Post-adoption Stage of E-government

Subject: Politics & Government
Pages: 10
Words: 2723
Reading time:
12 min
Study level: PhD

Introduction

Information Communication Technology [ICT] has become a fundamental element in countries’ economic transformation as evidenced by the emergence of the digital economy. Lee (2011, p.2366) posits that most governments around the world ‘appreciate the potential associated with employing ICT in supporting different government activities’. Most governments are increasingly implementing diverse web-based technologies in their service delivery, which has led to the emergence of the concept of e-government. Alalwan (2013) defines e-government as the usage of diverse web-based technologies by local, state, and federal agents in providing government services.

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The long-term sustainability of the implemented web technologies depends on the effectiveness with which the citizens appreciate the implemented technologies. Consequently, governments must adopt effective system adoption strategies. Ahuja and Thatcher (2005) argue that system adoption strategies are aimed at establishing the system, promoting its continued use, improving the level of user satisfaction, acceptance of the system, and promoting post-adoption behaviors. The adoption and usage of e-government technologies can be explained through three main stages, which include the pre-adoption, adoption, and post-adoption stage.

The post-adoption stage determines the future success of the implemented ICT. At the post-adoption stage, the intended users may continue using the implemented e-government technologies or abandon them. Alalwan (2013, p.58) asserts that if ‘they abandon a technology they may start to examine another technology at the same time in order to substitute their old technology’. This paper entails an investigation of the post-adoption stage of e-government to understand the effectiveness of developing continued usage behavior.

Definition of continuance intentions

Previous studies conducted have led to the formulation of diverse definitions of continuance intentions. Some of the common definitions are illustrated in the table below.

Authors Definitions
Santhanamergy and Ramayah (2013) Continuance usage intention refers to an individual’s decision to use a particular technology in the long term.
Chen, Hsu, Tseng, and Lee (2010) Continuance intention refers to the citizens’ repurchase intention or the decision to consume the various services delivered through web-based information systems.
Roca, Chiu, and Martinez (2006) Continuance intention refers to individuals’ decision to continue using the implemented information system more frequently as compared to the initial use.
Saeed and Abdinnour-Helm (2008) Continuance intention entails the process through which individuals decide to adopt and consume various electronic government services.
Lee (2011) Continuance intention entails the actual intention to repurchase or a particular service provided virtually through electronic platforms.

This study will adopt the definition of continuance intention by Chen, Hsu, Tseng, and Lee (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

To achieve the desired outcome through the implementation of e-government, governments must 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. Some of these models are explained herein.

Expectation-confirmation model [ECM]

This model is also referred to as the expectation-confirmation theory. Brown, Venkatesh, Kuruzovich, and Massey (2008) assert that the model is a two-stage model, which is used in studying the impact of information system (IS) on the users’ cognitive beliefs concerning the perceived usefulness and disconfirmation. The second stage evaluates how the information systems affect the citizens’ attitude and satisfaction during its utilization. The perceived usefulness component is used in explaining the extent to which the target consumer believes that utilizing the information system will improve his or her effectiveness in performing a particular task. Subsequently, it emphasizes the target users’ cognitive expectations on the performance of the implemented information system (Bhattacherjee 2001).

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Venkatesh et al. (2011, p.528) assert that 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 users’ expectations may change after experiencing the information systems’ performance. The users’ beliefs and attitudes about the IS may change after gaining hands-on experience. Consequently, the two-stage model cannot be generalized to understand the users’ beliefs (Venkatesh et al. 2011).

Unified Theory of Acceptance and Use of Technology [UTAUT]

Additional research has led to the identification of other core determinants of user behavior and user acceptance. These determinants include social influence, effort expectancy, performance expectancy, and facilitating conditions. Performance expectancy is similar to perceived usefulness. On the other hand, Venkatesh et al. (2011, p.530) posit, ‘facilitating conditions, social influence, and effort expectancy factor in the various aspects associated with the system use such as the usage environment, the costs involved and interpersonal considerations’. Venkatesh et al. (2011) emphasize that effort expectancy entails the amount of effort required in utilizing the information system. The rate of adoption is influenced by the degree of complexity associated with a particular innovation.

Venkatesh et al. (2011) believe that customers achieve a high level of satisfaction if the self-service technology implemented is easy to use. Therefore, effort expectancy can be a major hindrance to the continued usage of information systems. Consumers form certain perceptions and beliefs regarding the ease of using a particular technology before its utilization. However, these beliefs are adjusted after gaining hands-on experience (Lee-Post 2007).

Gaining hands-on experience may make the consumers develop a positive perception [such as user friendliness] regarding the technology (Venkatesh et al. 2011). Subsequently, the likelihood of developing continuance intention and a positive post-usage attitude increases.

