Introduction
In recent years, information and communication technology (ICT) has become a fundamental component in the economic transformation countries, as evidenced by the emergence of the digital economy. Available scholarship demonstrates that most governments around the world “appreciate the potential associated with employing ICT in supporting different government activities” (Lee 2011, p. 2366). Indeed, most governments are increasingly implementing diverse web-based technologies in their service delivery, which in turn has led to the emergence of the e Government concept, defined in the literature as the usage of varied web-oriented technologies by local, state, and federal agents in providing government services (Alalwan 2013).
The sustainability of the implemented web technologies depends on how citizens appreciate them and agree to continually use them, either on short-term or long-term basis. Consequently, it is imperative for governments to design and implement effective system adoption strategies. Ahuja and Thatcher (2005) argue that system adoption strategies are aimed at not only establishing the system, promoting its continued use and improving the level of user satisfaction, but also enhancing user acceptance of the system and promoting post-adoption behaviors.
The adoption and usage of e Government services can be explained through three main stages, namely pre-adoption stage, adoption stage, and post-adoption stage. The post-adoption stage determines the future success of the implemented e Government services. At the post-adoption stage, the intended users decide to either continue using the implemented e-government services or to abandon them altogether. As postulated by Alalwan (2013, p. 58), users who decide to abandon a particular technology may immediately commence an initiative to examine a new technology with the view to substituting their old technology. This section entails an investigation into the post-adoption stage of e-government in order to understand the effectiveness of developing continued usage behavior.
Definition of continuance intention
A number of research studies have led to the formulation of diverse definitions of continuance intentions, as demonstrated in the table below.
Table 1: Definitions of Continuance Intention
This study adopts the definition by Chen et al (2010), which asserts that continuance intention refers to the citizens’ repurchase intention or the decision to consume the various services delivered through web-based information systems.
Continued use intention models
In order to achieve the desired outcome through the implementation of e Government, it is critical for governments to nurture effective post-adoption behaviors amongst citizens. Alalwan (2013) asserts that the long-term viability of the information system implemented depends on the users’ continuance behavior. A number of theories and models have been formulated in an effort to explain the core factors that influence the consumers’ continued usage behavior, as demonstrated below.
Expectation-confirmation model [ECM]
This expectation-confirmation theory is a two-stage model that is often used in studying the impact of information system (IS) on the users’ cognitive beliefs with reference to the perceived usefulness and disconfirmation (Brown et al 2008). The second stage evaluates how information systems affect citizens’ attitude and satisfaction during the course of its utilization. The perceived usefulness component of the model has found usage by IS researchers in studies attempting to illuminate the extent to which users believe that utilizing a particular technology or innovation will enhance their effectiveness in undertaking a specific task or activity. Subsequently, the model emphasizes the target users’ cognitive expectations on the performance of the implemented information system (Bhattacherjee 2001).
As postulated by Venkatesh et al. (2011, p.528), the users’ expectations “about a product or a system are not necessarily restricted to the performance aspect, but rather they focus on many aspects such as ease of use.” The model also integrates the perceived enjoyment arising from utilizing the information technology (Thong, Hong & Tam 2006). Furthermore, the model acknowledges that users’ expectations may shift after experiencing the information systems’ performance, and that their beliefs and attitudes may change after gaining hands-on experience on a particular technology.
Unified Theory of Acceptance and Use of Technology [UTAUT]
Additional research has led to the identification of other core determinants of usage behavior and user acceptance, which include social influence, effort expectancy, performance expectancy, and facilitating conditions. Although performance expectancy is similar to perceived usefulness, Venkatesh et al (2011) acknowledge that other determinants such as facilitating conditions, social influence, and effort expectancy are immeasurably important in determining if users will continue adopting a particular technology after controlling for factors such as the usage environment, usage costs, and interpersonal concerns. These authors also emphasize that effort expectancy entails the amount of effort required in utilizing the information system, and that rate of adoption is influenced by the degree of complexity associated with a particular technology.
Available scholarship on UTAUT demonstrates that customers achieve a high level of satisfaction if the technology being implemented is easy to use, implying that effort expectancy can be a major hindrance in the continued usage of information systems because consumers form certain perceptions and beliefs regarding the ease of using a particular technology before its utilization (Venkatesh et al 2011). However, these beliefs are adjusted after gaining hands-on experience (Lee-Post 2007), implying that the concept of hands-on experience is of immense importance in making users to develop a positive perception regarding the continuous use of a particular technology (Venkatesh et al. 2011). These dynamics certainly increase the likelihood of developing continuance intention and a positive post-usage attitude towards a particular technology or e Government service.
