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Publication date: 20 April 2023

Ahmad Nadzri Mohamad, Allan Sylvester and Jennifer Campbell-Meier

This study aimed to develop a taxonomy of research areas in open government data (OGD) through a bibliometric mapping tool and a qualitative analysis software.

Abstract

Purpose

This study aimed to develop a taxonomy of research areas in open government data (OGD) through a bibliometric mapping tool and a qualitative analysis software.

Design/methodology/approach

In this study, the authors extracted metadata of 442 documents from a bibliographic database. The authors used a bibliometric mapping tool for familiarization with the literature. After that, the authors used qualitative analysis software to develop taxonomy.

Findings

This paper developed taxonomy of OGD with three research areas: implementation and management, architecture, users and utilization. These research areas are further analyzed into seven topics and twenty-eight subtopics. The present study extends Charalabidis et al. (2016) taxonomy by adding two research topics, namely the adoption factors and barriers of OGD implementations and OGD ecosystems. Also, the authors include artificial intelligence in the taxonomy as an emerging research interest in the literature. The authors suggest four directions for future research: indigenous knowledge in open data, open data at local governments, development of OGD-specific theories and user studies in certain research themes.

Practical implications

Early career researchers and doctoral students can use the taxonomy to familiarize themselves with the literature. Also, established researchers can use the proposed taxonomy to inform future research. Taxonomy-building procedures in this study are applicable to other fields.

Originality/value

This study developed a novel taxonomy of research areas in OGD. Taxonomy building is significant because there is insufficient taxonomy of research areas in this discipline. Also, conceptual knowledge through taxonomy creation is a basis for theorizing and theory-building for future studies.

Details

Online Information Review, vol. 48 no. 1
Type: Research Article
ISSN: 1468-4527

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