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A Predictive Model for Citizens’ Utilization of Open Government Data Portals

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Digital Libraries at Times of Massive Societal Transition (ICADL 2020)

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Abstract

Open government data (OGD) initiatives for building OGD portals have not yet delivered the expected benefits of OGD to the whole of society. Although citizens’ reluctance to use OGD has become a key problem in the present OGD development, limited studies have been carried out to investigate citizens’ actual usage of OGD and OGD portals. In order to fill this research gap, this study primarily focuses on predicting citizens’ actual utilization of OGD portals. To find features influencing citizens’ utilization of OGD portals and to predict their actual usage of OGD portals, an experiment was designed and carried out in China. A predictive model was built with C5.0 algorithm based on data collected through the experiment, with a predictive accuracy rate of 84.81%. Citizens’ monthly income, the compatibility of OGD portals, and citizens’ attentiveness regarding their interactions with OGD portals are found to be the most important factors influencing citizens’ actual utilization of OGD portals. Positive effects of compatibility, attentiveness, and perceived usefulness on citizens’ usage of OGD portals are noticed.

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Author information

Authors and Affiliations

  1. Wuhan University, Wuhan, 430072, Hubei, China

    Di Wang & Chuanfu Chen

  2. Macquarie University, Sydney, NSW, 2109, Australia

    Di Wang, Deborah Richards & Ayse Aysin Bilgin

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  1. Di Wang
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  2. Deborah Richards
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  3. Ayse Aysin Bilgin
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  4. Chuanfu Chen
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Correspondence to Di Wang .

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Editors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Emi Ishita

  2. National University of Singapore, Singapore, Singapore

    Natalie Lee San Pang

  3. Wuhan University, Wuhan, China

    Lihong Zhou

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Wang, D., Richards, D., Bilgin, A.A., Chen, C. (2020). A Predictive Model for Citizens’ Utilization of Open Government Data Portals. In: Ishita, E., Pang, N.L.S., Zhou, L. (eds) Digital Libraries at Times of Massive Societal Transition. ICADL 2020. Lecture Notes in Computer Science(), vol 12504. Springer, Cham. https://doi.org/10.1007/978-3-030-64452-9_14

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  • DOI: https://doi.org/10.1007/978-3-030-64452-9_14

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