Industrial revolution and environmental sustainability: an analytical interpretation of research constituents in Industry 4.0

被引:30
|
作者
Malik, Arun [1 ]
Sharma, Shamneesh [2 ]
Batra, Isha [1 ]
Sharma, Chetan [2 ]
Kaswan, Mahender Singh [3 ]
Garza-Reyes, Jose Arturo [4 ]
机构
[1] Lovely Profess Univ, Sch Comp Sci & Engn, Phagwara, Punjab, India
[2] UpGrad Educ Private Ltd, UpGrad Campus, Mumbai, India
[3] Lovely Profess Univ, Sch Mech Engn, Phagwara, India
[4] Univ Derby, Ctr Supply Chain Improvement, Derby, England
关键词
Text mining; Latent semantic analysis; Industry; 4; 0; Environmental sustainability; Revolution; TRENDS; CHALLENGES; KNIME;
D O I
10.1108/IJLSS-02-2023-0030
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
PurposeEnvironmental sustainability is quickly becoming one of the most critical issues in industry development. This study aims to conduct a systematic literature review through which the author can provide various research areas to work on for future researchers and provide insight into Industry 4.0 and environmental sustainability. Design/methodology/approachThis study accomplishes this by performing a backward analysis using text mining on the Scopus database. Latent semantic analysis (LSA) was used to analyze the corpus of 4,364 articles published between 2013 and 2023. The authors generated ten clusters using keywords in the industrial revolution and environmental sustainability domain, highlighting ten research avenues for further exploration. FindingsIn this study, three research questions discuss the role of environmental sustainability with Industry 4.0. The author predicted ten clusters treated as recent trends on which more insight is required from future researchers. The authors provided year-wise analysis, top authors, top countries, top sources and network analysis related to the topic. Finally, the study provided industrialization's effect on environmental sustainability and the future aspect of automation. Research limitations/implicationsThe reliability of the current study may be compromised, notwithstanding the size of the sample used. Poor retrieval of the literature corpus can be attributed to the limitations imposed by the search words, synonyms, string construction and variety of search engines used, as well as to the accurate exclusion of results for which the search string is insufficient. Originality/valueThis research is the first-ever study in which a natural language processing technique is implemented to predict future research areas based on the keywords-document relationship.
引用
收藏
页码:22 / 49
页数:28
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