The social influence dimension in the model explains the impact of others on an individuals’ continuance usage intention (Sanchez-Franco, Villarejo-Ramos, & Martin-Velicia 2011). The dimension asserts that the continuance usage intention is directly subject to an individuals’ social sphere. As acknowledged by Venkatesh et al (2011, p.534), the normative influence “can be considered as the result of integrating one’s expectations and feelings with significant others’ perceived expectations and feelings.” According to this model, an individual’s social influences play a fundamental role in the confirmation or disconfirmation of system usage. Indeed, Venkatesh et al (2011, p.538) argue that the impact of “social influences on the users’ confirmation or disconfirmation of expectations influences the level of satisfaction, post-usage attitude, and hence the consumers’ continuance intention.”
The final dimension of the UTAUT model entails the facilitating conditions, which refer to the extent to which consumers believe that there are effective technical and organizational infrastructures to support the implementation of information system. Facilitating conditions are correlated directly with the intention and usage of information systems. Research has found that lack of facilitating conditions has a negative impact on the target users’ attitude (Venkatesh et al 2011). Consequently, it can be argued that facilitating conditions underscore the importance of developing effective telecommunication infrastructures (e.g., adequate Internet penetration rates sufficient mobile phone uptake) with the view to enhancing the effectiveness of e Government.
The Self-Service Technology Attitude-Intention Model
This model contends that the consumers’ decision to adopt and use a particular technology is subject to the situation within which the technology is being used. Findings of a study conducted by Curran, Meuter, and Surprenant (2003) show that at least two main forces can motivate an individual to use the technology implemented during the service encounter. One of these forces is the user’s attitude towards the service provider or employees. For example, customer service representatives might not have adequate interpersonal skills, hence affecting the rate of customer satisfaction. The second force entails the consumers’ attitudes towards the implemented self-service technology.
Users of a particular service can develop a negative attitude towards the service provider, which in turn motivates them to use the self-service technology. For example, a user may develop a negative attitude towards bank tellers, which in turn enhances the likelihood of using implemented self-service technologies such as the Automated Teller Machines. Alternatively, the features associated by the self-service technology may be appealing to users, hence increasing its usage. For example, the ability of banks to provide different banking services such as time saving on a 24-hour basis is one of the features that might appeal to a large number of customers. Such an occurrence may enhance the customers’ intention to use the technology (Rokhman 2011).
IS Continuance Model
According to Kim and Crowston (2011, p.6), the “IS continuance mode is based on the similarity between individuals continuous IS usage decisions and consumers’ repeated purchase decisions using the expected confirmation theory.” The model emphasizes the importance of fostering satisfaction via positive post-adoption behavior. The level of satisfaction is influenced by two main constructs, which include the citizens’ emotions and cognitive ability. The level of satisfaction achieved from using e-government technologies influences the users’ attitude towards the implemented technology, hence the intention for its continued usage (Kim and Crowston, 2011).
Continued usage intention factors
Perceived Usefulness
This construct refers to the extent to which consumers’ believe that the implemented e Government service will augment the effectiveness and efficiency with which different tasks are undertaken. Wangpipatwong, Chutimaskul, and Papasratorn (2008) argue that perceived usefulness has a significant influence on the extent to which institutions such as governments adopt innovation. Indeed, these authors argue that a “person’s willingness to transact with a particular system is already considered as perceived usefulness” (p. 55). Consequently, the citizens’ perceptions on e Government services provided in Saudi Arabia are affected by their expectation on the implemented services.
Furthermore, the perceived usefulness of the implemented technology is influenced by the extent to which the user perceives the benefits associated with the implemented information system compared to the traditional methods of doing things. Perceived usefulness can be assessed by comparing the benefits associated with accessing different government services through offices compared to accessing the service through online protocols.
Available literature demonstrates a strong correlation between the users’ perceived usefulness and their behavioral intention (Beaudry & Pinsonneault 2005). Santhanamergy and Ramayah (2013, p.26) emphasize that people “form intentions toward behaviors they believe will increase their system use over and above whatever positive or negative feelings may be evoked towards the behavior.” According to the Technology Acceptance Model, the consumers’ behavior intention towards different information systems such as e Government technologies is based on a comprehensive appraisal of the system. Thus, the perceived usefulness construct contends that the likelihood of continued usage of the information system is affected by the extent to which the user is effective and efficient in undertaking various tasks (Saeed & Abdinnour-Helm 2008). Other elements that determine the perceived usefulness of a particular technology, according to these authors, include the ability to work more quickly, increased productivity, and ease of performing various tasks.
