• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.103-123
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• 2022

Purpose - This study investigates the effects of earnings management, related party transactions between chaebol affiliates on earnings management and ESG score on their profitability using return on assets (ROA). Design/Methodology/Approach - We use data including ESG (Environmental, Social, and Corporate Governance) score of the Korea Corporate Governance Service(KCGS), and financial data of 10,145 firm-year observations from the Total Solution 2000 (TS 2000) and Korea Companies-Information Service (KOKOInfo), and apply the finite lagged models to investigate the long-term effects of related party transactions between chaebol affiliates of earnings management on ESG scores and corporate performance. Furthermore, to take into consideration the simultaneous mutual effects on each other of main variables, we introduce finite distributed lags of five years. Findings - First, ESG-rated firms have a higher total asset return than non-ESG-rated firms. Second, chaebol firms have a higher profitability than non-chaebol firms. Third, profit management of related party transactions between affiliates within a chaebol has a positive effect on the short-term profitability and a negative effect on the long-term profitability. Fourth, chaebol ESG firms have a lower impact on profitability due to rating up (down) than non-chaebol ESG firms. Research Implications or Originality - Based on the above results, it can be concluded that firms used related party transactions for earnings management, the effects of related party transactions change over time, and chaebol firms manipulate earnings through related party transactions and ESG scores.

• Tunnel and Underground Space
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• v.32 no.5
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• pp.327-343
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• 2022

The standard work report for trucks was developed that records data on truck haulage operations in open-pit and underground mines, and to evaluate the performance of haulage operations. Work reports used in 5 mines in Korea was secured and analyzed, and items to be included in the standard work report were determined. By analyzing the formulas for key performance indicators (KPIs) proposed by the Global Mining Guidelines Group (GMG), it was possible to determine how to record time-related data. After selecting a limestone underground mine as a research area, the performance of haulage operations was evaluated using a standard work report. As a result, in terms of truck availability, uptime was 46.7%, and both physical and mechanical availability were 100%. In the case of utilization, use of availability was 88.2%, the asset utilization was 41.1%, and operating and effective utilization were 88.2% and 79.2%, respectively. Also, in terms of efficiency, operating efficiency was found to be 89.9%.

• Korean small business review
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• v.39 no.2
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• pp.89-107
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• 2017

This study investigates the effect of risk avoiding activities and characteristic of venture capital on the financial performance of invested companies. Based on the review on the literature, we present staged financing, syndicated investment and preferred stock investment as the measurements for risk avoiding activities. Types and age of venture capital were chosen as the variables for characteristic. The financial performance data of the invested companies was derived from their publicly announced yearly financial report. Data were analysed using logistic regression technique. The result show that syndicated investment and independent venture capital have positive influence on the growth of sales revenue and asset of the invested companies. Age of venture capital appears to be positively associated with growth of sales revenue. Staged financing and preferred stock investment, however, have no impact on any financial growth and profitability. Activities and characteristic of venture capital show no influence on the profitability.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.41-47
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• 2023

Financial instruments valuation continues to evolve due to various technological changes. Recently, there has been increased interest in valuation using machine learning and artificial intelligence, enabling the financial market to swiftly adapt to changes. This technological advancement caters to the demand for real-time data processing and facilitates accurate and effective valuation, considering the diverse nature of the financial market. Numerical analysis techniques serve as crucial decision-making tools among financial institutions and investors, acknowledged as essential for performance prediction and risk management in investments. This paper analyzes Korean patent trends of numerical analysis-based financial systems, considering the diverse shifts in the financial market and asset data to provide accurate predictions. This study could shed light on the advancement of financial technology and serves as a gauge for technological standards within the financial market.

• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.61-75
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• 2020

The scope of data for understanding or predicting stock prices has been continuously widened from traditional structured format data to unstructured data. This study investigates whether commentary data collected from SNS may affect future stock prices. From "Stock Discussion Room" in Naver, we collect 20 stocks' commentary data for six months, and test whether this data have prediction power with respect to one-hour ahead price direction and price range. Deep neural network such as LSTM and CNN methods are employed to model the predictive relationship. Among the 20 stocks, we find that future price direction can be predicted with higher than the accuracy of 50% in 13 stocks. Also, the future price range can be predicted with higher than the accuracy of 50% in 16 stocks. This study validate that the investors' sentiment reflected in SNS community such as Naver's "Stock Discussion Room" may affect the demand and supply of stocks, thus driving the stock prices.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.141-152
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• 2018

Although the 1997 Raw Data of the National Wealth Statistical Survey (South Korea) is timely 20-year-old data, it is meaningful as a benchmarking in the capital stock estimations of Korea, which is estimated by PIM (permanent inventory method). In the case of machinery/equipment, it is the data that can analyze in depth the changes in the industrial structure of Korea. In the case of ASL (average service life) which has economic concept, since the change of ASL is not so large, ASL yielded by the Raw Data of the 1997 National Wealth Statistical Survey is meaningful as reference value for the ASL estimated by the Bank of Korea and the National Statistical Office. As you know Japan has changed its service life due to changes in its industrial structure. However, many of its assets are still used for the years indicated in Showa (before 1989). The same trend with other countries such as Japan. However, the United States is constantly devoted to assessing the useful ASL and value of assets by distinguishing between the Hulten-Wykoff models and those not. Korea has also benchmarked the useful ASL of the United States and Japan when it conducted its own survey every 10 years by due diligence until 1997. In this study, the 'constraint' Iowa curve estimation by the Raw Data of the 1997 National Wealth Statistical Survey is based on the age records of the assets and the maximum age of the assets appropriately derived. And then we made modified Iowa curve by smoothing. From this modified one, we suggested ASL by asset. After 1997, the vintage disposal data directly were collected by the National Statistical Office with Oh Hyun Seung, Cho Jin Hyung, in order to estimate the useful ASL. Since then, the B/S team of the Economic and Statistics Bureau of the Bank of Korea has been working on a new concept of content training.

