The Effects of Online User Reviews on Movie Box-Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets

Gopinath, Shyam; Venkataraman, Sriram

doi:10.2139/ssrn.1331124

Cited by 219 publications

(318 citation statements)

References 18 publications

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“…However, in the present analysis, we find that valence does have non-negligible effects on movie box office revenues, which is consistent with the recent findings by Chintagunta et al [3]. Specifically, WOM with positive sentiment toward the movie is associated with higher movie sales in the subsequent week.…”

Section: Revenuesupporting

confidence: 92%

“…We used Total tweets, the total number of tweets mentioning a movie for each week, as an explanatory variable to capture the volume effect of Twitter WOM on movie box office revenues, which is consistent with the literature [3,7,9,11]. To capture the valence effect, we introduced Positive tweets ratio and Negative tweets ratio, which are the ratios of tweets with positive sentiment and negative sentiment among all tweets for each movie in each week, respectively.…”

Section: Variablesmentioning

confidence: 99%

“…As of April 2012, Twitter had more than 500 million registered users worldwide, who in total post an average of 340 million updates a day. 3 Sina Weibo and Tecent Weibo, two of the leading SBN in China, both reported more than 200 million registered users in 2011. Although there is little doubt about the significant social and political impact of these fast-growing SBN, an interesting question for the business world is whether the huge amount of brief messages generated and consumed by the vibrant SBN community in real time has any effect on product sales.…”

Section: Introductionmentioning

confidence: 99%

“…For example, [9] used WOM conversations from Usenet to study their influence on TV ratings, and they found that the dispersion of conversations across communities has explanatory power but that the volume of WOM does not. Many researchers have used posts from Yahoo!Movies to study the effect of WOM on movie box office revenue [3,7,11], and the results are mixed. For example, both [7,11] found that the volume of reviews matters, but the valence does not.…”

Section: Introductionmentioning

confidence: 99%

“…This view is also partially supported in [6], who found that future sales of a music album are positively correlated with the volume of blog posts about that album. On the other hand, [3] measured the effect of national online user reviews on designated market area-level local geographic box office performance of movies, and their findings suggest that it is the valence that drives box office performance, not the volume. A recent study by [18] seems to support this view, although their results were based on the analysis of a different product category.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Whose and what chatter matters? The effect of tweets on movie sales

Rui

Liu

Whinston

2013

Decision Support Systems

308

192

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a b s t r a c tSocial broadcasting networks such as Twitter in the U.S. and "Weibo" in China are transforming the way online word of mouth (WOM) is disseminated and consumed in the digital age. In the present study, we investigated whether and how Twitter WOM affects movie sales by estimating a dynamic panel data model using publicly available data and well-known machine learning algorithms. We found that chatter on Twitter does matter; however, the magnitude and direction of the effect depend on whom the WOM is from and what the WOM is about. Incorporating the number of followers the author of each WOM message had into our study, we found that the effect of WOM from users followed by more Twitter users is significantly larger than those followed by less Twitter users. In support of some recent findings about the importance of WOM valence on product sales, we also found that positive Twitter WOM is associated with higher movie sales, whereas negative WOM is associated with lower movie sales. Interestingly, we found that the strongest effect on movie sales comes from those tweets in which the authors expressed their intention to watch a certain movie. We attribute this finding to the dual effects of such intention tweets on movie sales: the direct effect through the WOM author's own purchase behavior, and the indirect effect through either the awareness effect or the persuasive effect of the WOM on its recipients. Our findings provide new perspectives to understand the effect of WOM on product sales and have important managerial implications. For example, our study reveals the potential values of monitoring people's intentions and sentiments on Twitter and identifying influential users for companies wishing to harness the power of social broadcasting networks.

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Section: Revenuesupporting

confidence: 92%

Section: Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Whose and what chatter matters? The effect of tweets on movie sales

Rui

Liu

Whinston

2013

Decision Support Systems

308

192

View full text Add to dashboard Cite

show abstract

How online word‐of‐mouth impacts receivers

Moore

Lafreniere

2019

Consumer Psychology Review

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Online word‐of‐mouth (WOM) can impact consumers’ product evaluations, purchase intentions, and choices—but when does it do so? How do those receiving WOM know whether to rely on a particular message? This article suggests that the multiple players involved in online WOM (receivers, senders, sellers, platforms, and other consumers) each have their own interests, which are often in conflict. Thus, receivers of WOM are faced with a judgment task in deciding what information to rely on: They must make inferences about the product in question and about the players who provide or present WOM. To do so, they use signals embedded in various components of WOM, such as average star ratings, message content, or sender characteristics. The product and player information provided by these signals shapes the impact of WOM by allowing receivers to make inferences about (a) their likelihood of product satisfaction, and (b) the trustworthiness of WOM players, and therefore the trustworthiness of their content. This article summarizes how each player changes the impact of online WOM, providing a lens for understanding the current literature in online WOM, offering insights for theory in this context, and opening up pathways for future research.

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Online consumer reviews and sales: Examining the chicken‐egg relationships

Ren

Yeoh

et al. 2017

Asso for Info Science & Tech

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This article examines the "chicken-egg" two-way relationships between online consumer reviews and sales, and assesses the dual influencer and indicator roles of online consumer reviews in relation to purchase behavior. Considering the time factor, we adopt the methodology of Granger causality test and track 3,390 products on Amazon.com over a 2-month period. The results reveal that a causality loop exists between online consumer review volume and sales. Specifically, our findings indicate that the volume of negative consumer reviews drive consumers' purchasing decisions, but the volume of positive consumers reviews only marginally affects purchasing decisions. Also, consumers generate more positive reviews than negative reviews after sales. Our results highlight the importance of negative consumer reviews; negative reviews not only lead to sales, but sales, in turn, lead to higher volume of negative reviews. The findings suggest an alternative strategy for practitioners to address negative online consumer reviews and highlight the awareness effect of online consumer review postings that can later convert into purchase behaviors.

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The Effects of Online User Reviews on Movie Box-Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets

Cited by 219 publications

References 18 publications

Whose and what chatter matters? The effect of tweets on movie sales

Whose and what chatter matters? The effect of tweets on movie sales

How online word‐of‐mouth impacts receivers

Online consumer reviews and sales: Examining the chicken‐egg relationships

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