Comparisons draw us close: The influence of leader‐member exchange dyadic comparison on coworker exchange
Bibliographic record
Abstract
Abstract Members compare their differential leader‐member exchanges (LMXs) to understand the triadic relationship (Member A, Member B, and their common leader); this will affect how members interact. Prior research based on balance theory assumes that the two members have a consensus on the structure of the triadic relationship, to argue that when Member A perceives their LMX to be lower than that of Member B, such an LMX imbalance would drive Member A to interact negatively with Member B. Comparison of LMX, however, reflects one's subjective perception, which may not be shared by the other. Therefore, we draw on social comparison theory to examine both members’ comparisons of LMX simultaneously and suggest that when they both perceive the other's LMX as better than their own, they may engage more in affiliative behaviors and develop a higher‐quality coworker exchange (CWX). The results of two studies consistently supported these hypotheses. This research extends our understanding of LMX in triadic relations and demonstrates that mismatched perceptions of LMX dyadic comparison between two members (i.e., both perceive an LMX imbalance) could motivate members to develop a positive relationship.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".