Does media richness influence job applicants' experience in asynchronous video interviews? Examining social presence, impression management, anxiety, and performance
Bibliographic record
Abstract
Abstract Asynchronous video interviews (AVIs) have become a popular alternative to face‐to‐face interviews for screening or selecting job applicants, in part because of their increased flexibility and lower costs. However, AVIs are often described as anxiety‐provoking or associated with negative applicant reactions. Building on theories of media richness and social presence, we explore if increasing the media richness of AVIs, by replacing “default” text‐based introductions and written questions with video‐based ones, can positively influence interviewees' experience. In an experimental study with 151 interviewees ( M age = 28.08, 56% female) completing a mock interview, we examine the (direct and indirect) impact of media richness on perceived social presence, interview anxiety, use of honest and deceptive impression management (IM) tactics, and ultimately interview performance. Results showed that media richer AVIs help increase interviewees perceived social presence and improve their interview performance. Higher perceived social presence was also associated with lower interview anxiety and facilitated using IM (especially other‐focused tactics). Our findings highlight that there might be ways for organizations to embrace the practical benefits of AVIs while still ensuring a positive experience for interviewees.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".