Optimized E-Government User Support Allocation and Its Influence on Citizens’ Adoption of E-Government: An Agent Based Approach

Optimized E-Government User Support Allocation and Its Influence on Citizens’ Adoption of E-Government: An Agent Based Approach

Shuang Chang (Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Kanagawa, Japan), Manabu Ichikawa (Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Kanagawa, Japan) and Hiroshi Deguchi (Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Kanagawa, Japan)
Copyright: © 2013 |Pages: 15
DOI: 10.4018/jkss.2013040101
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Abstract

In recent decades, E-government systems have been developed and deployed to provide more efficient, effective and transparent public services. However the citizen adoption rate is still relatively low. In order to encourage more citizens to utilize E-government services, there are many kinds of user support provided, though the effectiveness might vary among different social groups. Due to limited resources, if the authors allocate more resources to social groups who are not favoured by E-government service, it is very possible that in turn other social groups will not be satisfied and thus further influences the adoption rate. Therefore how to allocate the limited resources in an optimized way such that all the social groups are satisfied is a challenging and meaningful research problem. In this work they aim at resolving those conflicted objectives and achieving a Pareto optimal allocation of the resources among different social groups by using agent based approach with multi-objective genetic algorithm.
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1. Introduction

In recent decades, E-government research has attracted the attention of both researchers and government officers. According to OECD (OECD, 2003), E-government should modernize the public administration in order to prompt more citizen-centred services and to increase its adoption rate. Through years of development, the services provided by E-government have been evolved from the mere static information provision to more value-added services such as transactional services (Gil-Garcia & Luna-Reyes, 2003). However there is still no well-recognized and commonly agreed definition of E-government yet in literature (Halchin, 2004). Different definitions are proposed from various perspectives to grasp different aspects of this phenomenon (Jaeger, 2003; Brown & Brudney, 2001; Means & Schneider, 2000). There are several reasons for the difficulty of defining E-government. Firstly, besides the general purpose of E-government, there is no clear description of stakeholders’ activities involved. Therefore the meaning of E-government might depend on its particular context such as dominant social groups and enacted E-government related strategies (Yildiz, 2007; Halchin, 2004; Jaeger, 2003). Secondly, the needs and interests of different social groups might be diverse, thus most of the citizens might not grasp the overall clear image of E-government (Torres, Pina, & Acerete, 2005). As a result, understanding the particular social context and divergent citizens’ needs is an essential element of studying citizen-centred E-government system.

Generally the purpose of E-government is to promote transparency, convenience, and effectiveness to the citizens enabled by electronic means of public service delivery. According to UN and American Society for Public Administration (ASPA), E-government is “utilizing the Internet and the world-wide-web for delivering government information and services to citizens (UN & ASPA, 2002)”. Benefit of deploying E-government is obvious in the sense that through citizen-centred E-government, the quality of public service is improved and various services are provided to satisfy citizen’s diverse needs in a more flexible way (Venkatesh, Chan, & Thong, 2012). However, challenges are as well noticed. Around the world, the deployment rate and usage rate of transactional services are still relatively low (Sebie & Irani, 2005), and the user-side of E-government is almost overlooked.

In order to encourage more citizens to utilize E-government services, there are many strategies deployed such as improving E-government awareness and making the services more appealing. User support such as FAQ and service hotline that aim to help citizens use E-government in a much smoother way is also one of this kind. However from our previous work (Chang et al., 2012), we could observe that for different social groups, the effectiveness of user support varies. For social groups that have already utilized E-government frequently, the increased user support is more effective, which implies that special strategies should be carried out to improve the E-government adoption rate of social groups that haven’t utilized it frequently yet. On the other side, if we only pay special attention to the social groups who are not favoured by E-government such as elderly people, and allocate more resources to them, it is very possible that in turn other social groups will not be satisfied. Therefore in terms of user support, how to allocate the limited resources in an optimized way such that all the social groups are satisfied simultaneously is a challenging and meaningful research problem. In this work we aim to achieve a Pareto optimal (Sandler & Smith, 1982) allocation of the user support among different social groups. Furthermore, we evaluate the influence of this optimized resources allocation on citizens’ channel choice of taking up public services.

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