Which Enemy to Dance with? A New Role of Software Piracy in Influencing Antipiracy Strategies
Bibliographic record
Abstract
This paper studies how software firms should determine their antipiracy efforts and product prices. There are two unique aspects of our model. First, antipiracy efforts have both a direct effect and a cross effect on software piracy. Second, we capture two types of competitions when piracy exists: one between a legitimate product and its pirated counterpart, and the other between two pirated products. We show that due to pirated products’ buffer effect not studied before, eliminating piracy does not necessarily mean higher profit for firms. This reveals an unexplored advantage of desktop software comparing with Software as a Service that can eliminate piracy. Direct and cross effects have different impacts on firms’ decisions and profits. Opposite to what one might expect, when a firm’s antipiracy effort becomes more effective in increasing the cost of pirating its own product but not its competitor’s product, the firm becomes worse off under certain conditions. By contrast, if the effort’s cross effect is higher, therefore increasing the cost of pirating its competitor’s product, a firm will always be better off. The managerial implication is that if a firm ignores the cross effect, it could under-invest in anti-piracy effort, causing its profit to suffer.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".