With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes.

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Since the South Korean government enacted the Emission Trading Scheme (ETS), companies have been striving to simultaneously improve productivity and reduce carbon emissions, which represent conflicting goals. We used firm-level emissions and corporate variables to investigate how ETS enactment has affected carbon productivity, which is a firm-level revenue created per unit of carbon emission. Results showed that firm-level carbon productivity increased significantly under the ETS, and such a trend was more evident for high-emission industries. We also found that companies with high carbon productivity were (1) profitable, (2) innovative, and (3) managed by CEOs with experience in environmental fields. These findings suggest that to achieve the conflicting goals of increasing corporate profits while reducing emissions, firms have to invest in green technologies, and such decisions are supported by green leadership. Our findings also have implications for corporate leadership; data highlight the importance of managing human resources and deploying investment policies to respond to ETS.

In this study, we examine various effects of carbon emission regulation enacted in South Korea. We provide empirical evidence of regulated firms strategically hedging against potential risks by increasing the number of directors with environment-related backgrounds. We also find that this relationship is clearly evidenced when the firm is owned by a lower proportion of foreign investors. Further analysis shows that these directors successfully change their firms to become environmentally friendly. Overall, we conclude that the role of governments in promoting green finance is crucial. The findings of this study may be used as a guideline for decision makers and environmental policymakers to create systems and policies to increase the firm’s awareness about the environment in relation to corporate environmental responsibility (CER) ratings of firms.

This article investigates the effect of a firm's adoption of director liability reduction coverage laws on their directors' bad news hoarding behavior. Using unique Korean institutional settings, we find that, compared to directors of noncovered firms, those of covered firms are more likely to withhold negative information, proxied by stock price crash risk measures. Our regression analysis implies that legal protections of a company through DLR coverage makes directors relatively relaxed about litigation risks, which induces them to take advantage of the laws. Furthermore, we find that the relation manifests when the firm is owned by a high proportion of foreign investors, covered by many financial analysts, and is less regulated by listed exchange.

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