The performance implications of patenting – the moderating effect of informal institutions in emerging economies
Bibliographic record
Abstract
Research has highlighted the role of patenting on firms’ performance without elaborating on how patents can be leveraged to capture value. Drawing on the resource‐based view and institutional theory, this research attempts to extend patenting‐performance debate by examining the moderating effects of informal institutions – or specifically, political ties – on the patenting‐performance relationship. We argue that the leverage of political ties could eliminate uncertainty in patent rights by accessing information, reducing transaction costs, and facilitating bureaucratic arbitration, and therefore leads to better performance. Moreover, considering resource limitations and jurisdictional variations, the benefits of such a moderating approach may be more pronounced in a context characterized by high financial slack or low formal institutional development. Our analysis of panel data from 761 Chinese‐listed companies in the chemical, electronics, and pharmaceutical industries provides supports for both the moderating role of informal institutions on the patenting‐performance relationship and the contingency effects of financial slack and formal institutional development. This paper contributes to patent management literature by elaborating upon the mechanisms of how informal institutions can be leveraged to capture value from firms’ patent portfolios.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".