Predicting popularity: Machine learning insights into movie team patterns and online ratings.

According to the 2021 Film and Video Global Market Report, the global film and video market is expected to reach $318.2 billion by 2025 and $410.6 billion by 2030. The financial success of a movie, though, is largely uncertain due to many pertinent factors. Understanding which factors influence the financial success of a movie is crucial for the industry. The online movie rating is often used to measure a movie's success and has been investigated in many IS studies. The factors influencing online movie ratings can be classified into two major types: internal and external. Internal factors, such as actors, actresses, directors, and "stars," influence movie ratings and are related to the movie cast and crew. External factors, such as economics, motivations for moviegoing, and advertising effects, are movie rating influencers from the outside environment. Few studies focus on both the effect of individual and team characteristics on online movie ratings. Our research question is: How do team patterns, such as actors/actresses, experienced crew, and collaboration among team members, influence movie ratings? Using both decision tree model and neural network machine learning (ML) techniques, this research investigates the effects of movie team patterns on movie ratings. We make comparisons using OLS (ordinary least squares) regression for each ML technique. The results indicate that some crews and casts of a movie, especially experienced directors, play a critical role in determining the online movie rating. The results also demonstrate the power of the decision tree model and neural network in predicting online movie ratings. [ABSTRACT FROM AUTHOR]