Purpose – The purpose of this study is to examine the image similarity and attribute recognition of the top 10 rated spa destinations (Chungnam Deoksan, Chungnam Dogo, Busan Dongrae, Daejeon Yuseong, Chungnam Asan, Gyeongbuk Bomun, Chungbuk Suanbo, Gyeongnam Jangyu, Chungnam Onyang, & Gyeongbol Bugok) in Korea based on the visits to these spa places by the customers. Research design, data, and methodology – The survey of this study was conducted on the visitors to the top 10 spa destinations in Korea from April 8 ∼ April 21, 2017, and a total of 300 questionnaires were distributed. Of them, effective questionnaires used in the final study were a total of 241. In this study, empirical analysis was made through frequency analysis, factor analysis, and multidimensional scaling ALSCAL(spinning symmetry for image similarity and rectangle for attributes recognition) by using the Statistics Package SPSS 24.0. Results – According to the analysis result of spa destination image similarity, the stress level was 0.16453 and the level of the stress was good. Moreover, the coefficient of determination (RSQ) was, which had a description of each aspect of the spa destination, 0.79908. According to the results of attribute recognition, the stress value of 0.11805 represents a degree of conformity, and the coefficient of determination(RSQ) appeared at 0.98665. Therefore, the results of this analysis are that the similarities between spa destinations and the attribute recognition of the spa destinations is a suitable model that is properly expressed in two dimensions. Conclusions – First, according to the analysis result of image similarity, Deoksan & Dogo spa revealed similar images, as well as the Dongrae and Yuseong spa, while on the contrary Asan, Bomun, Suanbo spa has different images from the rest. Second, according to the results of attribute recognition, Asan and Onyang spa has competitiveness in terms of accessibility to spa destination; Yuseong, Dongrae, Jangyu spa in terms of spa facilities, spa tourism conditions, and service & shopping conditions. while spa water quality and spa costs showed low attribute reflection for all 10 spas. Therefore, the spa visitors cannot recognize the differentiation of spa water quality and spa costs.

Purpose – This current study will investigate the average financial ratio of top and failed five-star hotels in the Jeju area. A total of 14 financial ratio variables are utilized. This study aims to; first, assess financial ratio of the first-class hotels in Jeju to establishing variables, second, develop distress prediction model for the first-class hotels in Jeju district by using logit analysis and third, evaluate distress prediction capacity for the first-class hotels in Jeju district by using logit analysis. Research design, data, and methodology – The sample was collected from year 2015 and 14 financial ratios of 12 first-class hotels in Jeju district. The results from the samples were analyzed by t-test, and the independent variables were chosen. This was an empirical study where the distress prediction model was evaluated by logit analysis. This current research has focused on critically analyzing and differentiating between the top and failed hotels in the Jeju area by utilizing the 14 financial ratio variables. Results – The verification result of the accuracy estimated by logit analysis has shown to indicate that the distress prediction model’s distress prediction capacity was 83.3%. In order to extract the factors that differentiated the top hotels in the Jeju area from the failed hotels among the 14 chosen, the analysis of t-black was utilized by independent variables. Logit analysis was also used in this study. As a result, it was observed that 5 variables were statistically significant and are included in the logit analysis for discernment of top and failed hotels in the Jeju area. Conclusions – The distress prediction press’ prediction capability was compared in this research analysis. The distress prediction press prediction capability was shown to range from 75-85% by logit analysis from a previous study. In this current research, the study’s prediction capacity was shown to be 83.33%. It was considered a high number and was found to belong to the range of the previous study’s prediction capacity range. From a practical perspective, the capacity of the assessment of the distress prediction model in the top and failed hotels in the Jeju area was considered to be a prominent factor in applications of future hotel appraisal.