• Journal of Technology Innovation
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• v.22 no.4
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• pp.21-55
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• 2014

The rates of technological innovation and environmental change as well as market competition have recently accelerated, which makes it difficult for firms to satisfy the needs of their customers through existing product innovation or limited services. Some firms have attempted to find the solutions to this problem by conducting business model (BM) innovation. This study reviews the theoretical discussion to BM innovation and suggests propositions concerning the necessity of BM innovation and conditions of successful BM innovation. Furthermore, the study examines the applicability of the propositions and draws strategic implications by analysing the cases of two world-wide leading firms, AVL and ETAS, in the automobile R&D service sector. In particular, the study investigates how the firms with technological competence in the R&D service sector obtain market performance through BM innovation. Results of this study show that the case firms recognize the necessity of BM innovation based on product (or technology)-service fusion to effectively propose customer value and create corporate profit. Also, the firms exploit firm-specific complementary assets for successful BM innovation. This paper contributes to the literature of innovation management by promoting academic discussion concerning BM innovation in Korea and suggesting strategic implications for further development of R&D service sector and related firms in Korea.

• Management & Information Systems Review
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• v.37 no.2
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• pp.23-42
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• 2018

As the development of the Internet in China has led to the active shopping of the internet, the number of Chinese internet users and online shopping malls is increasing rapidly. Chinese internet shopping malls are experiencing satisfaction and discontent with their websites. Research has focused on the satisfaction of customers using internet shopping malls and internet shopping malls reported complaints to users as complaints about products and websites. The study looked at seven area Chinese internet users (Beijing, Harbin, Shenyang, Ganssu, Xian, Shanghai and Henan). The effect of product complaints and site complaints on shopping attitudes and behaviors of internet shopping users was studied, and economic benefits were studied as a control variable. As a result, There was no effect on controlling the economic benefits of the complaint against the product, but the controlling the economic benefits of the complaint on the site was effect. About 83 percent of those surveyed were in their 20s and 30s who had experience shopping online and in internet. And It is intended to present theoretical reviews and guidelines for Korean internet shopping malls operating here, as they plan to expand to China by analyzing their internet shopping mall users.

• Science of Emotion and Sensibility
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• v.19 no.1
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• pp.111-126
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• 2016

As the marketing value of Facebook advertisements increases, companies seek to create successful Facebook advertisements in order to promote their brands or products. This research aims to identify Facebook advertising factors that influence users' eye movements and attention, and thereby to investigate effective visual elements of Facebook advertising contents. Firstly, we identified two contributing factors influencing users' responses to Facebook advertisements: the formats of advertising contents(Text, Text in Image, and Movie) and the product properties(Involvement, Think/Feel). Based on theoretical reviews, eye tracking tests and surveys were conducted in order to examine how these two factors affect users' responses on Facebook, i.e. visual perception and users' purchasing responses. It was found that there were distinctive patterns of users' visual perceptions and purchasing behavioral responses according to the formats of the advertised contents. Meanwhile, the advertised products' properties influenced only the users' purchasing responses. Finally, the key findings of this research offer helpful guidelines for providers and developers to create effective SNS advertisements.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.405-415
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• 2011

The primary purpose of this paper is to identify the influencing factors on the new products adoption between countries and generations. For this purpose, a research is developed based on the relevant literature reviews. Data have been collected from 524 persons and were tested by t-test and various statistical methods. The results of this empirical study are summarized as follows. In the cultural factors, the groupism has high discretion in China old generation. The materialism and shopping preference have high discretion in two young generations. There is no difference between the two groups in the distance of power. In innovativeness of personality, Korea and China young generation have high discretion. Innovativeness has high discretion in Korea and China youngs. Cognition and sensory innovativeness are has low discretion in Korea old. In the social risk perception, physiological, functional general and financial risk has high discretion in China old. In risk reducing behavior, the normative taking level and ad, new product adoption has high discretion in Korea and China youngs. But, the influence of others has high discretion in China old generation. The safety and brand reputation are no influences. The findings have a several marketing strategies in generation and countries.

• The Journal of the Korea Contents Association
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• v.12 no.6
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• pp.182-190
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• 2012

Based on the study, the issues and trends in the current functional sandal designs on the same product line will be examined for the future developments for the functional sandals and the results were obtained as follows. First, the sandals must have a high front heel with wide floors that meet the ground. Second, the preference for the chromatic colour is stronger than the achromatic colors. Third, the sandals must be designed in curves in terms of the height and shape of the heels and design must consider the shoe's balance. Fourth, the product must appear big to provide a sense of stability. However, the sandals must not be designed too big to make them look crude or cause inconvenience while exercising. Fifth, the sandals must not be designed in too complex ways. This study has investigated and analyzed the external design of functional sandals and the user reviews on the actual sandals were not done. Through more detailed studies, the diversity in the design of functional sandals must be south and Korea's competitive edge in the industry and design must be secured for the future.

