CEO Risk-Taking Incentives and IT Innovation: The Moderating Role of a CEO’s IT-Related Human Capital
Bibliographic record
Abstract
Despite the importance of information technology (IT) innovation in today’s digitalized world, little research attention has been paid to examining how firms can incentivize IT innovation. To fill this gap, the current study investigates the impact of managerial incentives provided to chief executive officers (CEOs) on IT innovation, measured by the number of IT patents. In particular, we examine the role of risk-taking incentives provided to CEOs, captured by the sensitivity of CEO wealth to stock return volatility (i.e., Vega). Vega can motivate CEOs to engage in risky IT innovation projects by aligning their wealth with firm-specific risk. In so doing, we focus on how CEOs’ IT-related human capital (i.e., IT education and IT experience) moderates the relationship between Vega and IT innovation. Our empirical analyses reveal that a higher Vega encourages CEOs to support more IT innovation; more importantly, the impact of Vega on the amount of IT patents is stronger for firms with CEOs who have higher levels of IT education and IT experience. Our study contributes to research and practice by conceptualizing a CEO’s IT-related human capital and validating its moderating role in the relationship between risk-taking incentives provided to the CEO and the amount of IT innovation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".