Identification of success factors in E-service delivery of commercial order registration in the government of Islamic Republic of Iran

  • Authors:

  • Masoud Pourkiani;Sanjar Salajeghe;Mehdi Bagheri

  • Affiliations:

  • Department of management, Islamic Azad University, Kerman, Iran, University;Department of management, Islamic Azad University, Kerman, Iran, University;Department of management, Islamic Azad University, Kerman, Iran, University

  • Venue:
  • ASM'12 Proceedings of the 6th international conference on Applied Mathematics, Simulation, Modelling
  • Year:
  • 2012

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Abstract

The successful adoption of new technologies helps governments achieve efficiency in their implementation and delivery of public services to citizens. The objective behind various e-government initiatives has shifted in recent years towards establishing services that cater more to citizens' needs and offer greater accessibility. As a result, it is necessary to develop a well-founded theoretical framework to measure the success of such initiatives. The purpose of this thesis is to identify the success factors behind governmental eservice delivery from a citizen viewpoint. This research identifies and discusses three theoretical perspectives in approaching the research problem: IS and e-commerce success, success variables, and e-government success evaluation. A theoretical framework was developed to evaluate e-service delivery success. With study several disciplines (IS, e-commerce, and marketing), were made to develop a proposed success model for government e-services. Citizen satisfaction was proposed as a measure of e-government success, and its relationships were hypothesized with e-government system quality, information quality, e-service quality, perceived usefulness, perceived ease of use, and citizen trust. To test the proposed model, government e-order registration services in Iran was chosen as the application area, and a quantitative approach was deemed better suited to test the developed research model empirically. Correlation analysis was chosen as the statistical analysis techniques. The analytical results confirm most of the proposed relationships within the model.