Older and Wiser: How CEOs’ Time Perspective Influences Long‐Term Investments in Environmentally Responsible Technologies
Bibliographic record
Abstract
Abstract Most theories of corporate governance argue that chief executive officers (CEOs) take less risk as they near the end of their career, and therefore are less likely to make major investments. This prediction is based on decisions related to firm‐specific benefits; however, it may not be generalizable to decisions that involve broad societal goals. In terms of societal investments, CEOs with a longer time perspective may be more likely, rather than less likely, to invest. In this paper, we argue that a CEO's future time perspective is fostered by shorter career horizons, longer tenures, higher organizational ownership and less short‐term compensation. We test these hypotheses on 150 observations from the US investor‐owned electric power generation sector over a three‐year unbalanced sample (64.3% of the population). We applied random‐effects generalized least squares (GLS) estimations to test our hypotheses, and found support for three out of four hypothesized relationships.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".