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Proposing a framework of barriers to opening government data in China: A critical literature review

Ruhua Huang (School of Information Management, Wuhan University, Wuhan, China)
Tong Lai (School of Information Management, Wuhan University, Wuhan, China)
Lihong Zhou (School of Information Management, Wuhan University, Wuhan, China)

Library Hi Tech

ISSN: 0737-8831

Article publication date: 18 September 2017

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Abstract

Purpose

This paper reports on a critical literature review, which aimed to identify, understand and qualify barriers that hinder the release of open government data (OGD) in China. Moreover, the purpose of this paper is to develop and propose a theoretical framework, which can be adopted as a basis for empirical investigation in the future, and to articulate mitigating strategies.

Design/methodology/approach

This study adopted an inductive qualitative approach, retrieving 42 academic articles from three main Chinese academic databases: CNKI, Wanfang and CQVIP. A thematic analysis approach was employed for the literature analysis.

Findings

The literature analysis pointed to 15 barriers to the release of OGD in China. Furthermore, the barriers emerged in the following three main themes: institutional barriers, data integrity and quality barriers, and user participation barriers.

Originality/value

This paper reports on one of the early research efforts into the problems of releasing OGD in China. Although this study focusses on Chinese context and issues, the findings and lessons learnt can be shared across international borders.

Keywords

Acknowledgements

The research is sponsored by the project supported by the National Social Science Foundation of China (Grant No. 15ZDC025).

Citation

Huang, R., Lai, T. and Zhou, L. (2017), "Proposing a framework of barriers to opening government data in China: A critical literature review", Library Hi Tech, Vol. 35 No. 3, pp. 421-438. https://doi.org/10.1108/LHT-01-2017-0014

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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