4.7 Review

Analysis of the trend in the knowledge of environmental responsibility research

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 278, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.123402

Keywords

Environmental responsibility; Knowledge progress; Research opportunities; Knowledge graph

Funding

  1. National Natural Science Foundation of China [71471047]
  2. Research Grants Council of Hong Kong Special Administration Region [GRF PolyU 152031/17B]
  3. Hong Kong Polytechnic University [SB1F]

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This paper conducts a comprehensive knowledge review of environmental responsibility (ER) research by combining traditional bibliometric tools with the latest bibliometric software. Through methods like co-word analysis and social network analysis, it reveals the main research areas, evolution path, and research opportunities in ER studies.
With the rapid increase of environmental responsibility (ER) research in the past few years, it is common to see interdisciplinary studies in this field. Thus, it is important to conduct a comprehensive analysis of the knowledge progress of ER to better navigate the future research activities. This paper combines the advantages of the traditional bibliometric tools with the strengths of the latest bibliometric software CiteSpace to achieve a comprehensive knowledge review of ER. First, this work conducts a compre-hensive assessment and provides descriptive statistics on the sample papers. We extracted the top 52 keywords in 3656 ER papers from the Web of Science published in the past five years. By using co-word analysis, cluster analysis and social network analysis, this paper obtains keyword networks that cover five major categories of ER research: (1) stakeholder participation; (2) ER related theories; (3) management and performance; (4) sustainable development supply chain; (5) drivers. Using the method of multi-dimensional scaling, we proposed a spatial framework that identifies the main spatial structure of the current ER research by summarizing the high-frequency terminologies. We used the time-line mapping and strong citation burst in CiteSpace to present the knowledge evolution path of ER, revealing how ER-related research evolved over time, and to triangulate the results of cluster analysis. We also summarize three features of ER research and thus identifies research opportunities. At last, we build a knowledge graph model of ER research by integrating knowledge base, knowledge domain, and knowledge evolution in ER field. (C) 2020 Elsevier Ltd. All rights reserved.

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