A methodology for structured literature network meta-analysis. (4th November 2020)

• Record Type: Journal Article
• Authors: Murugaiyan, Pachayappan; Ramakrishnan, Venkatesakumar
• Is Part Of: Journal of modelling in management. Volume 17:Number 1(2022)
• Page Start: 4
• Page End: 48
• Publication Date: 2020-11-04
• Subjects: Literature review -- Meta-analysis -- Graph theory -- Evidence-based -- Gephi -- Social networks -- Data mining; Industrial management -- Mathematical models -- Periodicals; Industrial management -- Computer simulation -- Periodicals; Business -- Mathematical models -- Periodicals; Business -- Computer simulation -- Periodicals; 658.4033
• DOI: 10.1108/JM2-01-2020-0009
• Languages: English
• ISSNs: 1746-5664

Abstract

Abstract : Purpose: Little attention has been paid to restructuring existing massive amounts of literature data such that evidence-based meaningful inferences and networks be drawn therefrom. This paper aims to structure extant literature data into a network and demonstrate by graph visualization and manipulation tool "Gephi" how to obtain an evidence-based literature review. Design/methodology/approach: The main objective of this paper is to propose a methodology to structure existing literature data into a network. This network is examined through certain graph theory metrics to uncover evidence-based research insights arising from existing huge amounts of literature data. From the list metrics, this study considers degree centrality, closeness centrality and betweenness centrality to comprehend the information available in the literature pool. Findings: There is a significant amount of literature on any given research problem. Approaching this massive volume of literature data to find an appropriate research problem is a complicated process. The proposed methodology and metrics enable the extraction of appropriate and relevant information from huge quantities of literature data. The methodology is validated by three different scenarios of review questions, and results are reported. Research limitations/implications: The proposed methodology comprises of more manual hours to structure literature data. Practical implications: This paper enables researchers in any domain to systematically extract and visualize meaningful and evidence-based insights from existing literature. Originality/value: The procedure for converting literature data into a network representation is not documented in the existing literature. The paper lays down the procedure to structure literature data into a network.