• Information Systems Review
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• v.14 no.3
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• pp.53-74
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• 2012

IT outsourcing (ITO) is an integration of two firms-external vendor(s) and a client firm-IT resources by contract. According to resource-based view(RBV), three different resources-ITO vendor's resource, client firm's resource, and the relationship resource between two firms- may have an impact on ITO performance. However, there have been few previous studies considering all three IT resources simultaneously. There have been also few empirical studies in ITO context, which test Bharadwaj (2000)'s findings: 1) IT resources can be divided into tangible IT asset and intangible IT capability, and 2) only IT capability has an impact on the IT performance. Therefore we examined whether, in ITO context, all three different resources have a significant impact on ITO performance. Adopting the findings of previous IT studies, we also divided IT resource into IT asset and IT capability. To achieve this research objective, we analyzed 62 ITO cases of 45 companies being listed in Korean top 100 companies for recent 3 years. Also, we analyzed the data with the Partial Least Squares method. The results of this research lead to the following conclusions: First, only when partnership is high, ITO vendors' resource can have an influence on ITO performance. Second, only client firm's IT capability, not IT asset, is directly related to the ITO success. Third, a firm's IT capability can increase the partnership. Therefore, we concluded that 1) RBV is also an useful theory in ITO context, 2) Bharadwaj(2000)'s suggestion is valid in ITO context as well, and 3) the relationship resource is also important in ITO.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Distribution Research
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• v.17 no.4
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• pp.55-80
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• 2012

I. Introduction Developing the relationships between companies is very important issue to ensure a competitive advantage in today's business environment (Bleeke & Ernst 1991; Mohr & Spekman 1994; Powell 1990). Partnerships between companies are based on having same goals, pursuing mutual understanding, and having a professional level of interdependence. By having such a partnerships and cooperative efforts between companies, they will achieve efficiency and effectiveness of their business (Mohr and Spekman, 1994). However, it is difficult to expect these ideal results only in the B2B corporate transaction. According to agency theory which is the well-accepted theory in various fields of business strategy, organization, and marketing, the two independent companies have fundamentally different corporate purposes. Also there is a higher chance of developing opportunism and conflict due to natures of human(organization), such as self-interest, bounded rationality, risk aversion, and environment factor as imbalance of information (Eisenhardt 1989). That is, especially partnerships between principal(or buyer) and agent(or supplier) of companies within supply chain, the business contract itself will not provide competitive advantage. But managing partnership between companies is the key to success. Therefore, managing partnership between manufacturer and supplier, and finding causes of conflict are essential to improve B2B performance. In conclusion, based on prior researches and Agency theory, this study will clarify how business hazards cause conflicts on supply chain and then identify how developed conflicts have been managed by two control mechanisms. II. Research model III. Method In order to validate our research model, this study gathered questionnaires from small and medium sized enterprises(SMEs). In Korea, SMEs mean the firms whose employee is under 300 and capital is under 8 billion won(about 7.2 million dollar). We asked the manufacturer's perception about the relationship with the biggest supplier, and our key informants are denied to a person responsible for buying(ex)CEO, executives, managers of purchasing department, and so on). In detail, we contact by telephone to our initial sample(about 1,200 firms) and introduce our research motivation and send our questionnaires by e-mail, mail, and direct survey. Finally we received 361 data and eliminate 32 inappropriate questionnaires. We use 329 manufactures' data on analysis. The purpose of this study is to identify the anticipant role of business hazard (environmental dynamism, asset specificity) and investigate the moderating effect of control mechanism(formal control, social control) on conflict-performance relationship. To find out moderating effect of control methods, we need to compare the regression weight between low versus. high group(about level of exercised control methods). Therefore we choose the structural equation modeling method that is proper to do multi-group analysis. The data analysis is performed by AMOS 17.0 software, and model fits are good statically (CMIN/DF=1.982, p<.000, CFI=.936, IFI=.937, RMSEA=.056). IV. Result V. Discussion Results show that the higher environmental dynamism and asset specificity(on particular supplier) buyer(manufacturer) has, the more B2B conflict exists. And this conflict affect relationship quality and financial outcomes negatively. In addition, social control and formal control could weaken the negative effect of conflict on relationship quality significantly. However, unlikely to assure conflict resolution effect of control mechanisms on relationship quality, financial outcomes are changed by neither social control nor formal control. We could explain this results with the characteristics of our sample, SMEs(Small and Medium sized Enterprises). Financial outcomes of these SMEs(manufacturer or principal) are affected by their customer(usually major company) more easily than their supplier(or agent). And, in recent few years, most of companies have suffered from financial problems because of global economic recession. It means that it is hard to evaluate the contribution of supplier(agent). Therefore we also support the suggestion of Gladstein(1984), Poppo & Zenger(2002) that relational performance variable can capture the focal outcomes of relationship(exchange) better than financial performance variable. This study has some implications that it tests the sources of conflict and investigates the effect of resolution methods of B2B conflict empirically. And, especially, it finds out the significant moderating effect of formal control which past B2B management studies have ignored in Korea.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.