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Abstract
This study examines when and why online comments from different sources and platforms influence a movie’s box office receipts over time. Premised on the theory of information search, we hypothesize that consumers are more likely to engage in active search in the early stages of a movie’s release due to greater choice uncertainty, and passive attention is more likely to kick in for later stages of a movie’s release, as uncertainty decreases. To test the proposed hypotheses, we tracked over 1,500 sources of online expert and consumer reviews for cinematic movies released for an entire year and continuously monitored major social media sites (e.g., Twitter) for comments. We text-mined the comments to elucidate the sentiments and analyzed the data. Confirming our hypotheses, the results showed that expert reviews and pull-based peer comments have a significant influence in early stages of a movie’s release, and the effects decrease over time. In contrast, the volume of comments from push-based microblog platforms have a significant influence on later box office receipts. Our research demonstrates that online opinions are not always persuasive and useful, and our findings provide insights into when consumers are likely to pay attention to which types of online opinions.
Acknowledgments
We acknowledge research funding support provided by Nanyang Technological University (RG 1/10) and the Institute of Asian Consumer Insight.
Supplemental File
Supplemental data for this article can be accessed on the publisher’s website at 10.1080/07421222.2017.1394079
Notes
1. Major movie websites (e.g., IMDB, RottenTomatoes, etc.) can push updates and news-like messages about movies to users via RSS or Twitter, but they do not push peer comments. Generally, user comments posted on discussion forums are not pushed to users, likely because of the large volume of posts, which may be interpreted as spam. Therefore, posts made in user forums are basically not pushed to but pulled by moviegoers.
2. According to Alexa (www.alexa.com/siteinfo/twitter.com), the bounce rate of Twitter is 44 percent, indicating that almost half of its users navigate away from Twitter after viewing only one page. Thus, the probability of engaging in search within Twitter is low. Researchers have also highlighted that limited searches are made on Twitter .
3. Although some researchers have juxtaposed expert and peer opinions in their studies, they do not explore whether and how these opinions affect box office at different stages.
4. listed two papers on push-based comments [Citation4, Citation47], both of which collected data from Twitter for one or two weeks since movie release, and did not compare tweets with other online peer comments.
5. User reviews are collected from major movie websites (e.g., IMDB, RottenTomatoes, Metacritic, etc.), which categorize user reviews differently from expert reviews. Additionally, we collected comments from discussion forums indexed by BoardReader, which is a forum search engine. Specifically, we input keywords derived from the title of a focal movie, and collect results returned by BoardReader as raw data for user reviews on the movie. The returned results cover not only movie forums but also forums covering various topics. Finally, we randomly inspected the data set and verified that our data did not contain expert reviews.
6. We recognize that various movie websites (e.g., IMDB.com) provide RSS feeds to subscribers and push out messages to these subscribers. We did not include these as push-based peer comments as these messages are mainly news articles about upcoming movies and not user comments about movies.
7. Due to the sheer market share and popularity of Twitter in the microblog platform segment, many studies collect tweets to represent data from microblogging services (e.g., [Citation59, Citation66]) As Hennig-Thurau, et al. [Citation48, p. 3] pointed out, “although various microblogging services exist, Twitter has become synonymous with the concept.” Therefore, we believe Twitter, as the market leader, is properly representative of the microblog platform.
8. We center the main effects to compute the interaction terms. The beta values are identical with or without centering (with the exception of the intercept).
9. The analyses reported here use a 7-day moving average of the valence and volume of the reviews as key independent variables. To validate the robustness of our analyses, we computed moving averages of 5, 6, 8, 9, 10, 11, 12, 13, and 14 days for the reviews’ valence and volume, and found qualitatively similar results for each of the 5 estimators: RE, FE, IV-GMM (I), IV-GMM (II) and IV-GMM (III).
10. In our data, a small number of comments (less than 0.7 percent) span both pull- and push-based platforms. For example, expert reviews might be tweeted or repeated in forums, and forum comments may be forwarded via microblogs and vice versa. Based on the final source where the information is captured, such messages would have been classified as pushed or pull-based messages even if they originated from a different source. However, to ensure that messages spanning across platforms do not confound our findings, we perform a robustness check by dropping such data. The analysis, shown in Online Appendix D, shows that our results remain unchanged.
Additional information
Notes on contributors
Jianxiong Huang
Jianxiong Huang ([email protected]) is an assistant professor in the Department of Marketing and Electronic Business, School of Business at Nanjing University, China. She received her Ph.D. from Nanyang Technological University. Her primary research interests include social media, consumer behavior, and information processing and propagation. She has presented at conferences, including the International Conference on Information Systems, Hawaii International Conference on System Sciences, and Annual Meeting of the Academy of Management.
Wai Fong Boh
Wai Fong Boh ([email protected]) is a professor of information technology and operations management, and ACI Fellow at Nanyang Business School, Nanyang Technological University, Singapore. She received her Ph.D. from the Tepper School of Business at the Carnegie Mellon University. Her research interests are in the areas of knowledge and innovation management, and social media. She has published in Management Science, Journal of Management Information Systems, MIS Quarterly, Academy of Management Journal, Organization Science, and others. She is or has been on the editorial board of multiple journals, including Journal of Management Information Systems, Management Science, Information Systems Research, and Organization Science.
Kim Huat Goh
Kim Huat Goh ([email protected]; corresponding author) is an associate professor and ACI Fellow at Nanyang Business School at Nanyang Technological University. He received his Ph.D. in business administration with a specialization in economics and information systems from the Carlson School of Management at the University of Minnesota. His research interests include applying behavioral economic theories to explain consumer behavior in technology-mediated environments, examining electronic markets strategies and the payoff of information technology. His research has been published in Information Systems Research, Journal of Management Information Systems, and MIS Quarterly.