this research argues that Image has social, behavioral, and also cultural aspects and depends on an individual's personal behavioral ideology. We also argue that the construct perception of Image is a proponent of reasoning to use e-Gov and, thus, it is categorized in this research as the “Adherence” to use for e-Gov adoption. Therefore, we propose:

Content, organization, and presentation of information—i.e., information quality, which includes accuracy, current information, relevancy, fulfillment, linkage, completeness, integration, organization, timelines—are potential contributors to creating a perception of reliability that influences citizens to accept e-Gov (Collier & Bienstock, 2006 [73], Kim et al., 2006 [74], Kumar et al., 2007 [21], Parasuraman et al., 2005 [75] and Sebastianelli et al., 2006 [76]). The assurance and confidence in using e-Gov that information quality gives citizens is characterized in this research as the “Assurance” to use for e-Gov adoption. Gilbert et al. (2004) [19] conducted a survey in the UK among users of e-Gov that showed that information quality is a strong predictor of EG adoption. Another extensive empirical study conducted in Australia (AGIMO, 2003 [77]) revealed that there is an obvious expectation that information from government will be provided in accordance to fulfilling citizen needs rather than serving the convenience of government agencies. In order to ensure the success of an IS, DeLone & McLean, 1992 [78] and DeLone & McLean, 2003 [79] proposed the Information System Success Model (IS Model). The model asserted that information quality is the determinant of system use and user satisfaction that eventually leads to regular adoption (Wang & Liao, 2008 [27] and Wangpipatwong et al., 2005 [80]). Information quality asserts service requirements of consumers and the economic benefits of viewing and collecting information from a website in lieu of verbal interaction from traditional government offices. Therefore, information quality has a marketing aspect. Lin and Lu (2000) [81]asserted the conjecture that the features and accuracy of information posted on a website significantly affect users' behavioral attitude. Grounded on the aforementioned arguments, we propose:

Several e-Gov researchers address customer service as one of the important explanatory variables to satisfy customers and, thus, to ensure a recurring use of e-Gov (Lee & Rao, 2003 [82], Shareef et al., 2007 [49] and Wangpipatwong et al., 2005 [80]). Service quality is a strong predictor to differentiate performance of different organizations. From the behavioral point of view, recurring users of e-Gov will achieve beliefs and thus attitudes to adopt e-Gov if they perceive higher customer service in e-Gov. Traditional government service has a different approach than e-Gov. If citizens perceive a higher level of customer service in e-Gov than that offered in a traditional government office, they will purse the adoption of e-Gov. Kumar et al. (2007) [21] proposed that service quality leads to satisfaction that ensures regular use of e-Gov. Service quality can be considered from different dimensions, like technical or output quality, functional or process quality (Czepiel et al., 1985 [83], Grönroos, 1984 [84] and Lehtinen & Lehtinen, 1982 [85]), and direct customer service from employees (Shareef, Kumar, & Kumar, 2008 [86]). Since technical or output quality is already being assessed in PATU and PIQ and functional quality of the system is incorporated in PFB, we will only discuss here the customer service response to service quality. The reliability and assurance of service—which are intertwined with trust, security, privacy, and risk concepts of customer service—will also be discussed in a different section.

Due to the absence of any physical presentation, the service response of e-Gov has different aspects and properties. In e-Gov, the service response is generally assumed to be a recovery quality item. When there are problems or concerns, stakeholders always expect that customer service will resolve the problem promptly with complete sympathy. If citizens feel that they do not find any customer service in e-Gov when they require it, that they are being treated unfairly, or that the customer-oriented service policy of government websites is not credible, they are less likely to adopt e-Gov; rather they will go to a physical government office to seek services. A study in a developing country conducted by Shareef et al. (2009) [13] also found that service response has a significant effect on citizens' adoption of e-Gov. We argue that perceived service response (PSR) stimulates citizens' adaptability and satisfaction in using e-Gov and thus ensures recurring use. Therefore, this research categorizes the PSR construct as the “Adaptability” to use for e-Gov adoption. Based on the above arguments, we propose here:

In an online transaction, since different physical cues are absent, virtual transaction requires some extra facilities to perform transactions for individuals with different ethnic backgrounds. This criterion is especially very important for a country that comprises multicultural and multilingual groups. Additionally, e-Gov also has a global aspect. Considering these aspects, the multilingual option of e-Gov might enhance the adoption of e-Gov. The service quality research of EC also finds this factor to be an important cause for consumers to adopt a certain e-retailer's website (Collier & Bienstock, 2006 [73],Kim et al., 2006 [74], Parasuraman et al., 2005 [75] and Wolfinbarger & Gilly, 2003 [87]). Nantel, Sénécal, and Mekki-Berrada (2005) [88] conducted an empirical study to determine critical factors of online purchase. Their research captured a definite relation between the use of native language of a user in a website as the medium of instruction and information and adoption of the website by that user. If an individual can interact with a website using his/her primary language, he/she might feel more cultural connection and have a more positive attitude to use of that website. Through an extensive content analysis of 93 websites from local companies in China, India, Japan, and the USA, Singh, Zhao, and Hu (2005) [89] confirmed this statement. For citizens with less education, a single language option other than mother tongue for viewing, collecting, interacting, and transacting with e-Gov websites might create a significant barrier. This is an important cultural aspect (Michon & Chebat, 2004 [90]). If we borrow the speculations from the TAM and DOI, the relative advantage of e-Gov will trigger inclusion of a multilingual option in e-Gov web pages. However, beliefs in competence with an e-Gov interaction—which further promotes attitude towards the adoption of e-Gov according to the TPB—can be exaggerated if a known language option prevails in e-Gov websites. Therefore, the multilingual option has behavioral aspects (Foo, Hui, Leong, & Liu, 2000 [91]). If we look at the capability theory, the multilingual option in e-Gov might create equal and competitive capability, which ultimately enhances the economic power of minority stakeholders. Therefore, from an economic perspective, a multilingual option in e-Gov can create a level playing field for major stakeholder groups with a multilingual background. From the marketing, technological, behavioral, and cultural perspectives, this research argues that a multilingual option in e-Gov might enhance the processing and understanding capability of e-Gov. Therefore, since this criterion promotes the ability to use e-Gov by improving service quality, we categorize this plausible explanatory variable of e-Gov adoption as the “Ability” of stakeholders. This research thus proposes:

Many scholarly articles conducting research in e-Gov adoption have shown that security, privacy, uncertainty, and risk are predominant factors for adoption (Al-Adawi et al., 2005 [16],Parent et al., 2005 [47], Shareef et al., 2007 [49], Shareef et al., 2008 [86] and Welch & Pandey, 2005 [92]). Bélanger, Hiller, and Smith (2002) [93] found that pleasure, privacy, security, and web features are matters related to the perceived trustworthiness of a website. Research in e-Commerce and e-Gov found that uncertainty, security, privacy, and risk are all antecedents of perceived trust (Al-Adawi et al., 2005 [16], Balasubramanian et al., 2003,July [94],Parent et al., 2005 [47] and Soat, 2003 [95]).

