• Journal of the Korean Society of Clothing and Textiles
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• v.19 no.4
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• pp.637-650
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• 1995

The purpose of the study was to develop a model to explain how a fashion style is determined within a society and how the style diffuses. The research was carried out in two stages, theoretical study followed by empirical study. In the theoretical study, explanatory model about decision of fashion style and diffusion was developed and then fashion diffusion theories and fashion phenomenon of postholder society were explained by the model developed. The theoretical framework of the explanatory model was constructed in that fashion changes by ambivalence of pursuit values within an individual as well as within a society. The empirical study was carried out to validate the model by looking into fashion phenomenon in the postmodern society A questionnaire was developed including style image, pursuit value, preference style and administered to 19 to 30 year-old women living in Seoul area. Frequency distribution, discriminant analysis, one-way ANOVA. were used for the statistical analysis. As pursuit values differed in each style preference stoup, and pursuit value coincided with image of preference style it was confirmed that clothing selection behavior was determined by pursuit value. In a postmodern society where variety of values are pursued, appearance of various products and preference of all styles altogether considered, it could assume that subcultural collective selection phenomenon appeared.

• Korean Journal of Culture and Social Issue
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• v.26 no.3
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• pp.259-282
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• 2020

This study examines the validity of the Filial Responsibility Scale-Adult (Past), developed by Jurkovic and Thirkield (1999), among Korean university students in their twenties. First, a preliminary scale consisting of 30 items was developed by translating the original scale into Korean and item analysis and exploratory factor analysis were conducted on 249 subjects. Based on the exploratory factor analysis, items in the emotional parentification factor were either deleted or included in the other remaining factors, resulting in a two-factor model containing 15 items. In order to confirm this, a confirmatory factor analysis was conducted on 318 independent subjects. As a result of a confirmatory factor analysis of the two competing models - the three-factor model consisting of 30 items based on the original scale and the two-factor(emotional experience and caring behavior) model consisting of 15 items gained as a result of the exploratory factor analysis - the two-factor model showed more suitable and the original scale was revised accordingly. The convergent validity, discriminant validity and predictive validity were all found to be satisfactory. Based on such results, implications, limitations and suggestions on follow-up studies are discussed.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4881-4894
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• 2014

This study selected 83 manufacturing firms that had been delisted from the KOSDAQ market from 2009 to 2012 and the sample firms for the two-paired sampling method were compared with 83 normal firms running businesses with same items or in same industry. The 75 financial ratios for five years immediately before delisting were used for Mean Difference Analysis with those of normal firms. Fifteen variables assumed to be significant variables for five consecutive years out of the analysis were used to in the Dichotomous Classification Technique, Logistic Regression Analysis and Discriminant Analysis. As a result of those three analyses, the Logistic Regression Analysis model was found to show the greatest discrimination. This study is differentiated from previous studies as it assumed that the firm's failure proceeded slowly over long period of time and it tried to predict the firm's failure earlier using the five years' historical data immediately before failure, whereas previous studies predicted it using three years' data only. This study is also differentiated from the proceeding comparative studies by its statistically complex Multi-Variate Analysis and Dichotomous Classification Analysis, which general stakeholders can easily approach.

• Management & Information Systems Review
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• v.28 no.3
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• pp.1-23
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• 2009

Prior bankruptcy studies have established that bankrupt firm's pre-filing financial ratios are different from those of healthy firms or of randomly selected going concerns. However, they may not be sufficiently different from the financial ratios of other firms in financial distress to allow the development of a ratio-based model that predicts bankruptcy with reasonable accuracy. As the result, in the multiple discriminant model, independent variables divided firms into bankrupt firms and healthy firms are retained earnings to total asset, receivable turnover, net income to sales, financial expenses, inventory turnover, owner's equity to total asset, cash flow to current liability, and current asset to current liability. Moreover four variables Retained earnings to total asset, net income to sales, total asset turnover, owner's equity to total asset indicate that these valuables classify bankrupt firms and distress firms. On the other hand, Owner's Equity to borrowed capital, Ordinary income to Net Sales, Operating Income to Total Asset, Total Asset Turnover and Inventory Turnover are selected to predict bankruptcy possibility in the Logistic regression model.

