Tell Me More! Examining the Benefits of Adding Structured Probing in Asynchronous Video Interviews
Bibliographic record
Abstract
ABSTRACT The personnel selection field has observed the rising use of asynchronous video interviews (AVI). The current study investigates whether follow‐up questions (probes) can optimize the applicant experience in AVIs. Across two experimental studies with participants recruited from Prolific, we investigated whether AVIs with probing promote applicant reactions (e.g., the opportunity to perform perceptions) toward the AVI and how probing influences interview behaviors, applicant perceptions, and interview performance ratings. In Study 1, 404 participants were randomly assigned to either an AVI with probing or an AVI without probing. Results indicated that probing directly improved the opportunity to perform perceptions and interview performance ratings. In addition, probing positively impacted honest impression management and motivation to perform indirectly through participants' perceived opportunity to perform. However, mediation analyses suggested that the effect of probing on interview performance ratings was driven by response length. In Study 2 ( n = 271), we teased apart the effects of the inherently added response time that probing affords applicants with an additional condition that matched the response time of probes. Relative to Study 1, probing only slightly improved the opportunity to perform perceptions, but the effect of probing on the opportunity to perform perceptions was greater when compared to an AVI with an equivalent response time. In addition, probing positively impacted interview performance ratings, above and beyond their increased response time. Implications, limitations, and directions for future research are discussed.
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 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 | 0.032 | 0.192 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".