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dc.contributor.advisorLo, K. Y. Chris (ITC)en_US
dc.contributor.advisorYeung, C. L. Andy (LMS)en_US
dc.contributor.authorZhou, Yien_US
dc.descriptionviii, 164 pages : color illustrationsen_US
dc.descriptionPolyU Library Call No.: [THS] LG51 .H577P ITC 2019 Zhouen_US
dc.description.abstractThis research comprehensively examines the environmental issues in Operations management in the following three studies. The first study is a citation network analysis, which systematically reviewed 246 articles related to environmental concerns in the nine most reputable peer-reviewed OM journals. Analyzing the citation network of these 246 articles, we used the Girvan-Newman (2002) algorithm to identify four main clusters. In each cluster, we presented central knowledge development and suggested areas for further research. In this research, we identified the research gap and the needs to study the impact of environmental violations. In the second study, we conducted a short-term event study to estimate the impact of environmental violations on firms' short-term performance (i.e., market value). This study based on the 618 environmental violations data published by China's Institute of Public and Environmental Affair (IPE) and the financial data of the public manufacturing firms in China. We found that the market reacted negatively to the environmental violation announcements. Also, we investigated the financial, operational, and social factors that could moderate the relationship. Lastly, we found that environmental violations caused by Chinese firms can have a significantly negative impact on the market value of their overseas customers. In the third study, we extended the sample in the second study and conducted a long-term event study based on the 1600 violations committed by 439 manufacturing firms. We examined the long-term impact of environmental violations and tried to find out whether a firm can maintain its short-term economic benefits of violating environmental rules in the long run. Based on the concept of supervised machine learning, we discovered the significant factors that may lead to more pollution for a firm. Also, we developed a prediction model with the aim to help China government identifying high-risk firms before they pollute.en_US
dc.description.sponsorshipInstitute of Textiles and Clothingen_US
dc.publisherThe Hong Kong Polytechnic Universityen_US
dc.rightsAll rights reserved.en_US
dc.subjectProduction management -- Environmental aspectsen_US
dc.subjectProduction management -- Environmental aspects -- Chinaen_US
dc.subjectEnvironmental management -- Chinaen_US
dc.titleEssays on environmental issues in operations management : the impact of environmental violationsen_US
dc.description.degreePh.D., Institute of Textiles and Clothing, The Hong Kong Polytechnic University, 2019en_US
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