정하일 교수

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

In this study, we examine the effects of manager's perspectives on climate change on stock price crash risk. The analysis confirms that manager's climate change perspective is negatively associated with future stock price crash risk likelihood. Various channel tests show that investor attention and analyst coverage are potential channels through which a firm's climate change perspective improves financial stability and ultimately reduces crash risk. Our results are also robust to alternative cli

This paper investigates the effects of Emission Trading Scheme (ETS) adoption on the country-level reduction rate of carbon emission. We first used Environmental Kuznets Curve (EKC) tests to group countries into three categories: inverse U-shaped and gamma-shaped EKC for decoupled countries, and a positive linear EKC for non-decoupled countries. We then examined the effectiveness of ETS adoption. We found ETS was effective for both post-industrial and pre-industrial economies. Compared to countr

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 tre

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 friendl

This paper presents the development of a comprehensive, on-site industrial Optical Character Recognition (OCR) system tailored for reading text on iron plates. Initially, the system utilizes a text region detection network to identify the text area, enabling camera adjustments along the x and y axes and zoom enhancements for clearer text imagery. Subsequently, the detected text region undergoes line-by-line division through a text segmentation network. Each line is then transformed into rectangu

In the automobile manufacturing industry, inspecting the quality of heat staking points in a door trim involves significant labor, leading to human errors and increased costs. Artificial intelligence has provided the industry some aid, and studies have explored using deep learning models for object detection and image classification. However, their application to the heat staking process has been limited. This study applied an object detection algorithm, the You Only Look Once (YOLO) framework,

Abstract 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 relax

This study investigates the relationship between firm-level carbon productivity and volatility. With increasing interest in sustainable investing and inclusion of carbon productivity in financial assessments, we examine whether the market considers firms with high carbon productivity as less risky. Using U.S. firm-level carbon emission data, we find that carbon productivity is negatively associated with total and idiosyncratic volatilities. Our main findings hold under propensity score matching

This study investigates the relationship between the law of director liability reduction (DLR) and the level of corporate social responsibility (CSR). Using unique Korean institutional data, we show that firms that do not employ liability reduction coverage engage more heavily in CSR-related activities. This is primarily to control the litigation risk. Firms that have not adopted the DLR are vulnerable to litigation risks, and therefore, they strategically use CSR to hedge such risks. We also em

Abstract We investigate how a firm’s corporate pledgeable asset ownership (CPAO) affects the risk of future stock price crashes. Using pledgeable asset ownership and crash risk data for a large sample of U.S. firms, we provide novel empirical evidence that a firm’s risk of a future stock price crash decreases with an increase in its pledgeable assets. Our main findings are valid after conducting various robustness tests. Further channel tests reveal that firms with pledgeable assets increase the

Abstract This paper investigates the effects of Emission Trading Scheme (ETS) adoption on the country-level reduction rate of carbon emission. We first used Environmental Kuznets Curve (EKC) tests to group countries into three categories: inverse U-shaped and gamma-shaped EKC for decoupled countries, and a positive linear EKC for non-decoupled countries. We then examined the effectiveness of ETS adoption. We found ETS was effective for both post-industrial and pre-industrial economies. Compared

In the rapidly evolving field of printed circuit board (PCB) manufacturing, automated optical inspection (AOI) systems play a critical role but often face challenges such as computational inefficiencies, high costs, and limited defect data. To address these issues, we propose an ensemble methodology that combines lightweight models with custom data augmentation techniques to enhance defect classification accuracy in real-time production environments. Our approach mitigates overfitting in small d