Perceived security is crucial to users' confidence regarding the safety of a website. Based on previous research on security in e-Commerce (Bélanger & Carter, 2005 [37] and Schaupp & Bélanger, 2005 [96]), this current study visualizes perceived security as the protection of customers from any type of financial or non-financial risk during transactions on websites, such as any type of identity thefts including abuse of credit card, overcharging, non-payment, etc. These security factors are potential contributors in developing trust among citizens as the authentication of e-Gov financial transaction and protection of disposed information in e-Gov. Therefore, we conjecture that perceived security (PS) has a causal effect on perceived trust (PT).

Since transactions in e-Gov are basically virtual, no actual physical transaction takes place during interaction and payment by the clients, and the uncertainty construct of TCA can be a potential factor of non-accepting virtual environment of e-Gov. This, in turn, is related to perceived trust in e-Gov (Al-Adawi et al., 2005 [16]). In the virtual environment in particular, being able to place trust in a website is critical to consumers' successful interaction with the system (Gefen, Karahanna, & Straub, 2003 [97]). Cox and Rich (1964) [98]delineate perceived risk as the perception of uncertainty in a particular interaction situation that has a close relation to trust. Therefore, PT is dependent on PU (uncertainty).

In e-Gov, citizens provide written information in technology interface while interacting or receiving/paying through e-Gov websites. As a result, users of e-Gov always feel a lack of privacy. Several researchers (Angst & Agarwal, 2009 [99], Shareef et al., 2008 [86] and Yoo & Donthu, 2001 [100]), who conducted empirical studies regarding the acceptance of the online environment—e.g., e-Commerce and e-Gov—observed that perceived privacy is a major concern for internet customers during interaction with websites. Customers are afraid that websites can disclose, share, or misuse their personal information or that hackers can intercept their secret information (Brown & Muchira, 2004 [101] and Ranganathan & Ganapathy, 2002 [102]). During interaction with websites, customers may perceive there to be a privacy risk (Parasuraman et al., 2005 [75]). So, perceived privacy (PP) is also related to the confidence of users on the web, which finally indicates trustworthiness. Trusting the web can enhance the perceived privacy feeling of the customers during interaction in e-Gov (Kemp, 2000 [103]).

Warkentin et al. (2002) [48] extensively discussed the impact of trust in e-Gov adoption, and consequently proposed that institutional-based trust, characteristics-based trust, and process-based trust—which all together capture the essence of perceived security, privacy, and less uncertainty in e-Gov—will lead to the intention to adopt e-Gov. Thomas (1998) [104] outlined the three aspects by which trust in the government is produced. The first aspect is related to behavioral attitude that is supported by TPB. The second aspect of trust derives from institutional credibility. The third component captures the trusting attitude on typical outcomes of a process where the seller sells goods to buyers and buyers in return will pay for that. Nye and Zelikow (1997) [105] classified these causal factors as social, cultural, economic, and political. From the marketing aspect, if a customer does not have trust in the institution and process, he/she might not embrace that organization for interaction. So, we can conclude that PT in e-Gov has political, behavioral, social, organizational, technological, marketing, and cultural perspectives. Trust is an important factor in analyzing the adoption behavior of consumers in virtual environment, because citizens have few tangible and verifiable cues regarding the service provider's credibility and performance (Urban, Sultan, & Qualls, 2000 [106]). In light of the above discussion, we argue that PT creates confidence in the overall e-Gov performance and, thus, categorize this construct as the “Assurance” to use e-Gov. Thus, we propose:

The measuring scale items for all the exogenous constructs of the proposed model, developed and operationalized based on existing literature on e-Gov, e-Commerce, IS, marketing, and expert opinions, are shown in Appendix A [107]. The questionnaire was pretested by a group of people comprised of two scholarly researchers from Sprott School of Business, Carleton University, Canada who have expertise in analyzing the online adoption behavior, and eight PhD students from the social science and natural science departments of Carleton University who have extensive knowledge in using Canadian e-Gov to verify the structure, constructs, and respective measurement items of the questionnaire. For any exploratory research, if the surveyor happens to organize a group comprised of a couple of people, the participating pretesting method is better (Converse & Presser, 1986 [108]). In our pretesting procedure, we followed the participating method. A structured questionnaire was used to measure the independent and dependent variables of the study with a 5-point Likert scale ranging from 1 (strongly disagree/never) to 5 (strongly agree/always). The 5-point Likert scale is used to increase the response rate and response quality along with reducing the respondent's frustration level (Babakus & Mangold, 1992 [109]).

3.2. Dependent variables

According to the marketing theory, adoption of a new product begins with consumer awareness, leads to an attitude toward that product, then further advances to an intention to use as a trial basis, and finally ends as full acceptance and regular use with satisfaction (Pavlou & Fygenson, 2006 [110]). A new system can replace an old system. So, if an individual using traditional government systems perceives e-Gov to be more advantageous from any perspectives, he or she might adopt the online government systems—e-Gov. This research encompasses the adoption of e-Gov as a continuous process starting from awareness of the system, beliefs of the system benefits, attitude toward using it, intention to use, actual use, satisfaction, and recurring use. Some researchers (Al-Shehry et al., 2006, Sept. 11 [43], Chen & Thurmaier, 2005 [17], Kumar et al., 2007 [21], Moon & Norris, 2005 [45],Schedler & Summermatter, 2007 [25], Shareef et al., 2007 [49], Shareef et al., 2009 [13] and Wangpipatwong et al., 2005 [80]) have developed their adoption models and measuring items for the adoption construct by considering complete acceptance of the process.

