{"title":"工业革命与环境可持续性:工业4.0研究成分的分析解释","authors":"Arun Malik, Shamneesh Sharma, Isha Batra, Chetan Sharma, Mahender Singh Kaswan, J. Garza‐Reyes","doi":"10.1108/ijlss-02-2023-0030","DOIUrl":null,"url":null,"abstract":"\nPurpose\nEnvironmental 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.\n\n\nDesign/methodology/approach\nThis 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.\n\n\nFindings\nIn 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.\n\n\nResearch limitations/implications\nThe 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.\n\n\nOriginality/value\nThis 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.\n","PeriodicalId":48601,"journal":{"name":"International Journal of Lean Six Sigma","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Industrial revolution and environmental sustainability: an analytical interpretation of research constituents in Industry 4.0\",\"authors\":\"Arun Malik, Shamneesh Sharma, Isha Batra, Chetan Sharma, Mahender Singh Kaswan, J. Garza‐Reyes\",\"doi\":\"10.1108/ijlss-02-2023-0030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nEnvironmental 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.\\n\\n\\nDesign/methodology/approach\\nThis 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.\\n\\n\\nFindings\\nIn 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.\\n\\n\\nResearch limitations/implications\\nThe 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.\\n\\n\\nOriginality/value\\nThis 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.\\n\",\"PeriodicalId\":48601,\"journal\":{\"name\":\"International Journal of Lean Six Sigma\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2023-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Lean Six Sigma\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1108/ijlss-02-2023-0030\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Lean Six Sigma","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1108/ijlss-02-2023-0030","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Industrial revolution and environmental sustainability: an analytical interpretation of research constituents in Industry 4.0
Purpose
Environmental 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/approach
This 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.
Findings
In 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/implications
The 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/value
This 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.
期刊介绍:
Launched in 2010, International Journal of Lean Six Sigma publishes original, empirical and review papers, case studies and theoretical frameworks or models related to Lean and Six Sigma methodologies. High quality submissions are sought from academics, researchers, practitioners and leading management consultants from around the world. Research, case studies and examples can be cited from manufacturing, service and public sectors. This includes manufacturing, health, financial services, local government, education, professional services, IT Services, transport, etc.