How do corporate governance, artificial intelligence, and innovation interact? Findings from different industries
Bibliographic record
Abstract
Research in the field of corporate governance has been exhaustive, and recently many scholars have focused on the relationship between corporate governance attributes and artificial intelligence (AI), corporate governance attributes, and corporate innovation (Asensio-López et al., 2019), however, there are few studies that combine corporate governance, AI, and corporate innovation. This article examines the relationships among corporate governance attributes, AI, and corporate innovation. Adopting a new perspective, we have tried to help resolve this issue using a content-analysis that integrates data from over 50 companies that trade on National Association of Securities Dealers Automated Quotations (NASDAQ) to analyze the relationship between board attributes, the practice of AI and firm innovation for the time 2018–2022. The results suggest that particular aspects of boards, such as board size, board diversity, and ownership concentration show significant correlations with firm AI development and innovation for overall industries, but the levels of associations also vary depending on different innovation measurements and samples considered in specific industries. Corporate governance has more significant variables in the manufacturing and information technology service industries. Moreover, the mediating effects of AI and innovation are examined, respectively. This research offers implications to corporate decision-makers as to how to proceed if the intent is to offer commercialized AI advancements and successful breakthrough innovations.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".