• Journal of Distribution Science
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• v.10 no.2
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• pp.53-62
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• 2012

Well-being is a reflection of current sociocultural trends that focus on the quality of life based on economic growth. Furthermore, organic food is believed to help people maintain good health and therefore leads to increased consumption of organic foods. Therefore, consumer interest in organic food is increasing, causing its market to grow, and this trend will be maintained in the future. The abuse of agricultural pesticides, gene manipulation, and bovine spongiform encephalopathy has caused consumers to worry about food safety. The well-being trend has also contributed to consumers' growing interest inorganic food and organic agricultural products. A consumer's choice offood is a complex processes affected by various factors. In particular, organic food is considered an individualistic merit good, considering the consumers' preferences related to certification policies. Therefore, various factors such as personal characteristics and sense of value could affect consumers' decisions. This research focused on an analysis of the factors influencing consumers' purchasing intention for organic food on the basis of an increase in organic food consumption. The research method was based on the theory of planned behavior (TPB). Factors such as consumer characteristics regarding food consumption, purchasing frequency, and other factors affecting purchasing intention were presented. The hypothesis was set using advanced research and stated that it is easier to forecast purchasing intentions by combining the theory of planned behavior and personal characteristics of consumer. The results show that two dimensions, attitude and perceived behavioral control, have statistically significant influence on the purchasing intention. It can be said that a positive attitude toward organic foods in particular increases the possibility of purchasing intention. In addition, consumers who consume more organic food products are more likely to have positive attitudes, and, in the past, purchasing frequency has positively influenced purchasing intention of organic foods. Consumers' negative feelings about the non-purchase of organic foods also showed a negative influence on purchasing intentions. In other words, even though consumers feel uncomfortable when not consuming organic food products, they do not try to purchase such products because of this feeling of discomfort. Furthermore, the subjective norm and the behavioral control of food-related involvement do not have a statistically significant influence on the purchasing intention or attitudes. This research verified the influence of factors related to purchasing intention. This study has several limitations: (1) even though consumers' responses can change based on the type of food, the types of food were not classified in this study; (2) future studies are necessary to analyze the attitudes of consumers on the basis of their purchasing experiences with organic foods.

• Korean journal of food and cookery science
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• v.24 no.6
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• pp.788-800
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• 2008

This study was executed in order to analyze the purchasing practices, preferences, and satisfaction of consumers purchasing ready-to-eat foods. The study was executed from the $20\;May{\sim}30\;June$ 2007 in the Daegu and Gyeongsangbuk-do area. Questionnaires were distributed among 480 people. Of these, 410 questionnaires were collected and applied to a statistical analysis. Statistical analyses were conducted using the SPSS package program (version 14.0 for windows). The results of the statistical analyses were as follows. 42.4% males and 57.6% females participated in this study. The biggest motivation behind the purchase of ready-to-eat foods was convenience, 51.0%. The most common venues where these foods were purchased were supermarkets or discount marts, 67.6%. The ratio of substituting ready-to-eat foods for meals was 70.5%. The consumption practices of ready-to-eat foods of the consumers according to purchase place illustrated significant differences in the average expense per person each time (p<0.01), co-consumer (p<0.001), and age (p<0.05). The purchase preference was high with kimbaps (73.4%), hamburgers (29.8%), sushis (29.0%), and sandwiches (27.9%). Unmarried individuals showed a significantly higher overall liking and satisfaction for ready-to-eat foods than married individuals (p<0.001). Individuals living without a family was significantly higher overall liking and satisfaction than individuals living with a family (p<0.05). Individuals who spent an average of 10,000 won per time showed a significantly higher overall liking and satisfaction than those who spent below 10,000 won per time (p<0.05). In the presence of a friend as a co-consumer of ready-to-eat foods, satisfaction was significantly high (p<0.01). Consumers who purchased ready-to-eat foods at meal times, showed a significantly higher overall liking, compared to those who purchased such food products in between meal times (p<0.01). The preference and satisfaction degree of the consumer must be reflected by product development, quality improvement and marketing plan establishment.

• The Journal of Industrial Distribution & Business
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• v.10 no.6
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• pp.39-50
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• 2019

Purpose - Since the local liquor industry uses the agricultural products produced in the region as the main raw material, cultivation of the industry can be a way to expand the demand for domestic agricultural products and can contribute to the income increase of the farmers. In addition, the local liquor industry can be developed into a region-specific industry differentiated from other regions by linking with the region's tourism resources. Therefore, in order to achieve various derivative effects through promotion of the local liquor industry, it is necessary to precisely understand the factors related to the purchase and consumption of local liquor which then will lead to an increase in consumption. This study analyzes the current status and problems of local liquor industries and examines the determinants of consumers' purchasing decisions of regional specialties through questionnaires. We will then propose a strategy to promote consumption of locally produced alcoholic beverages. Research design, data, and methodology - An online Domestic Consumer Survey was conducted to identify the actual purchases and uses of local liquors for 500 consumers nationwide. Based on the questionnaire results, the analysis uses an ordered probit model. Results - As a result of analyzing the effect of consumer characteristics on the purchase of local liquors, it was found that the average cost of drinking, income, local specialty, brewery experience, and health concerns have a significant effect on gender, drinking frequency. All the variables except the participation in the training of the special provincial manufacturing method were found to be statistically significant. The statistical significance was at a 1% significance level for the remaining factors excluding the bottle design. This shows that the higher taste, quality, price, and harmony with food, the higher the probability of purchasing local liquors. Conclusions - In the analysis of factors influencing the purchase intention of local liquors, it was found that factors such as taste, quality, price, and harmony with food had a significant effect. Given the diversity of purchasing factors, the importance of diversification strategies is emphasized again. In particular, it will be important to secure wide publicity for local liquors through various PR strategies.

