A Parsimonious Predictive Model of Movie Performance: A Managerial Tool for Supply Chain Members

Mustafa Canbolat, Kyongsei Sohn and John T. Gardner
Additional contact information
Mustafa Canbolat: SUNY Brockport, Brockport, USA
Kyongsei Sohn: SUNY Brockport, Brockport, USA
John T. Gardner: SUNY Brockport, Brockport, USA

International Journal of Operations Research and Information Systems (IJORIS), 2020, vol. 11, issue 4, 46-61

Abstract: In this paper, the authors develop a parsimonious model that offers early prediction of potential success of a movie. In order to achieve this, a broad look at the drivers of movie success is required. Supply chain members will be making decisions regarding movie popularity with regard to licensing contracts, forecasting toy purchases, cross-promotions, etc. at varying times before a movie is released. A simple forecasting approach using publicly available data could improve supply chain decision making. Prior literature suggested that the virtual movie stock market, HSX, was a good predictor. Using a small set of variables including view counts, likes, and dislikes did offer some predictive value. However, HSX produces a forecast that dominates prior models while using a single readily available public data. Further, the HSX-based prediction showed consistency and convergence across a significant breadth of time.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJORIS.2020100103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:joris0:v:11:y:2020:i:4:p:46-61

Access Statistics for this article

International Journal of Operations Research and Information Systems (IJORIS) is currently edited by John Wang

More articles in International Journal of Operations Research and Information Systems (IJORIS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().