“I (might be) just that good”: Honest and deceptive impression management in employment interviews
Bibliographic record
Abstract
Abstract Applicant use of impression management (IM) tactics plays a central role in employment interviews. IM includes behaviors intended to create an impression of competence and likability, and avoid negative impressions. Applicants can influence interviewers’ impressions using both honest and deceptive IM, but measurement of IM has yet to distinguish these two constructs. The goal of the present research was to develop a self‐report Honest Interview Impression Management (HIIM) measure and use this to investigate differential antecedents and consequences of honest and deceptive IM. We report the results of five independent studies (total N = 1,470 interviewees). Studies 1–3 detail the creation of a self‐report measure of honest IM. Studies 4 and 5 utilize this measure to understand the relations between honest and deceptive IM, and their antecedents and consequences. Results demonstrate that honest and deceptive IM are positively related but distinct constructs that have unique antecedents (i.e., age, individual differences, attitudes, situational, and target characteristics) and differentially impact interview outcomes and ratings. Finally, we present a short measure of honest and deceptive IM to be used for time‐sensitive data collection.
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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 | 0.035 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".