Research on Movie Box Office Prediction Model With Conjoint Analysis

Research on Movie Box Office Prediction Model With Conjoint Analysis

Wei Lu (Communication University of China, Beijing, China) and Ruben Xing (Montclair State University, Montclair, USA)
DOI: 10.4018/IJISSCM.2019070104

Abstract

Based on the Chinese film market, considering the influence factors of the movie box office (MBO) from multiple dimensions, and using the conjoint-analysis method with a questionnaire survey and an expert interview to determine the main index system affecting MBO, this article then establishes a MBO forecast model through the neural network BRP method. In combination with the actual data of the film market along with the empirical analysis and verification this article provides valuable investment reference for film risk control and movie investment decisions.
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2. Summaries Of Mbo Prediction

2.1. Foreign Research Review

The first box office study began in the 40’s in the United States. Gallup and Handel, two important researchers, started the MBO study. In the late 80’s, Littman, another scholar from the United States proposed the first widely-influenced film-revenue prediction model. After that, scholars have put forward a variety of box office forecast models. Some of more representative researches includes: The models established have been successful in predicting MBO revenue from 2000 to 2002 in North America, greatly reducing the market risk of the film industry (Chang, & Ki, 2005). The feedback neural network algorithm was used to predict whether the movie is profitable, and a good prediction accuracy is shown locally (Barman, & Chowdhury, 2012). Multiple approaches were examined to improve the performance of the prediction model by using machine learning techniques (Lee et al., 2018). Several Data Mining techniques, namely neural networks, regression and decision trees were used to estimate the profit of a movie through the construction of a predictive model (Galvão, & Henriques, 2018). A set of indicators was proposed and validated for consumer engagement in social media to support for associated consumer engagement behavior and economic performance (Oh et al., 2017).

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