Financially insecure and less ethical: Understanding why and when financial insecurity inhibits ethical leadership
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the recent COVID-19 pandemic, among other crises (e.g., Russia–Ukraine conflicts and recession projections) threatening organizations’ financial conditions across the globe, supervisors may not only encounter challenges such as job cuts that test their ethical leadership, but also experience financial insecurity themselves. However, our knowledge of why and when supervisors’ ethical leadership behaviors may be affected in such a situation remains quite limited. In this research, we draw on uncertainty management theory (UMT) to examine the potential influence of financial insecurity on ethical leadership. Specifically, we suggest that financial insecurity triggers anxiety in supervisors, which inhibits their demonstration of ethical leadership. We also propose organizational pay fairness as a boundary condition for this process, such that supervisors who perceive their pay as fair are less susceptible to the anxiety resulting from financial insecurity than those who perceive their pay as unfair. Results from two multi-source, multi-wave studies supported our hypothesized model. We conclude by discussing the theoretical and practical implications of our findings.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it