• Journal of Distribution Research
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• v.7 no.1
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• pp.105-127
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• 2002

This paper presents an analysis of how the business models of organizations are getting transformed in the Marketspace created by the Internet. We use a research model comprising the transformation scores of four Ps(Product, Price, Promotion, and Place) as dependent variables and three dimensions, Demographics, Technology, and Community elements on the Websites, as explaining variables about the Cyber Transformation of the 4Ps. While most existing literatures have focused on Website's technology, our research model includes 22 five-point-scale items; 10 Demographics /Technology items and 12 Community items. To measure the 4P's transformation scores, the authors selected 14 workable items from the Marketspace Model by Dutta, Kwan, & Segev(1997). A sample of 123 shopping mall Websites comprising three categories(grocery, jewelry/accessory, and cosmetics) from the 100hot.co.kr list are evaluated and the data is analyzed by SPSSWIN 8.0 version. The result shows that there are five significant factors, Technology, Interaction, Connectedness, Business Features, and Domain, while the average transformation scores of 4Ps are at very low level. The factor scores are used in regression analysis for each P. Two factors, Technology and Interaction are influencing all four Ps; Connectedness is influencing only two, Product and Place. Organizations must not simply take their existing business models. They have to adopt the Technology items(navigation, logo, e-mail, guide, graphics) and to facilitate the Interaction items(consulting, number/quality of bulletin boards, participation, offline events) and Connectedness(club activation, contents, partner/site link, entertainment contents) in order to get transformed in the Marketspace successfully in the near future.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Fashion & Textile Research Journal
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• v.14 no.4
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• pp.614-628
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• 2012

The purpose of this study was to analyze research trends and make suggestions regarding the future of information technology (IT) in the fashion industry. In this study, 437 papers written regarding IT fashion from five major journals published between 2000 and 2011 were examined. The research areas were then organized by subject and keyword, and divided into 16 high-context categories. Two IT fashion maps were constructed, one from a fashion consumer's perspective, and the other based on the fashion industry's supply chain. This study identified important trends in IT fashion such as: 3D scanners, 3D digital renderings of the human form, 3D digital garments, smart garments, mass customization, production automation, online shopping, home shopping, online communities, e-commerce, digital media, virtual reality, e-tail, the digital generation, E-CRM, and education. Data from body scans was collected and applied to production, and research on smart textiles was also carried out. As for IT fashion's service areas, the majority of the research focused on online shopping or online communication. Additionally, research done on avatars and cyber space, and studies on social networking services are shown. The results of this study indicated that a new field of research has opened and that current research has been developing. Also, this study showed what is needed to expand and strengthen IT fashion.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.535-542
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• 2013

Since case-based reasoning(CBR) has many advantages, it has been used for supporting decision making in various areas including medical checkup, production planning, customer classification, and so on. However, there are several factors to be set by heuristics when designing effective CBR systems. Among these factors, this study addresses the issue of selecting appropriate neighbors in case retrieval step. As the criterion for selecting appropriate neighbors, conventional studies have used the preset number of neighbors to combine(i.e. k of k-nearest neighbor), or the relative portion of the maximum similarity. However, this study proposes to use the absolute similarity threshold varying from 0 to 1, as the criterion for selecting appropriate neighbors to combine. In this case, too small similarity threshold value may make the model rarely produce the solution. To avoid this, we propose to adopt the coverage, which implies the ratio of the cases in which solutions are produced over the total number of the training cases, and to set it as the constraint when optimizing the similarity threshold. To validate the usefulness of the proposed model, we applied it to a real-world target marketing case of an online shopping mall in Korea. As a result, we found that the proposed model might significantly improve the performance of CBR.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.19-25
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• 2021

This study contains the results obtained after two and a half years of developing a knitting customization website using a knitting machine. Recently in the fashion world, various services using customization are being provided, and devices that users can design directly using knitting machines are being developed. However the existing website for knitting machine does not provide a certain usability or layout, so it is difficult for users to use open source and custom design. Therefore, this study was conducted for the purpose of developing a website that provides ease of use to users who will use the knitting customizing service using a knitting machine. As a research method, the first usability evaluation was conducted by synthesizing the studies conducted for the knit customization website development work. As a result of the study, found the problems of the initial custom screen and the initial output screen were found, and convenience, intuition, and readability were improved. Secondary usability evaluation was conducted on the modified website and it was confirmed that the problem was corrected. Through the website finally derived from this study, it is expected that the new platform in the domestic knit market will be popularized and the usability of the custom website will be improved.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.39-56
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• 2023

Due to restrictions on offline activities caused by COVID-19, the use of mobile applications is increasing along with interest in online shopping, which are non-face-to-face commerce. Accordingly, mobile applications and various industries are combined, and the number of cases of using mobile applications in the fashion industry is increasing. In this study, the effect of e-service quality of fashion mobile applications on user's flow, user satisfaction, and service loyalty was examined. To conduct this study, a survey of 274 people who experienced the 'ABLY' fashion mobile application was used for analysis to verify the hypothesis. As a result of the analysis, it was found that informativity and responsiveness among the e-service quality of fashion mobile applications had a positive (+) effect on flow. And it has been confirmed that informativity, reliability, and responsiveness affect user satisfaction. In addition, flow has a positive (+) (+) effect on user satisfaction, and user satisfaction has a positive (+) effect on service loyalty. However, among the e-service quality of fashion mobile applications, reliability did not have a positive (+) effect on flow. And ease of use did not have a positive (+) effect on both flow and user satisfaction. Finally, it was confirmed that flow did not directly affect service loyalty. Through this study, we intend to contribute to the establishment of marketing strategies for fashion mobile application users, who are increasing with the development of mobile technology, and provide practical implications for the post-COVID-19 era.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• The Korean Journal of Pesticide Science
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• v.21 no.1
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• pp.49-61
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• 2017

The study was conducted for safety evaluation of 208 kinds of residue pesticides on 200 dried agricultural products, which are distributed in Gwangju. The method of monitoring was the second of Multi Class Pesticide Multi-residue Methods in Korean Food Code, and GC-ECD, GC-NPD, GC-MSD, and LC-MS/MS were used as evaluation equipment to analyze. The residue level in pesticides were 15.5% (31 of 200 samples) and 4 samples exceeded MRLs. 4.5 mg/kg of pyraclostrobin (MRL; 3.0 mg/kg) was detected in red pepper, 1.49 mg/kg of chlorpyrifos (MRL; 0.13 mg/kg) in daikon leaves, 38.26 mg/kg of pyridalyl (MRL; 0.25 mg/kg) in pepper leaves, 0.98 mg/kg of chlorpyrifos (MRL; 0.05 mg/kg), respectively. Pesticides were found on the 15 samples among the 21 samples of red pepper which is a fruit vegetable, and this resulted in high detection rate of 71%. In addition, pesticides were detected on chwinamul, shitake, siler divaricata, daikon leaves and others within MRLs. The frequent detected kinds of pesticides were insecticide (47.6%), fungicide (33.3%), acaricide (14.3%), nematicide (4.8%) in the order named, and pesticides were methoxyfenozide > pyraclostrobin > azoxystrobin, chlorantraniprole > novaluron, trifloxystrobin in frequent order.