Can’t get you o <i>u</i> t of my head: The stress-driven dual effects of LMX Ambivalence
Bibliographic record
Abstract
How can a complicated, ambivalent relationship with a boss be both draining and generative? This paper challenges the view that leader–member exchange (LMX) ambivalence is solely harmful. Using the Challenge–Hindrance Stressor Framework, we examine how conflicting feelings toward a leader can be experienced as both constraining and motivating. We focus on epistemic motivation—the tendency to seek deeper understanding—as a key factor that shapes how followers process such ambiguity. Across three studies, LMX ambivalence was linked to two distinct outcomes: emotional exhaustion and voice. These associations operated through different ruminative pathways: affective rumination, characterized by intrusive negative thoughts, and problem-solving pondering, involving reflective sense-making. Followers higher in epistemic motivation were less inclined toward affective rumination and more inclined toward problem-solving pondering, thereby strengthening the link between ambivalence and constructive voice while softening its association with exhaustion. Our findings highlight the hybrid nature of LMX ambivalence and suggest that it does not uniformly undermine followers but can also be associated with adaptive engagement. By unpacking the interplay of ambivalence, rumination, and epistemic motivation, this research provides a more balanced account of the complexities of leader–follower 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 | 0.003 | 0.009 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".