An Exploration of the Dark Side of LMX through a Relational Perspective: A Conceptual Framework
Bibliographic record
Abstract
LMX has been found to be positively related to multiple beneficial outcomes. However, recent evidence suggests it may have its downsides. This paper addresses this issue by developing a conceptual framework that seeks to explain why, at high levels, LMX may lead to negative follower-related outcomes. Drawing upon relational identification theory and the literature on blended friendship and multiplex relationships, we propose a model that describes the process by which a high LMX relationship may lead to various undesirable outcomes related to the follower well-being, interpersonal relationships, attitudes and behaviors. Specifically, we propose that a follower involved in a high LMX relationship may come to perceive the relationship with the supervisor as a blended friendship. We identify key variables that increase the likelihood of such a perception and argue that three mechanisms – role conflicts, contagion processes and role- relationship identity maintenance strategies - explain why it may lead to undesirable outcomes. We delineate propositions, and discuss our framework implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".