Business

The impact of online review helpfulness and word of mouth communication on box office performance predictions

S. Lee and J. Y. Choeh

This research conducted by Sangjae Lee and Joon Yeon Choeh explores how online review helpfulness and electronic word-of-mouth (eWOM) can significantly enhance the accuracy of box office revenue predictions. With a focus on the Korean movie market, the study reveals that movies with more helpful reviews lead to better prediction outcomes, making this an exciting insight for both filmmakers and marketers alike.

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Abstract

This study investigates the impact of online review helpfulness and electronic word-of-mouth (eWOM) on box office revenue prediction. Using data from the Korean movie market (Naver Movies), the study employs machine learning methods (random forest, boosted decision trees, k-nearest neighbor, discriminant analysis) to compare prediction performance between subsamples of movies with high and low review/reviewer helpfulness. Results indicate that movies with more helpful reviews or reviewers exhibit improved prediction accuracy, suggesting review and reviewer helpfulness are crucial moderators enhancing the predictive power of eWOM for box office revenue.

Publisher

Humanities and Social Sciences Communications

Published On

Sep 07, 2020

Authors

Sangjae Lee, Joon Yeon Choeh

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