Depending on the paradigms of the e-Gov adoption process, we find logical underpinnings on the premise that the adoption process of e-Gov involves the frequent and recurrent use of online services by citizens not only for obtaining information but also for interaction with government. Adoption construct has behavioral, organizational, economic, technological, political, marketing, social, and cultural perspectives. We have mentioned that this research is concerned to explore the objective by investigating adoption criteria into two different levels of service maturity: Static or Publishing stage and Interaction stage. Therefore, we have differentiated the dependent variable “Adoption” into two sub-groups:

•

Adoption 1: Decision to accept and use an EG system to view, collect information, and/or download forms for different government services as the user requires with the positive perception of receiving a competitive advantage.

•

Adoption 2: Decision to accept and use an EG system to interact with, and seek government services, and/or search for queries for different government services as the user requires with the positive perception of receiving a competitive advantage.

The endogenous/dependent variable “Adoption” was operationalized in a way that ensures measurement of the causal effects of the exogenous variables on the two levels of service maturity of e-Gov and increases response rate. It is obvious that citizens can view and interact with e-Gov for many tasks. Defining any specific task for adoption in the proposed questionnaire might reduce the response rate in terms of adoption. Therefore, this study formulates the instruments of “Adoption” not for any specific tasks but for general tasks to keep the questionnaire general for all respondents. A total of six scale items were selected to measure those two dimensions of the adoption construct (AGIMO (Australian Government Information Management Office), 2003 [77], Gil-Garcia & Martinez-Moyano, 2007 [11], Murru, 2003 [111], Sakowicz, 2007 [24] and Turner & Desloges, 2002 [52]) (scale items are shown in Appendix B [112]). Based on these arguments and identification, a model of e-Gov adoption (GAM) to investigate the plausible relations is proposed in Appendix C [113].

4. Methodology

The research methodologies we use in this research are those typically used in empirical business research. Based on the suggestions of Heeks and Bailur (2007) [8] about e-Gov research, and theories of Campbell & Fiske, 1959, March [114] and Bagozzi et al., 1991 [115] about reliability and validity of research, we designed our research methodology. In this research, the respondents are the users of the Canadian e-Gov system; anyone who has experience using Canadian e-Gov system could participate in the survey. This study was conducted in four large cities in Ontario, Canada. We selected the venue for the following purposes:

1.

Canada is one of the leading countries in terms of offering e-Gov services. Canada's e-Gov implementation and offered services are very mature, and have different services in the static, interaction, and transaction stages. Therefore, in terms of the development stage of e-Gov and mission, vision, and objectives, Canada can be viewed as one the most focused countries for e-Gov development (Cardin, Holmes, Leganza, Hanson, & McEnroe, 2006 [116]).

2.

The adoption rate (29.8%) and maturity of services of EG in Ontario is the highest in Canada according to the study by Parent et al. (2005) [47]. Since this research has set its objectives in detecting adoption criteria of citizens at different maturity levels of services offered by e-Gov, Ontario is assumed to fulfill the research objectives.

3.

The selected four cities are the most populated and largest cities in the respective regions of Ontario. These cities are also located strategically in important position and are prominent in multicultural assembly. Therefore, it is assumed that the sample should have enough variability.

To test the model in the most realistic way possible, the study was conducted through a survey (a self-administered questionnaire) of a broad diversity of citizens at several communities. From our previous experience, we assumed that the study would receive around a 10% response rate. Since there are 11 primary exogenous variables/constructs, the number of response should be at least 220 (20 samples per independent variable) for regression and factor analysis (Stevens, 1996, p. 143 [58]). However, a sample size of a minimum of 200 is good for structural equation modeling (SEM) (Kline, 2005, p. 110 [117]). Therefore, the questionnaire was distributed among 2200 citizens (or residents) in the previously mentioned four cities in Ontario, Canada, to meet the target and fulfill the statistical specifications. The specific way we used to distribute the questionnaire was:

1.

We maintained roughly the population ratio of the four cities, distributing 100 questionnaires in Sudbury, 200 in London, 500 in Ottawa, and 1400 in Toronto.

2.

We divided all the cities into five regions: east, west, north, south, and center.

3.

We then collected addresses from the Telephone White Pages of each city; we included houses, condominiums, and apartments located in the five regions we identified. We also collected the addresses of the residents living in the suburban areas in the east, west, north, and south regions immediately outside the city.

4.

We distributed the questionnaires by mail throughout the suburban areas in the east, west, north, and south regions outside each city. One half of the total questionnaires allocated for each city were distributed in this way. The other half was distributed physically to the houses, condominiums, and apartments in different areas in the five zones.

5.

We distributed 50% of the questionnaires in houses and condominiums and 50% in apartments.

6.

The survey was conducted over a three-month period.

We received a total of 241 questionnaires from the respondents. Two returned questionnaires were blank. Therefore, the eligible response number is 239. The response rate is around 11%. This is quite satisfactory based on our previous knowledge and also considering the length of the questionnaire—eight pages, including a one-page cover letter.

5. Statistical analysis

Several interrelated procedures were performed to organize, re-arrange, and summarize the raw data and make it amenable for analysis to get justified output. This section sequentially describes data preparation and analysis techniques for statistical analysis of this research.

5.1. Data reduction

Before performing any cause–effect relation, reliability, validity, and normality tests, we first conducted exploratory factor analyses (EFA) on the preliminary 57 scale items measuring the latent variables having direct causal relations to the adoption of e-Gov excluding the measuring items of the constructs perceived uncertainty (PU), perceived privacy (PP), and perceived security (PS), which are not hypothesized to have direct relation with adoption in our model. These three constructs are hypothesized to have causal relations with perceived trust (PT), which is an exogenous variable for the adoption model. We have also done EFA on the 10 measuring items of PU, PP, and PS separately because these three constructs are widely used as the exogenous variables for PT (Shareef et al., 2008 [86]). For EFA, we have used principal component analysis as the extraction method and varimax rotation as the rotation method. We used both the breaks-in-eigenvalues criterion (> 1) and scree plot to determine the number of factors to retain (Stevens, 1996, pp. 389–390 [58]).

