The cynical subordinate: exploring organizational cynicism, LMX, and loyalty
Bibliographic record
Abstract
Purpose Adopting a social exchange framework, this article examines the relationship between organizational cynicism and leader–member exchange (LMX) using two different methodologies. Design/methodology/approach Study 1 utilizes a longitudinal panel design ( N = 291) to examine the reciprocal relationships between organizational cynicism and LMX over time. Study 2 ( N = 348) positions loyalty as a possible mechanism through which organizational cynicism might impair LMX. Findings Study 1 provides evidence for the existence of some reciprocity in the relationships between organizational cynicism and LMX; however, organizational cynicism appears to be a stronger predictor of LMX than the obverse. The results of Study 2 suggest that cynical employees are less loyal to their supervisors, and this cynicism can interfere with the reciprocity process inherent in the creation and maintenance of high-quality social exchanges at work. Originality/value This is the first study to examine the relations between organizational cynicism and LMX in a longitudinal design. Additionally, the inclusion of loyalty and demonstration that organizational cynicism impacts loyalty to supervisors negatively represents a novel direction in organizational cynicism research.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".