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Investigating Impression Management Use in Asynchronous Video Interviews Across 10 Countries (WITHDRAWN)

2024· article· en· W4400442965 on OpenAlexaffabout
René Arseneault, Nicolas Roulin

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsImpressionImpression managementAsynchronous communicationComputer sciencePsychologySocial psychologyWorld Wide WebTelecommunications

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.362
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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