Investigating the effects of textual reviews from consumers and critics on movie sales

PurposeThe purpose of this paper is to investigate the sales impact of different types of online word-of-mouth based on their source (user vs critic) and form (structured vs unstructured).Design/methodology/approachThe paper proposed a model by adopting the heuristic-systematic perspective of information processing and tested it using online movie reviews collected from Rotten Tomatoes. A unique dataset was constructed, which matched critic reviews and user reviews with metadata such as box-office sales and advertisement spending for 90 movies. Sentiment information from the textual contents of both user and critic reviews were text-mined and extracted. Data analyses were used to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings and critic text reviews.FindingsCritic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives.Practical implicationsThis study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to maximize the economic value of online user-generated information.Originality/valueThe major contribution of this study is to extend the current understanding of the sales impact of online reviews to their textual aspect, as well as investigate how these textual narratives play different roles when offered by critics and users.

[1]  Christopher M. Snyder,et al.  The Influence of Expert Reviews on Consumer Demand for Experience Goods: A Case Study of Movie Critics , 2005 .

[2]  Bin Gu,et al.  Do online reviews matter? - An empirical investigation of panel data , 2008, Decis. Support Syst..

[3]  Ming-Yi Chen,et al.  Can two-sided messages increase the helpfulness of online reviews? , 2016, Online Inf. Rev..

[4]  Anindita Chakravarty,et al.  The Differential Effects of Online Word-of-Mouth and Critics’ Reviews on Pre-release Movie Evaluation , 2009 .

[5]  Wei Chen,et al.  The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings , 2011, Comput. Hum. Behav..

[6]  Dwayne D. Gremler,et al.  Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? , 2004 .

[7]  Sukki Yoon,et al.  The effects of eWOM volume and valence on product sales – an empirical examination of the movie industry , 2018, International Journal of Advertising.

[8]  Ya-Han Hu,et al.  Considering online consumer reviews to predict movie box-office performance between the years 2009 and 2014 in the US , 2018, Electron. Libr..

[9]  Yong Tan,et al.  Effects of Different Types of Free Trials and Ratings in Sampling of Consumer Software: An Empirical Study , 2013, J. Manag. Inf. Syst..

[10]  Michael A. Wiles,et al.  The Impact of Brand Rating Dispersion on Firm Value , 2013 .

[11]  Young Jin Lee,et al.  The Economic Value of Online User Reviews with Ad Spending on Movie Box-Office Sales , 2019 .

[12]  Ingoo Han,et al.  The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement , 2007, Int. J. Electron. Commer..

[13]  Steven M. Shugan,et al.  Film Critics: Influencers or Predictors? , 1997 .

[14]  Ann E. Schlosser Posting versus Lurking: Communicating in a Multiple Audience Context , 2005 .

[15]  Karen L. Xie,et al.  Effects of managerial response on consumer eWOM and hotel performance , 2016 .

[16]  M. Haan,et al.  Expert Judgment Versus Public Opinion – Evidence from the Eurovision Song Contest , 2005 .

[17]  Richard T. Gretz,et al.  Is everybody an expert? An investigation into the impact of professional versus user reviews on movie revenues , 2020, Journal of Cultural Economics.

[18]  Yibai Li,et al.  Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors , 2017, Int. J. Inf. Manag..

[19]  Renaud Lambiotte,et al.  Predicting links in ego-networks using temporal information , 2015, EPJ Data Science.

[20]  Morris B. Holbrook,et al.  Taste versus the Market: An Extension of Research on the Consumption of Popular Culture , 2007 .

[21]  Saurabh Kumar,et al.  EVALUATING THE PREDICTIVE POWER OF AN ENSEMBLE MODEL FOR ECONOMIC SUCCESS OF INDIAN MOVIES , 2016 .

[22]  S. Chaiken,et al.  The psychology of attitudes. , 1993 .

[23]  Tammo H. A. Bijmolt,et al.  The Effect of Electronic Word of Mouth on Sales: A Meta-Analytic Review of Platform, Product, and Metric Factors , 2016 .

[24]  Sungzoon Cho,et al.  Box-office forecasting based on sentiments of movie reviews and Independent subspace method , 2016, Inf. Sci..

[25]  Tom J. Brown,et al.  The Company and the Product: Corporate Associations and Consumer Product Responses: , 1997 .

[26]  JoongHo Ahn,et al.  Helpfulness of Online Consumer Reviews: Readers' Objectives and Review Cues , 2012, Int. J. Electron. Commer..

