Pilot, Pivot and Advisory Boards: The Role of Governance Configurations in Innovation Commitment
Bibliographic record
Abstract
This study examines how governance configurations comprised of board capital, CEO power and the presence of large shareholders are associated with innovation commitment in organizations. We take a configurational perspective, proposing that organizational innovation commitment is contingent upon how interdependent governance attributes associated with monitoring and resource provisioning can either enhance or constrain management’s discretion to invest in research and development (R&D). Using fuzzy-set qualitative comparative analysis (fsQCA), we identify complementarities which lead to three board archetypes that foster firm innovation commitment. ‘Pilot boards’ have both board capital breadth and depth allowing for active and close participation in innovation decision-making. ‘Pivot boards’ possess the depth of industry-specific expertise and linkages required for providing resources and oversight of powerful CEOs. And ‘advisory boards’ have less power but have outside directors who have breadth of expertise and relational capital that complements the oversight provided by powerful family owners so as to effectively advise management on innovation decisions. Our findings underscore that governance mechanisms work in tandem, not in isolation, to explain significant organizational outcomes, specifically those associated with innovation commitment.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| 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".