• 패션비즈니스
• /
• 제14권2호
• /
• pp.81-94
• /
• 2010

The online shopping environment has radically changed consumer shopping behavior. Without the actual physical shopping experience in a brick-and-mortar store, consumers make purchasing decisions over the Internet. They make an effort to obtain product information not only from online merchants, but also from previous purchasers in order to make an informed decision. Accordingly, customer comments are expected to have a significant impact on decisions to purchase goods and services online. This paper focuses on one type of electronic word-of-mouth, the online consumer review. It derives several motivations why customers post product reviews on shopping mall sites. Customer motives were identified through an in depth one-on-one interview with twenty female respondents conducted twice from June $17^{th}$ to September $11^{th}$, 2009. The interviews lasted between 40 and 60 minutes. The results showed that consumers write product reviews based on six motivations: to receive a reward or remuneration for writing a product review, to share information with other customers, to improve the quality of goods and services, to reduce customer dissatisfaction, to recommend products and services, and to derive pleasure.

• 한국전자거래학회지
• /
• 제27권2호
• /
• pp.21-36
• /
• 2022

최근 코로나로 인해 이커머스 시장이 확대되면서 인터넷 쇼핑몰 이용률 증가와 함께 다양한 형태의 소비 패턴을 보이는 고객들이 나타나고 있다. 기업은 고객 리뷰를 통해 고객의 의견과 정보를 얻을 수 있기 때문에 온라인 플랫폼에서의 고객 리뷰 관리에 대한 연구의 필요성이 증가하고 있다. 본 연구에서는 고객들을 군집화하고 분석하였으며, 이커머스 시장에 존재하는 고객 유형을 정의하고 시장세분화를 수행하였다. 구체적으로, 본 연구는 온라인 쇼핑몰 위메프(Wemakeprice)의 고객 리뷰 데이터를 수집하여 K-means 클러스터링을 진행하였으며, 그 결과로 6개의 군집이 도출되었다. 이후 6개의 군집으로 시장세분화 된 결과를 분석하여 각 군집의 특징을 정의하고 고객관리 방안까지 함께 제시하였다. 본 연구 결과는 이커머스 시장의 고객 유형 파악과 고객관리를 용이하게 하는 자료로 사용될 것이며, 다양한 온라인 플랫폼의 고객관리 비용 절감과 수익 창출에 기여할 것으로 기대된다.

• Asia pacific journal of information systems
• /
• 제31권4호
• /
• pp.472-490
• /
• 2021

The detection of products with manipulated reviews has received widespread research attention, given that a truthful, informative, and useful review helps to significantly lower the search effort and cost for potential customers. This study proposes a method to recognize products with manipulated online customer reviews by examining the sequence of each review's sentiment, readability, and rating scores by product on randomness, considering the example of a Russian online retail site. Additionally, this study aims to examine the association between brand awareness and existing manipulation with products' reviews. Therefore, we investigated the difference between well-known and unknown brands' products online reviews with and without manipulated reviews based on the average star rating and the extremely positive sentiment scores. Consequently, machine learning techniques for predicting products are tested with manipulated reviews to determine a more useful one. It was found that about 20% of all product reviews are manipulated. Among the products with manipulated reviews, 44% are products of well-known brands, and 56% from unknown brands, with the highest prediction performance on deep neural network.

• Fashion, Industry and Education
• /
• 제15권2호
• /
• pp.1-10
• /
• 2017

The purposes of this study were to explore (1) how fit reviews differ among height groups and (2) how overall numerical ratings differ depending on height groups and ifferent types of fit reviews. Content analysis was used to analyze systematically sampled online consumer reviews (OCRs) of formalwear dresses rented online. In part 1, 201 OCRs were analyzed to develop the coding scheme, which included three aspects of fit (physical, aesthetic, and functional), valence (negative, neutral, positive), and overall numerical rating. In part 2, 600 OCRs were coded and statistically analyzed. Differences in frequency were not found among height groups for any types of mentions (negative, neutral, and positive) in terms of the three aspects of fit in the OCRs. Differences in overall mean ratings were not found among height groups. Interestingly, valence of each aspect of fit reviews affected mean numeric ratings. This study is new in examining relationships among textual information (i.e., fit reviews), numerical information (i.e., numerical rating), and reviewer's characteristic (i.e., height). The results of this study offered practical implications for etailers and marketers that they should pay attention to the three aspects of fit reviews and monitor garments with negative fit evaluations for lower ratings. They may attempt to increase ratings by providing customers recommendations to get a better fit.

• 산경연구논집
• /
• 제14권1호
• /
• pp.67-74
• /
• 2023

Purpose: This study aims to investigate how the online review usefulness affect consumers' shopping behavior in non-face-to-face shopping, which is now very common format of shopping environment after COVID-19 pandemic. Factors influencing online reviews were determined as quantity of review, agreement of review and characteristic of review based on research by existing researchers. Research design, data, and methodology: Customers in their teens to 60s who had experience of checking online reviews and purchasing products were surveyed using a Google questionnaire form from January 15, 2022 to February 19, 2022. To verify the validity and reliability of the research model, confirmatory factor analysis and discriminant validity analysis were conducted. In addition, the causal relationship between factors was verified through path analysis. Results: As a result, quantity of review and agreement of review had a statistically significant effect on review usefulness. However, characteristic of review did not have a statistically significant influence on review usefulness. And review usefulness had a statistically significant effect on attitude and purchase intention. Conclusions: This study investigated the factors affecting usefulness of online reviews and empirically analyzed the effects of online reviews on consumer attitudes and purchase intentions providing practical and theoretical implications for corporate online review management.