[27]  Christopher S. G. Khoo,et al.  Comparing sentiment expression in movie reviews from four online genres , 2010, Online Inf. Rev..

[28]  Andrew Whinston,et al.  The Dynamics of Online Word-of-Mouth and Product Sales: An Empirical Investigation of the Movie Industry , 2008 .

[29]  Huaping Chen,et al.  Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-line Consumer Recommendations , 2009, Int. J. Electron. Commer..

[30]  Suman Basuroy,et al.  How Critical are Critical Reviews? The Box Office Effects of Film Critics, Star Power, and Budgets , 2003 .

[31]  Chuan-Hoo Tan,et al.  Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features , 2013, Int. J. Electron. Commer..

[32]  Yili Hong,et al.  Stimulating Online Reviews by Combining Financial Incentives and Social Norms , 2016, Manag. Sci..

[33]  T. M. Amabile Brilliant but cruel: Perceptions of negative evaluators. , 1983 .

[34]  Wenqi Zhou,et al.  Do Professional Reviews Affect Online User Choices Through User Reviews? An Empirical Study , 2016, J. Manag. Inf. Syst..

[35]  Xuefeng Liu,et al.  User Reviews Variance, Critic Reviews Variance, and Product Sales: An Exploration of Customer Breadth and Depth Effects , 2015 .

[36]  Yong Liu,et al.  When do Third-Party Product Reviews Affect Firm Value and what can Firms Do? The Case of Media Critics and Professional Movie Reviews , 2012 .

[37]  Carlos Delgado Kloos,et al.  Learning a Foreign Language in a Mixed-Reality Environment , 2011, IEEE Internet Computing.

[38]  Matthew K. O. Lee,et al.  Examining the influence of online reviews on consumers' decision-making: A heuristic-systematic model , 2014, Decis. Support Syst..

[39]  M K CheungChristy,et al.  Examining the influence of online reviews on consumers' decision-making , 2014 .

[40]  Mohammad Soleymani,et al.  A survey of multimodal sentiment analysis , 2017, Image Vis. Comput..

[41]  Wagner A. Kamakura,et al.  Reviewing the reviewers: The impact of individual film critics on box office performance , 2007 .

[42]  Kaiqiang Guo,et al.  The impact of online reviews on exhibitor behaviour: evidence from movie industry , 2017, Enterp. Inf. Syst..

[43]  Shuk Ying Ho,et al.  Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective , 2005, Inf. Syst. Res..

[44]  Yong Liu Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue , 2006 .

[45]  Yue Pan,et al.  Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews , 2011 .

[46]  S. Chaiken,et al.  Motivated Heuristic and Systematic Processing , 1999 .

[47]  Geng Cui,et al.  Terms of Use , 2003 .

[48]  X. Zhang,et al.  Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics , 2010 .

[49]  LiYibai,et al.  Business intelligence in online customer textual reviews , 2017 .

[50]  Yang Yu,et al.  Sentimental interplay between structured and unstructured user-generated contents: An empirical study on online hotel reviews , 2016, Online Inf. Rev..

[51]  Suchithra Rajendran,et al.  Topic-based knowledge mining of online student reviews for strategic planning in universities , 2019, Comput. Ind. Eng..

[52]  Beibei Li,et al.  Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowd-Sourced Content , 2011, Mark. Sci..

[53]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

[54]  Bin Gu,et al.  Research Note - The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products , 2012, Inf. Syst. Res..

[55]  Hao-Chiang Koong Lin,et al.  The asymmetric effect of review valence on numerical rating , 2019, Online Inf. Rev..

[56]  Christian Burgers,et al.  Do consumer critics write differently from professional critics? A genre analysis of online film reviews , 2013 .

[57]  S. Chaiken Heuristic versus systematic information processing and the use of source versus message cues in persuasion. , 1980 .

[58]  Judith A. Chevalier,et al.  Channels of Impact: User Reviews When Quality is Dynamic and Managers Respond , 2017, Mark. Sci..

[59]  D. Iacobucci,et al.  Dynamic Effects among Movie Ratings, Movie Revenues, and Viewer Satisfaction , 2010 .

[60]  Chrysanthos Dellarocas,et al.  Exploring the value of online product reviews in forecasting sales: The case of motion pictures , 2007 .

[61]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[62]  Dimple R. Thadani,et al.  The impact of electronic word-of-mouth communication: A literature analysis and integrative model , 2012, Decis. Support Syst..

[63]  Nan Hu,et al.  Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales , 2014, Decis. Support Syst..