Journal of the Korean Society of Food Science and Nutrition
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v.42
no.2
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pp.318-323
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2013
This study was carried out to investigate the levels of awareness of fermented vegetable beverages according to consumers' gender. The data were obtained through questionnaires completed randomly by 441 respondents and analyzed using the SPSS, t-tests, ANOVA, X-square tests, multiple regression, and logistic regression analyses. Among 441 respondents, 32.9% of male and 30.2% of female ever consumed fermented vegetable beverages. This beverage were consumed as an alternative for water in males (6.3%) and in females (7.9%) which was the most common reason for consumption. As for the most common reason for non-consumption, males (5.9%) and females (6.6%) responded that they did not consume fermented vegetable beverages because people around them have never consumed fermented vegetable beverages. The awareness that fermented vegetable beverages help reduce hunger was higher in females (3.2%) than and in males (2.9%). These results revealed that the awareness of fermented vegetable beverages as hunger alleviators was more prevalent in females than in males which can be used as preliminary data for research on the development of fermented vegetable beverages.
Journal of the Korean Society of Food Science and Nutrition
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v.42
no.12
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pp.2068-2075
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2013
The purpose of this study is to investigate the perception of university students on nutritional information according to food and nutrition labeling systems. A total of 310 customers, who visited family restaurant, were surveyed by a self-recorded questionnaire from March 2006 to April 2011. A total of 286 respondents were surveyed; of the respondents, 108 were males and 178 were females. Two surveys were conducted on the perception of the respondent's health: once in 2006 and once in 2011. According to these surveys, 63.6% and 54% of respondents perceived themselves as unhealthy, respectively. When ordering a meal, respondents were more concerned with price rather than taste, nutrition, new menu items, and food presentation. Compared with 2006, in 2011 more respondents felt that family restaurants provided enough nutritional information and practical use of that information to their customers. When surveyed, respondents felt that the total calories played a significantly higher role in ordering food than foods with higher nutritional values. There was a significant increase in satisfaction with the current nutrition labeling system; in 2006, $2.87{\pm}0.99$, and 2011, $3.35{\pm}0.84$. There was also a significant increase in individuals who felt that there was a need for an ingredient labeling system; $3.68{\pm}0.9$ in 2006 and $4.32{\pm}0.61$ in 2011. There was also a higher demand for nutritional information; $2.85{\pm}0.66$ in 2006, $3.06{\pm}0.65$ in 2011. From these results, it was concluded that the nutrition labeling system adopted by family restaurants did not affect the degree of customers' interest in nutritional information. Contrast to the results, the amount and frequency of nutritional information provided to customers have increased continuously since 2006. Therefore, the nutrition labeling system and recommended dietary allowance should be expanded in order to promote a healthy diet.
Loyalty programs are everywhere these days, and among them, coalition programs, where a hub company makes a network of participating companies and users can collect and use points in a large number of participating shops, are in increase. There is little research on the coalition loyalty program. Using the case of OKCashbag, the first and biggest coalition loyalty program in Korea, this study examines whether success factors of single operator program are relevant to coalition programs. This paper contributes first by turning attention to an unexplored area of coalition loyalty programs. By offering an opportunity to reflect on success factors of both single operator and coalition programs, it will help practitioners in loyalty programs to develop innovative loyalty programs using increasingly sophisticated data collection capabilities of IT.
In this study, to analyze the factors affecting the purchase of smart device applications, research model based on AHP(Analytic Hierarchy Process) model was employed and derived consumers' priorities of smart device game applications in the framework of contents characteristic factors. Survey was conducted with 10 experts who are involved in the smart game industry. The empirical result showed that the most important purchasing factor was story(0.217). And fame(0.171), graphics(0.134), operability(0.111), information(0.093), difficulty(0.085), speed(0.068), characters(0.053), price(0.042), genre(0.028) are presented in order in terms of the importance. The order of consumers' preferences to smart device game application was RPG, Tycoon, action, simulation, sports/leisure, quiz/puzzle/board, etc. gamble. It suggested that under the environment of smart devices consumers could enjoy not only simple puzzle and board game but also complicated and difficult games such as RPG and tycoon game due to the development of smart devices.