After conducting a series of EFA of those 57 measuring items, of the 11 exogenous variables and also examining the correlation matrix we found that nine constructs with 37 measuring items can be retained. However, to support this refinement in measuring items, we also looked at the correlation matrix, analyzed convergence through CFA, and thoroughly investigated theoretical aspects of those modifications. For PA, AOR, CSE, PI, PIQ, MLO, and PFB constructs, corresponding measuring items were loaded consistently (though some items were removed because of low loading or cross loading). Three PC items, three PATU items, and one PIQ item were loaded on a single factor. Though the items loaded under this factor are the measuring items from different hypothesized exogenous variables, after close examination of those seven items loaded under a single factor, we observed that all these items reflect certain personal beliefs and perceptions of ability of using e-Gov systems. This personal belief denotes both physical and psychological perception (resembles to PC) of ability to use e-Gov systems. Therefore, these seven items have very close functional alignment. We verified the correlation between these items and found moderate to strong correlations. This also justifies the convergence of these items under a single factor. We also verified the convergence of those seven measuring items in CFA by testing the appropriateness of a single factor or two factor model (Appendix D [118]). Based on the functional meaning, we argued that PATU can still cause those seven scale items and thus can be named PATU. However, the definition of PATU should be edited adding new psychological dimension. We have done that in Appendix E [119] where all the explanatory variables are defined. Four items of PT and five items of PSR loaded under a single factor in EFA. We can explain this behavior getting insight from the measuring items. On the one hand, perceived trust of citizens is related to the credibility of e-Gov; on the other hand, customer service of e-Gov also helps to enhance the perception of trust and credibility, particularly in the virtual environment, among the users of e-Gov. Therefore, we retained the name perceived trust (PT) for the combined measuring items loaded under a single factor. We also verified the correlation between these items and found moderate to strong correlations. This also justifies the convergence of these items under a single factor. However, we also verified the convergence of those nine measuring items in CFA by testing the appropriateness of a single factor or two factor model (Appendix D [118]). We also verified all nine factors with the measuring items individually by CFA and observed confirmation of EFA results (Appendix D [118]). However, since 1 item of the construct AOR (AOR3) was loaded in CFA with a loading factor of less than 0.50, we removed that item. In addition, since we could retain only two items for CSE with high internal correlations (more than 0.90) and two items for MLO with high correlations (more than 0.95) from EFA, we could not perform CFA for these two variables (since negative degree of freedom exists). So, we took the average scores of the respective measuring items for CSE and MLO respectively. Therefore, we retained a total of 34 measuring items with the nine exogenous variables (Appendix E [119]). We also retained two factors with nine indicators from the EFA of the 10 measuring items of PU, PS, and PP. However, four items of PS and two items of PP were loaded under the same factor. Although as an exploratory study we have hypothesized PS and PP as two different exogenous variables for PT, several researchers used PS and PP as a single construct by the name PS, because both the constructs are related to security of financial transactions, identity, and personal information (Gummerus et al., 2004 [120] and Wolfinbarger & Gilly, 2003 [87]). Therefore, we have provided the name of this construct as PS, however, its definition was revised (Appendix E [119]). We also verified the correlation between these items and found moderate to strong correlations. This also justifies the convergence of these items under a single factor. However, we also verified the convergence of those six measuring items in CFA by testing the appropriateness of a single factor or two factor model (Appendix D [118]). So, for PT as endogenous variable, we have retained two exogenous variables namely, PU (uncertainty) and PS.

The reliability scores for the constructs were measured by a coefficient alpha, which justified the reliability of the items in each dimension and thus internal consistency among the items in each dimension. The reliability scores for all the final exogenous and endogenous variables are ranged from 0.706 to 0.974, which suggest an acceptable internal consistency among the items in each dimension (Nunnally & Bernstein, 1994 [121]). The CFA results suggest that the scale items are reflective indicators of their corresponding latent constructs, which indicates construct validity (Chau, 1997 [122] and Segars & Grover, 1993 [123]). In this data analysis, the average variances extracted (AVE) for each factor and its measures all exceeded 0.50; thus, convergent validity is achieved (Fornell & Larcker, 1981 [124]). We also verified the correlation matrix of the items under each factor. All the items individually under each factor have moderate to strong correlation coefficients. This result also justified convergent validity. We also examined multicolinearity, normality, and outliers.

5.2. Model testing: causal relationship by path analysis

We have used LISREL for path analysis, a family of structural equation modeling (SEM), to test the causal relationships (the hypotheses) of the model. Since we have measured all of the exogenous and endogenous variables through Likert scale 1–5, data gathered from this empirical study is not perfectly continuous. Therefore, structural measurement through SEM by maximum likelihood (ML) is not appropriate for this type of data (Kline, 2005, p. 219 [117]). For structural measurement through SEM, one of the fundamental requirements is that latent variables should be continuous (Kline, 2005 [117]). Therefore, we took the average of the indicators of each of the latent variables individually for 239 cases and conducted a path analysis (Kline, 2005 [117]) to find out cause–effect relationships between exogenous and endogenous variables. In path analysis, all of the latent variables are treated as observed variables and their scores represent the average of the scores of their respective indicators. We have used the maximum likelihood procedure of LISREL for the purpose of analysis. Since, the measuring items of PC and PATU were integrated in a single construct and the measuring items of PT and PSR were also integrated in a single item, we have now nine hypotheses to test (with certain modifications in the composition and definition of PATU and PT constructs related to two hypotheses) from our proposed 11 hypotheses having direct relations with adoption. Only two hypotheses having direct relations with adoption were removed during statistical refinement in the previous section. For path analysis, we have used the correlation matrix as the input data for all the 11 exogenous variables (nine exogenous variables having direct relations with adoption, i.e., PA, AOR, CSE, PATU, PFB, PI, MLO, PIQ, and PT and two exogenous variables having indirect relations with adoption through PT, i.e., PU and PS) and one endogenous variable (for example ADOP1). Here PT is both an exogenous variable and endogenous variable (like a mediator variable for adoption). Therefore, for two models, i.e., GAM-S and GAM-I we have inputted two different 12 × 12-correlation matrices. Final Path models and fit indices are shown inAppendix F [125] and Appendix G [126]. For path analysis we have tested 11 hypotheses, through path analysis for our research questions of this research.

