Disruptive Technology as Antecedent to CEO Pay-Performance Sensitivity
Bibliographic record
Abstract
This study examines the influence of disruptive technology on firm performance, which in turn explains CEO compensation. Innovation consists of a series of S-curves which progress from introduction to growth, maturity and decline until they are replaced by new technology which has its own S curve. Each S curve involves the introduction of products that are cheaper, simpler, smaller and more convenient than established technology by catering to specific market needs currently unserved by existing technology. Disruptive technology is a special case of innovation characterized by its ability to displace a mainstream competitor, while innovation displaces ex- isting technology. This study follows on earlier empirical research that observed that a sample of technology-intensive US firms listed on the NASDAQ from 1993-2012 rewarded CEOs for engaging in disruptive technology by increasing their compensation in the form of stock option grants, suggesting that the effective implementation of disruptive technology results in superior firm performance which is rewarded with incentive pay such as stock option grants. We provide empirical support for the thesis that the implementation of disruptive technology increases net income and subsequently increases incentive pay. Such positive effects of disruptive technology on net income are strengthened for CEOs of higher age, longer tenure, more education and if the CEO is a founder or a celebrity and for larger firms. We draw on the literature on the influence of demographic characteristics on the implantation of innovation to explain the positive interaction of disruptive technology with demographic characteristics which in turn, improve corporate financial performance and consequently increase CEO incentive compensation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".