• Journal of the Korean Society of Food Culture
• /
• v.25 no.2
• /
• pp.134-140
• /
• 2010

This study focuses on testing the validity of dimensions of restaurants' menu prices. In addition, the effect of demographic variables on the perception of each price dimension was investigated. The subjects were people living in the capital region who have, at least on occasion, gone to family restaurants. The data were collected by self-administered questionnaires and analyzed by factor analysis, reliability analysis, confirmatory factor analysis, and the ANOVA t-test. The results were that consumers' perception of restaurant menu prices is not uni-dimensional, but has six dimensions: price-price schema, pricequality schema, value consciousness, low price proneness, price mavenism, sales proneness. Demographic variables partially affect the consumers' perception of each menu price dimension. The result of the t-test examining dimensions of price according to the demographic characteristic was that females have a higher sales proneness than males. The t-test result according to marriage indicated that married people were higher in price-price schema and quality proneness than unmarrieds. ANOVA according to age indicated that people between ages of 20 to 29 have a higher quality proneness than those of other ages.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.19 no.6
• /
• pp.1040-1050
• /
• 1995

The Purposes of this study were to investigate the choice dimensions in purchasing the medium-low priced casual clothing, the influence of them on the preference of medium-low priced casual clothing, and the brand images of six medium-low priced casual clothing using the perceptual map. The Questionnaires were administered to 540 college students living in Seoul (340) and County of Chungnam(200). The data were analyzed by frequency, factor analysis and multiple regression analysis. The results were summarized as follows: 1) The choice dimensions in purchasing the medium-low price casual clothing were identified as exclusiveness/style, intrinsic characteristics, promotion and price/distance. 2) Exclusiveness/style dimension influenced most on the preference of medium-low priced casual, intrinsic characteristics, price/distance dimension were followed. Promotion dimension appeared to have an insignificant influence. These results were consistent in both Seoul and the County of Chungnam. 3) Perceptual mapping showed Hunt and J-vim had the best brand images, Maypole and Omphalos were followed. Tipi Cosi and I-land appeared to have the worst brand image. The college students living in the County of Chungnam perceived that all six brands of medium low priced casual clothing to be exclusive in their style. In addition, it was perceived less promoted, more expensive and farther than Seoul counterparts.

• Journal of the Korean Society of Food Culture
• /
• v.10 no.4
• /
• pp.327-337
• /
• 1995

The purposes of this study were to: (a) identify college students' patronage behaviors, (b) develop an instrument measuring the attitudes of University Students towards university foodservices management practices, (c) determine university students' attitude towards the four types of university foodservices, and (d) provide recommendations on marketing strategies for university foodservice. Questionnaires were hand delivered to 600 Yonsei University students by designated coordinators. A total of 549 questionnaires were usable; resulting in an 93.3% response rate. The survey was conducted between November 28 to December 4, 1995. Statistical data analysis was completed using the SAS Programs for descriptive analysis, T-test, ${\chi}^2$ test, ANOVA, Factor Analysis and Stepwise Multiple Regression. Most (88.3%) of students were patronizing university foodservices for lunch. Underground student foodservice (40.1%) and Restaurants outside the campus (33.7%) were primarily used for lunch and dinner respectively. Eighty six percent of university students had 1 to 2 meals per day at university foodservices. The reasons given by students for patronizing university foodservices were as follows: location, time, price, menu, taste. Most of the respondents were least satisfied with hygiene, taste, menu and atmosphere. Data indicated strong support for eight priori dimensions in terms of food, menu, atmosphere, hygiene, employee attitude, facilities and convenience. After the factor analysis, price, fast service and foodservice location attributes were rearranged, combined and created a new dimension called as 'access'. Three dimensions in terms of menu, hygiene, convenience were important to students although performance was perceived as poor through importance-performance analysis. Most of students were not satisfied with all four types of university foodservices. In terms of food quality and price which university foodservices offer, most of respondents were moderately satisfied. According to multiple regression analysis, 93.31% of the variance respondents' satisfaction score could be explained by food, menu, price, atmosphere, hygiene, employee attitude, facilities, and convenience dimensions.

