Bibliographic record
Abstract
Purpose The purpose of this paper is to examine if the structure and design of CEO compensation has any effect on firm innovation. It further investigates the effectiveness of each component of portfolio of compensation incentives in encouraging innovation. Design/methodology/approach This study uses systems of simultaneous equations to model the interdependence between compensation incentives and measures of firm innovation. Findings Results indicate that the pay‐performance sensitivity of the CEO portfolio of compensation incentives is positively related to investment in R&D expenditures, number of patents and citations. Options in general are more effective than stocks. However, within the options portfolio, recently awarded and unvested options are more effective than previously awarded and vested options. Restricted stock is more effective than unrestricted stock. Research limitations/implications Measuring innovation output is difficult as innovation could take different forms, including business model innovation, which does not appear in the patent data. Practical implications Stock options encourage investment in value‐increasing innovations and should remain a significant part of managerial compensation. If the firm awards stock, it should only award restricted stock. Originality/value This study uses comprehensive measures of compensation incentives and firm innovation. It views incentives as a portfolio of stock and options and uses incentives in their entirety. It examines the effectiveness of each component of the portfolio in encouraging innovation. It measures innovation as investment into the innovation process (R&D expenditures) and the resulting success of that investment (patents and citations).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".