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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".