How Consumers’ Content Preference Affects Cannibalization: An Empirical Analysis on E-book Market
Bibliographic record
Abstract
Despite the increasing popularity of e-books and the growing maturity of e-book markets, there have been few studies on the e-book channel and its influence on the existing paper book channel. In order to fill this gap in the literature, we investigate the extent to which e-book release boosts or cannibalizes demand for the paper book. Using unique data on the actual sales of paper books and e-books, we conduct an empirical analysis on this question. Our results without addressing selection bias suggest that the e-book release boosts the demand for the paper book. However, this effect disappears once we control for selection bias by using matching. We also find that the impact of ebook release is moderated by consumers’ content preference. Specifically, the e-book release increases paper book sales as well as total sales for those books with light contents that consumers prefer to consume through a digital channel. In contrast, the books that appeared on the bestseller lists experience significant demand cannibalization from e-book release.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".