This study focuses on the effect of strategic deviance and managerial ability on cash flow from operating activities, which is financial performance. In addition, this paper examines the effect of managerial ability on the relationship between strategic deviance and cash flow from operating activities. The sample was extracted and analyzed for non-financial businesses among listed corporations with settlement of accounts in December from 2011 to 2020. As a result of the analysis, it was confirmed that strategic deviance found a significant negative effect on the cash flow from the operating activity, and managerial ability found a significant positive effect on the cash flow from the next operating activity. Strategic deviance was found to have a significant negative relationship with cash flow from next operating activities as managerial ability was higher, and this result was reconfirmed in the group with a high level of earnings management. This study is significant as it expands recent research related to management strategies by examining their impact on operating cash flow. Therefore, it seems that it is meaningful to identify the characteristics of managers in this relationship.
Purpose: The AEO (Authorized Economic Operator) program, created in 2001 in the United States due to 9.11 terrorist's attack, fundamentally changed the trade environment. Korea, which introduced AEO program in 2009, has become one of the world's top countries in the program by ranking 6th in the number of AEO certified companies and the world's No. 1 in MRA (Mutual Recognition Agreement) conclusions. In this paper, we examined what trade-economic and non-economic effects the AEO program and its MRA have in Korea. Research design, data and methodology: In this study we developed a model to verify the impact between utilization of AEO and trade-economic effects of the AEO and its MRA. After analyzing the validity and reliability of the model through Structural Equation Model we conducted a survey to request AEO companies to respond their experience on the effects of AEO program and MRA. As a result, 196 responses were received from 176 AEO companies and utilized in the analysis. Results: With regard to economic effects, the AEO program and the MRA have not been directly linked to financial performance, such as increased sales, increased export and import volumes, reduced management costs, and increased operating profit margins. However, it was analyzed that the positive effects of supply chain management were evident, such as strengthening self-security, monitoring and evaluating risks regularly, strengthening cooperation with trading companies, enhancing cargo tracking capabilities, and reducing the time required for export and import. Conclusions: When it comes to the trade-economic effects of AEO program and its MRA, AEO companies did not satisfy with direct effects, such as increased sales and volume of imports and exports, reduced logistics costs. However, non-economic effects, such as reduced time in customs clearance, freight tracking capability, enhanced security in supply chain are still appears to be big for them. In a rapidly changing trade environment the AEO and MRA are still useful. Therefore the government needs to encourage non-AEO companies to join the AEO program, expand MRA conclusion with AEO adopted countries especially developing ones and help AEO companies make good use of AEO and MRA.
This study suggests consulting directions for non-profit private organizations which were found to be inefficient in the efficiency analysis for the public activities support projects on those organizations performed by the Korean government. An ANOVA analysis on seven types of public activities support projects showed that there were differences among those types. By applying CCB-I, BCC-I, Super efficiency models among DEA, performance efficiencies were analyzed. Four input elements (age of the organization, supported amount, number of members, and the number of workers) and three output elements (project scores, financial scores, and comprehensive scores) were analyzed, and high efficient organizations were found as benchmarking objects, and, through super efficiency analysis, those objects were classified into short, mid, and long-term objects. Through such methods, this research provided organizations with the best information on other organizations to learn from and improve themselves.
Park, Chan Keun;Lee, Young Hak;Hong, Seok Min;Lee, Dal Won
Journal of The Korean Society of Agricultural Engineers
/
v.61
no.4
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pp.63-73
/
2019
Coastal and fishing facilities are gradually deteriorating in function due to the continual accumulation of soil sediments, which has affected local economic activities. Currently, there are many methods to remove soil sediments, but these methods are either a temporary solution or require a repetitive removal of the soil sediments, which is a huge financial burden for the maintenance of the facilities. To solve these problems, this study proposed a non-power soil cleaning system and evaluated field applicability by carrying out field model tests. The conditions for the evaluation focused on the drainage-elapsed time and drainage-outflow velocity according to the water level change in the water tank. In the field test, silty clay and sand were separately installed, and sedimentation soil removal test was practiced. As a result, the system was verified to have a sufficient outflow velocity for the removal of soil sediments. In addition, a generalization equation that can be used in different regions of the tide was suggested in this study. These results will greatly contribute to removing soil sediments in ports and dike gate facilities on the southwest coast. Since the system is an eco-friendly technology that does not require additional energy, thus it is expected to contribute to maintenance of sustainable facility performance as well as economic effect in the future.
