Once an Impression Manager, Always an Impression Manager? Antecedents of Honest and Deceptive Impression Management Use and Variability across Multiple Job Interviews
Bibliographic record
Abstract
Research has examined the antecedents of applicants' use of impression management (IM) tactics in employment interviews. All existing empirical studies have measured IM in one particular interview. Yet, applicants generally interview multiple times for different positions, and thus have multiple opportunities to engage in IM, before they can secure a job. Similarly, recent theoretical advances in personnel selection and IM research have suggested that applicant behaviors should be considered as dynamic and adaptive in nature. In line with this perspective, the present study is the first to examine the role of individual differences in both applicants' use of IM tactics and the variability in IM use across multiple interviews. It also highlights which honest and deceptive IM tactics remain stable vs. vary in consecutive interviews with different interviewers and organizations. Results suggest that applicants high in Extraversion or core self-evaluations tend to engage in more honest self-promotion but do not adapt their IM approach across interviews. In contrast, applicants who possess more undesirable personality traits (i.e., low on Honesty-Humility and Conscientiousness, but high on Machiavellianism, Narcissism, Psychopathy, or Competitive Worldviews) tend to use more deceptive IM (and especially image creation tactics) and are also more likely to adapt their IM strategy across interviews. Because deceptive IM users can obtain better evaluations from interviewers and the personality profile of those users is often associated with undesirable workplace outcomes, this study provides additional evidence for the claim that deceptive IM (or faking) is a potential threat for organizations.
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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 | 0.005 | 0.039 |
| 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 | 0.001 | 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".