Hollywood studio filmmaking in the age of Netflix: a tale of two institutional logics
Bibliographic record
Abstract
Abstract Online streaming services are challenging long-standing decision-making processes in the traditional motion picture industry, thus placing Hollywood major studios at a crossroads. We use the institutional logics perspective to examine how both traditional studios and online streaming services make strategic decisions on which films to produce and how these films are to be distributed. We then apply scenario analysis to explore how their interaction will likely evolve. We argue that the key criteria that studio executives use to make production and distribution decisions are shaped by what we define as a commitment institutional logic: decision-making heuristics that focus their attention on theatrical release and box-office intakes. In contrast, online streaming services follow a convenience institutional logic, the product of advanced data analytics to increase subscriptions. In the convenience institutional logic, the need to drive online traffic by providing users with an extensive catalogue of movies guides film production and distribution decisions. Whereas the commitment logic aims for mass-market hits in cinemas, the convenience logic seeks to reach a wide range of subscribers at home with micro-segmented offerings. We compare the two logics, develop four scenarios of how the interaction between them may shape the film industry, and offer recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".