After conducting path analysis for the adoption of e-Gov at static level, we find that PA and PATU have significant causal relations with ADOP1 with ‘t’ values of 3.98 and 7.01 respectively. Therefore, these two factors are significant at the 0.05 level (z score for the 0.05 level is 1.96). Even these two factors are significant at the 0.01 level (z score for the 0.01 level is 2.576). PFB is significant at the 0.1 level which has a ‘t’ value of 1.76 (z score for the 0.1 level is 1.645). PATU, PU, and PS are significant predictors on PT (Appendix F [125], Fig. 1). However, since PT is not a significant predictor on ADOP1, subsequently PT and its predictors are not related to adoption of EG. Apart from any issues related to the adoption of e-Gov, separate causal relations of PT with other exogenous variables are beyond the scope of this research. From path analysis for adoption of EG at the interaction level, we find that PA, PI, PT, PIQ, and PATU have significant causal relations with ADOP2 at the 0.05 level. PATU, PU, and PS are significant predictors on PT (Appendix F [125], Fig. 2). The final accepted hypotheses for the adoption of e-Gov at the static and interaction levels are listed in Appendix H [127]. Numerical formulation of the Path models for the static and interaction levels are shown in Appendix I [128].

6. Discussion

As was previously mentioned, e-Gov implementation passes through different phases of evolution as the services offered mature. Though these phases are mutually exclusive and not distinctive, however, as the services provided by e-Gov mature, its levels of interaction improve from the static level to the interaction, transaction, and integration levels. Especially in terms of technology, organizational structure, service quality, reliability, security, and privacy, the potential characteristics of the interaction level and static level might be significantly different. From the end users' perspectives, the two stages of services have significant differences in characteristics and functionality. ADOP1 indicates the adoption of EG at the static stage. This is the first stage of government service and information presence online. Consequently, ADOP1 has a higher mean between ADOP1 (3.673) and ADOP2 (3.2164). We found that PA, PATU, and PFB are significant predictors for the adoption of e-Gov at the static phase. Therefore, our hypotheses that PA, PATU, and PFB have positive effects on the adoption of e-Gov at the static stage are supported.

When citizens are aware that there is an alternative source of brick and mortar government service and information, for example e-Gov, they might be interested in looking for this. After that, if they find that they have sufficient technological and psychological ability to use it and also perceive that it provides absolute and relative advantages they will most likely adopt it.

Since this is only the static stage of e-Gov, at this stage citizens can only view, read, and collect government information relating to government policies, services, rules and regulations, and different other issues. Citizens do not adopt this stage to interact with government agencies; rather they do that only to be informed of government services that, instead, they could collect by physically going to different government offices. At this stage, availability of resources (AOR), perceived information quality (PIQ), perceived trust (PT), computer self-efficacy (CSE), multilingual option (MLO), and perceived image (PI) are not important for citizens to adopt e-Gov. These constructs basically contribute very little to the variances explained on ADOP1. Therefore, these hypotheses are not significant.

As Canada is a developed country and also advanced in modern ICT, AOR might not be an important predictor for the adoption of e-Gov. To adopt e-Gov, resources are mostly available. Here the internet adoption rate is around 84% (Internet World Stats, 2008 [129]). PIQ could be a potential predictor. However, since PFB is a significant predictor of e-Gov adoption, when citizens perceive that using e-Gov static webpage provides them with absolute and relative benefits, they are not concerned about PIQ. Same argument can be drawn for CSE. From our demographic analysis, since more than 80% of the respondents have at least undergraduate education and at least 80% have online experiences for more than 3 years, CSE is not an issue for citizens to adopt e-Gov. And, moreover, since PATU, which shared some essence of CSE, has already contributed enough in ADOP1, CSE is not a significant predictor. Since adoption of the static stage of e-Gov is very private, not an exposed matter to any other (as there are no communications anywhere), perceived image (PI) is expected not to be a predictor of e-Gov at the static stage. At the static stage, citizens are not interacting with government by any means, so they are not disclosing any personal or financial information in the virtual environment. So, PT has no significant causal effect on the adoption of e-Gov at this stage. MLO could be an important issue for citizens whose mother tongue is not English when they need to collect government information from published information in web pages. However, from demographic analysis, we found that around 83% of the respondents have mother tongue of English/French. Since Canadian government web pages are written in English and French, MLO cannot be an issue for use for respondents who speak both English and French. Respondents who have a mother tongue other than English or French are mostly new immigrants. Since the Canadian immigration policy is highly dependent on level of education, we found from cross tab analysis that around 90% of the respondents whose mother tongues are not English or French have at least an undergraduate education level. As we expected, language is not a barrier for them to collect information from government web pages that are written in English/French.

Now if we look at TAM (Davis, 1986 [130]) and DOI (Rogers, 1995 [68]), we can get support for our analysis that PATU, which captures the integrated view of PEOU of TAM and the complexity and compatibility of DOI, and PFB, which captures the overlapping essences of PU (perceived usefulness) of TAM and the relative advantage of DOI, are the critical factors for the adoption of e-Gov, a system driven by modern ICT. Since, government simply cannot abandon traditional government offices after launching e-Gov and since citizens have been concerned about the traditional service system for decades, citizens need to be aware of the alternative outlet of government service system that is offered in e-Gov. Therefore, PA is also an important predictor of e-Gov adoption that is not reflected in simple technology adoption as discovered by Davis (1986) [130]. From TPB (Ajzen & Fishbein, 1980 [55]), we know that a person's behavior is determined by the person's intention to perform the behavior and that this intention is, in turn, a function of the person's attitude and belief toward the behavior. Therefore, adopting e-Gov at the static stage, which we can view as the outcome of behavioral intention, is influenced by the beliefs of that person about the outcome of that behavior. We can clearly see that PATU and PFB are those beliefs of positive attitude toward adopting e-Gov, which in turn affect intention to use and, finally, the adoption of e-Gov at the static stage. However, also applicable in this theory that, for a system which has multidimensional aspects (including technological, social, cultural, behavioral, economic, organizational, and political) and which has an alternative outlet reflecting different behavioral outcomes (e.g., adopting traditional government services), for developing beliefs of the attitude and intention to adopt the system, perceived awareness (PA) is an important aspect to impart that beliefs.

From statistical analysis (see Appendix F [125] and Appendix I [128]), we observed that at both stages of service maturity of e-Gov, in the adoption model, perceived trust on e-Gov (PT) is affected by the causal effects of PATU, PU, and PS. While in our model development, we have conjectured that PU and PS have causal effects on PT; we did not make the hypothesis that PATU also has a causal effect on PT. Our path analysis has provided the result. However, since PT is not a potential factor for the adoption of e-Gov at the static stage, we do not pay attention to the causal relationships of PATU, PU, and PS with PT in this paragraph. Rather, in the next section, we will do that while explaining the significance of our findings for the adoption of e-Gov at the interaction stage where PT is also a significant predictor of adopting e-Gov at the interaction stage. We also conducted multiple regression analyses to verify our path analyses and got concrete support for the results (Appendix J [131]).