The social influence dimension 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. Venkatesh et al. (2011, p.534) believe that normative influence ‘can be considered as the result of integrating one’s expectations and feelings with significant others’ perceived expectations and feelings’. An individual’s social influences play a fundamental role in the confirmation or disconfirmation of system usage. Venkatesh et al. (2011, p.538) add 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 systems. Facilitating conditions are correlated directly with the intention and usage of information systems. Lack of facilitating conditions harms the target users’ attitude. Facilitating conditions highlight the importance of developing effective telecommunication infrastructures such as Internet penetration to enhance the effectiveness of e-government.

The Self-Service Technology Attitude-Intention Models

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.

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Consumers can develop a negative attitude towards the service provider [employee] hence motivating one to use the self-service technology. For example, a consumer may develop a negative attitude towards bank tellers, which enhances the likelihood of using the implemented self-service technologies such as the Automated Teller Machines. Alternatively, the features associated with the SST may appeal 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 ICT, hence the intention for its continued usage.

Continued usage intention factors

Perceived Usefulness

This construct refers to the extent to which consumers believe that the implemented e-government information system 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. Wangpipatwong, Chutimaskul, and Papasratorn (2008, p.55) add that a ‘person’s willingness to transact with a particular system is already considered as perceived usefulness. The consumers’ perception of the implemented information system is affected by their expectations of the implemented technology.

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 virtually.

The findings of previous studies conducted show that there is 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). Moreover, other elements that determine the technologies’ perceived usefulness include the ability to work more quickly, increased productivity, and ease the performance of 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. Consumers develop the willingness to repurchase a particular product or service if they attain maximum utility from the initial consumption. Consequently, the repeat purchase behavior is influenced by the extent to which the product or service purchased meets the consumers’ expectations (Garaca 2011).

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Similarly, the extent to which consumers develop the decision to continue using a particular technology is influenced by the extent to which he or she attains the desired expectations. Confirmation of 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 capacity, which might culminate in under-fulfillment. Therefore, the relevant stakeholders must assess the target users’ expectations before implementing the e-government system. This move will aid in balancing between over-fulfillment and under-fulfillment of the target users’ expectations. According to Brown et al. (2007), 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 technologies 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 aids in eliminating bureaucracies in the provision of diverse government services. Moreover, e-government 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.

To achieve this goal, respective governments should measure the users’ satisfaction with the implemented e-government system.

Different models of measuring the level of satisfaction amongst users have been formulated. One of these models is 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 a number of elements, which determine the level of satisfaction. Some of these elements include the level of accuracy, content provided, format, and timeliness.

The level of confirmation means that the technology has been effective in meeting the users’ expected benefits through the attained unique experiences. Thong, Hong, and Tam (2006, p.1821) think that as ‘in the expectancy-confirmation paradigm, perceived usefulness has a positive impact on satisfaction. This assertion arises from the view that satisfaction acts as the reference point for expectation confirmation.

Continuance intention

Chen, Hsu, Tseng, and Lee (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 city’s decision to continue using the implemented e-government technology. Bhattacherjee (2001) further opines that trust influences continuance intention through the post-usage attitude developed.

To develop continuance intention concerning e-government, the government must foster a high level of trust amongst citizens. According to Venkatesh et al. (2011, p.548), this goal can be achieved through the ‘integration of several dimensions, which include integrity, benevolence, and competence whereby benevolence is the belief that using the e-government system will act in the citizens’ best interests’.

On the other hand, integrity entails the degree of honesty associated with using the e-government system. Trust is a fundamental aspect of e-government systems due to the need to maintain the security and privacy of the user. Bhattacherjee (2001) asserts that one of the major risks facing information communication systems relates to a security threat, which has emanated from the high rate of hacking and phishing on computer systems.

According to Roberts, Hann, and Slaughter (2006) continuance intention of an 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 lack of citizen involvement in the implementation of e-government systems reduces the users’ perception of its creditability and reliability. On the other hand, citizen involvement fosters the completeness of the system. Bhattacherjee (2001) thinks that satisfaction is a fundamental determinant of IS continuance. Consequently, governments must adopt the 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 ICT depends on the post-adoption behaviors developed by the citizens. Thus, governments have a responsibility to ensure that citizens appreciate the implemented e-government technologies. By creating awareness, people will adopt the usage of ICT, which will in turn foster continued usage of e-government.

Different models have been formulated 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], the unified theory of acceptance, and use of technology model, and the self-service technology attitude-intention model. One of the core similarities in these models is their emphasis on the importance of fostering the 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, governments must ensure that the implemented e-government systems contribute to a high level of satisfaction amongst the citizen. This goal can be achieved by adopting a citizen-centric model.

Reference List

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, 2005, ‘Understanding user responses to information technology: A coping model of user adaptation’, MIS Quarterly, vol. 29, no. 3, pp. 493-524.

Bhattacherjee, 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, 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.

Garcia, 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, 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.

Rothman, 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.