Honest and Deceptive Impression Management in the Employment Interview: Can It Be Detected and How Does It Impact Evaluations?
Bibliographic record
Abstract
Applicants use honest and deceptive impression management (IM) in employment interviews. Deceptive IM is especially problematic because it can lead organizations to hire less competent but deceptive applicants if interviewers are not able to identify the deception. We investigated interviewers’ capacity to detect IM in 5 experimental studies using real‐time video coding of IM ( N = 246 professional interviewers and 270 novice interviewers). Interviewers’ attempts to detect applicants’ IM were often unsuccessful. Interviewers were better at detecting honest than deceptive IM. Interview question type affected IM detection, but interviewers’ experience did not. Finally, interviewers’ perceptions of IM use by applicants were related to their evaluations of applicants’ performance in the interview. Interviewers’ attempts to adjust their evaluations of applicants they perceive to use deceptive IM may fail because they cannot correctly identify when applicants actually engage in various IM tactics. Helping interviewers to better identify deceptive IM tactics used by applicants may increase the validity of employment interviews.
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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 | 0.054 | 0.270 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".