Fake it till you make it with your boss? Surface acting in interactions with leaders
Bibliographic record
Abstract
Due to its influence on important workplace outcomes, surface acting has drawn increasing attention from researchers in recent years. Most of the research in this area has focused on employees’ interactions with individuals external to the organization, such as customers and clients. With the current study, we contribute to and extend the literature by focusing on employees’ leader-directed surface acting and examining how leader-directed surface acting (i.e., faking positive emotions and suppressing negative emotions in interactions with one’s leader) relates to leader ratings of employee task performance. Data collected from 414 employees and 103 leaders showed that employees’ faking positive emotions in interactions with leaders was positively associated with employee withdrawal, but withdrawal was not significantly related to leader-rated task performance. In addition, male employees’ suppressing negative emotions in interactions with leaders was positively associated with leaders’ communication satisfaction, which was, in turn, positively related to leader-rated task performance. Yet, similar effects were not found for female employees. Theoretical and practical study implications are discussed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".