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

• Asia Marketing Journal
• /
• v.15 no.4
• /
• pp.33-49
• /
• 2014

Environmental concern has been an important issue for a few decades, and the extent of consumer demand for eco-friendly consumption has been increased. This study seeks to investigate consumers' willingness to pay (WTP) a premium for eco-friendly cosmetics. This study evaluates how much more a consumer is willing to pay for eco-friendly cosmetics and examines significant factors influencing consumers' WTP for eco-friendly cosmetics. Consumers' WTP is measured using four different ecofriendly cosmetics: low-priced skin care cosmetics, low-priced makeup cosmetics, high-priced skin care cosmetics, and high-priced makeup cosmetics. This study uses the contingent valuation method (CVM) to estimate consumer's WTP for eco-friendly cosmetics. Survey questions were designed using both dichotomous choice and payment card method of CVM. Through face to face interviews and on-line surveys, the data were collected from women between 20 and 49 years old residing in Seoul and Kyeonggi area, Korea, in May 2010. A total of 226 questionnaires (132 from interviews and 94 from on-line) were included for the analytical sample in this study. The data were analyzed using descriptive analysis, T-test and Log-Logit analysis. The findings are as follows: First, the WTP measured by dichotomous choice method was estimated using the Log-Logit analysis. The results showed that the estimated WTP for low-priced skin care cosmetics was 19,152 won, which was 27.7% higher than the reference price, 15,000 won. For low-priced makeup cosmetics, the estimated WTP was 18,524 won, and its green premium was 21.0%. The estimated WTP for high-priced skin care cosmetics was 59,128 won, which was 18.3% higher than the reference price, 50,000 won. For high-priced makeup cosmetics was 57,666 won, and its green premium was 15.3%. Second, the WTP measure by payment card method was estimated using descriptive analysis. The results showed that the respondents were willing to pay 17,955 won for low-priced skin care cosmetics, which was 19.7% higher than the reference price, 15,000 won and 17,595 won for low-priced makeup cosmetics, which was 17.3% higher than the reference price. For high-priced skin care cosmetics, the average WTP was 56,950 won which was 13.9% higher than the reference price, 50,000 won. For high-priced makeup cosmetics, the average WTP was 55,650 won, which was 11.3% higher than the reference price. Overall, the WTP was higher in order of low-priced skin care, low-priced makeup, high-priced skin care, and high-priced makeup. It means that consumers decide degree of premium based on the price and the attributes of eco-friendly products. Third, the findings showed that age, monthly income, and having children or not were statistically significant factors that influenced consumers' willingness to pay for eco-friendly cosmetics. Other explanatory variables such as education, marital status, job, purchase experience of eco-friendly products, and environmental concerns did not show any statistical significance. The major contribution of this study is the investigation of the value of green attributes of the products by using CVM. Unlike most previous researches, this research used two methods of CVM, the dichotomous choice and the payment card, so it enhanced the reliability of research. According to this study, consumers showed price sensitivity when they pay green premium. These findings can be used as useful information to establish marketing strategies for green cosmetics.

• The Journal of the Korea Contents Association
• /
• v.19 no.2
• /
• pp.340-348
• /
• 2019

This study analyzes the factors that influence commercial gentrification in Seoul by using both logit model analysis and machine learning with data cumulated from 2015 to 2018 regarding 158 market areas. Logit analysis indicates that log(market area average monthly rent) and the ratio of the purchasing amount by customers aged 40 and younger to total sales in the restaurant and retail business category are statistically significant at 1%; the increase in sales per female customer aged between 30 and 39 in the restaurant and retail business category is statistically significant at 5%; and the increase in number of retailers with a business history of less than two years in the franchise business category is significant at 10%. Machine learning indicates that significant factors ordered by importance are the total retail area, the existence of an industrial complex within the market area, the existence of a traditional market within the market area, the location of subway stations within the market area, the increase of entertainment facilities such as movie theaters within the market area, average monthly rent, and the growth rate of average monthly rent. The contribution of this research is threefold. First, this study analyzes the entire commercial area of Seoul, Korea. Second, this study provides a foundation for future research on predictive indicators by empirically investigating the factors that influence commercial gentrification in Seoul. Lastly, this study introduces various methods of research by utilizing a machine learning approach.

