Why Are People Honest? Internal and External Motivations to Report Honestly
Bibliographic record
Abstract
ABSTRACT We create and validate measures capturing internal and external motivations to report honestly as trait‐like characteristics. Both measures have high levels of reliability, as well as convergent and divergent validity. To test their predictive validity, we conduct two experiments. In the first experiment, MTurk participants have the opportunity and incentive to misreport with no immediate consequences, and in the second experiment, participants with management experience report how they would make a hypothetical accounting allocation decision. In both experiments, we find that participants who are higher in internal motivations to report honestly are more likely to report honestly than those lower in internal motivations, confirming this measure's predictive validity. Both experiments also provide limited support for the predictive validity of our external measure, finding that those who are higher in external motivation do not report differently than those who are lower in external motivations in the absence of controls. Our study also reveals that individuals who are higher in internal motivations have a diminished reaction to different management controls: MTurk participants to a control that punishes misreporting, and manager participants to a control that rewards honest reporting. Results suggest that management and those charged with governance should consider that some employees can react negatively to controls that are perceived as constraining. Our measures are useful to researchers who investigate honest reporting by allowing them to identify, ex ante, individuals who want to be honest versus wanting to appear honest.
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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 | 0.013 | 0.054 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".