Exploring the curvilinear relationship between LMX and negative affect during organizational change: can transformational leadership brighten the dark corners?
Bibliographic record
Abstract
Purpose While many studies confirm the benefits of high-quality leader-member exchange (LMX), the majority of previous research assumes that “more is better” (i.e. positive leader behaviors), failing to consider more complicated patterns in which the beneficial effect of positive behavior may start to wear out. In this study, drawing on the “too much of a good thing” framework, we aim to explore a curvilinear relationship between LMX and negative affect during an impactful organizational change. Design/methodology/approach To test our hypotheses, we conducted a survey with 160 employees going through a significant organizational change process during a merger and acquisition (M&A) situation. Findings We find that in very low and high levels of LMX, followers tend to experience higher negative affect. Moreover, we investigate whether transformational leadership moderates this curvilinear relationship and find that the curvilinear effect of LMX on negative affect is weaker when the manager displays transformational leadership. Originality/value With our findings, we contribute to the debate on the potential complexities of high-quality LMX relationships and respond to the calls to have a balanced examination of the costs and benefits LMX. Moreover, we discuss the bright and dark sides of leadership, particularly in a change and transformation context.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".