• Asia Marketing Journal
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• v.9 no.3
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• pp.117-149
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• 2007

It can be explained by congruity theory as a process that consumers engage in a matching process to identify personal community that is congruent with their self-images to find the identification between the self and the personal community. Personal community cues that evoke certain images are viewed as activating similar beliefs about the self (e.g., high status). Individuals prompt a comparison process to determine whether the personal community and self-image are congruent and imagine prototypical users of the personal community and select ones that maximize similarity to their actual or desired self-concept. Identity is devided into personal identity and social identity. Consumers are likely to be influenced by both personal identity and social identity. In this article the influencing factors of the commitment to on-line personal community are explored by the sources of both personal identification and social identification. The results are as follows. The maintenance expression and enhancement expression of personal self influence the level of personal identification positively and the maintenance expression and enhancement expression of social self influence the level of social identification positively. The level of both social and personal identification positively influence the commitment to on-line personal community which gives positive responses to the source enterprise that allows the cyberspace and the other benefits to be used.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.99-114
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• 2022

Crowdfunding is one of new financing alternatives and is innovative and creative. In order to proceed with crowdfunding, various functions are required, such as design for screen composition, marketing and promotion for the public, accounting to manage the collected funds, and product production and purchase for reward. In addition, since it is a project that must be completed in a short period of time, cooperation between team members is important. This paper studied how the characteristics of the team conducting crowdfunding affect the team performance in crowdfunding. In this study, we set functional background diversity and shared leadership necessary for crowdfunding as team characteristic variables and crowdfunding amount, completion of work and team innovation as team performance variables. This study tests the hypotheses from 220 university students in 79 teams. The findings suggest that functional diversity and shared leadership are positively related to the completion of work and team innovation but not related to crowdfunding amount. To date, few studies have studied the relationship between characteristics of the crowdfunding team and performance. Therefore, the study on functional diversity and shared leadership in crowdfunding can expand existing crowdfunding study area.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.3
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• pp.318-332
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• 2015

The maize production in South-eastern Asian countries showed a continuous increase with increasing poultry-livestock from the beginning of the 1990s to early 2010. Also the need for a new variety development of each contries was increased rapidly in the same period. Single-Cross hybrid varieties have been developed and supplied from 2001 instead of multi-cross maize varieties since 1992 in Indonesia. In Cambodia, CP group is mainly manufacturing feeds with most of the forage maize from farmers who are growing its seeds from the company. Cambodian main cultivars are varieties of multinational corporations such as DK8868 from Monsanto, NK6326, NK7328 from Syngenta and CP333 from CP group including local business company. Vietnam is the main maze importing country in South-Eastern Asia which had imported 13 times scale of amount compared to exports in average from 1990 to 2011. Vietnamese government has developed a range of varieties for improving their efficiency in production, such as the LVN-10 with political investments. Their production has been reached to 80% of the total. According to the 2012 MIFAFF (Ministry for Food, Agriculture, Forestry and Fisheries) data in Korea, domestic edible maize cultivation area was approximately 15,000ha. It showed 74,399 tons of production, 3.8% of food self-sufficiency in maize and around 0.9% of grain self-sufficiency rate. The consumption of grain is mostly rely on imports in Korea. To overcome the limit of the domestic seed market and increase maize self-sufficiency, the need to develop maze varieties for world-class is increasing at present through analyzing the market trend and prospect of the seed industry in South-eastern Asia.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.12
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• pp.1956-1964
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• 2009

Understanding ethical fashion consumers in Korea is essential for the expansion of the ethical fashion market. This study analyzed ethical consumers in Korea in an examination of the factors that influence ethical purchase behavior and attitudes. The differences between ethical fashion consumers and non-ethical fashion consumers were investigated using eight variables (perceived consumer effectiveness (PCE), self-direction, benevolence, universalism, social responsibility, perceived behavioral control, face saving, and group conformity). Data were collected by means of a questionnaire through both on-line and off-line surveys from April 20 to June 7, 2009. Only the respondents knowledgeable of ethical products or ethical consumption were asked to complete the questionnaire. A total of 494 samples were used for analyses. Using independent samples t-test, the differences in each variable between two groups were examined. There were significant differences between ethical fashion consumers and non-ethical fashion consumers in attitudes toward ethical consumption behavior, behavioral intention, PCE, self-direction, universalism, social responsibility, and face saving variables. The factors influencing attitude and behavior intention were investigated by step-wise regression analyses. For ethical fashion consumers, the attitudes to ethical consumption behavior were largely influenced by PCE and benevolence. Social responsibility was the most predictable variable in guiding behavioral intention. Behavioral intention was also influenced by benevolence and attitude. Group conformity was found to be negatively correlated with behavioral intention. The findings of this study provide significant guidance for marketers of ethical fashion products. This study is the start of ethical fashion consumer research in Korea and can develop into variable subfields in the future.

