Leaders' political skills and organizational change
Bibliographic record
Abstract
Abstract We studied the impact of leaders’ political skill on employee support for organizational change and the effect of the latter on change fatigue and change outcomes. Drawing from the theoretical characterization of the political skill construct domain proposed by Ferris et al. (2007), we posited that leaders’ political skill during organizational change plays an important role in garnering employee support for change and, ultimately, in achieving positive change outcomes. To conduct the study, we used survey data collected through SurveyMonkey on 197 participants working in Canadian organizations and used structural equation modeling techniques to analyze the data. We found that two dimensions of leaders’ political skill—their networking ability and apparent sincerity—positively affected employee support for change, which, in turn, positively affected change outcomes. However, neither of the other two dimensions examined (interpersonal influence and social astuteness) had a significant relationship with employee support for change. We also found that employee support for change and change fatigue were not significantly related, although change fatigue itself negatively affected change outcomes. From a theoretical perspective, this study is among the first to empirically test and confirm the impact of different dimensions of leaders’ political skill on employees’ change support. We also contribute theoretical knowledge by showing the detrimental effect of change fatigue on change outcomes. From a practical perspective, our findings imply that change leadership roles should be assigned to people who exhibit networking skills and apparent sincerity. Moreover, change leaders should prioritize seeking and securing employees’ support for change.
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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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".