Unethical Conduct Under Uncertainty: A Fear-Based Perspective
Bibliographic record
Abstract
Rising uncertainty in the business environment has coincided with a significant increase in unethical behaviors within organizations, posing substantial financial and reputational risks. Unethical conduct is estimated to cost organizations around the world more than USD 4.5 trillion per year, impacting corporate financial stability, investor confidence, and market integrity. Traditional risk assessment and predictive models, which rely on historical data, often fail to account for behavioral responses to uncertainty, creating blind spots in financial risk management and economic forecasting. This paper advances the literature by applying experimental methodologies to investigate the underlying emotional, fear-based mechanisms (namely short-term focus and self-concern) impacting decision-making under uncertainty. By utilizing two distinct types of experimental studies (comprising three studies in total), we empirically examine how uncertainty influences the types of unethical behaviors that are prevalent in today’s organizations. Our findings contribute to the fields of financial risk management and behavioral economics by offering evidence-based insights into the psychological drivers of unethical decision-making. We conclude with managerial implications, outlining proactive strategies to mitigate the financial and operational risks associated with individuals’ responses to uncertainty.
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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 | 0.006 | 0.026 |
| 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 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".