For the adoption of the interaction phase, we found that PA, PATU, PI, PT, and PIQ are the significant predictors. Therefore, our hypotheses that PA, PATU, PI, and PT have positive effects on the adoption of e-Gov at the interaction stage are supported. When citizens are aware that instead of going to brick and mortar government departments for seeking service and information, they can alternatively communicate with the respective departments through e-mail, chat rooms, etc., which are integrated with e-Gov websites, they develop an intention to use it. After that, if they find that they have the technological and psychological ability to use it and also perceive that this electronic communication is trustworthy, they will most likely adopt it. Since, in the interaction stage, citizens have some communication with others through modern ICT, they feel it to be prestigious. Citizens feel that instead of going to a physical government office, online communication with government departments can enhance their social status. As a result, PI was hypothesized to have a positive relation to the adoption of e-Gov at the interaction stage, and our findings support that hypothesis.

PIQ is also a significant predictor of e-Gov adoption at the interaction phase. However, it has a surprisingly negative relation to ADOP2, although our primary hypothesis predicted the causal relation to be positive. Although regression analysis (Appendix J [131]) supports this finding, the correlation matrix shows a positive relation between ADOP2 and PIQ. Basically PIQ has the lowest correlation coefficient among the other significant predictors of ADOP2 (0.239). From our literature review of EC, we have found that PIQ has positive relation with the use of EC. However, we should notice and consider some subtle issues in this respect. For EC or general adoption criteria of e-Gov (irrespective to service maturity level), better information quality encourages consumers or users to use that online system. However, the interaction stage of e-Gov has certain specific attributes. At this stage, citizens generally do some queries to get specific information that they do not find on the e-Gov websites, or to provide some information to the service providers. Therefore, if they find that information on the e-Gov websites is systematic, sequential, up-to-date, effective, complete, and also provide sufficient links to supplement information shortage, if any, citizens do not feel any urge to contact or interact with government agencies through electronic media. As a result, perceived information quality (PIQ) of e-Gov automatically reduces the interaction of citizens with government agencies through e-Gov websites. This argument justifies the findings of the statistical analysis, which depicts negative cause–effect relations between ADOP2 and PIQ.

Other exogenous variables—availability of resources (AOR), computer self-efficacy (CSE), multilingual option (MLO), and perceived functional benefits (PFB)—are not important for citizens to adopt e-Gov. These constructs, basically contribute very little to the variances explained on ADOP2. Therefore, these hypotheses are not significant. We have already explained the plausible reasons for the hypotheses formed by the exogenous variables AOR, CSE, and MLO to be non-significant on the adoption of e-Gov for Canadian users in the previous section. However, PFB was found significant for ADOP1 while it is non-significant for ADOP2. The hypothesis revealing a positive relation between PFB and ADOP2 was not proven from the statistical analysis. This is an interesting phenomenon. As we already explained, the interaction stage of e-Gov has certain characteristics. This stage is generally used to seek further information related to government services, policies, and rules and regulations; to provide government agencies with certain personal information; and to interact to ask some queries. Citizens typically perform these interactions through e-mail, chat rooms, etc. In the interaction stage, government agencies might not communicate with citizens. Therefore, in this stage, citizens are concerned mostly with the trustworthiness of e-Gov websites, not PFB. Contacting through e-mail is so common in this era that doing something (e-mail) through e-Gov websites does not create any perceptions of functional benefits among citizens as the plausible reasons of using e-Gov websites at the interaction stage (Ong & Wang, 2009 [132]). Reasonably, after PATU, PT is the second influential predictor, and PFB is a non-significant cause of the adoption of e-Gov at the interaction stage.

Based on TAM (Davis, 1986 [130]) and DOI (Rogers, 1995 [68]) we cannot exactly explain the adoption criteria of e-Gov at the interaction stage. The construct PATU, which captures the integrated view of PEOU of TAM and complexity and compatibility of DOI, is the most contributing construct of EG adoption at the interaction stage. We also find that perceived image (PI) influences the acceptance and use of e-Gov at the interaction stage. This finding is supported by DOI proposed by Moore and Benbasat (1991) [69]. Image refers to citizens' perceptions of adopting e-Gov to present themselves as superior to others in the society. Interaction with e-Gov systems, instead of using traditional government offices, reflects a perception by citizens of superior status. PI exactly captures this superior status perception. In this phase, PA is also a significant predictor, like ADOP1 which is an addition to TAM and DOI specific to e-Gov. Static and interaction stages of e-Gov are the primary stages when a country first launches e-Gov projects (Accenture, 2005 [41]). Since these two phases are the first introduction to online service of government and are the alternative of traditional government service systems, at these two levels of service of e-Gov, citizens' awareness is presumably an important critical factor for attitude and behavioral intention to use those systems. However, e-Gov, particularly at the interaction stage, has more aspects than simply adopting an innovation. Citizens' perceptions and expectations differ remarkably at this stage from simply viewing government information through static websites of e-Gov. Therefore, in this phase, PT and PIQ are also additions to traditional TAM and DOI constructs, which are introduced to capture the specific interaction characteristics of e-Gov services. We have already explained the reason for the negative impact of PIQ on ADOP2.

If we look at the sources of trust that citizens have while interacting with the government, based on Easton (1965) [133], we see that the trust that develops the satisfaction of citizens with governments due to their credible performance is very significant. This institutional-based trust is an important component while interacting in e-Gov web pages (Parent et al., 2005 [47]). Thomas (1998) [104] also supported that citizens required trust while interacting with e-Gov, a trust that derives from institutional credibility. If actors of e-Gov do not feel institutional trust, according to the institutional theory, they might not follow the institutional norms of e-Gov. Therefore, PT is a reasonable addition to TAM and DOI in predicting the adoption behavior of e-Gov at the interaction stage, which is not reflected in simple technology adoption as discovered by Davis (1986) [130]. From TPB (Ajzen & Fishbein, 1980 [55]), we can clearly see that PATU, PT, and PIQ are beliefs of attitude toward adopting e-Gov, which in turn affect intention to use and, finally, the adoption of e-Gov at the interaction stage. However, also related to this theory is that for a system which has multidimensional aspects (including technological, social, cultural, behavioral, economic, organizational, and political) and which has alternative outlets reflecting different behavioral outcomes (e.g., adopting traditional government services), PA and PI are important aspects for developing beliefs of attitude and intention of adopting this modern ICT-intense system.

