When Should a Firm Open its Source Code: A Strategic Analysis
Bibliographic record
Abstract
Deciding to open the source code of a software product has advantages and disadvantages. The disadvantage is that the firm loses the revenue from the software. The advantage is that the users' network can contribute to the quality of the software code, which increases the demand for the software and for a complementary product. Demand for the complementary product also goes up, because demand for a product increases when the price of its complement decreases, and under open source, the price of the software product drops down to zero. This paper examines the strategic interactions at work here, within a duopoly framework, and tries to determine the circumstances under which it is optimal for a firm to open its code. We find that firms open the source code when there is a competitive software‐product market, a less competitive complementary‐product market, and when the complementary product is of high quality. Furthermore, it is more profitable for the firm to open the source code if its competitor also does so. When this happens the incentive to open the code can even be higher than in a monopoly situation. More intense competition induces symmetric equilibria in which both firms choose the same strategy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".