Movies -a form of entertainment that has been around for centuries. Yet, the distribution and consumer perceptions of movies have shifted since the introduction of streaming services (Hennig-Hurau et al., 2021; Kumar, 2023). In 2010, the international expansion of Netflix across the globe marked a key point in the growth of on-demand content (Oberoj, 2024). The saturation and availability of the entertainment market have resulted in more critical consumers, which seemingly affects how audiences evaluate films (Hadida et al., 2020). Although research has been performed on movie ratings over time (e.g. Moon et al., 2010; Ramos et al., 2015), the effect of streaming remains rather unexplored.
Over the years, black-and-white deaf movies have advanced to colourful and effects-heavy motion pictures due to technological developments. This is especially prominent in animation, where disruptions in Computer Generated Images (CGI) and production techniques have significantly enhanced visual quality and storytelling opportunities (Sun, 2023). Consequently, audience evaluations of animated films could differ from live-action films. Yet, current research has not addressed this focus on animation.
All in all, the present study provides academic relevance by investigating the temporal dynamics on the quality perception of movies, while also accounting for the effect of animation. These insights are also socially relevant for filmmakers and distributors to better understand audiences. The focus of the research can be encapsulated with the following research question:
To what extent does the release year of a movie influence its average IMDb rating, and does this relationship differ between animated and non-animated films?
Ever since the introduction of Netflix, more streaming services have entered the market with players such as Disney+, Amazon Prime Video and HBO Max. Competitiveness rose, resulting in a greater variety of TV shows and movies offered (Thompson, 2024). Simultaneously, the more experienced a viewer is in watching movies, the more critical they tend to become (Moon et al., 2009). As such, it could be expected that newer movies are rated worse than older movies because the frame of reference has increased. Additionally, with the rise of digital platforms and social media, review-bombing has become an omnipresent phenomenon upon the release of new films. Moreover, there has been an increasingly vocal group that criticizes the franchise- and sequel-based strategies that movie corporations have adopted. Furthermore, it could be argued that movie consumers often experience nostalgia for films watched in their childhood, resulting in higher ratings for older movies. Authors such as Bollen et al. (2012) refer to this as a form of “positivity effect.” Overall, these findings underscore the expectation that newer films tend to be rated lower than older films, which will be tested with:
H1: There is a negative relationship between release year and rating.
Animated films are more easily regarded as timeless, whereas the effect of H1 might differ between the two categories. As such, animation remains memorable due to the distinct visual identity that is rooted in a fantasised setting. In addition, live-action characters' visible age or become outdated due to changes in fashion, technology or cultural norms, while animated characters remain fairly consistent over time (Larson, 2025). For example, Woody from Toy Story remains unchanged in sequels decades after his debut in 1995. On another note, animated films are often targeted at children and families, which strengthens the sense of nostalgia through intergenerational re-watches (Lizardi, 2020). The present study will test this with:
H2: The negative effect of release year on rating is weaker for animated films compared to non-animated films.
In order to test H1 and H2 and therefore answer the main research question, secondary data is consulted from IMDb.com. The database holds several datasets, of which two provide relevance in the context of the present study:
title.basics.tsv.gz, which contains information about the title, runtime, genre and release year of motion pictures.
title.rating.tsv.gz, which provides data on ratings from all titles. These IMDb ratings are derived from IMDb users and can comprise of movie consumers or professionals.
The dataset merged from the secondary data contains the following variables:
| Variable | Type | Definition |
|---|---|---|
| tconst | character | Unique IMDb identifier for each title |
| primaryTitle | character | The title most commonly used by the general public |
| startYear | integer | Year the title was released |
| runtimeMinutes | integer | Runtime expressed in minutes |
| genres | character | Genres separated by commas |
| averageRating | numeric | Weighted average IMDb user rating (0.0–10.0) |
| numVotes | integer | Number of votes received by the title |
| before_2010_dummy | dummy | Whether a movie is released before or after 2010 |
| animation_dummy | dummy | Whether a movie is animated or not |
To test the hypothesis, a linear regression was conducted in R Studio. Release year was treated as the independent variable, and average IMDb user rating as the dependent variable.
Whether a movie was an animation was dummy coded (0 = non-animated, 1 = animated) and included as a moderator to test whether it has a significant effect on the relationship from release year to the average rating.
Other potential factors that were not specifically hypothesised but could influence ratings were added as control variables in the regression. This included runtime, and number of votes. Furthermore, a dummy was created for whether a movie was released before 2010 or after, in order to test the effect of the rise in streaming services since the international introduction of Netflix.
TO BE DONE
- Describe the gist of your findings (save the details for the final paper!)
