• Knowledge Management Research
• /
• v.23 no.2
• /
• pp.229-251
• /
• 2022

Online reviews are useful for other consumers to make reasonable purchase decisions by providing previous buyers' experiences. However, when online reviewers are biased, online reviews do not accurately reflect the true quality of the product. Therefore, we investigated the characteristics of reviewers with underreporting bias to cope with the problem of declining reliability of online reviews. In this context, this study attempted to examine the characteristics of reviewers with underreporting bias using 14,165 reviews of Steam, an online game platform. As a result of the analysis, reviewers with underreporting bias mainly write reviews positively, write reviews within a short period from the game release date, but tend to write reviews after playing games for longer time, and write reviews when purchasing high-priced games. Since this study has explored the characteristics of reviewers showing underreporting bias, it will be meaningful as a basic study to cope with the problem caused by underreporting bias.

• Informatization Policy
• /
• v.20 no.3
• /
• pp.3-17
• /
• 2013

As electronic marketplaces grow and a large number of consumers exchange their opinions on products and services on the Internet, many studies have been conducted in the area of online consumer reviews. This paper analyzes the research trend of the online consumer reviews by investigating those studies in an attempt to provide future research directions. Many researchers have focused on the effects of online reviews on consumer behaviors as well as the usefulness of the online reviews. In particular, review contents, characteristics of reviewers/consumers and features of products/services have been identified as influencing factors on the effects of the online consumer reviews. For the review contents, the number and the volume of the contents have increasing effects on the online reviews, while the direction (positive vs. negative) of the contents has resulted in conflicting effects of the review. The reputation and trustfulness of reviewers, consumers' prior knowledge on the products, consumers' product involvement, and types of the products were investigated as these factors influence the effectiveness of the online consumer reviews. Social media (such as Facebook and Twitter) nowadays play an important role to disseminate online reviews among consumers. Thus, it is necessary to study how social media influence the effects of online reviews on consumers. Since some firms abuse the online reviews for their own sakes, we recognize the necessity for empirical studies on the side effects of the online reviews.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.171-189
• /
• 2022

Online reviews play an essential role in the consumer's purchasing decision-making process, and thus, providing helpful and reliable reviews is essential to consumers. Previous online review helpfulness prediction studies mainly predicted review helpfulness based on the consistency of text and rating information of online reviews. However, there is a limitation in that representation capacity or review text and rating interaction. We propose a CNN-RHP model that effectively learns the interaction between review text and rating information to improve the limitations of previous studies. Multi-channel CNNs were applied to extract the semantic representation of the review text. We also converted rating into independent high-dimensional embedding vectors representing the same dimension as the text vector. The consistency between the review text and the rating information is learned based on element-wise operations between the review text and the star rating vector. To evaluate the performance of the proposed CNN-RHP model in this study, we used online reviews collected from Amazom.com. Experimental results show that the CNN-RHP model indicates excellent performance compared to several benchmark models. The results of this study can provide practical implications when providing services related to review helpfulness on online e-commerce platforms.

• Proceedings of the Korean DIstribution Association Conference
• /
• 2005.05a
• /
• pp.41-58
• /
• 2005

본 연구는 eWOM(온라인 구전) 효과에 영향을 미치는 요인을 문화권(국가), 소비자특성(소비자 수용성), 그리고 온라인 경험 (인터넷 사용기간, 이용시간, 온라인 쇼핑횟수)로 나누어 살펴보았다. 문헌연구들을 통해 연구문제를 도출하고 이를 비교 문화적으로 실증하기 위해 총 1,176(한국 615명, 미국 561명)명의 온라인 리뷰 사용자를 대상으로 설문조사를 실시하였다. 그 결과 개인주의적인 미국보다는 집단주의적인 한국에서 eWOM의 효과는 더 큰 것으로 나타나 문화적 영향은 유의한 것으로 나타났다. 또한 소비자 수용성이라는 소비자 특성도 eWOM 효과(온라인 리뷰의 구매 직접영향정도)에 유의한 영향을 미치는 것으로 나타났다. 그리고 온라인 경험도 유의한 영향을 미쳤는데, 인터넷 사용기간 보다는 현재의 인터넷 이용시간이나 보다 관련이 있는 온라인 쇼핑횟수와 같은 변수가 eWOM 효과에 유의한 영향을 미치는 것으로 나타났다.

• The Journal of the Korea Contents Association
• /
• v.20 no.7
• /
• pp.316-330
• /
• 2020

The study analyzes reviews of hardware products, customer service products, and products that take the form of a convergence of hardware and cloud services in ICT using text mining. We derive keywords of each review and find the differentiation of words that are used to derive topics. A cluster analysis is performed to categorize reviews into their respective clusters. Through this study, we observed which keywords are most often used for each product type and found topics that express the characteristics of products and services using topic modeling. We derived keywords such as "professional" and "technician" which are topics that suggest the excellence of the service provider in the review of service products. Further, we identified adjectives with positive connotations such as "favorite", "fine", "fun", "nice", "smart", "unlimited", and "useful" from Amazon Eco review, an integrated product and service. Using the cluster analysis, the entire review was clustered into three groups, and three product type reviews exclusively resulted in belonging to each different cluster. The study analyzed the differences whereby consumer needs are expressed differently in reviews depending on the type of product and suggested that it is necessary to differentiate product planning and marketing promotion according to the product type in practice.

