Using Online User-Generated Reviews to Predict Offline Box-Office Sales and Online DVD Store Sales in the O2O Era

With the rapid growth of e-commerce and social media, customers post online reviews on various online shopping websites and social media after their consumption experience, which generated the electronic word of mouth effect. In the online-to-offline era, companies are using multi-channels to increase customer demand, and the Effect generated by online users’ reviews plays an important role in customer demand both online and offline. Few previous studies focused on using online user-generated reviews to forecast the demand of hyper-differentiated products online and offline, which is particularly hard to predict due to the various preferences of customers and complex relationship between factors. Via predictive global sensitivity analysis, this study uses online user-generated reviews posted on social media to predict customers’ demand for hyper-differentiated products both online and offline, with an example in the film industry. We generate forecasting equations, which can successfully predict movie box office and online DVD store sales. The effectiveness and reliability of our approach are proved by the numerical studies of 22 randomly selected movies. Managers can use our method to access and analyze online users’ review data and forecast future online and offline product sales.

[1]  Charles L. Munson,et al.  The impact of e-service offerings on the demand of online customers , 2017 .

[2]  B. Gu,et al.  The impact of online user reviews on hotel room sales , 2009 .

[3]  Hershey H. Friedman,et al.  A New Kind of Marketing: Creating Micro-Niches Using Resonance Marketing , 2008 .

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

[5]  Bo Yu,et al.  Does the Valence of Online Consumer Reviews matter for Consumer Decision Making? The Moderating Role of Consumer Expertise , 2011, J. Comput..

[6]  Chieh Lee,et al.  A predictive global sensitivity analysis approach to monitoring and modifying operational hedging positions , 2015 .

[7]  E. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry , 2006 .

[8]  Ling Liu,et al.  Manipulation of online reviews: An analysis of ratings, readability, and sentiments , 2012, Decis. Support Syst..

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

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

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

[12]  Cheol Park,et al.  Information direction, website reputation and eWOM effect: A moderating role of product type , 2009 .

[13]  Luc Muyldermans,et al.  Price determinants for remanufactured electronic products: a case study on eBay UK , 2015 .

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

[15]  Fred S. Zufryden,et al.  New Film Website Promotion and Box Office Performance , 2000, Journal of Advertising Research.

[16]  Jagdip Singh,et al.  Impact of User-Generated and Professional Critics Reviews on Bollywood Movie Success , 2015 .

[17]  Charles L. Munson,et al.  Robust Structural Equations for Designing and Monitoring Strategic International Facility Networks , 2013 .

[18]  N. Subramanian,et al.  Product delivery service provider selection and customer satisfaction in the era of internet of things: A Chinese e-retailers’ perspective , 2015 .

[19]  Jacqueline M. Bloemhof,et al.  Market prices of remanufactured, used and new items: Evidence from eBay , 2012 .

[20]  Yubo Chen,et al.  Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix , 2004, Manag. Sci..

[21]  Michail N. Giannakos,et al.  Shopping and Word-of-Mouth Intentions on Social Media , 2013, J. Theor. Appl. Electron. Commer. Res..

[22]  Fang Wu,et al.  Utilizing customer knowledge in innovation: antecedents and impact of customer involvement on new product performance , 2016 .

[23]  Ellen Garbarino,et al.  GENDER DIFFERENCES IN THE PERCEIVED RISK OF BUYING ONLINE AND THE EFFECTS OF RECEIVING A SITE RECOMMENDATION , 2004 .

[24]  B. Sparks,et al.  The impact of online reviews on hotel booking intentions and perception of trust. , 2011 .

[25]  Ramakrishnan Ramanathan,et al.  An empirical analysis on the influence of risk on relationships between handling of product returns and customer loyalty in E-commerce , 2011 .

[26]  B. Gu,et al.  First Step in Social Media - Measuring the Influence of Online Management Responses on Customer Satisfaction , 2011 .

[27]  Jae-Nam Lee,et al.  The long tail or the short tail: The category-specific impact of eWOM on sales distribution , 2011, Decis. Support Syst..

[28]  A. S. Cantallops,et al.  International Journal of Hospitality Management New Consumer Behavior: a Review of Research on Ewom and Hotels , 2022 .

[29]  Shuiqing Yang,et al.  Service cooperation and marketing strategies of infomediary and online retailer with eWOM effect , 2016, Inf. Technol. Manag..

[30]  M. Arce-Urriza,et al.  Private Labels and National Brands Across Online and Offline Channels , 2009 .

[31]  Wei Wang,et al.  Channel selection in a supply chain with a multi-channel retailer: The role of channel operating costs , 2016 .

[32]  Arik Ragowsky,et al.  Establishing Trust in Electronic Commerce Through Online Word of Mouth: An Examination Across Genders , 2008, J. Manag. Inf. Syst..

[33]  Joshua Fogel,et al.  Intentions to Use the Yelp Review Website and Purchase Behavior after Reading Reviews , 2017, J. Theor. Appl. Electron. Commer. Res..

[34]  Jaehun Joo,et al.  Roles of the Buyer's Trust in Seller in Posted-Price Model of Consumer to Consumer E-Commerce , 2015, J. Theor. Appl. Electron. Commer. Res..

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

[36]  John R. Rossiter,et al.  Toward a Valid Measure of E-Retailing Service Quality , 2007, J. Theor. Appl. Electron. Commer. Res..

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

[38]  Pei-Yu Sharon Chen,et al.  The Impact of Online Recommendations and Consumer Feedback on Sales , 2004, ICIS.

[39]  Suzan Burton,et al.  Online word‐of‐mouth: a comparison of American and Chinese discussion boards , 2006 .

[40]  P. D. Maeyer,et al.  Impact of online consumer reviews on sales and price strategies: a review and directions for future research , 2012 .

[41]  Albert Graf,et al.  Customer value from a customer perspective: a comprehensive review , 2008 .

[42]  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..

[43]  Dan J. Kim,et al.  A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents , 2019 .

[44]  Q. Ye,et al.  The impact of e-word-of-mouth on the online popularity of restaurants: a comparison of consumer reviews and editor reviews. , 2010 .