• Journal of Korean Society of societal Security
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• v.4 no.2
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• pp.73-78
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• 2011

In order to resolve the problem of change in global climate which is worsening as days go by and to preemptively cope with strengthened restriction on carbon emission, the government enacted 'Framework Act on Low Carbon Green Growth' in 2010 and selected green technology and green industry as new national growth engines. For this reason, the necessity to use the un-utilized waste heat across the whole industrial system has become an issue, and studies on and applications of recycling in the agricultural and fishery fields such as cultivation of tropical crops and flatfishes by utilizing the waste heat and thermal effluent generated by large industrial complexes including power plants are being actively carried out. In this study, we looked into the domestic and overseas examples of having utilized waste heat abandoned in the form of power plant thermal effluent, and carried out economic efficiency evaluation of sturgeon aquaculture utilizing thermal effluent of Yeongwol LNG Combined Cycle Power Plant in Gangwon-do. In this analysis, we analyzed the economic efficiency of a model business plan divided into three steps, starting from a small scale in order to minimize the investment risk and financial burden, which is then gradually expanded. The business operation period was assumed to be 10 years (2012~2021), and the NVP (Net Present Value) and economic efficiency (B/C) for the operation period (10 years) were estimated for different loan size by dividing the size of external loan by stage into 80% and 40% based on the basic statistics secured through a site survey. Through the result of analysis, we can see that reducing the size of the external loan is an important factor in securing greater economic efficiency as, while the B/C is 1.79 in the case the external loan is 80% of the total investment, it is presumed to be improved to 1.81 when the loan is 40%. As the findings of this study showed that the economic efficiency of sturgeon aquaculture utilizing thermal effluent of power plant can be secured, it is presumed that regional development project items with high added value can be derived though this, and, in addition, this study will greatly contribute to reinforcement of the capability of local governments to cope with climate change.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.3
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• pp.426-434
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• 2011

In order to increase the use of rice, ginseng Garaedduks and Topokki were made and the physicochemical and sensory properties were investigated. Topokki and Garaedduks were added with 0, 1, 3 and 5% ginseng powder and stored at $20^{\circ}C$ for 48 hours. The moisture contents were not different to the increasing amount of ginseng powder and increasing storage time for 48 hours. The moisture content of Garaedduks for control and 5% added ginseng powder were 48.08% and 49.62%, respectively. The L value in color of uncooked ginseng Garaedduk decreased according to the added amount of ginseng powder, and the b value in color increased significantly according to the added amount of ginseng powder and during 48 hours storage at $20^{\circ}C$. Textural analysis, measured using a texture analyzer, of Garaedduk revealed that hardness, cohesiveness, chewiness decreased significantly and adhesiveness increased according to the added amount of ginseng powder. In sensory evaluation, 5% ginseng Garaedduk (uncooked, cooked) scored higher in overall acceptability than those of the other samples. In cooking properties, water absorption and solid contents increased according to the added amount of ginseng powder. Therefore, Garaedduk containing 5% ginseng powder was the most preferable. These results implied that the degree of retrogradation of ginseng Garaedduk might be low due to its high dietary fiber content.

• Journal of the East Asian Society of Dietary Life
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• v.22 no.6
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• pp.744-757
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• 2012

This study was designed to obtain basic data for the globalization of Korean food and the expansion of food exports through contract foodservices. A survey of dietary habits and attitudes toward school foodservices was given to students in three American international schools served by a domestic contract foodservice management company located in Seoul and Gyeonggi area. The results showed an average of three meals taken daily 3.39 times for male students and 2.95 times for female students and the time required for a meal was about 24~26 minutes. The average breakfast frequency was 5.10 times(4.59 times for male students and 5.35 times for female students) and many students reported skipping breakfast due to a lack of time. The average weekly frequency of dining out was 1.78 times(2.15 times for male students and 1.60 times for female students). In all schools, irrespective of gender and grade, students responded that a desire for snacking was 'why they want to have cookies', and snacking hours were frequently listed as 'between noon and evening'. Many also responded that an unbalanced diet is the reason some snacks are 'not to their taste'. Overall, students were highly satisfied with the foodservice menu, although there was a significant difference in what was considered proper food temperature, proper food seasoning, suitable amounts of food, and freshness of food. Male and female students were specifically highly satisfied with the 'freshness of food materials' and 'variety of menu' respectively. Overall, all students were highly satisfied with the foodservice, including the 'cleanliness of tables and trays'.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.