• Knowledge Management Research
• /
• v.18 no.2
• /
• pp.65-83
• /
• 2017

Word-of-mouth (WOM), the communication between consumers offline, has transformed to include electronic word-of-mouth(eWOM), which has grown in its influence due to the advancements in communication technology. Despite the fact that many researchers have studied the impact of WOM and eWOM on the performance of movies in the movie industry, there still exists much controversy. Therefore, this study investigates the relationship of eWOM's volume and valence with the box office revenue for 2 years in Korean movies industry. The results show that the volume of eWOM, which is expected to related to awareness diffusion, is more important than the valence in the early stage of movie release. And in the later stage, the valence of eWOM which is expected to related to persuasion effect influences the box office revenue. In addition, the relationship of the volume and valence on box office revenue in both early and later stage can be increased through the interaction with the star power which raises the familiarity or the movie genre which causes the high arousal.

• Journal of Digital Convergence
• /
• v.16 no.3
• /
• pp.145-156
• /
• 2018

Unlike the past, when distributors distributed games through retail stores, they are now selling digital content, which is based on online distribution channels. This study analyzes the effects of eWOM (electronic Word of Mouth) on sales volume of game sold on Steam, an online digital content distribution channel. Recently, data mining techniques based on Big Data have been studied. In this study, emotion index of eWOM is derived by emotional analysis which is a text mining technique that can analyze the emotion of each review among factors of eWOM. Emotional analysis utilizes Naive Bayes and SVM classifier and calculates the emotion index through the SVM classifier with high accuracy. Regression analysis is performed on the dependent variable, sales variation, using the emotion index, the number of reviews of each game, the size of eWOM, and the user score of each game, which is a rating of eWOM. Regression analysis revealed that the size of the independent variable eWOM and the emotion index of the eWOM were influential on the dependent variable, sales variation. This study suggests the factors of eWOM that affect the sales volume when Korean game companies enter overseas markets based on steam.

• Asia Marketing Journal
• /
• v.12 no.2
• /
• pp.1-25
• /
• 2010

Motion pictures are so typical experience goods that consumers tend to look for more credible information. Hence, movie audiences consider movie viewers' reviews more important than the information provided by the film distributor. Recently many portal sites allow consumers to post their reviews and opinions so that other people check the number of consumer reviews and scores before going to the theater. There are a few previous researches studying the electronic word of mouth(eWOM) effect in the movie industry. They found that the volume of eWOM influenced the revenue of the movie significantly but the valence of eWOM did not affect it much (Liu 2006). The goal of our research is also to investigate the eWOM effects in general. But our research is different from the previous studies in several aspects. First, we study the eWOM effect in Korean movie industry. In other words, we would like to check whether we can generalize the results of the previous research across countries. The similar econometric models are applied to Korean movie data that include 746,282 consumer reviews on 439 movies. Our results show that both the valence(RATING) and the volume(LNMSG) of the eWOM influence weekly movie revenues. This result is different from the previous research findings that the volume only influences the revenue. We conjectured that the difference of self construal between Asian and American culture may explain this difference (Kitayama 1991). Asians including Koreans have more interdependent self construal than American, so that they are easily affected by other people's thought and suggestion. Hence, the valence of the eWOM affects Koreans' choice of the movie. Second, we find the critical defect of the previous eWOM models and, hence, attempt to correct it. The previous eWOM model assumes that the volume of eWOM (LNMSG) is an independent variable affecting the movie revenue (LNREV). However, the revenue can influence the volume of the eWOM. We think that treating the volume of eWOM as an independent variable a priori is too restrictive. In order to remedy this problem, we employed a simultaneous equation in which the movie revenue and the volume of the eWOM can affect each other. That is, our eWOM model assumes that the revenue (LNREV) and the volume of eWOM (LNMSG) have endogenous relationship where they influence each other. The results from this simultaneous equation model showed that the movie revenue and the eWOM volume interact each other. The movie revenue influences the eWOM volume for the entire 8 weeks. The reverse effect is more complex. Both the volume and the valence of eWOM affect the revenue in the first week, but only the volume affect the revenue for the rest of the weeks. In the first week, consumers may be curious about the movie and look for various kinds of information they can trust, so that they use the both the quantity and quality of consumer reviews. But from the second week, the quality of the eWOM only affects the movie revenue, implying that the review ratings are more important than the number of reviews. Third, our results show that the ratings by professional critics (CRATING) had negative effect to the weekly movie revenue (LNREV). Professional critics often give low ratings to the blockbuster movies that do not have much cinematic quality. Experienced audiences who watch the movie for fun do not trust the professionals' ratings and, hence, tend to go for the low-rated movies by them. In summary, applied to the Korean movie ratings data and employing a simultaneous model, our results are different from the previous eWOM studies: 1) Koreans (or Asians) care about the others' evaluation quality more than quantity, 2) The volume of eWOM is not the cause but the result of the revenue, 3) Professional reviews can give the negative effect to the movie revenue.

