Navigating the open innovation paradox: an integrative framework for adopting open innovation in pharmaceutical R&D in developing countries
Bibliographic record
Abstract
In this paper, we combine evidence from eight Indian pharmaceutical firms with extant literature and global best practices to conceptualize an integrative framework addressing the open innovation paradox (OIP), i.e., the tension between intellectual protection and openness. Firms in developing countries face additional challenges in the adoption of open innovation, such as the prevalence of open science norms, weak technology transfer systems, and mistrust between universities and industry; therefore, they employ open innovation selectively for pharmaceutical research. Prior research has examined the strategies to resolve OIP in the context of developed countries; the integrative framework proposed in this paper describes strategies for resolving the OIP in the context of developing countries. This framework illuminates the coping processes of the case firms and provides guidelines to uplift and accelerate the adoption of open innovation strategies in developing countries' pharmaceutical sectors, and thus provides value to both theory and praxis.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".