• 한국정보시스템학회지:정보시스템연구
• /
• 제31권4호
• /
• pp.161-187
• /
• 2022

Purpose This study analyzed the topic of game review contents and how the characteristics of game reviews affect the reviews helpfulness. In addition, this study explore the content of game reviews according to the game's sales strategy such as early access strategy and releasing without early access. Design/methodology/approach We collected a list of 3,572 action genre games released in 2020. 58,336 online reviews were collected by random sampling 50 reviews in each games, and topic modeling was performed on those reviews. We dynamized the results of topic modeling and analyzed the effect on review helpfulness with multiple regression analysis. Findings The results of analysis indicate that the longer the review is or the shorter the time it is written, the more helpful the review is. In addition the topic with positive and negative review has a significant effect on the review helpfulness. As a result of exploratory analysis, games from early access had relatively fewer reviews of story-related topics than games that were released without early access. These findings can present direct guidelines for collecting specific opinions from customers in the game industry when releasing games.

• 한국의류산업학회지
• /
• 제16권1호
• /
• pp.79-90
• /
• 2014

With the development of modern society, not only have the Internet and e-commerce been progressed but they also made 'consumption patten' diverse. Despite the internet clothing market growth, there is critical a disadvantage, which is consumers is not able to wear the products presented via online pictures. Thus, pictures on the internet are the only information customers can get, which has caused consciousness on the importance of dealing with 'customer review'. In spite of the fact that 'customer review' has undeniably evolved to be one of customers' essential requisites, the research on this subject is very limited. Until now, the studies on the internet shopping consumers' behavior mostly has to do with the features of 'customer review' such as 'a sense of exaggeration', 'usability', 'duality', 'purity', 'professionalism', 'reliability', and the 'similarity', etc.) Therefore, this study categorizes the characteristics of online shopping reviews to 'the number of reviews', 'the article-length', 'the existence of photos', 'the rewards for reviews', 'the contents of the reviews' and 'the freshness of the reviews' and reviews the impact of an features of 'customers' reviews' affecting the internet shopping sales promotion. Moreover, it is to contribute to the marketing strategies of a shopping mall by analyzing consumers' 'purchasing satisfaction', 'the intention of repurchasing', and 'the factors of viral marketing'.

• 한국IT서비스학회지
• /
• 제14권4호
• /
• pp.159-169
• /
• 2015

Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as Amazon.com used. However, there is another type of usefulness index utilized in TripAdviser.com or Yelp.com, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from Yelp.com to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.

• 한국IT서비스학회지
• /
• 제11권3호
• /
• pp.103-127
• /
• 2012

With the advent of SNS (Social Network Services), the product reviews by friends in SNS are intensively utilized for online marketing. However, there is a lack of empirical evidence on the actual marketing effect of SNS reviews, although we need to identify who can be the target of SNS marketing in terms of customer attributes, preferences, or experiences. In this study, we investigate the moderating role of customer attributes in identifying the effect of SNS reviews on customer purchasing decision. As the moderating variables, we adopt 'information search experience' and 'perception of information overload'. Research results evidence that, in order to understand the effect of SNS reviews in a comprehensive manner, we need to examine it in the context of various related factors such as 'information search experience' and 'perception of information overload'. The results show that the persuading effect of SNS reviews for product purchasing is stronger for the customers with the lower information search experiences as well as the lower perception on the information overload on the web. This result delivers managerial implications on who can be the target customers of SNS marketing.

• 지능정보연구
• /
• 제28권2호
• /
• pp.1-18
• /
• 2022

디지털 기술이 산업 전반의 전자상거래 시장에 융합되면서 온라인 거래의 활성화와 이용률을 증가시켰으며, 이러한 시장의 흐름은 최근 코로나와 같은 감염병이 확산함에 따라 더욱 가속화되어 다양한 상품 정보를 온라인을 통해 고객들에게 제공할 수 있게 되었다. 다양한 정보의 제공은 고객들에게 다양한 선택의 기회를 제공하지만 의사결정에 어려움을 주기도 한다. 추천시스템은 고객의 의사결정에 도움을 줄 수 있으나 기존 추천시스템 연구는 정량적 데이터만에 국한되어 있으며, 상품 및 고객의 세부적인 요인을 반영하지 못하였다. 이에 본 연구에서는 온라인 리뷰를 기반으로 정성적 데이터를 텍스트 마이닝 기법을 적용하여 상품 및 고객의 속성을 정량화하고 기존의 객관적 지표인 총평점과 감성 및 감정을 통합한 지능형 추천시스템을 제안한다. 제안된 지능형 추천모형은 총평점 위주의 추천 모형보다 우수한 추천성과를 보여주었으며, 상품 및 고객의 세부적 요소를 반영한 추천결과를 통해 새로운 비즈니스 가치를 창출할 것으로 기대한다.