Entertainment We Watch and Entertainment We Appreciate: Patterns of Motion Picture Consumption and Acclaim Over Three Decades

Are Enjoyment and Appreciation Both Yardsticks of Popularity?

“…Also as expected, the films on the Box Office Mojo list were less appreciated, M MOJO = 4.73, SD = 0.70; M other = 5.17, SD = 0.59; t (48) = −2.39, Cohen’s d = .68, p = .02, and more enjoyed, M MOJO = 5.85, SD = 0.28; M other = 5.26, SD = 0.57; t (48) = 4.70, Cohen’s d = 1.41, p < .001, than the films not on the IMDb list (see Table 1 for enjoyment and appreciation ratings for each of the 50 films). Films tagged as drama were enjoyed less ( M drama = 5.33, SD = 0.62; M non-drama = 5.77, SD = 0.38; t (48) = 3.14, Cohen’s d = 0.86, p = .003) appreciated more, ( M drama = 5.45, SD = 0.52; M non-drama = 4.65, SD = 0.61; t (48) = 4.77, Cohen’s d = 1.41, p < .001), had lower budgets, ( M drama = $60,955,478, SD = $72,192,712; M non-drama = $163,252,173, SD = $81,194,540; t (48) = 4.55, Cohen’s d = 0.58, p < .001) and made less money at the box office, ( M drama = $603,381,469, SD = $791,616,693; M non-drama = $583,030,463, SD = $106,446,312; t (48) = 4.55, Cohen’s d = 0.88, p < .001), consistent with Oliver et al (2014). Note the standard deviation for box-office gross is seven times larger for drama (vs. non-drama) films.…”

“…Only n = 3 of the films tagged as drama were in the Box Office Mojo list, which consisted primarily of films tagged with action, adventure, and/or fantasy genres. We present some analyses below using drama, comedy, and action as control variables to be consistent with previous research by Oliver et al (2014) as well as some analyses on both (IMDb vs. Box Office Mojo) samples separately.…”

“…This time, appreciation contributed unique variation to the model, Δ R 2 = .08, p < .001, in block 3 regardless of whether we controlled for the proportion of male raters. A third test used IMDb popularity, as in the Oliver et al (2014) content analysis. Again, appreciation contributed significantly to the model, Δ R 2 = .13, p = .003, regardless of whether we controlled for the proportion of male raters in our sample.…”

What Is Important When We Evaluate Movies? Insights from Computational Analysis of Online Reviews

“…If they interface with each other better, analyzing Big Data against a social-scientific background may help to improve recommender systems and user experiences within online re-view platforms, video streaming portals, or mixed-media channels. Although there are some notable but rare exceptions (e.g., Oliver, Ash, Woolley, Shade, & Kim, 2014), most entertainment researchers have not taken full advantage of the digital traces or responses that are publicly available online. Utilizing these data and applying computational methods to address open questions or supplement previous research could be a crucial factor for advancing both movie evaluation research and entertainment theory.…”

Shakespeare in Love: A Fictional Transliteration of the Grammar of Heterosexual Mating

“…It is generally true that romantic comedies do not provide a realistic picture of man-woman relationships, and that such representation may be potentially misleading for an inexperienced audience (Shapiro & Kroeger, 1991). However, it is equally true that not all fictional narratives share the same hedonic versus eudemonic valence (Oliver & Hartmann, 2010), elicit the same levels and forms of appreciation, and maintain the same cultural relevance through time (Oliver, Ash, Woolley, Shade, & Kim, 2014). Moreover, not all of them provide equally convincing accounts of the dynamic process leading to the successful or failed emergence of the heterosexual couple (Lucchi Basili & Sacco, 2017).…”