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      The impact of e-government information quality (EGIQ) dimensions on the adoption of electronic government services

      1 , 2
      Information Development

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          Abstract

          E-government's capacity to reform public service delivery is hinged on critical factors from the demand side of e-government. The demand side of e-government is the element that drives users’ behaviors to adopt e-government services and understanding these factors is fundamental to driving the realization of e-government targets. This study thus examined the extent to which e-government information quality dimensions can stimulate the acceptance and utilization of e-government services. The Unified Theory of Acceptance and Use of Technology (UTAUT) was used as the theoretical basis for this paper and the data congregated was evaluated with Analysis of Moment Structures (AMOS) statistical software using the structural equation modeling (SEM) approach. The results have revealed that both effort expectancy and facilitating conditions significantly drive the intention to use and recommend the adoption of e-government services. Also, e-government information quality dimensions such as availability, objectivity, utility, confidentiality, and integrity were all found to positively drive the intention to use e-government services. Additionally, people's intention to use e-government was significant in driving the recommendation behavior of e-government services to others. The practical and research repercussions of these discoveries on the development and deployment of e-government-empowered services are deliberated.

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          Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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            Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Information Development
                Information Development
                0266-6669
                1741-6469
                February 08 2023
                : 026666692311551
                Affiliations
                [1 ]Fujian Jiangxia University
                [2 ]Jiangxi University of Science and Technology
                Article
                10.1177/02666669231155164
                7ff211f1-b64f-4234-aaea-d9609ce57933
                © 2023

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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