• Asia Marketing Journal
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• v.13 no.3
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• pp.183-208
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• 2011

After more than 25 years of accumulated research evidence, there is little doubt that leadership behavior is related to a wide variety of positive individual and organizational outcomes. Indeed, leadership behavior has been empirically linked to increased employee satisfaction, organizational commitment, extra effort, turnover intention, organizational citizenship behavior, and overall employee performance. However, it is important to point out that although leadership behavior has been linked to a number of positive organizational outcomes, research regarding the antecedents of such behavior is limited. Especially there is little research dealing with the antecedents of inter-organizational leadership behavior. Supply chain leadership can be defined as the activities undertaken by the supply chain leader to influence the management programs and strategies of supply chain members. Supply chain performance is influenced by leadership of supply chain leader. Although research on supply chain leadership can be broadly categorized, many researchers have been preoccupied with analyzing supply chain leadership by the power-influence approach measuring such as control, power, and power bases. Also they have not examined the relationship between leadership and financial performance. This study has started to overcome those research gaps. The purpose of this study is to investigate the effect of relational behaviors on supply chain leadership, and the effect of such leadership behavior on financial performance of supply chain followers. In addition, this study also try to find out moderating variable existing in the relationship. To be concrete, First, this study develops a model of the antecedents of four conceptually distinct forms of relational behaviors such as training, fair reward, offering vision, and inter-organizational communication, and tests the hypothesized differential effects of relational behavior forms on supply chain leadership. Second, this study tests the effect of supply chain leadership on financial performance. Third, this study investigates the extent to which this leadership-performance relationship is moderated by leader ethicality. The reason why this study deals with convenience store supply chain is that there is very strong inter-dependence between a franchisor and its suppliers. Their strong inter-dependence makes their relationship as the relationship between a superior and subordinates and creates an atmosphere that leadership occur without difficulty. For the purpose of empirical testing, 217 respondents of suppliers of convenience store supply chain in Korea were surveyed and the analysis utilizing partial least square model indicated that training, fair reward, inter-organizational communication had positive effects on supply chain leadership, and such leadership had positive effect on financial performance of followers. On the other hand, offering vision had no effect on supply chain leadership. In addition, leader ethicality had moderating effect on the relationship between supply chain leadership and financial performance.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.541-559
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• 2015

VaR is now widely used as an important tool to evaluate and manage financial risks. In particular, it is important to select an appropriate volatility model for the rate of return of financial assets. In this study, both univariate and multivariate models are considered to evaluate VaR of the portfolio composed of KOSPI, Hang-Seng, Nikkei indexes, and their performances are compared through back testing techniques. Overall, multivariate models are shown to be more appropriate than univariate models to estimate the portfolio VaR, in particular DCC and ADCC models are shown to be more superior than others.

• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.189-220
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• 2009

This study tests the significance of momentum and contrarian strategy which challenge the weak efficient market hypothesis (EMH). If momentum and contrarian strategy can make extra return above the market, this can be a significant critics to the weak EMH. By using Monte Carlo simulation we have found that many existing returature, which test the significance of momentum and contrarian strategy, have a significance distortion problem. We test the significance of momentum and contrarian strategy by using reality check test of White(2000) which solve the problem of data snooping bias. The results are following. When we use the KOSPI index as the benchmark portfolio, we can get the best strategy of momentum strategy in the case of mean return. But in the case of Sharp ratio which is the performance measure adjusting risk, we find that the best strategy in the momentum and contrarian strategy can not dominate the performance of benchmark portfolio. Therefore we argue that weak EMH can not be rejected because of superior performance of momentum and contrarian strategy when we consider risk.

• Smart Media Journal
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• v.9 no.4
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• pp.26-35
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• 2020

Attempts to predict future stock prices have been studied steadily since the past. However, unlike general time-series data, financial time-series data has various obstacles to making predictions such as non-stationarity, long-term dependence, and non-linearity. In addition, variables of a wide range of data have limitations in the selection by humans, and the model should be able to automatically extract variables well. In this paper, we propose a 'sliding time step normalization' method that can normalize non-stationary data and LSTM autoencoder to compress variables from all variables. and 'moving transfer learning', which divides periods and performs transfer learning. In addition, the experiment shows that the performance is superior when using as many variables as possible through the neural network rather than using only 100 major financial variables and by using 'sliding time step normalization' to normalize the non-stationarity of data in all sections, it is shown to be effective in improving performance. 'moving transfer learning' shows that it is effective in improving the performance in long test intervals by evaluating the performance of the model and performing transfer learning in the test interval for each step.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of Environmental Policy
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• v.12 no.2
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• pp.33-57
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• 2013

