Is more always better? How preparation time and re-recording opportunities impact fairness, anxiety, impression management, and performance in asynchronous video interviews
Bibliographic record
Abstract
The present study examined how variations in the design of asynchronous video interviews (AVIs) impact important interviewee attitudes, behaviours, and outcomes, including perceived fairness, anxiety, impression management, and interview performance. Using a 2 × 2 experimental design, we investigated the impact of two common and important design elements on these outcomes: (a) preparation time (unlimited versus limited) and (b) the ability to re-record responses. Using a sample of 175 participants completing a mock AVI, we found that whereas providing such options (i.e., unlimited preparation time and/or re-recording) did not impact outcomes directly, the extent to which participants actually used these options did affect outcomes. For instance, those who used more re-recording attempts performed better in the interview and engaged in less deceptive impression management. Moreover, those who used more preparation time performed better in the interview while engaging in slightly less honest impression management. These findings point to the importance of investigating the effects of AVI design on applicant experiences and outcomes. Specifically, AVI design elements produce opportunities for applicants not typically present in synchronous interviews, and can alter interview processes in crucial ways. Finally, not all applicants use these opportunities equally, and this has implications for understanding interview behaviour and outcomes.
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".