• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.241-255
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• 2020

The purpose of this study is to analyze musical performances with the use of performance booking information from 2017 to 2019 which was obtained in Korea performing Arts Box Office Information system (KOPIS), and to make suggestions of Korean musical performances. Based on the data of KOPIS, relevant studies, internet based information, news articles, and magazines, musical performances were analyzed. In addition, the previous data of KOPIS and the data of the Broadway League were analyzed. The analysis results are as follows. Firstly, it is necessary to concentrate on Korean mid-sized theatre musical performances. Secondly, producers need to open their production costs invested in performances transparently. Thirdly, Off-Broadway system needs to be introduced after being modified in consideration of Korean situations. Thirdly, it is necessary to make long-run performances in order to achieve commercial success. Fifthly, it is necessary to make a bold attempt of theatre for performances just as in Broadway.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.19-43
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• 2021

The prediction of box office performance in performing arts institutions is an important issue in the performing arts industry and institutions. For this, traditional prediction methodology and data mining methodology using standardized data such as cast members, performance venues, and ticket prices have been proposed. However, although it is evident that audiences tend to seek out their intentions by the performance guide poster, few attempts were made to predict box office performance by analyzing poster images. Hence, the purpose of this study is to propose a deep learning application method that can predict box office success through performance-related poster images. Prediction was performed using deep learning algorithms such as Pure CNN, VGG-16, Inception-v3, and ResNet50 using poster images published on the KOPIS as learning data set. In addition, an ensemble with traditional regression analysis methodology was also attempted. As a result, it showed high discrimination performance exceeding 85% of box office prediction accuracy. This study is the first attempt to predict box office success using image data in the performing arts field, and the method proposed in this study can be applied to the areas of poster-based advertisements such as institutional promotions and corporate product advertisements.