• Human Ecology Research
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• v.59 no.4
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• pp.449-463
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• 2021

The aim of this study was to analyze the perceptions and entering status of small business online fashion retailers on portal shopping and fashion shopping malls. Case studies were conducted on a total of 10 research samples. The results were as follows: first, regarding the strategic factors of online fashion stores, 'price competitiveness' is important, especially in portal shopping and low-cost brands; 'product assortment' is important but not essential in all platforms; and 'differentiation' is important to continuously secure loyal customers in fashion shopping malls. Customer satisfaction leads to customer loyalty, and customer loyalty affects the sales conversion rate and brand growth of online sales channels. Factors that promoted sales activities in online sales channels were exposure, advertisements, SNS, events, special exhibitions, and events. Hindrance factors were low price competition, overheated competition, and the MD of sales channels. Second, the research samples used multiple online sales channels, including portal shopping malls and fashion shopping malls, in addition to their own malls. The selection factors were platform reputation and commission, branding, and customer inflow through exposure. Portal shopping malls were perceived as providing easy access, advertising/customer communication, exposure/search, price competitiveness, scalability, and intense competition, whereas fashion shopping malls were perceived as providing a brand image and concept, brand promotion, high commissions, difficult entry, and low profits. The factors for success in portal shopping malls were exposure/search, price competitiveness, and brand recognition, whereas the factors for success in fashion shopping malls were differentiation, brand, exposure/advertisement, product assortment, and MD.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.185-190
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• 2022

Open data has a lot of economic value. Not only Korea, but many other countries are doing their best to make various policies and efforts to expand and utilize open data. However, although Korea has a large amount of data, the data is not utilized effectively. Thus, attempts to utilize those data should be made in various industries. In particular, in the fashion industry, exchange and refund problems are the most common due to unpredictable consumers. Better feedback is necessary for service providers to solve this problem. We want to solve it by showing improved images of dissatisfactions along with user reviews including consumer needs. In this paper, user reviews are analyzed on online shopping mall websites to identify consumer needs, and product attributes are defined by utilizing the attributes of K-fashion data. The users' request is defined as a dissatisfaction attribute, and labeling data with the corresponding attribute is searched. The users' request is provided to the service provider in forms of text data or attributes, as well as an image to help improve the product.

• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.1
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• pp.50-65
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• 2024

By analyzing the style of creator avatars in the world of Metaverse, which is emerging as a fourth-generation social media platform, this study aims to identify the meta-fashion tastes of Generation Z (Gen Z) creators (born in the late 2010s and early 2020s) and to analyze the extent to which current trends in the fashion market are influencing meta-fashion. The research method uses a case study to compare meta-fashion and current fashion trends. First, five Gen Z fashion creators on ZEPETO were selected to analyze the meta-fashion styles presented by this group. In the end, a total of 100 fashion styles were analyzed by combining 50 items each from the current meta-fashion and real fashion trends. The fashion styles were found to be hip-hop, easy-casual, punk, lovely feminine, and sexy, and the main fashion items were analyzed as jeans, hip-hop style pants, sneakers, tight crop tops, dresses, tattoos, chain accessories, and dyeing. Meta-fashion is the emergence of items similar in shape to those popular in the current fashion market, but are more exaggerated or show off the human body than actual fashion items.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.501-503
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• 2012

오피니언 마이닝에서 특징기반으로 상품평을 요약할 때, 동일한 상품의 같은 특징에 대한 사용자의 표현이 일치하지 않아 같은 특징을 다른 것으로 인식하는 오류가 발생되어 효과적인 분석을 하는데 어려움이 있다. 본 연구에서는 이러한 문제점을 해결하기 위하여 온라인쇼핑몰의 상품평에서 명사와 형용사쌍 말뭉치를 이용하여 연관단어뭉치를 추출하고, 상관성이 높은 형용사를 각 명사의 특징으로 이용하여 대체어 목록을 자동으로 추출하는 방법을 제안한다.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.141-154
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• 2014

It is widely confirmed that online retailers can obtain crucial resources and greater growth potential by depending on the external web portal sites as it is explained in resource dependence theory. Nevertheless, recent studies show that the effect of dependence may not always be beneficial for firms and stress the importance of finding relevant contingent factors. In this study, we identify and suggest that firms' age and niche width strategy, whether generalist or specialist, are contributing factors on moderating the positive relationship between resource dependence and firm growth. To test our hypotheses based on the theory, we have collected monthly web traffic data of online retailers and portals from March 2000 and July 2008. The empirical results lend support to our theory of the firm age having a negative interaction effect on web traffic dependence. Moreover, results verified that positive effect of depending on the portals may become greater if the online retailer is a specialist in terms of niche width.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• 한국벤처창업학회:학술대회논문집
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• 2007.04a
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• pp.405-433
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• 2007

