Text Analysis on Green Supply Chain Practices of Electronic Companies

被引：0
作者：
Balan, Shilpa [1 ]
Conlon, Sumali [2 ]
Reithel, Brian [2 ]
机构：
[1] California State University, Los Angeles
来源：
International Journal of Decision Support System Technology | 2024年 / 16卷 / 01期
关键词：
Collocation; Data Mining; EICC; Electronics Industry; Frequent Pattern Mining; Green Supply Chain; Information Extraction; NLP;
D O I：
10.4018/IJDSST.358950
中图分类号：
学科分类号：
摘要：
The electronics industry is one of the major regulated industries in the United States that is profoundly impacted by environmental issues. In this study, we use natural language processing (NLP) techniques to analyze reports from major electronics companies to examine the impact on their environmental performance in alignment with the standards set by the U.S. Environmental Protection Agency (EPA). We applied collocation, semantic analysis and frequent pattern mining to evaluate the documented practices of green supply chain management used by firms in the electronics industry. The results from our study indicate that NLP analysis can be used on publicly available reports to highlight some of the best practices followed in a regulated industry. The electronic firms included in this study are found to be focused on energy efficiency implying that the firms are likely to be more environmentally sustainable. NLP tools present opportunities for investigating and documenting regulatory compliance. © 2024 IGI Global. All rights reserved.
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  • [1] Agrawal R., Imielinski T., Swami A., Database Mining: A Performance Perspective, IEEE Transactions on Knowledge and Data Engineering. Special Issue on Learning and Discovery in Knowledge-Based Databases, 5, 6, pp. 914-925, (1993)
  • [2] Balan S., Conlon S., Evaluating Best Practices in Green Supply Chain, AMCIS 2012 Proceedings, (2012)
  • [3] Balan S., Conlon S., Text Analysis of Green Supply Chain Practices in Healthcare, Journal of Computer Information Systems, 58, 1, pp. 30-38, (2016)
  • [4] Benzidia S., Makaoui N., Bentahar O., The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance, Technological Forecasting and Social Change, 165, (2021)
  • [5] Biahmou A., Stjepandic J., Towards agile enterprise rights management in engineering collaboration, International Journal of Agile Systems and Management, 9, 4, pp. 302-325, (2016)
  • [6] Brandenburg M., Govindan K., Sarkis J., Seuring S., Quantitative models for sustainable supply chain management: Developments and directions, European Journal of Operational Research, 233, 2, pp. 299-312, (2014)
  • [7] Brightwood S., Godwin O., Steve M., Best Practices for Implementing AI-Powered Chatbots in Sustainable Supply Chain Management, (2024)
  • [8] Camacho-Collados J., Pilehvar M., Navigli R., Nasari: Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities, Artificial Intelligence, 240, pp. 36-64, (2016)
  • [9] Chiu M. C., Wei C. J., Wang Y. C., Kao M. C., Applying unsupervised learning method to develop a regional risk model based on TCFD: A case study in the United States, Journal of Cleaner Production, 400, (2023)
  • [10] Chou D. C., Chen H. G., Lin B., Green IT and Corporate Social Responsibility for Sustainability, Journal of Computer Information Systems, 63, 2, pp. 322-333, (2022)
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