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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.
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.
Radio Frequency Identification (RFID) is a technology that not only serves to identify objects but also communicates other information, allowing the real-time monitoring of objects at each step in a mobile object network and the reporting of information on their current status. RFID has become one of the most promising research areas and has attracted increasing attention. This interest sparks a huge amount of literature in the field of RFID. However, the research has been conducted from different perspectives and, as a result, has led to a growing body of knowledge dispersed in different fields. To fill this gap, we carried out a systematic mapping study (SMS) based on a well-established research methodology from the medical and software engineering scientific communities, which aims to study and identify the approaches used, quantity and quality of publications, types of research, and publication trends that shaped the field of RFID research over the past two decades. Its results were based on 219 studies, rigorously selected from among 4294 studies identified in the IEEE Xplore, Scopus, and Web of Science digital libraries and classified according to the research type facet, research area facet, citation facet, and application domain facet. We synthesized and interpreted the results of this SMS to devise future research directions in the RFID domain. This breadth-first SMS provides a solid, comprehensive, and reproducible picture of state-of-the-art RFID technology; the obtained results may have implications for practitioners willing to understand and adopt RFID, including researchers, journal editors, reviewers, and universities. The results obtained revealed that (1) there is a considerable and continuous rise of RFID research activities around different parts of the globe, including in the USA and China, and other English-speaking developed countries, such as Australia, Canada, and the U.K., have a significant influence on this growth; (2) with the technological progress of RFID hardware components and increasingly demanding application domains, RFID technology brings opportunities in some new areas, such as "IoT applications", "Complex Environments", and "Industry 4.0"; (3) despite the high number of studies carried out in the field of RFID, especially in the hardware design and performances subfield, a limited number of works have detailed or focused on the "middleware" component of RFID systems, indicating that RFID data processing and management remain an open research issue; and (4) RFID domain challenges, gaps, and feasible future recommendations were highlighted in this study.
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