This study is conducted to examine how purchase behaviors of consumers have affected by the promotion of coupons in internet shopping mall. This study was conducted with the purpose of identifying the differences in purchase behavior based on consumer' s perception and experience of internet shopping mall coupons, and based on consumers' perception of cost and value of coupons, using a theoretic framework presented in previously conducted studies. The results of this study can be summarized as follows. First, based on the perception of coupons, there were significant differences in intent to use and intent to re-use at the time when coupons are offered, and at the time when coupons are offered, no significant differences were found between the level of interest and the importance of coupon at the time of visiting the shopping mall; however, significant differences were found in the overall purchase behavior based on perception of coupons. Second, when overall differences m purchase behavior based on experience in coupon use was observed, having experience in using coupons showed a higher average than did having no experience in using coupons, showing a significant difference. It was found that compared to those without experience in using coupons, those with experience with coupons had higher intent to use at the time when coupon is offered, intent to re-use at the time when coupon is offered, and higher level of purchase behavior in the importance of coupons at the time of visiting the shopping mall. Third, when relationship between purchase behaviors, cost of coupon, and perception of convenience was observed, a clear static relationship was found. This suggests that as the cost and perception of convenience of coupon increases, purchase behavior also increases. Such result suggests that there is a difference in purchase behavior based on experience in coupon use. When relationship of purchase behavior by variables of cost of coupon and perception of convenience is observed, it has a positive relationship with the perception that the use of coupon includes saving money, financial help, enjoyment of use, habitual use, has a short effective date, and has a negative relationship with the perception that it saves little money and is a waste of time. Therefore, it can be seen that purchase behavior has the highest relationship with enjoyment of coupon use and habitual coupon use. Such results suggest that purchase behavior will be significantly influenced based on cost of coupon and perception of convenience.

• Information Systems Review
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• v.25 no.4
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• pp.233-248
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• 2023

The increasing integration of intelligent information technologies within organizational systems has amplified the risk to personal information security. This escalation, in turn, has fueled growing apprehension about an organization's capabilities in safeguarding user data. While Internet users adopt a multifaceted approach in assessing a company's information security, existing research on the multiple dimensions of information security is decidedly sparse. Moreover, there is a conspicuous gap in investigations exploring whether users' evaluations of organizational information security differ across industry types. With an aim to bridge these gaps, our study strives to identify which information security attributes users perceive as most critical and to delve deeper into potential variations in these attributes across different industry sectors. To this end, we conducted a structured survey involving 498 users and utilized the analytic hierarchy process (AHP) to determine the relative significance of various information security attributes. Our results indicate that users place the greatest importance on the technological dimension of information security, followed closely by transparency. In the technological arena, banks and domestic portal providers earned high ratings, while for transparency, banks and governmental agencies stood out. Contrarily, social media providers received the lowest evaluations in both domains. By introducing a multidimensional model of information security attributes and highlighting the relative importance of each in the realm of information security research, this study provides a significant theoretical contribution. Moreover, the practical implications are noteworthy: our findings serve as a foundational resource for Internet service companies to discern the security attributes that demand their attention, thereby facilitating an enhancement of their information security measures.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.153-177
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• 2001

Nowadays, product recommendation is one of the important issues regarding both CRM and Internet shopping mall. Generally, a recommendation system tracks past actions of a group of users to make a recommendation to individual members of the group. The computer-mediated marketing and commerce have grown rapidly and thereby automatic recommendation methodologies have got great attentions. But the researches and commercial tools for product recommendation so far, still have many aspects that merit further considerations. To supplement those aspects, we devise a recommendation methodology by which we can get further recommendation effectiveness when applied to Internet shopping mall. The suggested methodology is based on web log information, product taxonomy, association rule mining, and decision tree learning. To implement this we also design and intelligent Internet shopping mall support system based on agent technology and develop it as a prototype system. We applied this methodology and the prototype system to a leading Korean Internet shopping mall and provide some experimental results. Through the experiment, we found that the suggested methodology can perform recommendation tasks both effectively and efficiently in real world problems. Its systematic validity issues are also discussed.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.13-23
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• 2022

In this paper, we propose a detailed component image classification algorithm by fashion item for unstructured data retrieval in the fashion field. Due to the COVID-19 environment, AI-based online shopping malls are increasing recently. However, there is a limit to accurate unstructured data search with existing keyword search and personalized style recommendations based on user surfing behavior. In this study, pre-processing using Mask R-CNN was conducted using images crawled from online shopping sites and then classified components for each fashion item through CNN. We obtain the accuaracy for collar of the shirt's as 93.28%, the pattern of the shirt as 98.10%, the 3 classese fit of the jeans as 91.73%, And, we further obtained one for the 4 classes fit of jeans as 81.59% and the color of the jeans as 93.91%. At the results for the decorated items, we also obtained the accuract of the washing of the jeans as 91.20% and the demage of jeans accuaracy as 92.96%.