Board characteristics, ownership structures and firm R&D intensity
Bibliographic record
Abstract
This study explores the impact of board characteristics and ownership structures on the strategic decisions taken for R&D investment. The study employs a sample comprising 1736 firm-year observations of 434 technological firms listed on the Taiwanese Stock Exchange (TWSE) between 2014 and 2017. Contrary to extant research, the findings reveal that board independence plays a crucial role relative to R&D intensity, as strong evidence reflects a positive and significant relationship thereon. Moreover, the empirical results demonstrate negative and significant relationships between CEO Duality, Board size (in big companies), Executive & Manager, Board of Directors and Top Blockholders; ownership structures, and firm R&D intensity. Interestingly, the ownership structure results emerging from this Taiwanese contextual study support, and are consistent with the predictions of the ‘entrenchment argument’ and are counter to the ‘convergence of interest’ argument. The findings emerging from this research provide an opportunity for further discussion and analysis regarding corporate governance principles and regulations. Firms seeking to optimize their R&D policy imperatives may benefit from such a study.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".