The Impact of Emotional Product Attributes on Consumer Demand: An Application to the U.S. Motion Picture Industry

Demand for products is often modeled as a function of product attributes. We propose that demand for experiential or hedonic products be modeled also as a function of “emotional product attributes” or emotions that a product might elicit from consumers. Our category of interest is the U.S. motion picture industry. We calibrate emotional attributes of a movie by mapping a movie’s plot keywords on a list of human emotions by using a word pattern recognition method called Latent Semantic Analysis (LSA). We propose a factor model to reduce this multidimensional representation of correlated emotional attributes to two factors - “emotional complexity” and “negative emotions”. These two factors are simultaneously incorporated in a random-utility choice model of 982 movies released in theaters in 1999-2005. We find that consumers prefer movies with greater emotional complexity. Demand for movies with negative emotions is moderated by consumers’ sense of well-being, as measured by the Consumer Confidence Index. Importantly, our method of capturing emotional product attributes is simple, off-the-shelf, inexpensive, and scalable to studying markets with a large number of products. Substantively, our findings combine insights from economics and psychology, and are of interest to studios and theaters in their production and release timing decisions.