The goal of this paper was to establish that the demand for movies is driven by their emotional content. We posited that plot keywords are a good proxy for information about the emotional content available to consumers as they make their choice of movie watching. To calibrate emotional content of a movie, we use a bag- of-words approach to map the semantic space of a movie’s set of keywords onto that of basic human emotions. We build a random utility demand model using movie emotional content and other movie characteristics, and estimate it on data for 1152 movies in theaters in 1999-2005.
We show that emotional content is a significant determinant of movie demand. By disentangling the impact of emotional attributes and differences by genre, we are able to generate insights into the level of emotional complexity and variety that consumers seek in their movie experiences in general, as well as within movie genres specifically. We also find that demand for emotional attributes is affected by both macroeconomic variables and one-time economic shocks. We are able to replicate several of the findings from the aggregate market share model in an individual ratings setting, which adds to the market share model by generating more nuanced insights
about consumer preferences that arise from individual characteristics. Overall, the insights from this paper are potentially of interest to both studios and theaters, who always seek to anticipate the kinds of movies that consumers will appreciate in future in making production, release and screening decisions. In addition the findings pertinent to individual preferences can be useful in audience segmentation and hence in targeted marketing of movies.
There are several avenues in which this research can be extended to understand other facets of the demand for movies. For example, individual level data from different countries can give insights as to how universal the demand for mood management is. Similarly, our approach can be used to examine if the demand for movies in the secondary channel, especially in the buying (and hence repeated watching) versus renting (single watching occasion, possibly after watching in the theatre), is different from demand for emotions in the primary theatrical channel. Yet another application would be capture time series of critics’ evaluation of movies, match it with their syndication geography, and see if their preferences of movie emotions change depending on the demographics of their audience, or with their own age and experience. This is explored in Chapter 2 of this dissertation.
The methodology proposed here can be used in examining other entertainment products. An example is the book industry, where many new products have failed, and where the industry is moving towards more risk-sharing with authors. It might be possible to improve forecasts of demand for books by calibrating their emotional content.
Finally, in contrast to the bag-of-words approach used in this paper, a sequential emotional measure might provide more insights into consumer demand for emotional content in entertainment. For example, it would seem that tense middle parts followed by happy endings might be preferred to the reverse sequence. Our
present methodology is unable to handle this sequence-of-words approach, and we are not aware of other methods to conduct such analyses.
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