• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.2
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• pp.212-223
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• 2004

The purpose of this study is 1) to identify Chinese environment for investment and apparel market, 2) to analyze the current situations of Korean fashion brands＇entry to Chinese market, 3) to analyze the marketing strategies to China according to product category, and 4) to identify merits and problems of Chinese market. For data collection, secondary resources were collected, and the telephone interview with merchandisers were implemented with brand managers. Twenty-one fashion brands were included for the study. Results of the study were as followed： 1) China was a big potential apparel market due to its rapid economic growth. Apparel purchase behavior and clothing preference of Chinese consumers were various by regional groups. 2) The motives of entry to China were to competition in domestic markets, saving raw material cost. The entry modes to China were direct export, license and regional manufacturing system. 3) Marketing strategies were to pursue high quality branding, high pricing and placing strategies with high-class department stores. Also star marketing were used with ＂Han Rue＂. Also various promotion strategies were implemented such as fashion show and unique VMD. 4) The merits of Chinese market were high potential market for export, close proximity, cultural similarity and Han-Ryu syndrome. Problems of Chinese market for export were lack of experts on Chinese market, fierce competition in China, and unstable economic policies.

• Journal of Fashion Business
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• v.15 no.6
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• pp.71-84
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• 2011

This study is purposed to categorize external image types of Korean male idol stars, and to understand characteristics of fashion and beauty design for each type and through such, the study is meant to identify the various types of male image and to provide data of image making. As part of the empirical study, it selected well-known 15 groups of male idol stars who were active during February of 2008 and August of 2010, categorized the image types by their photographic data, and analyzed characteristics of fashion and beauty design for each type. In order to measure the degree of image types, a survey was also conducted. The image types of male idol stars, which were classified through a workshop by a group of image-related experts, were categorized with 6 types that are Elite, Urban Chic, Charisma, Rebellious child, Toy boy, and Homme Fatale. The six types of male idol stars have a great relation with the modern male trends and images, and that the male idol stars have been leading the trends and presenting the image by the public preference. In addition, as shown the above, the recent male image appears as a mixed image of femininity and masculinity, showing a complex aspect that does not dominates with one image, and that it was found out to constantly be changing in the flow of times.

• Journal of the Korean Society of Food Culture
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• v.22 no.5
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• pp.597-602
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• 2007

For the hotel industry, the situations having difficulties in management are becoming we planed by the rises of the cost and labor costs, the imbalance between supply and demand, stiffening competitions between the hotels. Therefore, there has been a plan for a great change to attract customers, escaping from the existing form of management in order to secure competitive powers in the food and beverage field. For that purpose, we plan to investigate into the preference of buffet restaurants in ten 5star hotels in Seoul. By the analysis, we also plan to present the menu concepts that stand out and are preferred by the customers in managing semi-buffet restaurants. Therefore, the linear and planar coordinate values of the H Hotels and I Hotels came out both positive(+) as results of a similarity analysis using MOS, we can predict that they would be positioning on the same dimension. Furthermore we can predict that the menu of antipasto, sushi, sashimi and desserts would be positioning on the same dimension as a result of analysis of the most preferred menu by customers for each station in managing a semi-buffet restaurant. Based on these results, there must be continuous supervision over the menu of buffet restaurants.

• Culinary science and hospitality research
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• v.22 no.4
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• pp.254-264
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• 2016

This research was conducted over about four weeks from May 1, 2015 until May 30. First, of the hotel restaurant visit motivations, awareness image B=0.524 (p<.001), food B=0.121 (p<.05), and distinctiveness B=0.251 (p<.001) had a significantly positive impact on consumer attitude. Second, the hotel restaurant, visit motivation and preference image B=0.577 (p<.001), Social B=0.192 (p<.001), distinctiveness B=0.173 (p<.01) was significantly positive on consumer attitudes there were influential (+). Third, there was the influence of motivation and purchases, visit the hotel restaurant between images of B=0.705 (p<.001), Around B=0.113 (p<.01) significantly positive (+) on purchase of consumer attitudes to food B=-0.135 (p<.01) and social B=-0.123 (p<.001) are part had the effect of (-). Addition, consumers also look at the attitude and awareness Revisit B=0.464 (p<.001), Rating B=0.321 (p<.001), even the purchase of B=0.205 (p<.001) all significantly positive (+) with there was influence. If the five-star hotel from carrying out marketing activities around awareness, preference, and purchase intention judges will give a great help in hotel management.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.93-106
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• 2019

This study is purposed to make sure of the Effects of Hotel Brand Equity on Enterprise Trust and Customer Citizenship Behavior. For this study, the effects of Hotel Brand Equity on Enterprise, the effects of Hotel Brand Equity on Customer Citizenship Behavior and the effects of Enterprise Trust on Customer Citizenship Behavior were analyzed statically by the method of multiple regression analysis. Hotel Brand Equity consists of 3 elements(Perceived Service Quality, Brand Image, Brand Awareness), Enterprise Trust is made of 2 elements(Enterprise Ability, Enterprise Sincerity) and Customer Citizenship Behavior has 2 element(Guest Preference, Guest Helping). As a result, three things have been found. First, Perceived Service Quality has a positive influence on both of Enterprise Trust and Customer Citizenship Behavior which means that Perceived Service Quality is the most essential element for hotel marketing performance. Second, Brand Image has a positive influence partly on Enterprise Trust, that is, only on Enterprise Ability, not Enterprise Sincerity and also on both Guest Preference and Guest Helping whereas Brand Awareness has a positive influence on both of Enterprise Ability and Sincerity but it has no influence on Customer Citizenship Behavior. Third, Enterprise Sincerity has a positive influence on both of two elements in Customer Citizenship Behavior whereas Enterprise Ability has a positive influence on only Guest Helping.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used