What if my coworker builds a better LMX? The roles of envy and coworker pride for the relationships of LMX social comparison with learning and undermining
Bibliographic record
Abstract
Summary Although the extant literature has demonstrated the benefits of building a higher leader–member exchange (LMX) relationship with a leader, it has overlooked the efforts by lower LMX employees to leverage the difference from higher LMX coworkers. Integrating social comparison theory and EASI theory, we contend that lower LMX social comparison (LMXSC) is associated with positive (self‐improving) and negative (undermining) behavior via different emotional mechanisms and that the focal employee's perceptions of the comparison coworker's pride play a critical role in qualifying the effects of lower LMXSC. The results from a time‐lagged field study and an online experiment reveal that lower LMXSC is associated with both benign and malicious envy, which in turn respectively relate to the focal employee learning and socially undermining the superior coworker. The negative indirect effect of LMXSC on learning behaviors via benign envy is stronger when the coworker compared is perceived to be higher (vs. lower) in authentic pride, whereas the indirect effect of LMXSC on social undermining via malicious envy is stronger when the coworker compared is perceived to be higher (vs. lower) in hubristic pride. We conclude with theoretical and practical implications.
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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 | 0.005 | 0.020 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".