Investigating Impression Management Use in Asynchronous Video Interviews Across 10 Countries (WITHDRAWN)
Bibliographic record
Abstract
This cross-cultural study investigates how interviewees from 10 culturally-distinct countries differ in their use of impression management (IM) tactics in asynchronous video interviews (AVIs), and the relationship(s) between those tactics and interview performance. A total of 582 participants from ten countries (India, Canada, South Africa, Poland, Spain, Iran, Germany, Chile, Philippines, China) completed an 8-question AVI for a mock position as a manager in a bank. We drew upon GLOBE’s cultural framework to predict and explain observed differences in self-reported IM use and performance. Interviewees from our ten countries differed in their IM use for various tactics, but the observed differences were largely inconsistent with GLOBE-based predictions. Partially-consistent with previous in-person interview research, honest IM tactics (e.g., self promotion) were positively, but deceptive tactics (e.g., image creation) negatively, associated with interview performance. This research is the first to investigate cross-cultural IM differences in AVIs, thus addressing a critical gap in the selection literature at a time when many organizations conduct interviews virtually to save costs, streamline the hiring process, or simply conduct most of their activities remotely.
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 | 0.008 | 0.021 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".