• Korean Journal of Community Nutrition
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• v.24 no.4
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• pp.300-308
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• 2019

Objectives: This research was conducted to identify the consumers' food choice factors that affect the consumers' replacement of soft drinks with carbonated water. Methods: The present study used secondary data from a consumer panel survey conducted by the Rural Development Administration of Korea, and the data included the panel members' purchase records based on their monthly spending receipts. The survey asked the participants about their food choice factors and their personal responsibility for their health. This survey included independent variables for the consumers' food purchase factors. As a dependent variable, two types of groups were defined. The replacement group included those people who increased their purchase of carbonated water and decreased their purchase of soft drinks. The non-replacement group included those people who did not change their purchase patterns or they increased their purchase of soft drinks and they decreased their purchase of carbonated water. Logistic regression analysis was conducted to determine the consumers' food choice factors that were associated with replacing soft drinks with carbonated water. Results: The replacement group was significantly associated with (1) a younger age (OR=0.953), (2) being a housewife (OR=2.03), (3) higher income (OR=1.001) and (4) less concern about price (OR=0.819) when purchasing food. This group also showed (5) higher enjoyment (OR=1.328) when choosing food and (6) they took greater responsibly for their personal health (OR=1.233). Conclusions: This research is the first study to mainly focus on soft drinks and carbonated water. The result of this research showed that young, health-conscious consumers with a higher income and who are more interested in food have more possibilities to replace soft drinks with carbonated water. These research findings may be applied to consumers who have characteristics that are similar to the young health-conscious consumers and the results can help to suggest ways to reduce sugar intake and improve public health. However, this research has a limitation due to the application of secondary data. Therefore, a future study is needed to develop detailed survey questions about food choice factors and to extend these factors to all beverages, including soft drinks made with sugar substitutes, so as to reflect the growth of alternative industries that use artificial sweeteners or different types of sugar to make commercially available drinks.

• Culinary science and hospitality research
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• v.9 no.4
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• pp.163-178
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• 2003

The purpose of this study is to find the effect of restaurant brand image upon service quality, price acceptability, and revisit intention, and to propose the importance of brand image to operators and managers who manage restaurants. To accomplish the purpose of this study, sampling was taken among customers who visit six deluxe hotels and six family restaurants in Seoul. Six hundreds questionnaires were distributed to each hotel and restaurant and 487 valid samples were selected for statistical analysis. The questionnaire consists of 77 items about demographical characteristics, brand image, service quality, revisit intention, price acceptability, and spending patterns. SPSS WIN 10.0 was used for statistical analysis. A research model was built up and three null hypotheses were established. Based on theses research model and three null hypotheses, the test was conducted, and the results are as follows. Brand image has an effect upon service quality, and furthermore this can be preceding variable of service quality. Also Service quality has an effect upon price acceptability and revisit intention.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.