Explanatory and predictive analytics for movie production efficiency by online word-of-mouth

Abstract

It has become increasingly important to consider the efficiency of movies in creating box revenue while using fewer movie resources. Further, there is a lack of eWOM (online-word-of-mouth) studies regarding using the production efficiency of movies as a dependent outcome measure replacing box revenue. This study shows that production efficiency can be suggested by comparing movie resources powers, i.e., powers of actors, directors, distributors, and production companies, which are input for movie production, and the box office. For testing the validity of the measure of production efficiency, this study examines the effect of eWOM attributes, i.e., review depth, volume, rating, review sentiment, and helpfulness on production efficiency. Data envelopment analysis is adopted to produce the efficiency of movies. This study provides insights into a current movie study on eWOM by showing the effect of interaction between eWOM (review rating) and helpfulness on production efficiency. Further, this study purports to test the prediction power in predicting production efficiency using decision trees, neural networks, and logistic regression. These results show that k nearest neighbor and automated neural networks outperform the other machine learning methods in classifying efficient movies.