• Knowledge Management Research
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• v.24 no.2
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• pp.87-115
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• 2023

This study aims to examine the causal relationships between sales and employment for public technology-based startups. Although there is a limit to statistical generalization due to the poor understanding of the actual conditions of public technology start-up companies, these companies were analyzed by classifying them into high-growth companise, potential growth companies, and other companies. In order to understand the causal relationship, and to estimate the time required to be effective, panel vector autoregression was applied. As a result, the performance creation mechanism was identified as government supoort and private investment was mutually causal with employment, sales did not cause employment, and employment caused sales. In other words, it was found that employment plays an mediator role in public technology based startups' performance mechanism. In addition, private investment had the effect of improving employment and sales in the short time than governments support, and showed that firms with high employment can attract government support and private investment. This study are academically meaningful in that they empirically revealed the process of performance creation, whereas previous studies had only shown whether there was an effect on performance. It also has a policy contribution by suggesting the need for effective policy promotion by considering the 'employment' factor, such as human resource support, as more important.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Environmental Policy
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• v.13 no.4
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• pp.3-49
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• 2014

Growing interest of stakeholders on corporate responsibilities for environment and tightening environmental regulations are highlighting the importance of environmental management more than ever. However, companies' awareness of the importance of environment is still falling behind, and related academic works have not shown consistent conclusions on the relationship between environmental performance and economic performance. One of the reasons is different ways of measuring these two performances. The evaluation scope of economic performance is relatively narrow and the performance can be measured by a unified unit such as price, while the scope of environmental performance is diverse and a wide range of units are used for measuring environmental performances instead of using a single unified unit. Therefore, the results of works can be different depending on the performance indicators selected. In order to resolve this problem, generalized and standardized performance indicators should be developed. In particular, the performance indicators should be able to cover the concepts of both environmental and economic performances because the recent idea of environmental management has expanded to encompass the concept of sustainability. Another reason is that most of the current researches tend to focus on the motive of environmental investments and environmental performance, and do not offer a guideline for an effective implementation strategy for environmental management. For example, a process improvement strategy or a market discrimination strategy can be deployed through comparing the environment competitiveness among the companies in the same or similar industries, so that a virtuous cyclical relationship between environmental and economic performances can be secured. A novel method for measuring eco-efficiency by utilizing Data Envelopment Analysis (DEA), which is able to combine multiple environmental and economic performances, is proposed in this report. Based on the eco-efficiencies, the environmental competitiveness is analyzed and the optimal combination of inputs and outputs are recommended for improving the eco-efficiencies of inefficient firms. Furthermore, the panel analysis is applied to the causal relationship between eco-efficiency and economic performance, and the pooled regression model is used to investigate the relationship between eco-efficiency and economic performance. The four-year eco-efficiencies between 2010 and 2013 of 23 companies are obtained from the DEA analysis; a comparison of efficiencies among 23 companies is carried out in terms of technical efficiency(TE), pure technical efficiency(PTE) and scale efficiency(SE), and then a set of recommendations for optimal combination of inputs and outputs are suggested for the inefficient companies. Furthermore, the experimental results with the panel analysis have demonstrated the causality from eco-efficiency to economic performance. The results of the pooled regression have shown that eco-efficiency positively affect financial perform ances(ROA and ROS) of the companies, as well as firm values(Tobin Q, stock price, and stock returns). This report proposes a novel approach for generating standardized performance indicators obtained from multiple environmental and economic performances, so that it is able to enhance the generality of relevant researches and provide a deep insight into the sustainability of environmental management. Furthermore, using efficiency indicators obtained from the DEA model, the cause of change in eco-efficiency can be investigated and an effective strategy for environmental management can be suggested. Finally, this report can be a motive for environmental management by providing empirical evidence that environmental investments can improve economic performance.

• 한국노년학
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• v.40 no.1
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• pp.197-216
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• 2020

This study aimed to suggest leisure activities and policy-level support in the light of the characteristics and needs among the elderly by examining constraint factors of leisure activities among the elderly. Data of 3887 elderly with the age of 65 and above with economic burden and health problems from the 6th Korean Retirement and Income study were used for the statistical analyses. Hierarchical linear models were tested by entering factors stepswise; demographic factors(age, gender, marriage status, single household, region, living expenses, health status) in the first step, leisure factors(leisure time, leisure motivation) in the second step, and lastly leisure activity factors(desired leisure activities, undesired leisure activities) in the third step. The results were as follows: First, major factors that constrict leisure activities of the elderly were financial burden and health problems. Second, there were significant differences among three(financial constraint, health constraint, and financial and health constraint) groups. Financial constraint group was the highest in the level of leisure satisfaction but leisure time was the shortest. The major reason to do leisure activities of the financial constraint group was to keep relationships with families and friends. In terms of desired leisure activities, health constraint group wanted resting, financial constraint group wanted hobbies and entertainment, and the financial-and-health constraint group wanted social activities. Third, financial constraint group demonstrated higher levels of leisure activity satisfaction when they wanted to take care of pets or gardens; however, they showed lower levels of leisure activity satisfaction when they wanted to domestic trips for desired leisure activities. In case of health constraint group, they demonstrated lower levels of leisure activity satisfaction whether or not they wanted resting like watching TV or listening to the radio. And, the showed higher levels of leisure activity satisfaction when they wanted social activities such as participation in religion or social gathering organizations. For the financial-and-health constraint group, whereas they showed lower levels of leisure activity satisfaction when they wanted walking around or watching TV, and domestic trips for desired leisure activities, they demonstrated higher levels of leisure activity satisfaction when they wanted entertainment doing the game of go, or chess, and hobbies like hiking and social activities. Practice and policy level suggestions to offer leisure activities that meet the needs of the elderly were made based on the study results.