A comparative assessment of videoconference and face-to-face employment interviews
Bibliographic record
Abstract
Purpose – Based on theories of media richness and procedural justice, the authors aim to examine the influence of videoconferencing (VC) technology on applicant reactions and interviewer judgments in the employment interview, the most commonly used employee selection device. Design/methodology/approach – MBA students participated in simulated VC and face-to-face (FTF) interviews. Applicant perceptions of procedural justice and interviewer characteristics were collected. Interviewers provided ratings of affect toward the applicant, perceived applicant competence, overall interview performance, as well as an overall hiring recommendation. Findings – Applicants perceived VC interviews as offering less of a chance to perform and as yielding less selection information. They also viewed VC interviews as less job-related than FTF interviews and had significantly less favorable evaluations of their interviewer (on personableness, trustworthiness, competence, and physical appearance) in VC interviews. Finally, applicants in VC interviews received lower ratings of affect (likeability) and lower interview scores, and were less likely to be recommended for the position. Research limitations/implications – The authors' findings suggest that VC technology can adversely affect both applicant reactions and interviewer judgments. They propose several precautionary steps to help minimize the risks associated with conducting VC interviews. Originality/value – The authors extend prior research concerning the use of VC interviews by directly assessing applicant perceptions of both procedural justice and of interviewer characteristics associated with the probability that job offers will be accepted. They also add to the literature in showing that VC interviews tend to result in less favorable evaluations of applicants than FTF interviews.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".