The differing roles of success drivers across sequential channels: An application to the motion picture industry

In several product categories, it is typical to release products sequentially to different markets and customer segments. Conventional knowledge holds that the roles of various product success drivers do not differ significantly across these sequential channels of distribution. The authors examine sequential distribution channels within the motion picture industry and develop a model that proposes that such differences exist between a primary (short- and long-term theatrical box office) and a sequential (video rental) channel. The authors test their model with a sample of 331 motion pictures released in theaters and on video during 1999–2001 using partial least squares. Results reveal differences in the impact of success factors across channels. For example, cultural familiarity enhances box office success but relates negatively to video rental success, and distribution intensity and date of release enhance box office outcomes but have no impact on rental revenues.

[1]  Robert E. Krider,et al.  Competitive Dynamics and the Introduction of New Products: The Motion Picture Timing Game , 1998 .

[2]  Irwin P. Levin,et al.  Movie Stars and Authors As Brand Names: Measuring Brand Equity in Experiential Products , 1997 .

[3]  Walt W. Rostow.  How It All Began , 2017, Exit Berlin.

[4]  Suzanne Mary Donahue,et al.  American film distribution: The changing marketplace , 1987 .

[5]  W. Walls,et al.  Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Office? , 1999 .

[6]  Markus Christen,et al.  Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures , 2003 .

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

[8]  Donald R. Lehmann,et al.  Sales through Sequential Distribution Channels: An Application to Movies and Videos , 2000 .

[9]  J. Jacoby,et al.  Components of perceived risk in product purchase: A cross-validation. , 1974 .

[10]  Charles B. Weinberg,et al.  Profits Out of the Picture: Research Issues and Revenue Sources Beyond the North American Box Office (Draft December 19, 2003) , 2003 .

[11]  J H Chloropos,et al.  How it all began. , 1967, JAMA.

[12]  Robert E. Krider,et al.  Clustering at the Movies , 1998 .

[13]  Ronald J. Faber,et al.  Effect of Media Advertising and other Sources on Movie Selection , 1984 .

[14]  Jay Prag,et al.  An empirical study of the determinants of revenues and marketing expenditures in the motion picture industry , 1994 .

[15]  Cheryl Burke Jarvis,et al.  A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research , 2003 .

[16]  A. Rao,et al.  No Pain, No Gain: A Critical Review of the Literature on Signaling Unobservable Product Quality , 2000 .

[17]  J. J. D. Dana,et al.  REVENUE SHARING AND VERTICAL CONTROL IN THE VIDEO RENTAL INDUSTRY , 2003 .

[18]  G. Zinkhan,et al.  An integrated framework for the conceptualization of consumers’ perceived-risk processing , 2004 .

[19]  E. Rogers.  Diffusion of Innovations , 1962 .

[20]  Charles B. Weinberg,et al.  Silverscreener: a Modeling Approach to Movie Screens Management , 1999 .

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

[22]  Elise Prosser.  How Early Can Video Revenue Be Accurately Predicted , 2002, Journal of Advertising Research.

[23]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[24]  J. Bettman.  Perceived Risk and Its Components: A Model and Empirical Test , 1973 .

[25]  P. Nelson.  Information and Consumer Behavior , 1970, Journal of Political Economy.

[26]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[27]  Brian T. Ratchford,et al.  A Study of Consumer Perceptions of Decisions , 1974 .

[28]  Pradeep K. Chintagunta,et al.  A Bayesian Model to Forecast New Product Performance in Domestic and International Markets , 1999 .

[29]  G. Zinkhan,et al.  Market valuation models of the effect of advertising and promotional spending: A review and meta-analysis , 2005 .

[30]  H. Winklhofer,et al.  Index Construction with Formative Indicators: An Alternative to Scale Development , 2001 .

[31]  Margaret C. Campbell,et al.  The Moderating Effect of Perceived Risk on Consumers’ Evaluations of Product Incongruity: Preference for the Norm , 2001 .

[32]  George A. Marcoulides,et al.  Modern methods for business research , 1998 .

[33]  R. Rust,et al.  Service Quality: Insights and Managerial Implications from the Frontier , 1994 .

[34]  S. Ravid.  Information, Blockbusters and Stars? A Study of the Film Industry , 1997 .

[35]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[36]  Barry Litman.  Predicting Success of Theatrical Movies: An Empirical Study , 1983 .

[37]  Rebecca J. Slotegraaf,et al.  The paradox of a marketing planning capability , 2004 .

[38]  Gianfranco Walsh,et al.  An Investigation into the Factors Determining the Success of Service Innovations: The Case of Motion Pictures , 2001 .

[39]  Debabrata Talukdar,et al.  An Empirical Investigation of Signaling in the Motion Picture Industry , 2006 .

[40]  Thomas C. Kinnear,et al.  Individual Differences and Marketing Decision Support System Usage and Satisfaction , 1987 .

[41]  J. Eliashberg,et al.  A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures , 1996 .

[42]  Joey Reagan.  Effects of Cable Television on News Use , 1984 .

[43]  Joel B. Cohen,et al.  Alternative Models of Categorization: Toward a Contingent Processing Framework , 1987 .

[44]  Charles B. Weinberg,et al.  A Concise Handbook of Movie Industry Economics: Profits out of the Picture: Research Issues and Revenue Sources Beyond the North American Box Office , 2005 .

[45]  Roland T. Rust,et al.  Service Quality: New Directions in Theory and Practice , 1993 .