EMPIRICAL ANALYSIS OF FACTORS INFLUENCING BOX OFFICE REVENUE OF ANIMATED FILMS: EVIDENCE FROM AI-MEDIATED DIGITAL MARKET SIGNALS

Abstract

The global animated film sector experienced a significant structural shift between 2015 and 2023, largely spurred by the rise of AI-driven digital platforms, the swift growth of subscription streaming services, and the increasing commercial impact of algorithmic recommendation systems on audience behaviour. In this changing environment, the traditional framework of film economics, which primarily focuses on production budgets and critical reviews, fails to adequately explain the commercial success of animated films. This dissertation empirically investigates the factors that affect worldwide box office revenue for animated films released during this timeframe, paying particular attention to whether AI mediated digital market signals have independent commercial value beyond conventional production-related factors. The research utilises a secondary dataset comprising 13,440 observations of animated films obtained from The Movie Database (TMDB), the Internet Movie Database (IMDb), and Box Office Mojo, encompassing animated theatrical releases globally from 2015 to 2023. The dependent variable in this study is worldwide box office revenue. In contrast, the four independent variables include production budget, the TMDB algorithmic popularity score, total audience vote count, and average vote. The primary analytical method employed is Ordinary Least Squares (OLS) multiple linear regression, conducted using IBM SPSS Statistics (Version 26) with the Enter method. A Pearson bivariate correlation analysis is performed as a supplementary diagnostic step before regression estimation. The regression model exhibits considerable explanatory power, accounting for roughly 69 per cent of the variance in worldwide box office revenue (R² = 0.690, Adjusted R² = 0.690, F = 7,475.594, p < 0.001). All four predictors are statistically significant at the 0.001 level. Vote count is the most influential predictor (β = 0.607, t = 81.434, p < 0.001), indicating that the level of AI-mediated audience interaction on digital rating platforms is the primary driver of commercial success. Production budget follows in importance (β = 0.218, B = 0.989, p < 0.001), with an unstandardized coefficient near 1, suggesting that animated films tend to recoup their production costs through box-office iv earnings at the margin. The TMDB algorithmic popularity score, which serves as the main AI-mediated signal variable in this study, reveals a notable positive independent coefficient (β = 0.146, B = 140,205.597, t = 28.866, p < 0.001), indicating that visibility generated by AI on platforms significantly influences box office revenues, independent of budget and audience engagement. The vote average shows a small yet statistically significant negative partial effect (β = −0.024, t = −4.961, p < 0.001), reflecting a market-segmentation dynamic rather than a negative correlation between film quality and commercial performance in absolute terms. The hierarchy of standardised effects ranks vote count (0.607), budget (0.218), popularity (0.146), and vote average (0.024 in absolute value), providing a clear, evidence-based ranking of the commercial drivers for the animated film industry in the digital age. This study contributes in three main ways: it offers a large-sample empirical analysis of the global animated film market for a contemporary period not previously studied at this scale; it explicitly operationalizes and empirically tests an AI-generated algorithmic metric as a distinct category of revenue predictor; and it provides directly interpretable economic magnitudes that producers, investors, and digital marketers can utilize to assess the commercial returns on investments in digital engagement. Keywords: Animated films; box office revenue; AI-mediated digital signals; TMDB popularity score; vote count; film economics; OLS regression; digital platform economics; algorithmic visibility; audience engagement.