Software Patents and Firm Value: A Real Options Perspective on the Role of Innovation Orientation and Environmental Uncertainty
Bibliographic record
Abstract
Our paper shows that software-based patents can contribute significantly to the value of firms. Our paper provides managers with insights into how different types of software-based innovations affect firm value in market environments exhibiting different levels of competitiveness and dynamism. Using a large-panel data set consisting of 602 U.S. firms, we find that firms with a software patent portfolio having higher levels of explorative innovation orientation achieve higher market value in environments with high competitiveness and low dynamism. By contrast, firms with a software patent portfolio exhibiting high levels of exploitative innovation orientation achieve higher market value in low competitiveness and high dynamism environments. Although some practitioners are still skeptical about the value of software patents, we provide empirical evidence that a firm’s software patents do contribute to firm performance, thereby helping practitioners to justify their investments in software innovation and assess the value of their software patents. Furthermore, our paper highlights key factors—both internal (i.e., innovation orientation) and external (i.e., environmental uncertainty)—that may affect the value of software patents. This can help firms formulate the appropriate innovation strategy for software patents that can lead to the greatest returns.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".