• Journal of Information Processing Systems
• /
• v.13 no.5
• /
• pp.1277-1298
• /
• 2017

Related to the maximum vector problem, a skyline query is to discover dominating tuples from a set of tuples, where each defines an object (such as a hotel) in several dimensions (such as the price and the distance to the beach). A tuple, an instance of an object, dominates another tuple if it is equally good or better in all dimensions and better in at least one dimension. Traditionally, skyline queries are defined upon single-instance data or upon objects each of which is associated with an instance. However, in some cases, an object is not associated with a single instance but rather by multiple instances. For example, on a review website, many users assign scores to a product or a service, and a user's score is an instance of the object representing the product or the service. Such data is an example of multi-instance data. Unlike most (if not all) others considering the traditional setting, we consider skyline queries defined upon multi-instance data. We define the dominance calculation and propose an algorithm to reduce its computational cost. We use synthetic and real data to evaluate the proposed methods, and the results demonstrate their utility.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.1-17
• /
• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.33 no.7
• /
• pp.1025-1037
• /
• 2009

This study- 1) identifies the distribution of customer equity in luxury brands, 2) identifies customer equity dimensions of luxury brands, 3) identifies the properties that influence the customer equity of a luxury brand, and 4) compares the differences in the properties of the luxury brands that influence customer equity by brand type and relationship duration. In this research, the survey method was conducted in Seoul and 500 responses were used for analysis. For the data analysis, descriptive statistics (i.e. frequency and percentage), t-test, factor analysis, and multiple-regression analysis were used through the utilization of the SPSS 12.0 program. The results of this study are as follows: First, the distribution of customer equity are found to be 50.8% of the customer equity distributes under 1 million Korean won and 34.8% between 1 million won and 3 million won. Second, the luxury brand dimension consists of 6 factors, 'differentiated brand image', 'personal ties', 'qualitative trust relation', 'rational price value', 'store value', and 'convenience value'. Third, the higher 'differentiated brand image', 'personal ties', 'qualitative trust relation', 'store value', and 'convenience value' were related to a higher customer equity. Fourth, in the case of the consumer group having a long-term relationship, the higher' differentiated brand image', 'personal ties', and 'store value' were related to a higher customer equity. Also, in the case of the consumer group of the traditional luxury brands, the higher 'personal ties', 'differentiated brand image', 'qualitative trust relation', and 'store value' were related to a higher customer equity.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.39 no.4
• /
• pp.613-623
• /
• 2010

The purpose of this study was to examine parents' perceptions towards the importance and performance levels of foodservices quality at kindergartens. The questionnaire was developed to measure the thirty-one quality attributes of foodservice operations. A questionnaire survey was conducted to 500 parents and the return rates were 62.4%. The survey period was from July 15 to August 8, 2008. The parents had a high level of perception toward the need for foodservice, earning 4.46 points out of 5 point. Their perceptions of foodservice quality were examined by six dimensions of importance and performance level. While the parents gave 4 points or greater of 5 points to most quality attributes of importance level, they gave 4 points or less out of 5 points to most quality attributes of performance level. As for the importance and performance level of the quality dimensions of foodservice, parents regarded sanitation as the most important dimension. IPA showed that 'ventilation', 'sanitation of tableware' and 'sanitation of dining tables and chairs' were included as 'focus' areas. The overall satisfaction level for foodservice was 3.74 out of 5 points A higher level of satisfaction was shown at self-operated foodservice system of kindergartens. According to multiple regression analysis, 46.3% of the variance in the respondents' overall satisfaction scores was explained by factors such as food, menu and price, facilities, sanitation, atmosphere and foodservice effects.