Expectation confirmation
According to Bhattacherjee (2001, p.357), expectation confirmation is one of the most widely used components in understanding the consumers’ behaviors and it is “used in evaluating the consumers’ level of satisfaction, which affects their post-purchase behavior.” The theory of expectation confirmation argues that consumers repeat purchase decision is influenced by the experience achieved from the initial consumption. As indicated in the literature, users develop the willingness to repurchase a particular product or service if they attain maximum utility from the initial consumption, implying that the repeat purchase behavior is influenced by the extent to which the product or service purchased meets the expectations of users (Garaca 2011).
Similarly, the extent to which consumers develop the decision to continue using a particular technology is influenced by the extent to which they attain the desired expectations. Confirmation on the effectiveness of the implemented technology is achieved by evaluating the difference between the perceived performance and the actual performance. Brown et al. (2007, p.54) assert that expectations “serve as an anchor such that there is an ideal point of experience in which the difference between the expectations and the experience is minimized.” In some instances, the expectations by the users of the information system may exceed the capacity of the information system’s capacity, which in turn might lead to under-fulfillment. Consequently, it is imperative for the relevant stakeholders to assess the target users’ expectations prior to implementing the needed e Government services. Such a move, according to Brown et al (2007), will aid in providing a balance between over-fulfillment and under-fulfillment of the target users’ expectations. These authors further argue that the ideal point, which is characterized by optimal satisfaction, occurs if the users’ expectations are equivalent to the experiences received.
Satisfaction
The decision by most governments to implement e Government platforms is motivated by the need to achieve a high level of operational efficiency in the provision of diverse government services. Bhattacherjee (2001) argues that e Government not only aids in eliminating bureaucracies in the provision of diverse government services, but also enhances the efficiency with which government services are decentralized. Different e Government platforms have been developed over the years; however, it is fundamental for governments to determine the level of satisfaction achieved by utilizing a particular e-government technology. In order to achieve this goal, respective governments should measure the users’ satisfaction with the implemented e Government platforms.
Different models of measuring the level of satisfaction amongst users have been formulated, with one of the mostly used being the end-user computing satisfaction model. Bhattacherjee (2001) asserts that the users’ satisfaction is subject to the perceived usefulness of the implemented information system and the level of confirmation. The end-user model assesses elements such as level of accuracy, content provided, format and timeliness, with the view to determining the level of satisfaction that a particular technology can provide to users. The level of confirmation means that the technology has been effective in meeting the users’ expected benefits through the attained unique experiences. It is important to mention that perceived usefulness has a direct effect on the level of satisfaction that consumers get from using a particular service, in large part because satisfaction acts as the reference point for expectation confirmation (Thong, Hong, and Tam 2006).
Continuance intention
Chen et al (2010) define continuance intention as the citizens’ decision to continue using e-government technologies. One of the factors that affect the users’ continuance intention is the level of trust developed amongst the target users. Fadel (2012) asserts that trust is a fundamental element in determining the citizen’s decision to continue using the implemented e Government platforms. Bhattacherjee (2001) further opines that trust influences continuance intention through the post-usage attitude developed.
In order to develop continuance intention with regard to e Government, it is imperative for the government to foster a high level of trust amongst citizens. According Venkatesh et al. (2011, p.548), this goal can be achieved through the integration of a number of dimensions, which include integrity (degree or level of honesty associated with using available e Government platforms), trust (ensures the maintenance of and privacy of the user), benevolence (conviction that using a particular e Government platform will act in the citizens’ best interests), and competence. Bhattacherjee (2001) asserts that one of the major risks facing information communication systems relates to security threat, which has emanated from the high rate of hacking and phishing experienced on computer systems.
According to Roberts, Hann, and Slaughter (2006), continuance intention of information system is affected by the degree to which citizens are involved in its implementation and the level of satisfaction achieved. Bhattacherjee (2001) emphasizes that inadequate or absence of citizen involvement in the implementation of e Government systems reduces the users’ perception on their creditability and reliability. On the other hand, citizen involvement fosters completeness of the system. Bhattacherjee (2001) is of the opinion that satisfaction is a fundamental determinant of IS continuance. Consequently, it is imperative for governments to adopt citizen-centric model in the course of implementing various e Government platforms.
Conclusion
The development of ICT has presented governments with an opportunity to improve their service delivery through the implementation of various e Government technologies. However, the long-term success of the implemented technologies depends on the post-adoption behaviors developed by the citizens. Thus, governments have a responsibility to ensure that citizens appreciate the implemented electronic platforms intended to deliver various e Government services. By creating awareness, people will adopt the usage of the various electronic platforms, which will in turn foster continued usage of e Government services.
