• The Korean Journal of Franchise Management
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• v.3 no.2
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• pp.39-54
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• 2012

Franchise system in Korea has been developed in different way compared with American way of franchising based on mutual contract and intellectual property context. Korean franchising is mostly based on product distribution franchise concept rather than business format franchise in which franchisor makes revenue sources from providing their products as much as possible thru group purchasing and logistics rather than receiving royalty. Many franchise enterprises from Korea drive to enter into global franchise market based on the successful performance of Korean way of franchising. Korean enterprises are required to prepare completely for research and survey regarding local culture, custom, way of life and legal matters etc. when entering into global franchise market to gain a substantial performance. CaffeBene recently entered into American franchise business with success, and many other Korean franchise enterprises have a deep interest in proceeding with global franchise business modeling CaffeBene case. There is no Korean franchise enterprise in United Kingdom in which service franchise area in particular with personal service is considered to become a promising and potential franchise business and many people show a great interest in Oriental foods and beverages with well-being trend. Korean franchise enterprises have now access to United Kingdom easier because IT industry including internet of the country have been developed by leaps and bounds since London Olympic in 2012. The purpose of this study is to suggest key success factors and basic strategy such as situation analysis, selecting business format, and marketing strategy for successful launching of franchise business in United Kingdom.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.515-528
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• 2023

With the growth of the e-commerce market, consumers increasingly rely on user reviews to make purchasing decisions. Consequently, researchers are actively conducting studies to effectively analyze these reviews. Among the various methods of sentiment analysis, the aspect-based sentiment analysis approach, which examines user reviews from multiple angles rather than solely relying on simple positive or negative sentiments, is gaining widespread attention. Among the various methodologies for aspect-based sentiment analysis, there is an analysis method using a transformer-based model, which is the latest natural language processing technology. In this paper, we conduct an aspect-based sentiment analysis on multilingual user reviews using two real datasets from the latest natural language processing technology model. Specifically, we use restaurant data from the SemEval 2016 public dataset and multilingual user review data from the cosmetic domain. We compare the performance of transformer-based models for aspect-based sentiment analysis and apply various methodologies to improve their performance. Models using multilingual data are expected to be highly useful in that they can analyze multiple languages in one model without building separate models for each language.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Food Hygiene and Safety
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• v.29 no.3
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• pp.195-201
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• 2014

This study was conducted to investigate the current status of hygiene performance and food preparation/storage condition in restaurants during the summer season in order to evaluate the sanitary management systems in restaurants against climate change. Total 30 restaurants located in Gyeonggi participated in a survey in which they were asked current hygiene performance, food preparation/storage condition, and purchasing practices for 5 food ingredients. As results, regarding the performance degree of respondents on food hygiene management, the average scores of 9 questions were well over 4 points. However, only 6.83% of the respondents claimed that they use sanitizers (chlorine) to disinfect food ingredients. About food storage condition, a high proportion of respondents said that they store food materials in plastic bags or airtight containers following pretreatment and use refrigerator for the storage of pretreated food materials. However, 5.55% and 14.85% of respondents answered that they store pretreated food materials in the kitchen or inside of dining room, respectively. Respondents (21.50%) answered that they store pretreated food materials for more than 6 hours before cooking. Therefore, food materials need to be disinfected properly with sanitizer to remove microbial contamination and stored at refrigerator using closed bags or containers before cooking in order to prevent foodborne disease in restaurants especially during summer season.

• Journal of Distribution Research
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• v.6 no.2
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• pp.1-17
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• 2002

The recent dot-com craze has been one of the main causes that accelerated the growth of internet-related companies in diversity as well as in size. Meanwhile, the domestic market of supplies and equipment for internet businesses has been dominated by major foreign companies. To regain their market positions, the domestic manufacturers had to find the way to build up their competitive advantages, such as meeting their customers needs and reducing overall costs. In this study, one domestic PC server manufacturer, which competes fiercely with foreign manufacturers for the top place, has been chosen as a model to evaluate its current supply chain and to find an area that can be improved for a better performance. System Dynamics is used throughout the study. The central concept to system dynamics is understanding how all the objects in a system interact with one another. It focuses on feedback and secondary effects to think through how a strategy might or might not work, depending on how organizational changes are received, and what kinds of consequences emerge. Then, computerized models were built for simulations, each with different conditions, and, finally, the results were evaluated based on some criteria which are considered to be important and meaningful. The inefficiency that exists in the supply chain was proved to be a thirty-day long purchasing order leadtime, and it was expected that more effective supply chain could be formed if the leadtme were reduced to 14 days or 7 days. The results of simulations showed that the overall expected costs in supply chain was the least with the purchasing leadtime being 7 days. The lower average number of parts held as inventory, along with the reduced lost sales, acted as the factor reducing the expected overall costs. Although there was a slight increase in the average number of final products held as inventory and the total ordering cost, the benefits from lower parts inventory and reduced lost sales were large enough to justify the overall cost reduction.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.6
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• pp.1-14
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• 2018