• Journal of Global Scholars of Marketing Science
• /
• v.20 no.3
• /
• pp.269-277
• /
• 2010

Previous studies have shown that the most important factor affecting customer loyalty in the service industry is service quality. However, on the subject of whether service quality has a direct or indirect effect on customer loyalty, scholars' views apparently vary. Some studies suggest that service quality has a direct and fundamental influence on customer loyalty (Bai and Liu, 2002). However, others have shown that service quality not only directly affects customer loyalty, it also has an indirect impact on customer loyalty by influencing customer satisfaction and perceived value (Cronin, Brady, and Hult, 2000). Currently, there are few domestic articles that specifically address the relationship between service quality and customer loyalty in the mobile communication industry. Moreover, research has studied customer loyalty as a whole variable, rather than breaking it down further into multiple dimensions. Based on this analysis, this paper summarizes previous study results, establishes an effect mechanism model among service quality, customer satisfaction, and customer loyalty in the mobile communication industry, and presents a statistical test on model assumptions by using customer investigation data from Heilongjiang Mobile Company. It provides theoretical guidance for mobile service management based on the discussion of the hypothesis test results. For data collection, the sample comprised mobile users in Harbin city, and the survey was taken by random sampling. Out of a total of 300 questionnaires, 276 (92.9%) were recovered. After excluding invalid questionnaires, 249 remained, for an effective rate of 82.6 percent for the study. Cronbach's ${\alpha}$ coefficient was adapted to assess the scale reliability, and validity testing was conducted on the questionnaire from three aspects: content validity, construct validity. and convergent validity. The study tested for goodness of fit mainly from the absolute and relative fit indexes. From the hypothesis testing results, overall, four assumptions have not been supported. The ultimate affective relationship of service quality, customer satisfaction, and customer loyalty is demonstrated in Figure 2. On the whole, the service quality of the communication industry not only has a direct positive significant effect on customer loyalty, it also has an indirect positive significant effect on customer loyalty through service quality; the affective mechanism and extent of customer loyalty are different, and are influenced by each dimension of service quality. This study used the questionnaires of existing literature from home and abroad and tested them in empirical research, with all questions adapted to seven-point Likert scales. With the SERVQUAL scale of Parasuraman, Zeithaml, and Berry (1988), or PZB, as a reference point, service quality was divided into five dimensions-tangibility, reliability, responsiveness, assurance, and empathy-and the questions were simplified down to nineteen. The measurement of customer satisfaction was based mainly on Fornell (1992) and Wang and Han (2003), ending up with four questions. Based on the study’s three indicators of price tolerance, first choice, and complaint reaction were used to measure attitudinal loyalty, while repurchase intention, recommendation, and reputation measured behavioral loyalty. The collection and collation of literature data produced a model of the relationship among service quality, customer satisfaction, and customer loyalty in mobile communications, and China Mobile in the city of Harbin in Heilongjiang province was used for conducting an empirical test of the model and obtaining some useful conclusions. First, service quality in mobile communication is formed by the five factors mentioned earlier: tangibility, reliability, responsiveness, assurance, and empathy. On the basis of PZB SERVQUAL, the study designed a measurement scale of service quality for the mobile communications industry, and obtained these five factors through exploratory factor analysis. The factors fit basically with the five elements, indicating the concept of five elements of service quality for the mobile communications industry. Second, service quality in mobile communications has both direct and indirect positive effects on attitudinal loyalty, with the indirect effect being produced through the intermediary variable, customer satisfaction. There are also both direct and indirect positive effects on behavioral loyalty, with the indirect effect produced through two intermediary variables: customer satisfaction and attitudinal loyalty. This shows that better service quality and higher customer satisfaction will activate the attitudinal to service providers more active and show loyalty to service providers much easier. In addition, the effect mechanism of all dimensions of service quality on all dimensions of customer loyalty is different. Third, customer satisfaction plays a significant intermediary role among service quality and attitudinal and behavioral loyalty, indicating that improving service quality can boost customer satisfaction and make it easier for satisfied customers to become loyal customers. Moreover, attitudinal loyalty plays a significant intermediary role between service quality and behavioral loyalty, indicating that only attitudinally and behaviorally loyal customers are truly loyal customers. The research conclusions have some indications for Chinese telecom operators and others to upgrade their service quality. Two limitations to the study are also mentioned. First, all data were collected in the Heilongjiang area, so there might be a common method bias that skews the results. Second, the discussion addresses the relationship between service quality and customer loyalty, setting customer satisfaction as mediator, but does not consider other factors, like customer value and consumer features, This research will be continued in the future.