• 한국IT서비스학회:학술대회논문집
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• 2008.11a
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• pp.366-369
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• 2008

현재 우리나라의 오픈마켓은 저가 구조로 수익과 성장에 한계를 맞고 있지만 이미 외국에서는 소셜 네트워크를 중심으로 소셜 쇼핑이 소비자 주도의 쇼핑몰로 인기를 얻고 있으며 새로운 쇼핑몰 형태로 각광받고 있다. 소셜쇼핑은 블로그나 커뮤니티와 같은 사회적 네트워킹 서비스를 기반으로 어떤 특정 인터넷 쇼핑몰에 구애됨 없이 다양한 상품을 각 개인이 퍼 오거나 게시하고 이에 대한 상품평과 사용후기를 개인 블로그나 커뮤니티에 올리거나 올리게 하여 거래가 일어나도록 하는 전자상거래 방식이다. 소셜 쇼핑은 사람/정보/쇼핑 검색이 가능하고 블로그나 커뮤니티를 통한 정보 공유가 가능하며 타 판매 사이트로부터 쇼핑 구매도 가능하다. 소셜 쇼핑은 또 하나의 플랫폼을 제공하며 다양한 판매자, 소비자, 광고주 등과 제휴가 가능하고 네트워킹이 가능하기 때문에 변화하는 소비자들의 특성을 반영하고 수익의 한계를 돌파하기 위한 진정한 web 2.0 쇼핑형태로써 발전 가능성이 크다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.571-572
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• 2019

일상생활에서 사람들은 각자 자신의 맞춤형 생활을 원한다. 특히 쇼핑이나 강의 등 직접 사용한 자의 후기에 따라 구매를 하는 경우에는 선택이 중요하다. 따라서 이 연구를 통해 머신러닝의 속성 범주화로 사용자에게 꼭 맞는 제품과 강의를 연결 할 수 있도록 한다.

• Journal of Korea Technology Innovation Society
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• v.11 no.4
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• pp.450-475
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• 2008

The successful diffusion of innovation depends on an understanding of the consumer innovators, although they represent a small group of consumers. Researchers have studied the personalities of innovators to explain how these are associated with innovativeness. This paper examined the relationships of need for uniqueness, susceptibility to interpersonal influence, attention to social comparison information, and role-relaxed consumption to individual innovativeness. Data were collected from 319 students in Seoul. The results supported by large hypothesized relationships among these variables. Based on the results of the analysis, practical implications were discussed.

• Journal of Information Technology Applications and Management
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• v.27 no.6
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• pp.53-73
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• 2020

Many prior studies have been conducted that positive reviews increase the intention to purchase. However, there are very few papers that have studied the impact of review search on purchase satisfaction. It is meaningful to study the impact of review search on purchase satisfaction as it can lead the business successfully by inducing repurchase. There is also no study of how review search have different effects on purchase satisfaction among countries. Given the growing number of cross-border e-commerce, we believe that the need for research is high because identifying these differences between countries can have a very important impact on a company's successful overseas expansion. Therefore, in this study, the impact of positive and negative review search on purchase satisfaction and the national impact were set up as a research model. In order to verify this research model, the survey was distributed to those who experienced online purchase in Korea and China, and a total of 234 copies were collected, including 125 copies in Korea, 109 copies in China, and the research model was verified using Smart-PLS structural equation analysis tools. First, positive review search has been shown to positively affect purchase satisfaction. Second, it has been shown that negative review search also has a positive effect on purchase satisfaction. Third, the impact of positive and negative review search on purchase satisfaction was different between Korea and China. While Korea is more aggressive in review search than China due to its high tendency to avoid uncertainty, China is less likely to avoid uncertainty than Korea and is more likely to rely on brand familiarity. Therefore, according to the uncertainty avoidance moderation effect the impact of positive and negative review search on purchase satisfaction was higher in Korea than in China. In this study, Shopping mall managers need to take strategic measures to maximize shopping mall performance by recognizing positive aspects of negative review search on purchase satisfaction. Companies and managers in Korea and China can establish strategies to promote product sales when companies enter the global market.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.321-326
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• 2020

