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Examining the Factors of Open Government Data Usability From Academician's Perspective

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  • Muhammad Mahboob Khurshid

    (Department of Information Systems, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia)

  • Nor Hidayati Zakaria

    (Department of Information Systems, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia)

  • Ammar Rashid

    (NUR International University, Punjab, Lahore, Pakistan)

  • Muhammad Nouman Shafique

    (Dongbei University of Finance and Economics, Dalian, China)

Abstract

This article examines factors that can be argued to influence the academician's behavioral intentions in using open government data (OGD). Policy-makers and practitioners will determine policy instruments in increasing acceptance and use of OGD by maintaining a good understanding of these factors. In this article, Rogers' Diffusion of Innovations (DOI) theory has been proposed and used in order to empirically examine these factors taking perceived characteristics of innovations. Relevant hypotheses have been developed through the literature review, forming a preliminary research model, while respective influences of the factors on the behavioral intention to use open government data have been statistically tested. Results have shown that compatibility and voluntariness have had a strong influence on behavioral intention, whereas a 66.2% variance has been found in academicians' behavioral intentions to use open government data.

Suggested Citation

  • Muhammad Mahboob Khurshid & Nor Hidayati Zakaria & Ammar Rashid & Muhammad Nouman Shafique, 2018. "Examining the Factors of Open Government Data Usability From Academician's Perspective," International Journal of Information Technology Project Management (IJITPM), IGI Global, vol. 9(3), pages 72-85, July.
    • Handle: RePEc:igg:jitpm0:v:9:y:2018:i:3:p:72-85
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    Cited by:

    1. Jinhua Chu & You-Yu Dai & Anyuan Zhong, 2023. "Factors Influencing the Effectiveness of Open Government Data Platforms: A Data Analysis of 61 Prefecture-Level Cities in China," SAGE Open, , vol. 13(3), pages 21582440231, August.
    2. Junyoung Jeong & Keuntae Cho, 2024. "Proposing Machine Learning Models Suitable for Predicting Open Data Utilization," Sustainability, MDPI, vol. 16(14), pages 1-23, July.

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