This paper analyzed the statistical status of public properties, management organization, operational performance focused on Gyeonggido. And This paper tried to get some implications through comparative analysis of public property management system of Gyeonggido and other local governments including Seoul, Incheon. The survey was also conducted on the whole practitioners of public property management in Gyeonggido. As a result of analysis, although Gyeonggido has an urgent need to raise funds because financial independence is insurfficient, non-tax revenues from it's own public properties are insignificant compared to holdings. The major reason would be pointed out that the dedicated management department on public property is not composed and lack of workforce. The survey results conformed that establishment of the dedicated department to public property management is urgent by reorganization of management structure for the higher operational performance.
Journal of Korean Society of Industrial and Systems Engineering
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v.43
no.3
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pp.143-155
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2020
The relationship between companies in the supply chain is a core competency of the company and key indicator which determines the survival of a company. Therefore, companies are investing in efforts for inter-company relations, and related studies have been conducted for a long time. However, in the supply chain, the positions and characteristics of suppliers and buyers are not the same. Therefore, research is needed to better understand and respond to other characteristics of the relationship between suppliers and buyers. The purpose of this study was to identify the characteristics of the resources held between the buyer and the supplier through social capital, which is a value asset that can be used as a resource created through social relations, and whether it affects the commitment of the relationship. In addition, The core of this study was to statistically analyze the differences between suppliers and buyers through this analysis. This study was conducted by surveying companies that are suppliers and buyers along the supply chain. The difference between the supplier and the buyer was revealed through empirical analysis, and statistically, the difference between the two groups was also revealed. As a result of the analysis, the higher the involvement of the buyer, the more significant the result of structural capital was, and the result was statistically opposite to the supplier. As for the relationship capital, quantitative and qualitative relationship capital had different effects on the commitment. Both the supplier and the buyer had a positive effect on relationship performance. However, the effect of emotional commitment on non-financial relationship performance has a greater degree of influence on suppliers, and it appears in statistical differences. This study revealed differences in the relationship between suppliers and buyers, and found that different investments and efforts were required for each group.
Journal of the Korea Society of Computer and Information
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v.29
no.5
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pp.155-164
/
2024
This study proposes an unsupervised learning-based clustering model to estimate the ESG ratings of domestic public institutions. To achieve this, the optimal number of clusters was determined by comparing spectral clustering and k-means clustering. These results are guaranteed by calculating the Davies-Bouldin Index (DBI), a model performance index. The DBI values were 0.734 for spectral clustering and 1.715 for k-means clustering, indicating lower values showed better performance. Thus, the superiority of spectral clustering was confirmed. Furthermore, T-test and ANOVA were used to reveal statistically significant differences between ESG non-financial data, and correlation coefficients were used to confirm the relationships between ESG indicators. Based on these results, this study suggests the possibility of estimating the ESG performance ranking of each public institution without existing ESG ratings. This is achieved by calculating the optimal number of clusters, and then determining the sum of averages of the ESG data within each cluster. Therefore, the proposed model can be employed to evaluate the ESG ratings of various domestic public institutions, and it is expected to be useful in domestic sustainable management practice and performance management.
The purpose of this study is to verify whether companies residing in industry-academic convergence zones designated by the government are achieving policy goals and to seek policy implications and directions for improvement through analysis. For the study, business activities targeting resident companies were divided into infrastructure, business content, management, and system aspects, and business performance indicators, resident company satisfaction surveys, and differences in sales increase between resident companies and non-resident companies were analyzed through t-test. Based on statistical analysis results, performance indicators, and corporate survey analysis results, we track joint industry-academia R&D projects to maximize the effectiveness for companies, develop and operate human resources management for teams, and provide financial support for ordinances of metropolitan local governments. Improvements such as stipulation, antenna facilities at the corporate research center, and improvement of the researcher's residential environment were suggested. This study is the first to quantitatively verify policy performance targeting companies residing in industry-academic convergence zones, a large-scale government project, and future follow-up research is needed, including analysis of policy effects based on various variables such as employment indicators and corporate financial indicators.
Communications for Statistical Applications and Methods
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v.18
no.1
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pp.119-130
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2011
This paper measures and analyzes the performance of insurance companies in Korea in respect to sustainable development and suggest strategic implications based on the analysis. The correlation, regression, ANOVA, and t-test are employed. The results of this study are summarized as follows. First, it shows tat social index is important in the life insurance industry; however, the environmental index, is important in the non-life insurance industry. Second, the result gained by regressing the size and financial soundness on the performance of sustainable development demonstrates that the size variable is statistically significant. It suggests that size is a necessary condition for sustainable development. Finally, ANOVA shows that the small and medium sized companies have a significantly poor performance compared to the large companies concerning the social index and reputation index in the life insurance industry. The small and medium sized companies in the non-life insurance industry exhibit a significantly poor performance compared to the large companies in respect to all the indexes, except for the social index. Therefore, the small and medium sized companies make every endeavor in the poor indexes to improve performance.
Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.
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