• Knowledge Management Research
• /
• v.21 no.3
• /
• pp.177-195
• /
• 2020

The social consciousness on fake reviews has triggered researchers to suggest ways to cope with them by analyzing contents of fake reviews or finding ways to discover them by means of structural characteristics of them. This research tried to collect data from blog posts in Naver and detect habitual patterns users use unconsciously by variables extracted from blogs and blog posts by a machine learning model and wanted to use the technique in predicting fake reviews. Data analysis showed that there was a very high relationship between the number of all the posts registered in the blog of the writer of the related writing and the date when it was registered. And, it was found that, as model to detect advertising reviews, Random Forest is the most suitable. If a review is predicted to be an advertising one by the model suggested in this research, it is very likely that it is fake review, and that it violates the guidelines on investigation into markings and advertising regarding recommendation and guarantee in the Law of Marking and Advertising. The fact that, instead of using analysis of morphemes in contents of writings, this research adopts behavior analysis of the writer, and, based on such an approach, collects characteristic data of blogs and blog posts not by manual works, but by automated system, and discerns whether a certain writing is advertising or not is expected to have positive effects on improving efficiency and effectiveness in detecting fake reviews.

• Information Systems Review
• /
• v.23 no.1
• /
• pp.45-68
• /
• 2021

As the influence of online reviews on consumer decision-making increases, concerns about review manipulation are also increasing. Fake reviews or review manipulations are emerging as an important problem by posting untrue reviews in order to increase sales volume, causing the consumer's reverse choice, and acting at a high cost to the society as a whole. Most of the related prior studies have focused on predicting review manipulation through data mining methods, and research from a consumer perspective is insufficient. However, since the possibility of manipulation of reviews perceived by consumers can affect the usefulness of reviews, it can provide important implications for online word-of-mouth management regardless of whether it is false or not. Therefore, in this study, we analyzed whether there is a difference between the review evaluated by the consumer as being manipulated and the general review, and verified whether the manipulated review negatively affects the review usefulness. For empirical analysis, 34,711 online book reviews on the LibraryThing website were analyzed using multilevel logistic regression analysis and Poisson regression analysis. As a result of the analysis, it was found that there were differences in product level, reviewer level, and review level factors between reviews that consumers perceived as being manipulated and reviews that were not. In addition, manipulated reviews have been shown to negatively affect review usefulness.

• The Journal of the Korea Contents Association
• /
• v.18 no.5
• /
• pp.367-375
• /
• 2018

The study aims to determine which groups leave longer(more active) online reviews(comments) on the film by separating groups, one that satisfied with the movie while the other group dissatisfied with the movie. The data used were rating scores and reviews(comments) from Naver Movie API, and break-even point data provided by Korea Film Commission. We analyzed the relationship between movie rating and review length, before and after movie opening, the characteristics of review length according to the box office, and whether the movie rating affects the review length.

• The Journal of the Korea Contents Association
• /
• v.17 no.1
• /
• pp.80-91
• /
• 2017

The purpose of paper is to confirm the role of E-WOM(Electronic Worth of Mouth) in decision making of selecting the mobile application via smart-phone or tablet pc. Particularly i wished to confirm the effects of others' positive or negative reviews in purchasing(free downloading) mobile applications. To resolve these research questions, the secondary data or previous research were collected and arranged theoretically. From literature research, i made out the proposed model to explain the relationships between the variables, executed the operational definitions and 14 Hypotheses were established, collected the survey data of 228 mobile application users. Using the empirical test analysis, previous performances to confirm the construct validity and internal consistency and PLS(Partial Least Square) modelling method was executed. The test result showed that proposed relations of variables was empirically identified, therefore, i got the conclusion as followings; First, attributes of mobile application users' reviews have the effects positively to usefulness perception and expected performance. Second, it was significantly tested Usefulness of Online Review and Expected Performance. Second, Usefulness of Online Review, Source Credibility and Expected Performance have effect positively to Intention of Review Adoption.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.882-884
• /
• 2019

오늘날 정보화 시대에서는 온라인 쇼핑의 상품리뷰 등 대용량의 텍스트 문서가 존재하며 제품에 대한 정서적인 의견뿐만 아니라 제품 선호도 및 상품 비교와 같은 유용한 정보를 제공한다. 본 논문에서는 사용자가 작성한 상품 리뷰로부터 제품의 특성을 비교하는 비교의견을 추출하기 위해 적용한 다양한 텍스트 마이닝 기법의 비교 결과를 제시한다.