• Asia Marketing Journal
• /
• v.15 no.4
• /
• pp.61-101
• /
• 2014

This study investigates when and how disagreements in online customer ratings prompt more favorable product evaluations. Among the three metrics of volume, valence, and variance that feature in the research on online customer ratings, volume and valence have exhibited consistently positive patterns in their effects on product sales or evaluations (e.g., Dellarocas, Zhang, and Awad 2007; Liu 2006). Ratings variance, or the degree of disagreement among reviewers, however, has shown rather mixed results, with some studies reporting positive effects on product sales (e.g., Clement, Proppe, and Rott 2007) while others finding negative effects on product evaluations (e.g., Zhu and Zhang 2010). This study aims to resolve these contradictory findings by introducing preference heterogeneity as a possible moderator and causal attribution as a mediator to account for the moderating effect. The main proposition of this study is that when preference heterogeneity is perceived as high, a disagreement in ratings is attributed more to reviewers' different preferences than to unreliable product quality, which in turn prompts better quality evaluations of a product. Because disagreements mostly result from differences in reviewers' tastes or the low reliability of a product's quality (Mizerski 1982; Sen and Lerman 2007), a greater level of attribution to reviewer tastes can mitigate the negative effect of disagreement on product evaluations. Specifically, if consumers infer that reviewers' heterogeneous preferences result in subjectively different experiences and thereby highly diverse ratings, they would not disregard the overall quality of a product. However, if consumers infer that reviewers' preferences are quite homogeneous and thus the low reliability of the product quality contributes to such disagreements, they would discount the overall product quality. Therefore, consumers would respond more favorably to disagreements in ratings when preference heterogeneity is perceived as high rather than low. This study furthermore extends this prediction to the various levels of average ratings. The heuristicsystematic processing model so far indicates that the engagement in effortful systematic processing occurs only when sufficient motivation is present (Hann et al. 2007; Maheswaran and Chaiken 1991; Martin and Davies 1998). One of the key factors affecting this motivation is the aspiration level of the decision maker. Only under conditions that meet or exceed his aspiration level does he tend to engage in systematic processing (Patzelt and Shepherd 2008; Stephanous and Sage 1987). Therefore, systematic causal attribution processing regarding ratings variance is likely more activated when the average rating is high enough to meet the aspiration level than when it is too low to meet it. Considering that the interaction between ratings variance and preference heterogeneity occurs through the mediation of causal attribution, this greater activation of causal attribution in high versus low average ratings would lead to more pronounced interaction between ratings variance and preference heterogeneity in high versus low average ratings. Overall, this study proposes that the interaction between ratings variance and preference heterogeneity is more pronounced when the average rating is high as compared to when it is low. Two laboratory studies lend support to these predictions. Study 1 reveals that participants exposed to a high-preference heterogeneity book title (i.e., a novel) attributed disagreement in ratings more to reviewers' tastes, and thereby more favorably evaluated books with such ratings, compared to those exposed to a low-preference heterogeneity title (i.e., an English listening practice book). Study 2 then extended these findings to the various levels of average ratings and found that this greater preference for disagreement options under high preference heterogeneity is more pronounced when the average rating is high compared to when it is low. This study makes an important theoretical contribution to the online customer ratings literature by showing that preference heterogeneity serves as a key moderator of the effect of ratings variance on product evaluations and that causal attribution acts as a mediator of this moderation effect. A more comprehensive picture of the interplay among ratings variance, preference heterogeneity, and average ratings is also provided by revealing that the interaction between ratings variance and preference heterogeneity varies as a function of the average rating. In addition, this work provides some significant managerial implications for marketers in terms of how they manage word of mouth. Because a lack of consensus creates some uncertainty and anxiety over the given information, consumers experience a psychological burden regarding their choice of a product when ratings show disagreement. The results of this study offer a way to address this problem. By explicitly clarifying that there are many more differences in tastes among reviewers than expected, marketers can allow consumers to speculate that differing tastes of reviewers rather than an uncertain or poor product quality contribute to such conflicts in ratings. Thus, when fierce disagreements are observed in the WOM arena, marketers are advised to communicate to consumers that diverse, rather than uniform, tastes govern reviews and evaluations of products.

• Journal of Distribution Science
• /
• v.15 no.10
• /
• pp.41-50
• /
• 2017

Purpose - Online product review has become a crucial part of the online retailer's market performance for a wide range of products. This research aims to investigate how an individual reviewer's review frequency and timing affect her/his average attitude toward products. Research design, data, and methodology - To conduct reviewer-level analysis, this study uses 42,172 posted online review messages generated by 6,941 identified reviewers for 59 movies released in the South Korea from July 2015 to December 2015. This study adopts Tobit model specification to take into account the censored nature and the selection bias arising from the nature of J-shaped distribution of movie rating. Results - Our estimation results support that the negative impact of review frequency and timing on valence. Furthermore, review timing has an inverted-U relationship with the user's average valence and enhance the negative effect of review frequency. Conclusions - This study contributes to the growing literature on the understanding how eWOM is generated at the individual consumer level. On the basis of the main empirical findings, this study provides insights into building a recommendation system in online retail store based on the consumer's review history data - frequency, timing, and valence.

• The Journal of Society for e-Business Studies
• /
• v.16 no.3
• /
• pp.93-112
• /
• 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
• /
• v.21 no.2
• /
• pp.49-67
• /
• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.