A person‐centered view of impression management, inauthenticity, and employee behavior
Bibliographic record
Abstract
Abstract Impression management (IM)—the strategies through which employees create, maintain, or alter a desired image towards others—is a ubiquitous part of organizational life. To date, scholars studying this interpersonal phenomenon have largely focused on Jones and Pittman's (1982) taxonomy of IM strategies, examining consequences associated with the tactics of ingratiation, self‐promotion, exemplification, supplication, and intimidation on others’ reactions to, and perceptions of, the actor. Thus, scholarly understanding surrounding the implications of IM for employees’ own well‐being is nascent. We integrate ideas from the emotional labor and IM literatures to develop and test theory that explains the impact of IM strategies on the actors themselves. Across three complementary studies spanning 2337 full‐time employees, we use latent profile analysis to investigate how the conjoint use of multiple IM tactics—each of which is associated with a distinct, and sometimes conflicting, image—yields unique consequences for employees’ feelings of inauthenticity at work. In addition, we also explore how profiles of IM tactics differentially relate to theoretically relevant work outcomes, namely coworker ratings of employee performance, work withdrawal, absenteeism, and perceived sincerity. Taken together, our work sheds light on the prevalence and impact of employees combining IM tactics during work interactions.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".