• International Journal of Highway Engineering
• /
• v.11 no.2
• /
• pp.195-203
• /
• 2009

This research wished to risk type and examine closely driver special quality and relation of traffic accidents by occurrence type of traffic accidents and traffic accidents seriousness examine closely relation with Severity. Fractionate traffic accidents type by eight, and driver's special quality for risk group's classification did to distinction of sex, vehicle type, age etc. analyzed relation with injury degree adding belt used putting on availability for security the objectivity with wave. Used log-Linear model and Logit model for analysis of category data. A head-on collision and overtaking accident, right-turn accident are high injury or death accident and possibility to associate in relation with accident type and seriousness degree. In risk group analysis The age less than 20 years in motor-cycle driver, taxi driver in 41 years to 50 years old are very dangerous. The woman also was construed to the more risk group than man from when related to car, mini-bus, goods vehicle etc. Therefore, traffic safety education and Enforcement for risk group that way that can reduce accident that produce to reduce a loss of lives at traffic accidents appearance a head-on collision and overtaking accidents, right-turn accidents should be studied and as traffic accidents weakness class may have to be solidified.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.2
• /
• pp.248-258
• /
• 2021

The study analyzed the financial determinants of corporate R&D intensity that require more attention from academics and practitioners in the Korean capital market. Domestic small and medium enterprises (SMEs) may face with developing substitutes by making more R&D investments in scale and scope, given the unprecedented economic conditions such as the limitation of importing core components and materials from other nation(s). KOSDAQ-listed SMEs were selected as sample data, whose R&D expenditures may be less than those of large firms during the post-global financial turmoil period (2010~2018). Static panel data model was applied, along with Tobit and stepwise regression models, for examining the validity of results. Logit, probit, and complementary log-log regressions were also employed for a relative analysis. R&D expenditures in the prior year, the interaction effect between the previous R&D intensity and high-tech sector, firm size, and growth rate were significant to determine R&D intensity. Moreover, a majority of explanatory variables were found to change between the years 2011 and 2018, while time-lagged effects between the R&D intensity and growth rate exist. Results of the study are expected to be used for future research to detect optimal levels of R&D expenditures for the value maximization of SMEs.

• The Korean Journal of Applied Statistics
• /
• v.4 no.2
• /
• pp.117-127
• /
• 1991

Coefficient of determination $R^2$ is most frequently used descriptive measure in practical use of linear regression analysis. But there have been controversies on defining this measure in the cases of linear regression without the intercept, weighted linear regression and robust linear regression. Several authors such as Kvalseth(1985) and Willet and Singer(1988) proposed many variations of $R^2$ to meet the situations. However, theire measures are not satisfactory due to the lack of a universal principle. In this study, we propose a unfied approach to defining the coefficient of determination $R^2$ using the concept of likelihood distance. This new measure is in good accordance with typical $R^2$ in linear regression and, moreover, can be applied to nonlinear regression models and generalized linear models such as logit and log-linear models.

• The Journal of the Korea Contents Association
• /
• v.21 no.7
• /
• pp.171-180
• /
• 2021

Places exposed to mass media induce behavior by forming curiosity and expectations for potential travelers. The places reported through mass media influence viewers. Among TV programs, the most influential genre is drama, and reality programs that provide immersion with different characteristics from dramas influence viewers' choice of destination. CVM is mainly used for estimating the value of objects that cannot be evaluated in the market, such as tourist destinations. This study conducted an economic valuation of filming locations for dramas and travel entertainment programs using CVM, and then compared and analyzed the research results of the two filming locations. Linear and log logit analysis were performed to measure the willingness to pay for the filming location of the drama/travel entertainment program, and the payment amount was derived. The conclusion of the study is that as the travel cost required to visit the filming location of the drama/travel entertainment program increased, the intention to visit decreased. The amount payable when visiting the filming location of the drama/travel entertainment program was higher than the average consumption amount for a day trip, and the amount payable for the drama was higher than that of visiting the filming location of the travel entertainment program.