Analyzing sustainability literature in maritime studies with text mining

Shin, S, Kwon, O, Ruan, X, Chhetri, P, Lee, P and Shahparvari, S 2018, 'Analyzing sustainability literature in maritime studies with text mining', Sustainability, vol. 10, no. 10, pp. 1-19.


Document type: Journal Article
Collection: Journal Articles

Title Analyzing sustainability literature in maritime studies with text mining
Author(s) Shin, S
Kwon, O
Ruan, X
Chhetri, P
Lee, P
Shahparvari, S
Year 2018
Journal name Sustainability
Volume number 10
Issue number 10
Start page 1
End page 19
Total pages 19
Publisher MDPIAG
Abstract Since the world's first Earth Summit in Rio de Janeiro in 1992, sustainability has become a focal point of significant debate for industry, government, and international organizations. As a result, research on sustainability of maritime logistics is on the rise, yet fragmented in terms of conceptual development, empirical testing and validation, and theory building. The aim of this paper is therefore two-fold: the first aim is to present a literature review of key journal articles in the field of maritime studies published between 1993 and 2017 using a technique of topic modelling; and the second is to provide future research directions with respect to major topics, themes and co-authorship patterns. Mapping and consolidation of sustainability issues are achieved by conducting a generative probabilistic text-mining technique, called latent Dirichlet allocation (LDA), for latent data discovery and relationships among text document data. Moreover, bibliometric analysis is conducted to visualize the landscape of sustainability research. Based on the results, a new intellectual structure of sustainability research is created, the underlying themes are identified, key trends and patterns are extracted and future research development trajectories are mapped for the field of maritime studies.
Subject Logistics and Supply Chain Management
Keyword(s) Latent Dirichlet Allocation
LDA
Maritime studies
Research trend
Sustainability
Topic modeling
DOI - identifier 10.3390/su10103522
Copyright notice © 2018 by the authors. Creative Commons Attribution 4.0
ISSN 2071-1050
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