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

Di Wang; Deborah Richards; Ayse Aysin Bilgin; Chuanfu Chen

doi:10.1007/978-3-030-64452-9_14

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

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

6 Citations (Scopus)

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 language	English
Title of host publication	Digital Libraries at Times of Massive Societal Transition
Subtitle of host publication	22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Proceedings
Editors	Emi Ishita, Natalie Lee Pang, Lihong Zhou
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Pages	159-175
Number of pages	17
ISBN (Electronic)	9783030644529
ISBN (Print)	9783030644512
DOIs	https://doi.org/10.1007/978-3-030-64452-9_14
Publication status	Published - 2020
Event	22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020 - Kyoto, Japan Duration: 30 Nov 2020 → 1 Dec 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12504 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020
Country/Territory	Japan
City	Kyoto
Period	30/11/20 → 1/12/20

Keywords

Open data utilization
Open government data portal
Predictive model

Access to Document

10.1007/978-3-030-64452-9_14

Cite this

Wang, D., Richards, D., Bilgin, A. A., & Chen, C. (2020). A predictive model for citizens’ utilization of open government data portals. In E. Ishita, N. L. Pang, & L. Zhou (Eds.), Digital Libraries at Times of Massive Societal Transition: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Proceedings (pp. 159-175). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12504 LNCS). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-64452-9_14

Wang, Di ; Richards, Deborah ; Bilgin, Ayse Aysin et al. / A predictive model for citizens’ utilization of open government data portals. Digital Libraries at Times of Massive Societal Transition: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Proceedings. editor / Emi Ishita ; Natalie Lee Pang ; Lihong Zhou. Cham, Switzerland : Springer, Springer Nature, 2020. pp. 159-175 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7c6d39d0e625400fa9a6dab070068f74,

title = "A predictive model for citizens{\textquoteright} utilization of open government data portals",

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{\textquoteright} reluctance to use OGD has become a key problem in the present OGD development, limited studies have been carried out to investigate citizens{\textquoteright} actual usage of OGD and OGD portals. In order to fill this research gap, this study primarily focuses on predicting citizens{\textquoteright} actual utilization of OGD portals. To find features influencing citizens{\textquoteright} 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{\textquoteright} monthly income, the compatibility of OGD portals, and citizens{\textquoteright} attentiveness regarding their interactions with OGD portals are found to be the most important factors influencing citizens{\textquoteright} actual utilization of OGD portals. Positive effects of compatibility, attentiveness, and perceived usefulness on citizens{\textquoteright} usage of OGD portals are noticed.",

keywords = "Open data utilization, Open government data portal, Predictive model",

author = "Di Wang and Deborah Richards and Bilgin, \{Ayse Aysin\} and Chuanfu Chen",

year = "2020",

doi = "10.1007/978-3-030-64452-9\_14",

language = "English",

isbn = "9783030644512",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer, Springer Nature",

pages = "159--175",

editor = "Emi Ishita and Pang, \{Natalie Lee\} and Lihong Zhou",

booktitle = "Digital Libraries at Times of Massive Societal Transition",

address = "United States",

note = "22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020 ; Conference date: 30-11-2020 Through 01-12-2020",

}

Wang, D, Richards, D , Bilgin, AA & Chen, C 2020, A predictive model for citizens’ utilization of open government data portals. in E Ishita, NL Pang & L Zhou (eds), Digital Libraries at Times of Massive Societal Transition: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12504 LNCS, Springer, Springer Nature, Cham, Switzerland, pp. 159-175, 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Kyoto, Japan, 30/11/20. https://doi.org/10.1007/978-3-030-64452-9_14

A predictive model for citizens’ utilization of open government data portals. / Wang, Di; Richards, Deborah ; Bilgin, Ayse Aysin et al.
Digital Libraries at Times of Massive Societal Transition: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Proceedings. ed. / Emi Ishita; Natalie Lee Pang; Lihong Zhou. Cham, Switzerland: Springer, Springer Nature, 2020. p. 159-175 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12504 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - A predictive model for citizens’ utilization of open government data portals

AU - Wang, Di

AU - Richards, Deborah

AU - Bilgin, Ayse Aysin

AU - Chen, Chuanfu

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Open data utilization

KW - Open government data portal

KW - Predictive model

UR - https://www.scopus.com/pages/publications/85097564930

U2 - 10.1007/978-3-030-64452-9_14

DO - 10.1007/978-3-030-64452-9_14

M3 - Conference proceeding contribution

AN - SCOPUS:85097564930

SN - 9783030644512

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 159

EP - 175

BT - Digital Libraries at Times of Massive Societal Transition

A2 - Ishita, Emi

A2 - Pang, Natalie Lee

A2 - Zhou, Lihong

PB - Springer, Springer Nature

CY - Cham, Switzerland

T2 - 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020

Y2 - 30 November 2020 through 1 December 2020

ER -

Wang D, Richards D , Bilgin AA, Chen C. A predictive model for citizens’ utilization of open government data portals. In Ishita E, Pang NL, Zhou L, editors, Digital Libraries at Times of Massive Societal Transition: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Proceedings. Cham, Switzerland: Springer, Springer Nature. 2020. p. 159-175. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-64452-9_14

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

Abstract

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Keywords

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Implementation of a conversational virtual assistant for open government data portal: effects on citizens

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