Different models have been formulated in an effort to emphasize the significance of fostering continuance intention and positive post-adoption behavior. Some of these models include the technology acceptance model, the expectation-confirmation model (ECM), unified theory of acceptance and use of technology model, and the self-service technology attitude-intention model. One of the core similarities of these models is their emphasis on the importance of fostering perceived usefulness of the technology and the perceived ease of use. Moreover, the analysis shows that continuance usage intention is subject to the level of satisfaction and expectation confirmation. Subsequently, it is imperative for governments to ensure that the implemented e Government platforms contribute to a high level of satisfaction amongst the citizen. This goal can be achieved by adopting a citizen-centric model.
Reference
Ahuja, M & Thatcher, J 2005, ‘ Moving beyond intentions and towards the theory of trying; effects of working environments on gender and post-adoption information technology’, MIS Quarterly, vol. 29, no. 3, pp. 191-224.
Alalwan, J 2013, ‘Continuance intention to use government 2.0 services; the impact of citizens satisfaction ad involvement’, International Journal of Electronic Government Research, vol. 9, no. 3, pp. 58-73.
Beaudry, A & Pinsonneault, A 2005, ‘Understanding user responses to information technology: A coping model of user adaptation’, MIS Quarterly, vol. 29, no. 3, pp. 493-524.
Bhattacherjee, A 2001, ‘Understanding information systems continuance; an expectation-confirmation model’, MIS Quarterly, vol. 25, no. 3, pp. 351-370.
Brown, S, Venkatesh, V, Kuruzovich, J & Massey, A 2008, ‘Expectation confirmation; an examination of three competing models’, Organizational Behavior and Human Decision Processes, vol. 105, no. 4, pp. 52-66.
Chen, Y, Hsu, Y, Tseng, H & Lee, Y 2010, ‘Confirmation expectation and satisfaction with the internet shopping; the role of internet self-efficacy’, Computer and Information Science, vol. 3, no. 3, pp. 1-9.
Curran, J, Meuter, M & Surprenant, C 2003, ‘Intentions to use self-service technologies; a confluence of multiple attitudes’, Journal of Service Research, vol. 5,no. 3, pp. 209-224.
Fadel, K 2012, ‘User adaptation and infusion of information systems’, Journal of Computer Information Systems, vol. 52, no. 3, pp. 1-10.
Garaca, Z 2011, ‘Factors related to the intended use of ERP system’, Management, vol. 16, no. 2, pp. 23-42.
Kim, Y & Crowston, K 2011, Technology adoption and use theory review for studying scientists’ continued use of cyber-infrastructure, School of Information Studies, Syracuse.
Lee, Y 2011, ‘m-Brand loyalty and post adoption variations for the mobile data services; gender differences’, Computers in Human Behavior, vol. 27, pp. 2364-2371.
Lee-Post, A 2007, ‘E-learning success model: An information systems perspective’, Electronic Journal of e-Learning, vol. 7, no. 1, pp. 61-70.
Roca, J, Chiu, C & Martinez, F 2006, ‘Understanding e-learning continuance intention; an extension of the Technology Acceptance Model’, International Journal of Human Computer Studies, vol. 64, pp. 683-696.
Roberts, J, Hann, I & Slaughter, S 2006, ‘Understanding the motivations, participation, and performance of open sources software developers; a longitudinal study of the apache projects’, Management Science, vol. 52, no. 7, pp. 984-999.
Rokhman, A 2011, ‘E-government adoption in developing countries; the case of Indonesia’, Journal of Emerging Trends in Computing and Information Sciences, vol. 2, no. 5, pp. 78-91.
Saeed, K & Abdinnour-Helm, S 2008, ‘Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems’, Information and Management, vol. 45, no. 3, pp. 376-386.
Sanchez-Franco, M, Villarejo-Ramos, A & Martin-Velicia, F 2011, ‘Social integration and post adoption usage of social network sites; an analysis of effects on learning performance’, Procedia Social and Behavioral Sciences, vol. 15, no. 3, pp. 256-262.
Santhanamergy, T & Ramayah, T 2013, ‘The effect of personality traits on user continuance usage intention of e-filing system’, Journal of Economics, Business and Management, vol. 1, no. 1, pp. 25-30.
Thong, J, Hong, S & Tam, K 2006, ‘The effects of post-adoption beliefs on the expectation confirmation model for information technology continuance’, International Journal of Human Computer Studies, vol. 64, pp. 799-810.
Venkatesh, V, Thon, J, Chan, F, Hu, P & Brown, S 2011, ‘ Extending the two-stage information systems continuance model; incorporating UTAUT predictors and role of context’, Information Systems’ Journal, vol. 21, no.2, pp. 527-555.
Wangpipatwong, S, Chutimaskul, W & Papasratorn, B 2008, ‘ Understanding citizen’s continuance intention to use e-government website; a composite view of technology acceptance model and computer self-efficacy’, Electronic Journal of E-Government, vol. 6, no. 1, pp. 55-64.