The evolution of Industrial Management & Data Systems over the past 25 years: A bibliometric overview. Issue 1 (8th February 2019)

• Record Type: Journal Article
• Authors: Wang, Chao; Zhao, Longfeng; Vilela, André L.M.; Lim, Ming K.
• Is Part Of: Industrial management & data systems. Volume 119:Issue 1(2019)
• Page Start: 2
• Page End: 34
• Publication Date: 2019-02-08
• Subjects: Literature review -- Citation analysis -- Bibliometric -- Industrial Management & Data Systems; Industrial management -- Periodicals; Electronic data processing -- Periodicals; Business -- Periodicals; Industrial management -- Great Britain -- Periodicals; 658.05
• DOI: 10.1108/IMDS-11-2018-0506
• Languages: English
• ISSNs: 0263-5577

Abstract

Abstract : Purpose: The purpose of this paper is to examine publication characteristics and dynamic evolution of the Industrial Management & Data Systems ( IMDS ) over the past 25 years from volume 94, issue 1, in 1994 through volume 118, issue 9, in 2018, using a bibliometric analysis, and identify the leading trends that have affected the journal during this time frame. Design/methodology/approach: A bibliometric approach was used to provide a basic overview of the IMDS, including distribution of publication and citations, articles citing the IMDS, top-cited papers and publication patterns. Then, a complex network analysis was employed to present the most productive, influential and active authors, institutes and countries/regions. In addition, cluster analysis and alluvial diagram were used to analyze author keywords. Findings: This study presents the basic bibliometric results for the IMDS and focuses on exploring its performance over the last 25 years. And it reveals the most productive, influential and active authors, institutes and countries/regions in IMDS . Moreover, this study detects the existence of at least five different keywords clusters and discovers how themes have evolved through the intricate citation relationships in IMDS . Originality/value: The main contribution of this paper is the use of multiple analysis techniques from a complex network paradigm to emphasize the time evolving nature of the co-occurrence networks and to explore the variation of the collaboration networks in the IMDS . For the first time, the evolution of research themes is revealed with a purely data-driven approach.