Executive Summary
This research investigates how various movie ratings from professional critics, amateur communities, and viewers themselves influence key movie performances, such as movie revenues and new movie ratings (as a viewer satisfaction measure). The authors compare how these ratings influence movie performances using two types of movie data: movie-specific data and individual viewer-specific data. Their empirical findings are fivefold. First, they find that strong revenues can generate more positive reviews during the course of the movie. Because prior research has already proved that highly rated movies tend to generate high box office revenues, they argue that high ratings effectively sustain high movie revenues and vice versa over the not-so-long life of the movie. Second, the empirical analysis shows that positive ratings enhance the effectiveness of advertising spending to raise more revenues. In other words, the positive impact of movie ratings on revenues materializes successfully when movies with high advertising spending are rated high by critics and/or amateurs. Third, the authors find that sequel movies tend to reap more revenues but receive lower ratings than contemporaneous original movies. With generous production budgets and heavy advertising based on the originals’ brand power, sequels usually achieve box office success, even if they do not tend to meet the box office levels attained by their parent movies. Yet, although sequels can make money, they are often rated less favorably than original movies. It is because the original movie’s success leads to high expectation for the sequel, which will be difficult to satisfy, thus leading to less satisfaction. Fourth, they find that individual viewers’ consumption experiences with movies make their ratings more critical over time. That is, ordinary movie viewers with more viewing experiences rate movies lower, similar to critics’ ratings, because of the critical nature of their reviews. Fifth, the findings indicate that there are two opposing effects in determining the relationship between genre preference and genre rating: the upward “preferred genre effect” and the downward “seen set effect.” On the one hand, it may be expected that viewers give their favorite genres (more precisely, their frequently viewed genres) high ratings because they are internally predisposed to like certain aspects of their favorite genre movies (upward preferred genre effect). For example, children are huge fans of animation movies. On the other hand, as viewers choose more movies in particular genres, they settle for less attractive movies because they have exhausted their top choices in certain genres (downward viewed set effect). Thus, the relationship between genre preference and genre rating may not be straightforward because of these two conflicting effects. From a managerial perspective, this study illuminates how high ratings can explain enhanced movie performances. For example, for movie rental companies (e.g., netflix.com), member satisfaction represented by favorable movie ratings is a key to long-term customer retention. This study provides insights into how to recommend satisfactory movies on the basis of the individual member’s own viewing and rating history, the member community’s overall rating patterns of the movie of interest, and the movie’s characteristics (e.g., genre, running time).
Biography
Sangkil Moon (PhD, University of Iowa) is Associate Professor of Marketing in the Business Management Department at North Carolina State University. Moon’s research interests include empirical modeling, online word-of-mouth effects, entertainment products, customer relationship management, price/promotion, spatial models, and text mining. Moon’s primary teaching experiences include courses in marketing research for both MBA and undergraduate students and marketing analytics for MBA students. He is a recipient of two teaching awards, one from University of Iowa (2002) and the other one from North Carolina State University (2006). He has publications in Journal of Marketing Research, Management Science, Journal of Retailing, International Journal of Research in Marketing, and Journal of Business Research, among others. He won the 2002 Marketing Science Institute Alden G. Clayton Dissertation Proposal Competition and the 2008 Davidson Award (best-paper award in Journal of Retailing).
Paul K. Bergey is Associate Professor of Information System in the Department of Business Management of the Jenkins Graduate School of Management at North Carolina State University (2000–present). His research on applied decision modeling has appeared in Decision Support Systems, Personnel Psychology, OMEGA, and Annals of Operations Research, among others. He is an active member of the Institute for Operations Research and Management Science and the Decision Sciences Institute. He is a recipient of the outstanding graduate teaching award in the College of Management at North Carolina State University.
Dawn Iacobucci is E. Bronson Ingram Professor of Marketing and Associate Dean in the Owen Graduate School of Management at Vanderbilt University. Previously, she was Professor of Marketing in the Kellogg School of Management at Northwestern University (1987–2004), the Coca-Cola Distinguished Professor of Marketing, Professor of Psychology, and Head of the Marketing Department at the University of Arizona (2001-2002), and the John Pomerantz Professor of Marketing in the Wharton School at the University of Pennsylvania (2004–2007). Her research on social networks and satisfaction and services and her methodological research has appeared in various marketing and psychology journals. Iacobucci is recent editor of both Journal of Consumer Research and Journal of Consumer Psychology. She edited Networks in Marketing, Handbook of Services Marketing and Management, Kellogg on Marketing, and Kellogg on Integrated Marketing; she is author of Marketing Management, Mediation Analysis; and she is coauthor on Gilbert Churchill’s lead text on Marketing Research.
Journal of Marketing, Volume 74, Number 1, January 2010
View Table of Contents