Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry
Bibliographic record
Abstract
Anecdotal evidence suggests that counterfeiting/piracy can help create online word of mouth (WOM) and through this boost demand, but how powerful is such WOM? To answer this question, we conduct a descriptive study with some attempts to establish near causality. We estimate the impact of piracy on WOM and ultimately revenue by applying a panel data method to all movies widely released in the United States from 2015 to 2017. In identifying the effects of piracy we make inventive use of Russian piracy data to construct instrument variables for piracy in the United States. This is possible because the key piracy site, The Pirate Bay, has been blocked in Russia since 2015. We find movies with prerelease piracy are associated with lower revenues despite the WOM effect. Critically, however, we show a positive correlation between postrelease piracy and WOM volume, and we extend the field by finding that the presence of postrelease piracy is associated with an approximately 3.0% increase in box office revenue. We also note the impact of a raid by the Swedish Police that temporarily took down The Pirate Bay website in December 2014. The period when the site was down experienced a decline in WOM volume and revenues, consistent with the effect of lower postrelease piracy predicted by our models. Our findings suggest approaches to target scarce antipiracy resources, such as focusing on tackling damaging prerelease piracy. This paper was accepted by Juanjuan Zhang, marketing.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".