• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.53-68
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• 2015

This study analyzed online word-of-mouth information using content analysis to help practical categorization of two-sided eWOM. A total of 402 online consumer reviews on search goods and experience goods were collected. Descriptive characteristics(information direction, length of review line) and content structural characteristics(product benefit types, information presentation methods) were used as analysis criteria. The study results are as follows. First, the types of two-sided e-WOM direction were made of positive/negative, negative/positive, positive/negative/ positive, and negative/positive/negative. Second, the length of two-sided eWOM was longer than the length of one-sided eWOM and blended type accounted for the highest proportion both one-sided and two-sided eWOM at the aspect of product benefit. Third, holistic presentation method was overwhelmingly high in one-sided eWOM, whereas blended and analytic presentation methods were somewhat high in two-sided eWOM. Fourth, holistic presentation method was high in search goods, whereas blended and analytic presentation methods were high in experience goods. Based on these results, implications for two-sided e-WOM study and further research issues were discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.316-318
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• 2021

영화 수익에 있어 영화의 흥행 여부는 중요한 영향을 끼친다. 영화 흥행 요인은 영화 산업의 규모가 커지면서 많은 제작사들 및 투자자들이 고려해야 하는 사항이 되었다. 따라서 영화의 흥행을 예측하기 위한 많은 모델이 연구되었다. 본 연구의 목적은 선행연구에서 흥행에 유의미한 영향을 끼친다고 밝혀진 스크린 수, 감독명, 제작사명 등의 내재적인 속성과 더불어 온라인 구전 변수를 사용하여 영화 흥행 예측 모델을 만드는 것이다. 이때 기사 수, 블로그 수와 같이 온라인 구전의 크기를 나타내는 변수들을 사용하는 대신 개봉 후 첫 주간의 관람객 리뷰를 텍스트마이닝을 이용하여 전체 리뷰 중 긍정 리뷰의 비율에 따라 점수를 매긴 후 독립변수로 사용한다. 그 후, 데이터 마이닝 기법을 활용하여 만든 모델에 앞서 언급한 독립변수를 입력 값으로 사용하여 영화의 흥행을 예측한다. 최종적으로 의사결정트리와 로지스틱회귀를 수행한 결과 영화 흥행에 영향을 주는 독립변수를 찾고 모델의 성능을 평가하였다. 로지스틱회귀의 결과 관객 수, 평점이 영화의 흥행에 특히 유의한 영향을 끼치는 변수로 선정되었고 리뷰 역시 유의한 변수로 선정되었다. 이때 만들어진 모델은 약 90%의 높은 수준의 정확도를 보여주었다. 의사결정트리의 결과 관객 수가 가장 중요한 변수로 선정되었다.

• Information Systems Review
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• v.25 no.2
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• pp.103-123
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• 2023

One of the valid perspectives in the e-commerce platform literature is the seller signaling strategy in the information asymmetry situation. In this study, a research model was constructed based on signaling theory and shopping goal theory to systematically explore the effects of a seller's signaling strategy on consumer decision-making. Specifically, the study examined whether the signaling effects (i.e., reputation, electronic word-of-mouth, price) provided by the seller differed based on consumers' shopping goals. For the empirical analysis, the Gaussian Copula method was employed, utilizing 26,246 data collected from Amazon, a leading e-commerce platform. The analysis revealed that the signals provided by the seller positively impacted sales, and this effect was moderated by consumers' shopping goals. Drawing on shopping goal theory, this study contributes to signaling theory and e-commerce literature by discovering differences in the effectiveness of a seller's signaling strategy based on the keywords input by consumers.

• Information Systems Review
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• v.17 no.3
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• pp.183-202
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• 2015

In recent years, various Social Network Service (SNS) is emerging as a new means of communication were enjoying a lot of popularity among consumers. Accordingly, an online word-of-mouth marketing through the SNS is prevalent. At this moment, the majority of companies selects the SNS used as resources of online word-of-mouth marketing on the assumption that the more a SNS is popular (followers or visitors based), the more it has an influence. In addition, the existing studies about the popularity or influence on the SNS were not distinguish them separately. The former researchers used popularity mixed with Influence. Therefore, this study, we have conducted a survey with people in their twentieswho use SNS most to do an empirical analysis of the relationship between popularity and Influence on the SNS. According to the results of this study, it has a weak correlation between popularity and Influence. So, it is necessary to distinguish between popularity and influence.

