Unintended Consequences of Interview Faking: Impact on Perceived Fit and Affective Outcomes
Bibliographic record
Abstract
Drawing on signalling theory, we propose that use of deceptive impression management (IM) in the employment interview could produce false signals, and individuals hired based on such signals may incur consequences once they are on the job—such as poor perceived fit. We surveyed job applicants who recently interviewed and received a job to investigate the relationship between use of deceptive IM in the interview and subsequent perceived personjob and person-organization fit, stress, well-being, and employee engagement. In a twophase study, 206 job applicants self-reported their use of deceptive IM in their interviews at Time 1, and their perceived person–job and person–organization fit, job stress, affective well-being, and employee engagement at Time 2. Deceptive IM had a negative relationship with perceived person–job and person–organization fit. As well, perceived fit accounted for the relationship between deceptive IM and well-being, employee engagement, and job stress. The findings indicate that using deceptive IM in the interview may come at a cost to employees.
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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 | 0.008 | 0.044 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".