A theoretical model of cross‐cultural impression management in employment interviews
Bibliographic record
Abstract
Abstract With organizations being increasingly multinational and multicultural, there is a need for understanding the implications of having job applicants and interviewers from various cultural backgrounds interacting in an employment interview. We propose a theoretical model for understanding how cultural values translate into preferences for, and use of, impression management (IM) tactics in employment interviews. Building upon previous cross‐cultural IM models and relying on GLOBE cultural framework, we suggest that various cultural dimensions are associated with subsequent differences in applicants' use of both honest and deceptive forms of self‐focused, other‐focused, and defensive IM tactics in interviews. Our model also predicts that cultural distance, and indirectly difference between applicant IM use and interviewers' expectations, will determine how interviewers evaluate applicant interview performance. We highlight the importance of organizations taking responsibility in developing culturally conscientious selection methods to avoid potentially biased hiring decisions.
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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".