An Exploration of the Dishonest Side of Self–Monitoring: Links to Moral Disengagement and Unethical Business Decision Making
Bibliographic record
Abstract
The majority of research on self–monitoring has focused on the positive aspects of this personality trait. The goal of the present research was to shed some light on the potential negative side of self–monitoring and resulting consequences in two independent studies. Study 1 demonstrated that, in addition to being higher on Extraversion, high self–monitors are also more likely to be low on Honesty–Humility, which is characterized by a tendency to be dishonest and driven by self–gain. Study 2 was designed to investigate the consequences of this dishonest side of self–monitoring using two previously unexamined outcomes: moral disengagement and unethical business decision making. Results showed that high self–monitors are more likely to engage in unethical business decision making and that this relationship is mediated by the propensity to engage in moral disengagement. In addition, these negative effects of self–monitoring were found to be due to its low Honesty–Humility aspect, rather than its high Extraversion side. Further investigation showed similar effects for the Other–Directedness and Acting (but not Extraversion) self–monitoring subscales. These findings provide valuable insight into previously unexamined negative consequences of self–monitoring and suggest important directions for future research on self–monitoring. Copyright © 2013 European Association of Personality Psychology
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".