Predicting Innovation Success in the Motion Picture Industry: The Influence of Multiple Quality Signals
Authors: Bharadwaj Neeraj , Noble Charles H. , Tower Annette , Smith Leah M. , Dong Yuexiao 
Published: July 2017

In settings characterized by frequent product entries and rapid market exit, predicting innovation success is a difficult task. The challenge in such short life‐cycle markets is exacerbated when the offering is an experience good (i.e., product quality is not known prior to consumption). This article investigates a variety of information disequilibrium‐reducing cues that can signal a new product's quality, and posit that those cues affect knowledge of and attitude toward (i.e., customers' mindset) the item, and in turn, demand. The work draws on signaling theory in an experience good context (motion pictures) to set up a nomological net that includes three sources of quality cues as predictors of consumer demand, namely: (i) traditional prelaunch decisions (e.g., production budget, advertising budget, and sequel), (ii) volume and valence of movie critics' reviews, and (iii) text contained in the movie critics' reviews. Based on a stratified, random sample of movie introductions (n = 115), the results show that the prelaunch (structured numeric) variables alone explain a meaningful proportion of the variance in domestic box office sales. The postlaunch (structured numeric) variables, volume and valence of critics' reviews, add more explanatory power, and analyzing over two million words from the (unstructured) text of critics' reviews adds further explanatory power. This research answers a recent call to model the variety of structured and unstructured variables to predict innovation success in data‐rich environments, and thereby represents the pioneering study to combine all three sources of quality cues—(i) prelaunch, structured variables; (ii) postlaunch, structured variables; and (iii) postlaunch, unstructured variables derived from text reviews—as determinants of demand. The approach taken also substantiates film as innovation and as an interesting case of new product development. For managers in a short life‐cycle, experience good settings, the results support the inclusion of a broader “variety” of quality signals to improve forecasting in today's data‐rich environments.

Practitioner Points

The results here highlight the importance of considering both marketing decisions and expert reviews in forecasting approaches for new product launches. Exploring a nuanced approach to analyzing expert reviews seems to be particularly promising in predicting product successes and failures.

This research also helps us develop a deeper understanding of the nature of critics' reviews. Beyond just “thumbs up/thumbs down” sort of recommendations, we identify specific words that are influential in making an impression on consumers and, ultimately, driving product success or failure.

This work also emphasizes the challenges of predicting success in markets for short life‐cycle experience goods such as films. In these markets, expert reviews likely carry even more weight since consumers have great difficulty in assessing the product in advance.