From Appendix F [125] we can see that at both stages of service maturity of e-Gov, in adoption model—perceived trust on e-Gov (PT)—is affected by the causal effects of PATU, PU, and PS. We find that PS is the strongest predictor of PT. In all phases of e-Gov, citizens have trust in e-Gov if they perceive that the e-Gov websites ensure security of financial and personal information. Although we did not conjecture any relation between PATU and PT in our hypothesis development process, we find a strong causal relation of PATU on PT. The perceived technological and psychological ability to use e-Gov increases trust in e-Gov. This relation is suggested by path analysis for better fitness of the model and we find several logical aspects for this relation. Warkentin et al., 2002 [48] and Parent et al., 2005 [47] both discovered that an important component of trust is characteristic and process-based. If citizens perceive that they are sufficiently capable of handling online systems, their characteristic and process-based trust also increase (Warkentin et al., 2002 [48]). In light of TPB, we also see that if a user believes that he/she is capable of using an online system, he/she feels attitude and intention to use that system, which implies his/her development of characteristic-based trust. Therefore, the causal relation of PATU with PT has a strong theoretical base. We conjectured that perceived uncertainty (PU) has a negative relation to PT; however, surprisingly, we find that PU has a positive relation to PT (loading factor 0.25, significant at the 0.01 level). That is, the perception of uncertainty basically increases the perception of trust in EG. Apparently, this result is confusing. Therefore, we looked at the result from different statistical and conceptual perspectives. The correlation matrix shows that PU has a very weak positive correlation with PT (0.109). PU has weak negative correlations with PATU and PS. We have also done regression analysis of PU, PS, and PATU (independent variables) with PT (dependent variable) (Appendix J [131]). We see that all these variables are significant positive predictors of PT. However, in stepwise regression, while only PU is inputted, we see that PU has a negative non-significant effect on PT. While inputting PU, PS, and PATU—either in path analysis or in regression analysis—PU has the least positive effect on PT with very weak correlation with PT. This simply signifies that, while measuring the combined effect of PATU, PU, and PS, PU has a positive effect on PT. In our sample, more than 80% of the respondents are at least undergraduates; have had online experience for more than three years; and are working in the government, private services, and other services, or are university students. This demographic property of the respondents simply indicates that they are very familiar, comfortable, and experienced with online behavior. More than 84% of the citizens in Canada have internet experience. Therefore, uncertainty in virtual environment is a known psychological phenomenon for them. In addition, basically, Canadian citizens enjoy this uncertain virtual environment and perceive it prestigious for them. (We find that PI is significant for e-Gov adoption at the interaction stage.) When users of e-Gov believe that they have sufficient ability to handle this uncertain environment of e-Gov, and security in e-Gov is ensured by the government, the virtual environment of e-Gov does not hamper their trust in e-Gov. Rather, in the presence of PATU and PS, they enjoy the apparently uncertain environment of e-Gov, and the proper virtual characteristics of online system positively affect their perceived trustworthiness behavior. Consequently, PU has a positive relation with PT; however, this condition is applicable in the combined presence of PATU and PS.

Therefore, finally, we propose that 1) Attitude to use (measured by PA), 2) Ability to use (measured by PATU), and 3) Adherence (Reasoning) to use (measured by PFB) are the critical factors for the adoption of e-Gov at the static stage (GAM-S). Furthermore, we also find that 1) Attitude to use (measured by PA), 2) Ability to use (measured by PATU), 3) Assurance to use (measured by PT and PIQ), and 4) Adherence (Reasoning) to use (measured by PI) are the critical factors for the adoption of e-Gov at the interaction stage (GAM-I).

Finally, we can conclude that e-Gov functional characteristics are not only different at different levels of service maturity, but adoption factors at different levels of service maturity are also potentially different. From static to interaction phase, citizens perceive different factors to be important for creating the behavioral attitude and intention to accept the e-Gov system and to use it. Static and interaction levels especially offer different modes of service with different levels of association of technology. As a result, adoption criteria for different stages by citizens have significant implications for the policy-makers.

7. Implications and recommendations

To accomplish the objectives, this research explained the related concepts and theories and developed a research framework grounded on a strong theoretical and literature review background. After conducting an empirical study in Ontario, Canada, we have performed rigorous statistical analysis to validate our models of e-Gov adoption at different levels of service maturity. It is clearly observed from the findings that the viewpoint of the prime stakeholder, i.e., citizens, is crucial in selecting the critical factors for the adoption model and also identifying the effect of different levels of service maturity in the critical factors of adoption. The contextual setting, i.e., the level of service maturity of respective e-Gov websites, is important for exploring critical factors. Academicians, practitioners, researchers, and policy-makers can be benefited from this research and from successive findings.

Without addressing the requirements and the fundamental demands of citizens to accept an e-Gov system, e-Gov will fail to replace the traditional brick and mortar government system. This paper has validated two separate adoption models GAM-S and GAM-I which can be deeply investigated, addressed, and used for developing and designing the citizen-centric e-Gov framework. No researchers have attempted to address and identify the adoption framework for e-Gov at different levels of service maturity, which is the primary source of success for both government agencies and citizens (Carter & Bélanger, 2005 [15]). While implementing e-Gov and setting strategic initiatives to develop e-Gov services by incorporating different functions, public administrators and policy-makers should understand that perceived awareness (PA) and perceived ability to use (PATU) are the two potential contributors for adopting e-Gov. Therefore, all of the countries, particularly developing countries, should be very conscious to make citizens, especially those who are living in rural areas, aware and familiar with e-Gov. Not attending to these issues is likely to create a severe digital divide, thereby jeopardizing the mission of e-Gov.

Taking insufficient measures to familiarize under-privileged populations with ICT, different countries, particularly developing countries, are implementing e-Gov projects frequently; this is a classic mistake of policy-makers. Before implementing e-Gov, different programs should be initiated to make less educated citizens aware, familiar with, and capable of using e-Gov. It is worthy to note here that any e-Gov readiness index prepared by United Nations is sometimes very misleading since these are prepared based on average country readiness (which do not reflect rural populations' readiness).

