A HAND ON THE RUDDER OF INNOVATION: INVESTIGATING THE INFLUENCE OF BOARD OF DIRECTORS AND TOP MANAGEMENT TEAMS.
Bibliographic record
Abstract
This study provides one of the rare evidences based on a field study regarding the influence of corporate governance on innovation. Drawing on semi-structured interviews, it investigates how the internal governance chain (board of directors and top management teams) contribute to foster innovation in their organization. The interviewees' statements highlight the considerable impact that directors and managers can have on innovation, and in that sense that they certainly have "a hand on the rudder of innovation". However, the collected data also shows that other factors such as organizational characteristics (e.g. sector of the firm and partners) play a major role, thus revealing that many other aspects and stakeholders also have "a hand on the rudder of innovation". The in-depth analysis contained in the present paper gave rise to a conceptual framework that includes 5 dimensions and 19 sub-dismensions. This framework promotes a more holistic approach when studying the link between the internal governance chain and innovation. It also emphasises the complexity of this relationship and thus helps to better tackle it.
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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 | 0.028 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".