Ambivalence in the Leader-Follower Relationship: Dispositional Antecedents and Effects on Work-Related Well-Being
Bibliographic record
Abstract
Although ambivalence is a long-standing topic of interest in the social sciences, leadermember exchange (LMX) ambivalence and other measures of ambivalence in work settings have only recently attracted attention in the Management literature.To enhance our understanding of the nature of LMX ambivalence, this research investigated specific dispositional antecedents of LMX ambivalence, and whether and how it may influence employee work-related well-being.Using a two-wave design, survey data were collected from employees and their supervisors in a large public organization.Results revealed that specific personality traits, including both supervisor dominance-and prestige-seeking and employee hostility, were significant predictors of LMX ambivalence.Furthermore, LMX ambivalence was found to be significantly associated with two focal measures of work-related well-being: work engagement and emotional exhaustion.Moderated mediation analyses indicated that these relationships were mediated by employee psychological need fulfillment; however, these effects were contingent on two moderating factorsemployee collectivism, and perceived meaning in one's work.Overall, these results suggest that supervisor and subordinate dispositional characteristics may contribute to the development of LMX ambivalence.Moreover, complementing previous work (Lee et al., 2019), these findings signal that LMX ambivalence contributes unique variance in predicting key employee work outcomes beyond traditional operationalizations of LMX.Further research is needed testing the nomological net surrounding LMX ambivalence, and when and how LMX ambivalence affects different employee attitudes and behaviors.
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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 | 0.002 | 0.007 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".