As interest and consumption of luxury goods have become more popular, luxury goods market is growing rapidly. Consumers can acquire psychological satisfaction with material abundance by purchasing and using luxury goods. Also, from the view of corporations, luxury goods have price inelastic characteristics, so they can enjoy price premium and it is good to produce good performance. That is the reason why they should pay much attention to securing luxuriousness. This study examined the establishment of brands luxuriousness in Korean SMEs. First, it examined the world market of luxury goods industry and the present condition of Korean market. Then it identified the constituents of luxuriousness by examining the prior studies and related literatures, and designed a research model based on the theoretical grounds to suggest the methods of brand luxuriousness building of Korean SMEs. Luxuriousness can be defined as the attribute of product that distinguishes luxury goods from other products by consumers' perceptions, and the factor that provides situational benefits that motivate consumers' purchasing behavior. In this study, I identified the sub-dimensions of luxuriousness according to whether there are product related attributes and consumers' benefit in consideration of the problems of existing studies. Product related luxuriousness are classified into superiority(functional benefit) and scarcity(experiential benefit), while non-product related luxuriousness are classified into differentiation(symbolic benefit) and traditionality(exclusive benefit). The following are the ways to build brand luxuriousness. First, company can use product factors. High quality, excellent design, high recognized brand with strong, favorable and unique images can enhance the luxuriousness of brand. Second, company can use price factors. Consumers tend to perceive luxury goods as high-priced items, so lowering the price of product can undermine the luxuriousness of product. Third, company can use distribution factors. It is effective for making consumers to perceive the differentiation and scarcity of luxuriousness through limited distribution channel. In addition, store atmosphere suitable for luxury brands should be created. Fourth, company can use promotion factors. The more consumers are exposed to advertisements, the more positive attitudes toward luxury brands are made, and consumers recognize luxuriousness higher. Price promotion negatively affects consumers' perception of luxuriousness. Fifth, company can use corporate factors. Consumer evaluations of products are influenced not only by the product attributes but also by the corporate association and corporate image surrounding the product. Considering the existing researches, it is possible to enhance the brand luxuriousness through high corporate competence and good corporate reputation. In order to increase the competence of the enterprise, it is useful to approach multidimensionally in relation with the knowledge creation capability. In corporate reputation, the external stakeholders' reputation is important, but the internal members' reputation is also important. Korean SMEs will be able to build brand luxuriousness by establishing marketing strategies as above and/or mix(integrate) them according to the situation.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of Distribution Science
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• v.14 no.2
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• pp.47-56
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• 2016

Purpose - The purpose of this study is to investigate the difference in the degree of significance and satisfaction perceived by college students and ordinary consumers on the HMR product attributes. Comparison of the difference on HMR product attributes between ordinary consumers and college students who belong to the current and future consumption groups of HMR will provide information for clear marketing strategies and PR on target consumers from the aspects of companies. Also, overall difference on HMR was investigated through IPA(importance-performance analysis) on significance and satisfaction with each product attribute. This result will provide information to food companies that produce or supply HMR products to be supplemented and improved. Finally, IPA was conducted between groups on product attribute to find which difference exists between groups. This result is also expected to provide crucial information to companies as suggested in the first purpose. Research design, data, and methodology - The procedure of analysis is as follows. First, independent sample t-test was conducted on the significance and satisfaction on HMR product attributes. Second, with using IPA, the significance and satisfaction on HMR product attributes of the respondents were checked to investigate marketing strategy direction on overall HRM products. Third, the difference between generations was verified using IPA on the college student and consumer groups. According to this result, the direction of marketing strategy on HRM products was to be proposed to food companies. Results - It was known that consumers consider HMR product attributes statistically and significantly such as nutrient content(nutrition), country of origin, brand, main raw material, packaging, and awareness of manufacturer. They keep after purchase more importantly than college students who considered only volume and price than consumers. In comparison with the difference in satisfaction on HMR product attributes, the college student group was more satisfied than ordinary consumers only in flavor, condition of food additives, and volume. Also, HMR related food companies must maintain taste, cooking method, manufacturing date, expiration date, and safety on current products continuously. Finally, as a result of analysis from the groups, the attributes such as cooking method, manufacturing date, expiration date, and safety were considered significantly with high achievement by the two groups. It was known that college students considered food texture to be important, but consumers considered storage method to be important after purchasing it. Conclusions - There is necessity to differentiate effectiveness of products when releasing HMR products subject to consumers and college students. The result will give great assistance to the improvement of companies, produce or supply HMR products. It will also provide entry strategies on target groups of companies that are planning for entry. The factors that consumers commonly considered not to be significant were brand, package form(appearance), cooking time, and sale(purchase) location, which were found in the comparison with the groups that awareness about manufacturers and storage method after purchase corresponded to college students and that distribution route corresponded to ordinary consumers.

• Journal of Global Scholars of Marketing Science
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• v.10
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• pp.1-26
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• 2002

The present study examines the role of subjectively perceived factors of the attitude toward web shopping in forming an intention to use a web shopping intention. An integrative research model is presented and tested empirically. It includes the following three aspects of belief in Davis' TAM: perceived usefulness, perceived ease of use, perceived enjoyment. Specially, internet self-efficacy, or the belief in one's capabilities to organize and execute courses of Internet actions required to produce given attainments, is a potentially important factor in efforts to gain more favorable attitude toward web shopping close the digital divide that separates experienced Internet users from novices. Prior research on Internet self-efficacy has been limited to examining specific task performance and narrow behavioral domains rather than overall attainments in relation to general Internet use, and has not yielded evidence of reliability and construct validity. Survey data were collected to develop a reliable operational measure of Internet self-efficacy and to examine its construct validity. Also, much previous research has established that perceived ease of use is an important factor influencing user acceptance and usage behavior of information technologies. However, very little research has been conducted to understand how that perception forms and changes over time. The present study examines that higher internet self-efficacy is more getting favorable web shopping attitude, and web shopping intention as more as usefulness, enjoyment through the internet.