This paper test the relationship among determinants of environmental investments, level of environmental investments, eco-efficiency (carbon productivity). The results show that profitability, leverage and R&D costs have a negative impact on environmental investments, and controlling ownership have a positive impact on environmental investments as well as environmental protection costs. The analysis also show that firms increasing environmental investments are able to gain superior environmental performance ($CO_2$ emission), but are negatively relationship with financial performance. Finally, the findings prove that differences exist in the relationship between determinants and effect of environmental investments when grouped by industry characteristics.

• Asia-Pacific Journal of Business
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• v.9 no.1
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• pp.91-106
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• 2018

An appreciable number of Chinese firms have successfully expanded their businesses into foreign economics although they have limited resources. Advocating that home country government supports can mitigate firms' resource-disadvantages in international expansions, we attempted to investigate whether and how the Chinese government's support enables Chinese firms to compete in foreign markets. Based heavily on the knowledge-based theory of the firm and the resource-based theory of the firm, we developed a model that explain and predicts the effects of home-country government-supports on superior financial performance. The model was empirically tested using a accounting dataset regarding Chinese firms' 323 international expansion events from 2008 to 2015. Empirical evidence presents that the Chinese government's support has a positive effect on Chinese firms' international success and that these firms' marketing, technological, and managerial resources positively moderate the effect of the government support on the firms' international success. Nonetheless, because we employed an event-study method, the limitations of the method can be applied to the current research. In addition, because of the empirical context, the results of the research might lack generalizability. We, however, provided an understanding how firms from emerging countries can succeed in international expansions specifically when they have lack of resources for international competition.

• Journal of Distribution Science
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• v.20 no.2
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• pp.43-53
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• 2022

Purpose: This research aims to provide solutions for human resource problems in public educational institutions to improve employee performance. Research design, data, and methods: The study used a quantitative approach with a survey method. Data were obtained through questionnaires and documentation. Meanwhile, the model used path analysis using Analysis Moment Structure (AMOS) software. Results: Results showed that there was a significant relationship between locus of control and redistribution variables on employee empowerment as well as on employee performance. This result implied that good management through the locus of control and employee redistribution in public organizations could be better to serve the community and organizations. Public change to be superior and demanded by the community to make it a good place to learn. Employees' good behavior and increasing competence can satisfy users of educational and sustainable institutions. Conclusion: To sum up, research on management development of locus of control and employee redistribution is needed to make public organizations, especially those engaged in education. This study provides academic implications by revealing that the locus of control factor and employee redistribution in public organizations are needed to improve institutional services.

• Communications for Statistical Applications and Methods
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• v.20 no.2
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• pp.115-127
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• 2013

This paper provides an effective stock portfolio screening tool for socially responsible investment (SRI) based upon corporate social responsibility (CSR) and financial performance. The proposed approach utilizes nonparametric frontier models. Data envelopment analysis (DEA) has been used to build SRI portfolios in a few previous works; however, we show that free disposal hull (FDH), a similar model that does not assume the convexity of the technology, yields superior results when applied to a stock universe of 253 Korean companies. Over a four-year time span (from 2006 to 2009) the portfolios selected by the proposed method consistently outperform those selected by DEA as well as the benchmark.

• Journal of the Korean Society of Systems Engineering
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• v.5 no.1
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• pp.57-65
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• 2009

The purpose of Defense Acquisition Project is that the superior weapons validated needs and performance are supplied to military user with limited financial resources and time. The Warship Acquisition Project is not only like this, But also has special characteristics of long project period and first-constructed ship's operation employment. So, The Warship Acquisition Project need systematic and efficient procedure & management. And this paper researches System engineering application of imaginary WIG(Wing-In-Ground Effect) ship acquisition project based Systems Engineering Handbook ver.3.1 published by INCOSE, the lead of field. The Imaginary WIG(Wing-In-Ground Effect) ship acquisition project applied the four processes(technical project, Enterprise & Agreement, Enabling Systems), the basis of INCOSE Engineering Handbook ver.3.1, and the each process output compared with DAPA(Defense Acquisition Program Administration)'s warship acquisition procedure.