A predictive model for citizens’ utilization of open government data portals

Di Wang*, Deborah Richards, Ayse Aysin Bilgin, Chuanfu Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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.

Original languageEnglish
Title of host publicationDigital Libraries at Times of Massive Societal Transition
Subtitle of host publication22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Proceedings
EditorsEmi Ishita, Natalie Lee Pang, Lihong Zhou
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages159-175
Number of pages17
ISBN (Electronic)9783030644529
ISBN (Print)9783030644512
DOIs
Publication statusPublished - 2020
Event22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020 - Kyoto, Japan
Duration: 30 Nov 20201 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12504 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020
CountryJapan
CityKyoto
Period30/11/201/12/20

Keywords

  • Open data utilization
  • Open government data portal
  • Predictive model

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