• Information Systems Review
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• v.17 no.3
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• pp.159-181
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• 2015

This study proposes a comprehensive model of purchasing intention of customers in agricultural products online shopping malls. In this study, we derived the factors through the literature reviews and logical reasoning and classified the factors as a business point of view, an information systems point of view and an agricultural characteristics point of view, and developed the integrated research model which is the factors affect purchase intentions by mediating trust and the perceived usefulness. A total of 329 samples of a valid survey data from the members of small agricultural online shopping malls were collected and the research model was empirically analyzed by a confirmatory factor analysis and path analyses using structural equation modeling with the data. The results show that the product quality and the service quality of the business point of view have effects on the trust, however the price adequacy and entertainment have no effect on the trust and the perceived usefulness respectively, also the advertising exposure has no effect on the trust but it has an effect on the purchase intention directly. The information quality and the ease of use of the information systems point of view have an effect on the trust and perceived usefulness. At last, the seasonal product of the agricultural characteristics point of view has effects on perceived usefulness but the regional brand has no effect on the trust. The results of this study provide strategic implications for successful development and operation of agricultural products online shopping malls.

• Korean journal of food and cookery science
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• v.19 no.4
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• pp.477-492
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• 2003

The purposes of the study were to establish HACCP-based standards and guidelines for conducting a plan review to build, or renovate, hospital food service establishments, and ensure the safety of foodservice and reduce the risk of food borne illness. The scope of the study included suggestion for the planning of hospital foodservice facilities: layout, design, equipment and modeling. The results of this study can be summarized as follows: 1) The development of a foodservice facility plan based on the results of a survey, literature reviews and the results of interviews with foodservice managers from 9 general hospitals. This was composed of operational policies in foodservices, layout characteristics, space allocation, selection, design, specification standards for equipment and the construction principles of foodservice facilities. 2) Two foodservice facility models were developed, one for general hospitals with 900 beds (2,000 patients and 2,500 employee meals per day) and the other for general hospitals with 300 beds (600 patients and 650 employees meals per day). 3) The suggested kitchen space requirements for the foodservice facility models were 341.2 ㎡ (W 17,100mm x L 23,700mm) and 998.8㎡ (W 35,600mm x L 32,800mm) for the 300 and 900 beds hospitals, respectively, with both designs being rectangular. The space requirements for the equipment, in relation to the total operational area, in terms of ratios were 1:3.5 and 1:3.8 for the 300 and 900 beds hospitals, respectively. The recommended space allowances per bed for the developed foodservice facility models were 1.15 ㎡ and 1.11 ㎡ for the 300 and 900 beds hospitals, respectively, which were increased by more than 30% compared to those suggested in the precedent study, and considered appropriate for the implementation of the HACCP system. 4) The hospital foodservice facilities plans and models were developed based on the general sanitation standards, guidelines and the HACCP system, and included foodservice facility layout, product flow, physical separation between contaminated and sanitary areas, foodservice facility specifications with a 1/300 scale for a 300 bed, and a 1/400 scale for a 900 beds blueprint. 5) The main features of the developed foodservice facility plans and models were; physical separation between contaminated and sanitary areas to prevent cross contamination, product flow in one direction from the arrival of the raw material to the finished product, and separation of different work areas and the process of receiving & preparation of products, refrigeration & storage, cooking, assembly, cleaning & disinfection, employee areas and janitorial facilities. The proposed models from this study were presented as examples for those wanting to build, or renovate, their facility for the production of foods.

• Food Science and Industry
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• v.52 no.1
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• pp.40-51
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• 2019

For better understanding the relationship between consumers' perception and sensory characteristics of products, diverse types of rapid sensory profiling technique have been suggested as alternatives to conventional descriptive analysis. Among these, check-all-that-apply (CATA) method has gained popularity for studying consumers' perception and intuitive responses to products due to their simplicity, speed, and ease of use. CATA method has been used to gather consumers' perception derived from sensory characteristics of products as well as consumers' emotion responses to products in recent years. Moreover, many researchers reported that CATA method can be used to provide valuable information for product optimization by applying a penalty analysis and collecting responses to ideal product. Thus, this article reviews recent research using CATA in the field of sensory and consumer science and introduces practical applications to achieve various business objectives in food industry.

• 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.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.