• Land and Housing Review
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• v.13 no.4
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• pp.13-26
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• 2022

This study analyzes housing choice determinants of the youth and newlyweds households by using housing survey data in Incheon. A multinomial logit model is employed for analysis with the following variables: housing characteristics, housing market characteristics, and residential and neighborhood environment characteristics. The findings from the analysis are as follows. First, for the continued residence of the youth, the important factors were the relief assistance of housing maintenance costs. For the newlyweds, the important factors were the quality improvement of residential environments to ensure residential stability. Second, the housing choice factors to attract the youth were residential support for rent, maintenance costs, and relocation, and the improvements of residential environments such as security, noise levels, and medical facilities. For the newlyweds, the important factors were housing loan assistance for a home purchase or a cheonsei deposit and residential quality improvements for air pollution and parking facilities. Third, the youth were likely to move out due to high rental costs, and the newlyweds were likely to move out for the purchase of a new apartment or higher-quality housing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.6
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• pp.869-877
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• 2022

This study aims to analyze the factors affecting the re-subscription period upon initial termination of the reverse mortgage subscription. The study utilized the Korea Housing Finance Corporation's database to extract the information regarding re-subscribers of the reverse mortgage from July 2007 to June 2021. The ordered logit model was employed and found that a set of user (subscriber) characteristics are influential towards the re-subscription period. Among the individual characteristics, changes in age group, marital status from married to single-living, maintaining single-living, and the initial subscription period were found statistically significant, highlighting that the increase in the initial subscription period decreased the re-subscription period. Among the housing (home equity) characteristics, changes in housing price and ownership type (single and partial ownership) were statistically significant, indicating that the change in ownership type decreases the re-subscription period. Lastly, the variables related to loan terms were found significant, revealing that changes in payout method and schedule were both increasing factors of the re-subscription period. Based on the findings, necessary policy implications can be considered to secure the returning subscribers of the reverse mortgage effectively.

• Journal of the Korean Regional Science Association
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• v.39 no.3
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• pp.13-34
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• 2023

Jeju is making multifaceted efforts to foster and attract businesses in order to increase its GRDP, which is only at the level of 1% nationwide. A firm's choice of location selection is such a significant decision that it can affect the growth of the firm. The concentration of firm locations in one region means that the characteristics of the region conduce to corporate profit maximization. Therefore, the analysis of the characteristics of regions preferred by firms and the reflection of the results thereof in policies for attracting firms will be helpful in inducing regional innovation and development. This study investigates the distribution of firm locations in Jeju, and analyzes the effects of regional characteristics on the determination of firm location by using the conditional logit model. The analysis results indicate that Jeju has various kinds of firms concentrated, regardless of the industry type, and a large economically active population in thinly populated areas. Additionally, firms in the knowledge-based industry tend to locate in areas where more firms in the same field are located in Jeju. This study is significant in that it is the basic analysis of the determinants of firm location in Jeju, which has never carried out, for the purpose of establishing policies for firm and industry promotion and local development in Jeju.

• Environmental and Resource Economics Review
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• v.22 no.3
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• pp.521-545
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• 2013

This study applied choice experiment(CE) method(which is included in the stated preference method) to estimate values of some important attributes(i.e. type of estuary, water quality of river in estuary, water quality of sea in estuary, biodiversity level of estuary) of 4 major river(Hangang, Guemgang, Yeongsangang, Nakdonggang) estuaries in Korea. Although the multinomial logit model(MNL) is generally applied to analyse the CE data, testing for IIA assumption with the Hausman and McFadden test in MNL model shows that the IIA assumption in our data is rejected. Therefore, the heteroscedastic extreme value model(HEV) and the multinomial probit model(MNP) which are not based on the IIA assumption are used to analyse our CE data. As results, the coefficients and the elicited economic values of MNL model are seriously distorted if the IIA assumption is not satisfied in MNL model. The estimation results of MNP model show that the economic values are elicited as 352.3 billion won(95% C.I. 261.1 - 477.8 billion won) for natural estuary, 411.5 billion won(95% C.I. 338.5 - 525.5 billion won) for one grade improvement of river water quality in estuary, 358.9 billion won(95% C.I. 292.5 - 457.0 billion won) for one grade improvement of sea water quality in estuary, and 151.9 billion won(95% C.I. 99.0 - 218.6 billion won) for one grade improvement of biodiversity level of estuary. Therefore, the value of estuary is reached to 2,197.0 billion won(95% C.I. 1,721.0 - 2,879.9 billion won) if any natural estuary in 4 major rivers has good water quality of river in estuary(i.e. 2nd grade), good water quality of sea in estuary(i.e. 1st grade), and good biodiversity level of estuary.