• 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.

• ETRI Journal
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• v.31 no.3
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• pp.304-314
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• 2009

Component-based software reuse has been generally regarded as a promising approach to improving software productivity and quality within software development. However, progress in component-based software reuse has been slower than expected. Much of the software reuse literature points to the lack of software components that can maximize users' benefits as the most important source of the slow progress. Considering that the underlying processes behind component-based software reuse are strikingly similar to commercial software marketing, this paper attempts to identify the aspects of software components that consumers value and to establish relationships between the identified aspects and consumer behavior in the software component market. More specifically, this paper focuses on the perceived subjective features of software components. This study was conducted in a web-based artificial market environment called "SofTrade."

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• Korean Journal of Organic Agriculture
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• v.28 no.2
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• pp.189-208
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• 2020

The purpose of this study aims to estimate consumption selection attribute, part-worth of organic instant rice through the use of conjoint analysis method. The conjoint analysis is to trace the development of consumer preference among multi-attribute alternatives. The selection attribute was including 4 factors preferred Type of rice, Capacity, Brand and payment price. For this research, a total of 192 questionnaires was collected of which 200 were completed. The research design was a full profile method by orthogonal design then 9 main profiles, 3 holdout sets were created. The results of this research were as follows. Consumers of organic instant rice are consider their importance of selection attributes was in order to price (25.87%), Type of rice (27.231%), Brand/Purchase channel (24.013%) and Capacity (18.494%). The findings of this study have identified 3 clusters for each experience visitors. Each cluster has a different and showed the relative importance or preference values for each accessible attribute of the segmentation.

• Proceedings of the ESK Conference
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• 1996.04a
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• pp.212-217
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• 1996

This study investigated whether consumer's characteristics affect their preference of size labeling systems. The survey included 443 women 20 to 64 years of age. The results show that the consumers' preferences of five size labeling systems were different significantly. Subjects strongly prefered the size label in use for many years at the marker. The consumers' preference for various systems was different by resident area, educational level, and body types of the subjects. The system with information on body measrements was prefered by the resident in the metro city area than the resident in the small city. The simplest system was strongly prefered by the subjects having no difficulties on purchasing ready-to-wear garments that reasonably fit their bodies. The subjects belonged to the low educational level strongly prefered the simplest system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.2
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• pp.9-18
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• 2011

Vender prioritization process is one of the most critical tasks of production and logistics management for many companies. Determining the most critical criteria for vender prioritization process is a vital means for a purchasing company to improve its supply chain productivity. This study discuss the use of a Fuzzy analytic network process (Fuzzy ANP) model which is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different criteria which are not independent. Also, the comparison of classical ANP and Fuzzy ANP is described using simulation with triangular distribution random number generation. It is shown that Fuzzy ANP model possesses some attractive properties and could be used as an alternative to the known vender prioritization methods.

• Korean journal of food and cookery science
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• v.26 no.6
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• pp.745-752
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• 2010

The purpose of this study was to investigate the perceptions and preferences held by the general public concerning jeungpyun. Self-administered questionnaires were collected from 484 residents in Daegu and Gyeongbuk. The questionnaires contained questions on the recognition of jeungpyun, purchasing, frequency, purchasing factor, and preferences for and purchasing of jeungpyun containing different kinds of makgeoilli. According to the survey, females were higher than males (31.4%) by 69.7%, and that for ages 20~30' was the highest. Most of them were students (39.7%), and 81.7% of them had an income of under 4,000,000 won per month. The results of the eating frequency rate showed that over half of the people had jeungpyun once a month, and the recognition of jeungpyun for females (3.63) was higher than that for males (3.09). The respondents who joyed eating jeungpyun mentioned 'good taste and texture' as the top reason for eating jeungpyun, and 'different food and bad flavor' was mentioned as the reason for disliking it. As a result, popularization of jeungpyun was based on two factors: 1) fundamental factor and 2) external factor, and both significantly influence the preference for and purchasing of jeungpyun,-. Therefore, future studies should be conducted to improve preference for jeungpyun as a functional food. The results of preference and purchasing show that jeungpyun prepared with rice, rubus coreanum miquel, black bean, grape and green mume makgeoilli.