• Journal of Global Scholars of Marketing Science
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• v.17 no.3
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• pp.1-19
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• 2007

1. Purpose Rural tourism is made by individuals with different characteristics, needs and wants. It is important to have information on the characteristics and preferences of the consumers of the different types of existing rural accommodation. The stud aims to identify the determinants of consumer preference by type of accommodations. 2. Methodology 2.1 Sample Data were collected from 1000 people by telephone survey with three-stage stratified random sampling in seven metropolitan areas in Korea. Respondents were chosen by sampling internal on telephone book published in 2006. We surveyed from four to ten-thirty 0'clock afternoon so as to systematic sampling considering respondents' life cycle. 2.2 Two-step cluster Analysis Our study is accomplished through the use of a two-step cluster method to classify the accommodation in a reduced number of groups, so that each group constitutes a type. This method had been suggested as appropriate in clustering large data sets with mixed attributes. The method is based on a distance measure that enables data with both continuous and categorical attributes to be clustered. This is derived from a probabilistic model in which the distance between two clusters in equivalent to the decrease in log-likelihood function as a result of merging. 2.3 Multinomial Logit Analysis The estimation of a Multionmial Logit model determines the characteristics of tourist who is most likely to opt for each type of accommodation. The Multinomial Logit model constitutes an appropriate framework to explore and explain choice process where the choice set consists of more than two alternatives. Due to its ease and quick estimation of parameters, the Multinomial Logit model has been used for many empirical studies of choice in tourism. 3. Findings The auto-clustering algorithm indicated that a five-cluster solution was the best model, because it minimized the BIC value and the change in them between adjacent numbers of clusters. The accommodation establishments can be classified into five types: Traditional House, Typical Farmhouse, Farmstay house for group Tour, Log Cabin for Family, and Log Cabin for Individuals. Group 1 (Traditional House) includes mainly the large accommodation establishments, i.e. those with ondoll style room providing meals and one shower room on family tourist, of original construction style house. Group 2 (Typical Farmhouse) encompasses accommodation establishments of Ondoll rooms and each bathroom providing meals. It includes, in other words, the tourist accommodations Known as "rural houses." Group 3 (Farmstay House for Group) has accommodation establishments of Ondoll rooms not providing meals and self cooking facilities, large room size over five persons. Group 4 (Log Cabin for Family) includes mainly the popular accommodation establishments, i.e. those with Ondoll style room with on shower room on family tourist, of western styled log house. While the accommodations in this group are not defined as regards type of construction, the group does include all the original Korean style construction, Finally, group 5 (Log Cabin for Individuals)includes those accommodations that are bedroom western styled wooden house with each bathroom. First Multinomial Logit model is estimated including all the explicative variables considered and taking accommodation group 2 as base alternative. The results show that the variables and the estimated values of the parameters for the model giving the probability of each of the five different types of accommodation available in rural tourism village in Korea, according to the socio-economic and trip related characteristics of the individuals. An initial observation of the analysis reveals that none of variables income, the number of journey, distance, and residential style of house is explicative in the choice of rural accommodation. The age and accompany variables are significant for accommodation establishment of group 1. The education and rural residential experience variables are significant for accommodation establishment of groups 4 and 5. The expenditure and marital status variables are significant for accommodation establishment of group 4. The gender and occupation variable are significant for accommodation establishment of group 3. The loyalty variable is significant for accommodation establishment of groups 3 and 4. The study indicates that significant differences exist among the individuals who choose each type of accommodation at a destination. From this investigation is evident that several profiles of tourists can be attracted by a rural destination according to the types of existing accommodations at this destination. Besides, the tourist profiles may be used as the basis for investment policy and promotion for each type of accommodation, making use in each case of the variables that indicate a greater likelihood of influencing the tourist choice of accommodation.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.667-675
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• 2016

This study aims to investigate the effects of regular participation in physical activity on personal happiness by analyzing the "2014 National Leisure Sports Participation Survey". By using cross-tabulation and the binary logit model, it is found that there is a positive correlation between those two variables. The effect of positive affect on happiness index was found to be influenced by age, education level, occupation, income, marital status, appear. The results of the analysis of the relationship between the presence of sports facilities and the participation of regular physical activity using crosstab analysis and the analysis of physical activity showed positive relationship between two variables. Hence, it is expected that providing more opportunities to participate in sports programs can lead the public to more regular participation in physical activity.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.187-189
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• 2004

This paper investigates the effect of weather factors(such as winds, rain, snows, temperature, clouds and humidity) on transmission line outages. The result shows that weather variables have significant effects on the transmission line historical outages and the relationship between them is nonlinear. Multiple regression analysis using Logit model is proved to be appropriate in forecasting line failure rate in KEPCO systems. It could also provide system operators with useful informations about system operation and planing.

• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.113-123
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• 2012

This study introduces a new type of a modal split model that use zonal data instead of cost data as independent variables. It has been indicated that the ones using cost data have deficiencies in the multicollinearity of travel time and cost variables and unpredictability of independent variables. The zonal data employed in this study include (1) socioeconomic data, (2) land use data and (3) transportation system data. The test results showed that the proposed modal split model using zonal data performs better than the other does.

• Korea journal of population studies
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• v.24 no.2
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• pp.235-253
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• 2001

As we are entering the information society, there are increasing concerns about information gaps which are believed to create serious obstacles to social integration and development. Previous studies on the information gaps in Korea, despite their contributions to our understanding of the issue, appear to be descriptive. This study attempts to analyze the relative importance of residential area, gender, age education, and household income for information gaps and their changes in recent years. Based on the data from two surveys conducted by the Information Culture Center, the study run multivariate logit model analysis of the sue of computer and internet. The result shows that all the variables except residential area have influences on the use of computer and internet. In terms of time change, gender-based difference in the use of digital media has decreased between 1998 and 2000 while the differences by all the other variables have remained constant or increased.

• Journal of Labour Economics
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• v.27 no.2
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• pp.139-159
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• 2004

This paper explores the determinants of re-employment of retired military personnel who served more than ten years in Korea. Recently, the re-employment rate of veterans is less than 30%. Considering the reduction in military forces in future, this very low rate of re-employment may be one of important social and economic problem. Using a survey and econometric analyses, we generate several important results. First, spouses' income is higher in the group who chose to run their own business than in the group who decided to become salary workers. Second, those who gave economic activities had longer the period of military service, higher ranks, and higher rate of being in bad health. Third, the longer the military service period is, the shorter the period of job search. And those who not taking the program of vocational guidance have short search period. If, however, one was more educated or one served longer in Seoul area, then she is more likely to have a longer search period. These results imply that the current important factors in government policies for veterans such as vocational guidance programs, information for employment, and military experience should be improved to be more oriented to the requirements of employers.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.17-29
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• 2013

The major aim of this research is to examine the effect of computer and internet literacy and cellular phone possession on depression and suicidal ideation among elderly. This study used data of 2011 national survey results on the elderly life conditions. To determine the effectiveness of computer and internet literacy and cellular phone possession, a total of 6,774 respondents over 60 years of age was selected. The SPSS package was used to analyze the data. Multiple linear regression and logit analysis was run to verify influence of information conditions(computer and internet literacy and cellular phone possession) on depression and suicidal ideation. The results are as follows. First, the elder who is male, younger, has higher education and economic level and lives with spouce is in good information conditions. On the contrary to this, the elder who is female, older, low level of education and economic, single and lives with grandchildren is in information minority group. They have high level of depression and rate of suicidal ideation. Second, computer and internet literacy and cellular phone possession associate with level of depression significantly. Third, computer and internet literacy do not associate with suicidal ideation significantly. The results of this study provide significant source to plan informatization policy and welfare services for socially isolated older people.

• Land and Housing Review
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• v.13 no.1
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• pp.55-65
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• 2022

This paper investigates the distinct characteristics of freehold and leasehold households living in the seven largest cities and the other areas. We employ the two-stage logit regression analysis to identify the marginal effects of wealth and income after controlling for the other one. We document the following results. First, households with more net wealth are more likely to reside in their own houses, regardless of living areas. Second, the pure income effect after controlling for wealth and other variables lowers the tendency of freeholders to live in the seven largest cities while increasing the tendency to live in the other areas. Furthermore, the income effects reduce the tendency to live in the former regions. Our results suggest that the pure income effects enhance preferences for a better living environment (e.g., larger spaces, better school districts, etc.), whereas the wealth effect increases the likelihood of freeholds.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Korean Journal of Social Welfare
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• v.63 no.3
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• pp.29-53
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• 2011

This paper analyzed factors to affect needs for personal assistance service in the workplace for people with disabilities. The statistical analyses adapted in this study were the Frequency Analysis, Ordered Logit Analysis, Multiple Regression Analysis. The results of analysis are summarized as follows. First, through the Frequency Analysis many people with severe disabilities in the workplace found to have a high desire of the personal assistance service in the workplace, and factors like types of disability, the degree of disability, income basis found to include in the selection criteria of personal assistance service in the workplace. Second, through Ordered Logit Analysis and Multiple Regression Analysis factors to affect needs for personal assistance service in the workplace for people with disabilities found to include factors like residential district, age, level of education, assistance activities of family. By the results of analysis, it is suggested practical implications and policy implications.