• The Journal of Society for e-Business Studies
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• v.16 no.3
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• pp.93-112
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• 2011

For the success of an online retail market, it is important to allow consumers to get more helpful reviews by figuring out the factors determining the helpfulness of online reviews. On the basis of elaboration likelihood model, this study analyzes which factors determine the helpfulness of reviews and how the factors affecting the helpfulness of an online consumer review differ for product price. For this study, 75,226 online consumer reviews were collected from Amazon.com. Furthermore, additional information on review messages was also gathered by carrying out a content analysis on the review messages. This study shows that both of peripheral cues such as review rating and reviewer's credibility and central cues such as word count of review message and the proportion of negative words influence the helpfulness of review. In addition, the result of this study reveals that each consumer focuses on different information sources of reviews depending on the product price.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.43-61
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• 2016

Customer reviews help potential customers make purchasing decisions. However, the prevalence of reviews on websites push the customer to sift through them and change the focus from a mere search to identifying which of the available reviews are valuable and useful for the purchasing decision at hand. To identify useful reviews, websites have developed different mechanisms to give customers options when evaluating existing reviews. Websites allow users to rate the usefulness of a customer review as helpful or not. Amazon.com uses a ratio-type helpfulness, while Yelp.com uses a count-type usefulness index. This usefulness index provides helpful reviews to future potential purchasers. This study investigated the effects of sentiment and readability on useful votes for customer reviews. Similar studies on the relationship between sentiment and readability have focused on the ratio-type usefulness index utilized by websites such as Amazon.com. In this study, Yelp.com's count-type usefulness index for restaurant reviews was used to investigate the relationship between sentiment/readability and usefulness votes. Yelp.com's online customer reviews for stores in the beverage and food categories were used for the analysis. In total, 170,294 reviews containing information on a store's reputation and popularity were used. The control variables were the review length, store reputation, and popularity; the independent variables were the sentiment and readability, while the dependent variable was the number of helpful votes. The review rating is the moderating variable for the review sentiment and readability. The length is the number of characters in a review. The popularity is the number of reviews for a store, and the reputation is the general average rating of all reviews for a store. The readability of a review was calculated with the Coleman-Liau index. The sentiment is a positivity score for the review as calculated by SentiWordNet. The review rating is a preference score selected from 1 to 5 (stars) by the review author. The dependent variable (i.e., usefulness votes) used in this study is a count variable. Therefore, the Poisson regression model, which is commonly used to account for the discrete and nonnegative nature of count data, was applied in the analyses. The increase in helpful votes was assumed to follow a Poisson distribution. Because the Poisson model assumes an equal mean and variance and the data were over-dispersed, a negative binomial distribution model that allows for over-dispersion of the count variable was used for the estimation. Zero-inflated negative binomial regression was used to model count variables with excessive zeros and over-dispersed count outcome variables. With this model, the excess zeros were assumed to be generated through a separate process from the count values and therefore should be modeled as independently as possible. The results showed that positive sentiment had a negative effect on gaining useful votes for positive reviews but no significant effect on negative reviews. Poor readability had a negative effect on gaining useful votes and was not moderated by the review star ratings. These findings yield considerable managerial implications. The results are helpful for online websites when analyzing their review guidelines and identifying useful reviews for their business. Based on this study, positive reviews are not necessarily helpful; therefore, restaurants should consider which type of positive review is helpful for their business. Second, this study is beneficial for businesses and website designers in creating review mechanisms to know which type of reviews to highlight on their websites and which type of reviews can be beneficial to the business. Moreover, this study highlights the review systems employed by websites to allow their customers to post rating reviews.