Propositions for R&D Governance Regimes: A Behavioral Perspective
Bibliographic record
Abstract
Purpose: Our paper intends to bring systematic attention to the transaction costs and corresponding governance structures in the corporate R&D contexts. Design/methodology/approach: The current conversation on the governance of R&D has addressed topics related to investments, resource allocation, knowledge sharing, or managing intellectual property rights. While these are important aspects of innovation cost and benefit, they do not address an equally important issue of transaction cost arising out of human behavior. We adopt a systematic synthesis of 16 qualitative teaching case studies to validate the existence of human behavior-related transaction cost issues in the context of R&D and identify firms’ governance responses to mitigate or eliminate their effect. Findings: Our findings suggest that transaction costs issues can be mitigated by appropriate governance models ranging from centralized R&D to open innovation. While structure mitigates the transaction cost some ex-ante and ex-post controls may also be required. Practical implications: For management practice, the insights of our study provide evidence-based advice for R&D managers regarding the practical, effective ways of limiting and controlling the transaction costs associated with R&D activity. Originality/value: This paper uses teaching-case studies to analyze a variety of transaction cost dilemmas in the R&D context and brings in a new perspective to R&D governance.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".