• 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 Korean Society of Transportation
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• v.26 no.3
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• pp.211-221
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• 2008

This dissertation studied the psychological cost, which converted the mental pain suffered by the victim of a traffic accident and his/her family, friends and people around him/her into social costs. Three methodologies - Choice Experiments, Direct Question and Dichotomous Choice Question - were used to design questionnaires, and models were built for each questionnaire design method. When building models, a logit model was used, which is used most frequently in probabilistic choice model. And the tobitmodel was used to make direct questionnaires. When verifying these models, although there were some differences in each model, suitability of most models and credibility of each coefficient were meaningful around the credibility level of 95%. According to the analysis, domestic psychological cost produced through the assessment model of psychological cost was 15.63 million won per person or 5.1 trillion in total, assuming 37.1% of total traffic accident cost.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.125-133
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• 2014

This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which demonstrated high efficiency, can certainly provide great help in decision making in the field of hotel management and in deciding on the bankruptcy or financial solidity of each branch of serviced residence hotel.

• Knowledge Management Research
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• v.23 no.1
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• pp.187-201
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• 2022

Online reviews written by tourists provide important information for the management and operation of the tourism industry. The star rating of online reviews is a simple quantitative evaluation of a product or service, but it is difficult to reflect the sincere attitude of tourists. There is also an issue; the star rating and review content are not matched. In this study, a star rating prediction model based on online review content was proposed to solve the discrepancy problem. We compared the differences in star ratings and sentiment by continent through sentiment analysis on tourist attractions and hotels written by foreign tourists who visited Korea. Variables were selected through TF-IDF vectorization and sentiment analysis results. Logit, artificial neural network, and SVM(Support Vector Machine) were used for the classification model, and artificial neural network and SVR(Support Vector regression) were applied for the rating prediction model. The online review rating prediction model proposed in this study could solve inconsistency problems and also could be applied even if when there is no star rating.

• The Journal of Society for e-Business Studies
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• v.15 no.2
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• pp.55-75
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• 2010

The primary purposes of this study are to find a proper model for government's selections of Mainbiz and present what are the better weights of the current indexes. We prepared three sets of models：first one using original 10 variables; second one using 9principally composed variables; third one using 7 principally composed variables. Among 3 models, the last one had higher explanation power than the other two models. Therefore, if index weights are adjusted according to the third newly developed model, the credibility in evaluating and selecting Mainbiz will be improved. When transforming the index weights and running the analysis, 5 variables(organization process, marketing management, management process, production-facility states, the level of forecasting) have more direct influences than other 4 variables(innovation strategies, knowledge management, achieving level, operational level) on selecting Main-biz.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.1
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• pp.123-138
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• 2013

Keyword advertising has been used as a promotion tool rather than the advertising itself to online retailers. This is because the online retailer expects the direct sales increase when they deploy the keyword sponsorship. In practice, many online sellers rely on keyword advertising to promote their sales in short term with limited budget. Most of the previous researches use direct revenue factors as dependent variables such as CTR (click through rate) and CVI (conversion per impression) in their researches on the keyword advertising[14, 16, 22, 25, 31, 32]. Previous studies were, however, conducted in the context of aggregate-level due to the limitations on the data availability. These researches cannot evaluate the performance of keyword advertising in the individual level. To overcome these limitations, our research focuses on conversion of keyword advertising in individual-level. Also, we consider manageable factors as independent variables in terms of online retailers (the costs of keyword by implementation methods and meanings of keyword). In our study we developed the keyword advertising conversion model in the individual-level. With our model, we can make some theoretical findings and managerial implications. Practically, in the case of a fixed cost plan, an increase of the number of clicks is revealed as an effective way. However, higher average CPC is not significantly effective in increasing probability of purchase conversion. When this type (fixed cost plan) of implementation could not generate a lot of clicks, it cannot significantly increase the probability of purchase choice. Theoretically, we consider the promotional attributes which influence consumer purchase behavior and conduct individuals-level research based on the actual data. Limitations and future direction of the study are discussed.