Classifying Movies Based on Audience Perceptions: MTI Framework and Box Office Performance

Abstract

This research examined the current status of the movie genre usage in movie research and film industry and introduced a new method to classify movies. Using a large-scale audience survey data, the authors clustered movies into 9 distinct types based on 8 audience-perceived movie characteristics such as fun, eye-catching, discomfort, and feel-good. The authors validated their method by comparing movie types vs. movie genres in terms of their box-office revenue explanatory power. All three types of box-office revenues (opening week revenue, total revenue, revenue-per-screen) differed significantly across movie types, whereas only the opening week revenue showed a significant difference across movie genres, suggesting that movie types may be a better predictor of a movie's box-office performance than movie genres that have been frequently used in prior research on box-office performance prediction.

ACKNOWLEDGMENTS

We thank the CJ Entertainment & Media movie research center staff for helping us throughout the MTI scale development, data collection, and empirical validation process.

Additional information

Notes on contributors

Ji-Hyun Shon

Ji-Hyun Shon is a PhD at KAIST Business School, Korea Advanced Institute of Science and Technology, Seoul, Republic of Korea.

Young-Gul Kim

Young-Gul Kim is a Professor at the Graduate School of Information & Media, KAIST Business School, Korea Advanced Institute of Science and Technology, Seoul, Republic of Korea.

Sang-Jin Yim

Sang-Jin Yim is a Senior Vice President at CJ E&M Pictures (CJ Entertainment), Seoul, Republic of Korea.