Another potential implication of this study for the policy-makers who are associated with e-Gov development stages is that if information quality is very good, interaction stage does not bear potential value to the users. They can simply use static stage. Therefore, designers of e-Gov should be keen in organizing and formatting information in the e-Gov website. Since the e-Gov environment is virtual, unlike traditional government services, in e-Gov, public administrators should be very careful in developing institutional-based trust, characteristics-based trust, and process-based trust among citizens. For many potential customers, this raises real concerns about the trustworthiness of the service providers and guarantees of satisfaction. If e-Gov fails to develop perception of trust among citizens, it will not attain its full potential. Perceived functional benefit (PFB) is a significant factor for citizens to adopt it at the static stage. Policy-makers may think to provide some incentives for adopting e-Gov services instead of paper-base government services. Policy-makers can learn it from private e-Commerce (EC) organizations. Citizens still believe that using e-Gov might upgrade their social status. Governments can promote this concept among unprivileged populations to motivate them to adopt it. Policy-makers of any government, while developing e-Gov, should clearly understand that upgrading e-Gov services from the static to the interaction stage is not only different from their perspective, but that the requirements of citizens are also different for different levels of service of e-Gov. Therefore, while upgrading service patterns, functions, and features of e-Gov, citizens' requirements should be considered and incorporated. In this way, the static stage of e-Gov in any country has been adopted by citizens significantly. From this experience, governments have been encouraged to develop further interaction stage. But this stage can be abruptly failed if citizens' different perceptions for this stage are not properly reflected while designing this next service maturity stage.

From 2005, around 175 national governments were using the internet to provide government information and services to their stakeholders. A survey of chief administrative officers at government agencies in the US reveals that 74.2% of government agencies have a website, but that 90.5% of them have never conducted a survey to capture the users' requirements (ICMA, 2002 [134]).Therefore, based on the findings from this research about citizens' requirements, we recommend some essential points to service providers of Canadian e-Gov as well as other governments and policy-makers of e-Gov:

1.

Since functional characteristics and technological sophistication of different levels of service offerings—such as static and interaction—are quite different, the government should understand that the requirements of citizens for using those different service levels are different. Therefore, governments can focus on fixing their e-Gov and fulfilling citizens' needs separately, based on service patterns.

2.

Citizens perceive their technological and psychological ability as one of the most important factors to develop beliefs, attitude, intention, and final acceptance of e-Gov with recurring use at any level of service patterns, functions, and maturity. Since, this factor is always a significant issue for citizens in making a decision as to whether they will use e-Gov or not, the government should pay close attention to this issue and focus on the ancillary factors to enhance citizens' technological and psychological capability to use online government service. Online service should be flexible, easy to navigate and download, and fully available. At the same time, citizens should get technological tips regarding the handling of technological interfaces associated with e-Gov and the mental motivation to use the system.

3.

Government departments/agencies should focus on how they can make citizens familiar with their online presence. Citizens have asserted that awareness is an important aspect of using e-Gov systems at both phases. In addition, every year a significant number of immigrants come to Canada, particularly Ontario. They should be made aware that e-Gov is an alternative for obtaining government services.

4.

Since the static presence of government services is the initial and premature stage, at this period citizens should be made aware of the benefits of using this system. In this way, citizens would increase their usage of e-Gov and government could significantly decrease their cost for physical office maintenance.

5.

In the interaction stage, citizens are concerned about the trustworthiness of the system. At this stage, citizens communicate or contact government agencies to provide personal information or to seek information. However, they might be afraid whether their information will reach and be stored in the proper location on time. To develop the trust of citizens, governments can look to create instant acknowledgement procedures, so that users would be assured that the system is effective.

6.

To develop citizens' trust in e-Gov systems, a virtual environment that is sometimes termed as uncertain is not a problem. Citizens do not perceive uncertainty as having a negative impact on trust development in e-Gov. However, their ability to use e-Gov and security are important contributors in developing trust in e-Gov.

7.

Security is a vital issue to develop trust on e-Gov. So, there should be a definite tradeoff between the complexity of the security and the user-friendliness of the software. In our survey, several respondents expressed their concern about the security policy of governments. Governments should continuously update their security system. However, the most important thing in this aspect is to explicitly publish their security, safety, and privacy policies on the web pages, so that users can be completely assured that in any devastating situation the government has the final responsibility to resolve the problem.

8.

For general adoption of e-Gov, other than resolving any specific problems, citizens do not perceive customer service as an important aspect for them. Therefore, in the virtual environment, if e-Gov service providers can develop the e-Gov systems in such a way that they will automatically enhance user ability to use the system, they do not need to pay as much attention to providing regular customer service.

8. Limitations and future research directions

This research is at an exploratory level as not enough empirically supported research is available. So the limitations of exploratory research are applicable in this study. We have also chosen respondents of this empirical study considering, to some degree, the convenience of the sampling process. Though Ontario, Canada is a good place to study the adoption behavior, the generalization of this study and proper validity of the theory can only be achieved if this study can be replicated in several other countries. We have developed our theoretical framework considering the general aspect. As a result, we have predicted some exogenous variables, which might not be significant for developed countries. However, these might have enormous value for developing countries. They include availability of resources (AOR) and computer self-efficacy (CSE). Therefore, for generalizing the model, this study should be conducted in some developing countries.

Citizens are the prime users of e-Gov. However, business organizations are also an important stakeholder of e-Gov. We did not include business organizations in our study, because individual behavior and organizational behavior should be analyzed considering different aspects. Therefore, future research could separately explore the adoption criteria of business organizations for different levels of service maturity. The device which is used to gain access to the internet might be wired (computers as end user devices) or wireless (mobile phones or computers connecting via wireless ports). Mobile applications are beginning to be implemented in e-Gov systems. In this interaction technology, the device is a mobile phone or any handheld mobile device. Therefore, mobile-government (m-government) is a dynamic sub-classification of e-Gov. This is a new trend in e-Gov which is now widely used by consumers, especially in European and Asian countries. However, it is not clear whether this new trend can explicitly affect our study findings. This new trend of e-Gov should be investigated thoroughly.

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Fig. 1 Adoption model of EG at static level (GAM-S).

Fig. 2 Adoption model of EG at interaction level (GAM-I).

References