The impact of videoconference technology, interview structure, and interviewer gender on interviewer evaluations in the employment interview: A field experiment
Bibliographic record
Abstract
Despite the growing use of communication technologies, such as videoconferencing, in recruiting and selection, there is little research examining whether these technologies influence interviewers' perceptions of candidates. The present field experiment analysed evaluations of 92 real job applicants who were randomly assigned either to be interviewed face‐to‐face (FTF) ( N = 48) or using a desktop videoconference system ( N = 44). The results show a bias in favour of the videoconference applicants relative to FTF applicants, F (1,91) = 7.35, p = .01. A significant interaction of interview structure and interviewer gender was also found, F (1,91) = 3.70, p < .05, with female interviewers using an unstructured interview rating applicants significantly higher than males or females using a structured interview. Interview structure did not significantly moderate the influence of interview medium on interviewers' evaluations of applicants. These findings highlight the need to be aware of potential biases resulting from the use of communication technologies in the hiring process.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".