Earticle

초록

영어

Forecasting daily box-office performance is critical for planning the distribution of marketing resources, and by extension, maximizing profits. For certain movies, the number of viewers increases rapidly at the beginning of their theatrical run, and the increments slow down later. Other movies are not popular in the beginning, but the audience sizes grow rapidly afterward. Thus, the audience attendance of movies grow in different trajectories, which are influenced by various factors including marketing budget, distributors, directors, actors, and word of mouth. In this paper, we propose a method for predicting the daily performance trajectory of running movies based on the hierarchical linear model. More specifically, we focus on the effect of online word of mouth on the shape of the growth curves. We fitted the mean trajectory of the cumulative audience size as a cubic function of time, and allowed the intercept and slope to vary movie-to-movie. Moreover, we fitted the linear slope with a function of online word of mouth predictors to help determine the shape of the trajectories. Finally, we provide performance predictions for individual movies.

목차

ABSTRACT
 Ⅰ. Introduction
 Ⅱ. Data Preparation
  2.1. Data Collection
  2.2. Predictor Variables
 Ⅲ. Hierarchical Linear Models
 Ⅳ. Results
  4.1. Parameter Estimates and Mean Trajectories
  4.2. Model Validation
 Ⅴ. Conclusion
 

저자

• Jongmin Park [ Graduate Student, Graduate School of Business Administration, Kookmin University, Korea ]
• Yeojin Chung [ Assistant Professor, College of Business Administration, Kookmin University, Korea ] Corresponding Author
• Yoonho Cho [ Professor, College of Business Administration, Kookmin University, Korea ]

참고문헌