• Korean Security Journal
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• no.57
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• pp.205-230
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• 2018

The purpose of this study is to examine the structural model between servant - transformational leadership, organizational citizenship behavior and organizational performance of private security organizations. The security guards working in private security companies in Seoul and Gyeonggi - do were selected as population, random sampling method. The survey was conducted from September 1, 2016 to November 30. Accoridng to the purpose of the study, this study conducted factor analysis(EFA/CFA), reliability analysis, convergence and discriminant validity analysis, and covariance structure analysis using SPSSWIN 21.0 and AMOS 21.0. The conclusions of this study are as follows. First, servant leadership has a positive (+) effect (${\beta}=.406$) on organizational citizenship behavior statistically at .001 level. Second, transformational leadership has a positive (+) effect (${\beta}=.373$) on organizational citizenship behavior statistically at .001 level. Third, organizational citizenship behavior has a positive (+) effect (${\beta}=.615$) on organizational performance statistically at .01 level. Fourth, servant leadership does not affect the organizational performance statistically (${\beta}=.211$). Fifth, transformational leadership does not affect the organizational performance statistically (${\beta}=.058$). Sixth, organizational citizenship behavior has statistically positive (+) mediation effect (${\beta}=.249$) in the relationship between servant leadership and organizational performance. Seventh, organizational citizenship behavior has statistically positive (+) mediating effects (${\beta}=.230$) on the relationship between transformational leadership and organizational performance. Innovation and Improvement of National Emergency Management System in Korea.

• (The) Korean Journal of Educational Psychology
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• v.31 no.1
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• pp.59-81
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• 2017

This study examined the relationship of grit, goal perception and academic adjustment of adult female students in a distance university. We additionally investigated how academic work-family conflict and social support influenced the grit-adjustment relationship. Grit is defined as passion and perseverance for long-term goals and it has been known as one of the predictors of adjustment indicators including academic achievement. A total of 642 female students in a distance university were participated in the survey and a Structural Equation Modeling (SEM) was utilized for data analysis. Results showed that our model fit data well, specifically, two components of grit (i.e. consistency of interest and perseverance of effort) positively predicted academic adjustment. Results from analyses of specific indirect effects revealed that consistency of interest was found to have a positive direct effect on academic adjustment while perseverance of effort had a positive indirect effect via goal perception, suggesting the discriminant predictability of grit's two components. Lastly, academic work-family conflict was found to negatively predict academic adjustment while social support predicted a positive academic adjustment via goal perception.

• Korean Journal of Culture and Social Issue
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• v.28 no.4
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• pp.627-659
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• 2022

This study aimed to translate and validate the Positive and Negative Ex-Relationship Thoughts (PANERT), a scale measuring the positive and negative valence of thoughts about past relationships in early adulthood. For this purpose, PANERT was translated into Korean and the study surveyed on 337 single male and female adults in their 20. Then, the gender difference between major variables was analyzed. After going through item analysis, all twelve original items were used to construct the Korean version of PANERT. The confirmatory factor analysis(CFA) supported the two factors structure of the Korean version of PANERT: positive vs, negative thought content valence. Also, the reliability coefficients of each two factors were all satisfactory. As a result of a correlation analysis, the criterion-related validity of the two sub-factors was good with other related scales(Intrusive rumination scale of K-ERRI, K-DASS-21-D, and K-PANAS-Revised) except for changes of self-perception. Finally, the research model was built to examine the mediating effect of two affect responses(positive and negative) in the relationship between two thought content valences and depression. In this process, the convergence and discriminant validity of the Korean version of PANERT were confirmed and the indirect effect was also confirmed in the structural equation model. In conclusion, the Korean version of PANERT consists of two factors and twelve items in total. Also, it is a reliable and valid tool for measuring the thought content valences in the romantic relationship breakup experience of early adults.