Analysis and synthesis of Industry 4.0 research landscape: Using latent semantic analysis approach. Issue 1 (21st June 2019)

• Record Type: Journal Article
• Authors: Wagire, Aniruddha Anil; Rathore, A.P.S.; Jain, Rakesh
• Is Part Of: Journal of manufacturing technology management. Volume 31:Issue 1(2020)
• Page Start: 31
• Page End: 51
• Publication Date: 2019-06-21
• Subjects: Technological innovation -- Latent semantic analysis -- Industry 4.0 -- Advanced manufacturing technology; Computer integrated manufacturing systems -- Periodicals; Manufacturing processes -- Automation -- Periodicals; Industrial engineering -- Periodicals; Manufacturing industries -- Management -- Periodicals; 670.5
• DOI: 10.1108/JMTM-10-2018-0349
• Languages: English
• ISSNs: 1741-038X

Abstract

Abstract : Purpose: In recent years, Industry 4.0 has received immense attention from academic community, practitioners and the governments across nations resulting in explosive growth in the publication of articles, thereby making it imperative to reveal and discern the core research areas and research themes of Industry 4.0 extant literature. The purpose of this paper is to discuss research dynamics and to propose a taxonomy of Industry 4.0 research landscape along with future research directions. Design/methodology/approach: A data-driven text mining approach, Latent Semantic Analysis (LSA), is used to review and extract knowledge from the large corpus of the 503 abstracts of academic papers published in various journals and conference proceedings. The adopted technique extracts several latent factors that characterise the emerging pattern of research. The cross-loading analysis of high-loaded papers is performed to identify the semantic link between research areas and themes. Findings: LSA results uncover 13 principal research areas and 100 research themes. The study discovers "smart factory" and "new business model" as dominant research areas. A taxonomy is developed which contains five topical areas of Industry 4.0 field. Research limitations/implications: The data set developed is based on systematic article refining process which includes the keywords search in selected electronic databases and articles limited to English language only. So, there is a possibility that other related work may not be captured in the data set which may be published in other than examined databases and are in non-English language. Originality/value: To the best of the authors' knowledge, this study is the first of its kind that has used the LSA technique to reveal research trends in Industry 4.0 domain. This review will be beneficial to scholars and practitioners to understand the diversity and to draw a roadmap of Industry 4.0 research. The taxonomy and outlined future research agenda could help the practitioners and academicians to position their research work.