Does It Take Two to Tango? Examining How Applicants and Interviewers Adapt Their Impression Management to Each Other
Bibliographic record
Abstract
Abstract Although research has long examined applicants’ use of impression management (IM) behaviors in the interview, interviewers’ IM has only been recently investigated, and no research has attempted to combine both. The aim of this research was to examine whether and how applicants and interviewers adapt their IM to one another. To answer this question, we bring together IM, signaling theory, and the concept of adjacency pairs from linguistics, and carried out two studies. Study 1 was an observational study with field data ( N = 30 interviews including a total of 6290 turns of speech by interviewers and applicants). Results showed that both applicants and interviewers are more likely to engage in IM in a way that can be considered as a “preferred” (vs. “dispreferred”) response pattern. That is, self-focused IM is particularly likely to occur as a response to other-focused IM, other-focused IM as a response to self-focused IM, and job/organization-focused IM as a response to job/organization-focused IM. In study 2, we used a within-subjects design to experimentally manipulate interviewer IM and examine its impact on ( N = 120) applicants’ IM behaviors during the interview. Applicants who engaged more in “preferred” IM responses were evaluated as performing better in the interview by external raters. However, “preferred” IM responses were not associated with any other interview outcomes. Altogether, our findings highlight the adaptive nature of interpersonal influence in employment interviews, and call for more research examining the dynamic interactions between interviewers and applicants.
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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".