The inter-relationship among corporate governance, artificial intelligence, and innovation
Bibliographic record
Abstract
Although research in the field of corporate governance has been exhaustive recently many scholars have focused on the relationship between corporate governance attributes and artificial intelligence, corporate governance attributes and corporate innovation, there are few studies that combine corporate governance, artificial intelligence and corporate innovation. The main reason is due to the quantitative difficulties in measuring and distinguishing artificial intelligence activities and corporate innovation activities in enterprises. This study examines the relationships among corporate governance attributes, artificial intelligence, and corporate innovation. Adopting a new perspective, we have tried to help resolve the issue using a content analysis that integrates data from over 50 United States companies to analyze the relationship between board attributes, practice of artificial intelligence (AI) and firm innovation for the period 2018–2022. The results suggest that certain aspects of boards, such as board size, board diversity, and ownership concentration show the most 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. Moreover, the mediating effects of AI and innovation are examined, respectively. Lastly, we also discovered changes in the industry’s attention to AI development before and after COVID-19 (2020). 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".