Social Comparisons, Self-Conceptions, and Attributions: Assessing the Self-Related Contingencies in Leader-Member Exchange Relationships
Bibliographic record
Abstract
Research on leader-member exchange (LMX) has demonstrated that, in addition to the value of LMX as an indicator of quality relationships with leaders, employees also evaluate how their relationship with the leader compares to other employees' relationship with the leader. This finding led to the emergence of LMX social comparison (LMXSC). This study examines how LMX vs. LMXSC relates to work outcomes and considers the employee and perceived supervisor self-concept levels as moderators. We posit that LMX predicts work performance through increased organizational commitment. We further suggest that the relational and collective levels of the self-concept act as contingencies of the relationships among LMX, LMXSC, commitment, and performance. A sample of 250 employee-supervisor dyads was used to test the hypotheses. LMX predicted commitment and, indirectly, performance. The employee and perceived supervisor relational self-concepts acted as moderators of LMXSC, and the perceived supervisor collective self-concept acted as a moderator of LMX and LMXSC. However, not all moderation hypotheses were supported. Unexpected moderating effects involving the employee and perceived supervisor individual self-concepts, as well as main effects, were also uncovered. This study helps differentiate LMX from LMXSC and understand the role of self-conceptions, including self-conceptions attributed by employees to the leader, in leader-member relationships.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".