Lights, camera, persuasion : examining the impacts of impression management tactics on predictive validity
Bibliographic record
Abstract
The study explored the use of self presentation behaviours (i.e., deceptive, and honest impression management; IM) in asynchronous video interviews (AVIs).Applicants use of IM in interviews has the potential to enhance (honest IM) or detract (deceptive IM) the predictive validity of interviews.However, scant research exists on the potential impacts of IM on predictive validity of job interviews.The current study employed a novel approach by collecting data in two stages: first, participants (n = 212) completed a mock AVI; next, participants (n = 168) completed two in-basket HR tasks to capture performance.Results indicated that honest self-promotion had a positive relationship with interview and task performance, and a significant indirect effect.Other IM tactics lead to some mixed and contrary findings.Proposed moderators (experience, age, and anxiety) did not impact results.Overall, honest and deceptive IM demonstrate their importance to both interview and job performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".