Small Companies and Value Capture from their Intellectual Properties: a Qualitative Study
Bibliographic record
Abstract
Small companies and their intellectual properties (IPs) play an increasingly crucial role in a “well-functioning market economy”. In recent empirical studies, it is recognized that small companies carried out breakthrough IPs. However, more studies are needed to investigate how small companies strategically capture value from their IPs given their resource constraints. By analyzing the empirical case findings in the light of IP management theory and resource-based view (RBV), this study attempted to answer 1) how small companies capture value from their intellectual properties and 2) in their value capture, how small companies utilize their physical, organizational, and human capital resources and overcome resource constraints, if any. Interview data with seven case companies which possess valuable and radical IPs were used to identify patterns and differences among the value capture strategies. The results were reported on a within- and cross-cases basis, which led to the discussion of three propositions. Overall, this thesis identified how small companies commercialize their IPs and the crucial roles of network and radical patents for small companies.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".