This study aimed to develop a new diffusion model for box-office forecasting by modifying the generalized Bass diffusion model with incorporation of search trend data and historical movie-audience data. To that end, first, movie-audience data (i.e., the number of moviegoers) and NAVER search trend data for each of the top 30 movies released in Korea in 2018 were collected by day. Then, the modified generalized Bass diffusion model, newly proposed in this paper, was applied in order to estimate the diffusion parameters. The results of our empirical case study on the Korean film market show that NAVER search trend data plays an important role in box-office forecasting after a movie is released. This study contributes to the extant literature by proposing a new diffusion model, which is a novel online big-data-driven methodology of box-office forecasting. In addition, comparison analysis with two other representative diffusion models was conducted, and the proposed model showed superior prediction power.

本研究旨在通过结合搜索趋势数据和历史电影观众数据来修改广义的Bass扩散模型，从而开发出一种用于票房预测的新扩散模型。为此，首先，按日收集了韩国2018年发行的前30部电影中的每部电影的电影观众数据（即，上电影的人数）和NAVER搜索趋势数据。然后，应用本文新提出的改进的广义巴斯扩散模型，以估计扩散参数。我们对韩国电影市场的实证研究结果表明，NAVER搜索趋势数据在电影上映后在票房预测中起着重要作用。这项研究通过提出一种新的扩散模型，为现存文献做出了贡献，这是一种新颖的在线大数据驱动的票房预测方法。此外，与其他两个代表性扩散模型进行了比较分析，所提出的模型显示出优越的预测能力。