- How are the findings/end product of the project deployed?
- Explain the relevance of these findings/product.
Test graph
The structure of our repository is as follows:
project-name/
├─ reporting/
│ ├─ report.Rmd
│ └─ start_app.R
├─ src/
│ ├─ analysis/
│ │ ├─ 1_descriptive_statistics.R
│ │ ├─ 2_exploratory_visualisations.R
│ │ ├─ 3_pre_assumption_check.R
│ │ ├─ 4_regression_analysis.R
│ │ ├─ 5_post_assumption_check.R
│ │ ├─ analysis_raw.R
│ │ ├─ assumption check_raw.R
│ │ ├─ makefile
│ │ └─ README.md
│ └─ data-preparation/
│ ├─ 1_download_raw_data.R
│ ├─ 2_merge_raw_data.R
│ ├─ 3_select_variables.R
│ ├─ 4_handling_missing_values.R
│ ├─ 5_filtering.R
│ ├─ 6_feature_engineering.R
│ ├─ makefile
│ └─ README.md
├─ .RDataTmp
├─ .gitignore
├─ README.md
├─ makefile
└─ team-project-team2.Rproj
For data downloading, cleaning and analysing R and R studio were used.
# Install the following packages
install.packages("googledrive")
install.packages("tidyverse")
install.packages("mice")
install.packages("car")
install.packages("lmtest")
install.packages("broom")
install.packages("nortest")This project is set up as part of the Master's course Data Preparation & Workflow Management at the Department of Marketing, Tilburg University, The Netherlands. The project is implemented by team 2:
- Britt van Haaster
- Isah Huijbregts
- Lars van der Kroft
- Amanda van Lankveld
- Amy Quist
- Stefan Valentijn
Billy Thompson. (2024, May 25). The Rise and Fall of Streaming TV? – Michigan Journal of Economics. Michigan Journal of Economics. https://sites.lsa.umich.edu/mje/2024/05/25/the-rise-and-fall-of-streaming-tv/
Bollen, D., Graus, M. P., & Willemsen, M. C. (2012). Remembering the stars?: effect of time on preference retrieval from memory. Proceedings of the sixth ACM conference on Recommender systems.https://doi.org/10.1145/2365952.2365998
Hadida, A. L., Lampel, J., Walls, W. D., & Joshi, A. (2020). Hollywood Studio Filmmaking in the Age of Netflix: a Tale of Two Institutional Logics. Journal of Cultural Economics, 45(2), 213–238. https://doi.org/10.1007/s10824-020-09379-z
Hennig-Thurau, T., Ravid, S. A., & Sorenson, O. (2021). The Economics of Filmed Entertainment in the Digital Era. Journal of Cultural Economics, 45(2), 157–170. https://doi.org/10.1007/s10824-021-09407-6
Kumar, L. (2023, April). A Study On The Impact Of The OTT Platform On The Cinema With The Special Reference On The Cinema Audience. ResearchGate; unknown. https://www.researchgate.net/publication/376650380_A_Study_On_The_Impact_Of_The_OTT_Platform_On_The_Cinema_With_The_Special_Reference_On_The_Cinema_Audience
Larson, V. J. (2025). Philosophy in filmmaking: Animation vs. live action (Honors Program Theses No. 976) [Undergraduate thesis, University of Northern Iowa]. University of Northern Iowa Repository. https://scholarworks.uni.edu/cgi/viewcontent.cgi?article=2004&context=hpt>
Lizardi, R. (2020). The future of nostalgia is inevitable: Reflections on mediated nostalgia. In M. H. Jacobsen (Ed.), Nostalgia Now: Cross-disciplinary perspectives on the past in the present (1st ed., pp. 147-161). Routledge. https://doi.org/10.4324/9780429287602-8>
Moon, S., Bergey, P. K., & Iacobucci, D. (2009). Dynamic Effects among Movie Ratings, Movie Revenues, and Viewer Satisfaction. Journal Of Marketing, 74(1), 108–121. https://doi.org/10.1509/jmkg.74.1.108
Oberoi, S. (2024, December 3). The Evolution of Netflix: from DVD Rentals to Global Streaming Leader. Seat11a.com. https://seat11a.com/blog-the-evolution-of-netflix-from-dvd-rentals-to-global-streaming-leader/
Ramos, M., Calvão, A. M., & Anteneodo, C. (2015). Statistical patterns in movie rating behavior. PLOS ONE, 10(8), e0136083. https://doi.org/10.1371/journal.pone.0136083>
Sun, Z. (2023). What does cgi digital technology bring to the sustainable development of animated films?. Sustainability, 15(14), 10895.https://doi.org/10.3390/su151410895