본 연구에서는 한국어 감성분석 성능 향상을 위한 DECO(Dictionnaire Electronique du COreen) 한국어 전자사전과 LGG(Local-Grammar Graph) 패턴문법 기술 프레임에 의존파서 및 LSTM을 적용하는 하이브리드 방법론을 제안하였다. 본 연구에 사용된 DECO-LGG 언어자원을 소개하고, 이에 기반하여 의미 정보를 의존파서(D-PARS)와 페어링하는 한편 OOV(Out Of Vocabulary)의 문제를 LSTM을 통해 해결하여 자질기반 감성분석 결과를 제시하였다. 부트스트랩 방식으로 반복 확장될 수 있는 LGG 언어자원 및 알고리즘을 통해 수행되는 자질기반 감성분석 프로세스는 전용 플랫폼 DecoFESA를 통해 그 범용성을 확장하였다. 실험을 위해서 네이버 쇼핑몰의 '화장품 구매 후기글'을 크롤링하였으며, DecoFESA 플랫폼을 통해 현재 구축된 DECO-LGG 언어자원 기반의 감성분석 성능을 평가하였다. 이를 통해 대용량 언어자원의 구축과 이를 활용하기 위한 어휘 시퀀스 처리 알고리즘의 구현이 보다 정확한 자질기반 감성분석 결과를 제공할 수 있음을 확인하였다.

• Knowledge Management Research
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• v.22 no.3
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• pp.273-293
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• 2021

Due to the COVID-19 pandemic, the size of the e-commerce has been increased rapidly. This pandemic, which made contact-less communication culture in everyday life made the e-commerce market to be opened even to the consumers who would hesitate to purchase and pay by electronic device without any personal contacts and seeing or touching the real products. Consumers who have experienced the easy access and convenience of the online purchase would continue to take those advantages even after the pandemic. During this time of transformation, however, the size of information source for the consumers has become even shrunk into a flat screen and limited to visual only. To provide differentiated and competitive information on products, companies are adopting AR/VR and steaming technologies but the reviews from the honest users need to be recognized as important in that it is regarded as strong as the well refined product information provided by marketing professionals of the company and companies may obtain useful insight for product development, marketing and sales strategies. Then from the consumer's point of view, if the ratings of reviews are widely diverged how consumers would process the review information before purchase? Are non-converged ratings always unreliable and worthless? In this study, we analyzed how consumer's regulatory focus moderate the attitude to process the diverged information. This experiment was designed as a 2x2 factorial study to see how the variance of product review ratings (high vs. low) for cosmetics affects product attitudes by the consumers' regulatory focus (prevention focus vs. improvement focus). As a result of the study, it was found that prevention-focused consumers showed high product attitude when the review variance was low, whereas promotion-focused consumers showed high product attitude when the review variance was high. With such a study, this thesis can explain that even if a product with exactly the same average rating, the converged or diverged review can be interpreted differently by customer's regulatory focus. This paper has a theoretical contribution to elucidate the mechanism of consumer's information process when the information is not converged. In practice, as reviews and sales records of each product are accumulated, as an one of applied knowledge management types with big data, companies may develop and provide even reinforced customer experience by providing personalized and optimized products and review information.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.547-559
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• 2018

This study deals with the factors influencing acceptance of social media-based smart commerce service through personal innovativeness by quantitative empirical research method. Researchers have viewed that personal innovativeness affects the aesthetic criteria, hedonic value, economic value, and subjective norms, which are subdivided elements of emotion and characteristics. The hypothesis is that the emotional and characteristics variables affect the variables such as purchase intention and word of mouth intention of the TAM and the post acceptance model (PAM). The research model proposed in this study is a integrated model of proven models in previous studies, and it is expected that there will be a theoretical and practical contribution of research. The researchers hope that this study will make a significant contribution to the industry and policy in dealing with the acceptance of smart commerce services emerging in the area of e-commerce and social media marketing.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.101-110
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• 2020

Devices that add emotion-specific services or various functions are appearing in AI speakers and related devices. To this end, this study performed topic modeling analysis on the topics of post-purchase texts written by AI speaker users, and compared them with the data collected via survey questionnaires. Furthermore, data on the emotional intelligence of AI speakers and relationship quality were collected from 600 users and analyzed using structural equation modeling. The findings of the study are as follows: First, the analysis results of topic modeling showed that most of the articles mainly mention the functional aspects of AI speakers. Second, emotional intelligence of AI speaker perceived by consumer affected relationship quality, and relationship quality had a positive effect on customer satisfaction. Therefore, this study expands the area of AI research by integrating the concept of emotional intelligence and relationship quality to provide new theoretical and practical implications.