Essays on environmental issues in operations management : the impact of environmental violations

Abstract

This 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.