In this study, a survey of online coupon user in their 20's and 30's was undertaken to explore how e-coupon attribute, perceived risk affect the intention to continuously use online coupons through mediation of online coupon user satisfaction. Four variables were used as E-coupon attribute variables(economy, amusement, reliability, usefulness) with coupon type and gender being used as moderating variables. The analysis results of relationship between e-coupon attributes, perceived risk and online coupon user satisfaction indicated that amusement, usefulness significantly affected online coupon user satisfaction, and this, in turn, significantly affected the intention to continuously use online coupons. In addition, the test of difference between groups in coupon type and gender, two moderating variables used in this study, found that with respect to coupon type, a moderating effect existed in reliability, usefulness whereas for gender, a moderating effect existed in only reliability. It is considered that this study will provide a credible evidence basis for Internet shopping malls in establishing a coupon promotion strategy including a specific promotional mix best suited to their needs and resources, while implicating that offering highly reliable online coupons will contribute to enhancing satisfaction level among customers using online coupons.
Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.
It is generally regarded a design system in post-industrial society, which products designed by in-house designers or design consultancy are manufactured in factory and distributed in market for the consumer. Although it is treated an old design system in traditional society, the traces of vernacular design has been remaining in the state of adopted to the periodical needs in these days, also proving the attribute of design culture to constitute human's material environment as well as existing design systems. There were discovered various design artifacts in daily surroundings vary from the established design in several manners, user modifications or manufactures in everyday lives formalized them. It was approached a case study that analyze the changes of artifact configuration and designer/user context and creation process of the non-professional design artifacts, Creativity Template Theory and ACM(Artifact Context Model) have been utilized for the analysis model. From the analysis result, It assume that the everyday artifacts may be ordinary but extra-ordinary including particular ideas and identity represented by everyday designers or users. Beside these characteristics induce the potentiality that reflect on creative motives for the designers or a complementary artifact generator filling up with drawbacks in established design system. The everyday design domain, various explorations and alternatives are made, is seems to be another design practice domain dissimilar to the one in the industry-based design. Moreover it provides an more easily accessability for the approaching user-friendly design, user customization because they conduct the reliable modeling of consumer and end-user. Finally, based on the exploratory study regarding interpretation of context and configuration in the everyday artifacts, new approach for the design process and design education through more detailed cognitive modeling of everyday designers will be a further study.
With the advent of cryptocurrencies such as Bitcoin in 2009, the paradigm of money, a means of payment, has been changing significantly. And it has a great impact on our daily lives. Thus central banks have attempted various analyzes on the issuance and impact of digital currencies including electronic payments but a study on which issuance method is suitable is insufficient. In this study, the issuance of digital currency was analyzed compared to the electronic payments which are currently used. As a result, the account-based model did not show any significant differences from the current RTGS(real-time gross settlement systems) and retail payment systems. But the token-based model is expected that it can improve the efficiency of finance and induce technological innovation in the financial field. However, it was analyzed that this model would weaken the intermediary function of financial institutions such as loans due to the characteristics of digital signature technology. In addition, in order to protect consumers against security attacks such as hacking and phishing of CBDCs, legal and institutional supports similar to the current electronic payment method are required, and continuous technology development efforts are also required for the CBDC issuance model to maintain convenience and anonymity equivalent to cash.
Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.
While e-commerce market(B2C) grows rapidly, many experts argue that EC(B2C) transactions have not reached its full potential. A notable difference between online and offline consumer markets that is suppressing the growth of EC(B2C) is the decreased presence of human and social elements in the online shopping environments. Generally online shopping lacks human warmth and sociability. In this study, social presence in online shopping mall was proposed as a substitute for face-to-face social interaction in the traditional commerce and author explored what variables affect social presence(human warmth and sociability) on online shopping malls and how human warmth and sociability can influence on online store loyalty. To achieve research objectives, we reviewed literatures related with marketing, psychology and communication research areas. Based on literature review, we proposed a research model on the online shopping mall. To examine the proposed research model, we gathered data by using a self-report questionnaire. Respondents consists of online shoppers with at least five or more times of purchase experience in online shopping malls. Because social presence is a feeling which needs frequent contacts with malls to experience, respondents must have enough purchase experiences. The empirical results are as follows : First, shopping mall's customization efforts influence perceived social presence on the mall significantly. Second, shopping mall's responsiveness influences perceived social presence significantly. Third, perceived activity of community of online shopping mall influences perceived social presence significantly. Mall managers have to activate their customer community to reinforce social presence, resulting in trust building. Finally, perceived social presence influences trust and enjoyment on the mall significantly. And then trust and enjoyment on the mall affect store loyalty significantly. From these findings it can be inferred that perceived social presence appears determinant which is critical to the formation of core variables(trust and loyalty) in existing online shopping papers.
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