Reactions to asynchronous video interviews: The role of design decisions and applicant age and gender
Bibliographic record
Abstract
Abstract Asynchronous video interviews (AVIs) are a form of one‐way, technology‐mediated selection interview that can help streamline and increase flexibility in the hiring process and are used to hire millions of applicants per year. Although applicant reactions to AVIs in general tend to be more negative than with traditional interview modalities, AVIs can differ widely in how they are designed. For instance, applicants can be provided with more or less preparation time, response length, rerecording options, or rely on different question formats. This study examines how AVI design features impact applicant reactions, as well as the moderating role played by applicant age and gender. Data from 27,809 real job applicant's AVI experiences were collected in 11 countries (69.3% English‐speaking) from 33 companies and relating to 72 types of positions. Data were fitted with linear mixed‐effects models to account for nesting. Results showed that allowing more preparation time and offering the opportunity to rerecord responses were related to more favorable reactions, while including more questions was related to more negative reactions. Applicants above the age of 31 reacted especially negatively to AVIs with more questions while those below the age of 30 preferred being allocated longer maximum response lengths. Women reacted more positively to increased preparation time. These findings might help both AVI vendors and hiring organizations design AVIs that facilitate a positive applicant experience. Our research also expands knowledge on applicant reactions to interviews, highlights crucial differences from traditional formats, and calls for integrating applicant characteristics into current theoretical frameworks on applicant reactions to AVIs.
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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 | 0.024 | 0.073 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".