Antecedents and outcome of employee change fatigue and change cynicism
Bibliographic record
Abstract
Purpose Organisations implement changes either to address real business imperatives or to follow trends in their industries. But frequent changes in an organisation often lead to employee change fatigue and change cynicism. The purpose of this study is to investigate the impact of the change logic of appropriateness and the logic of consequences on change fatigue and change cynicism and the impact of change fatigue and change cynicism on change success. Design/methodology/approach To carry out this study, the authors collected data on a sample of 320 participants from diverse organisations, and they used structural equation modelling (SEM) techniques to test our hypotheses depicted in the research model. Findings The authors found that the change logic of consequences reduces both change fatigue and change cynicism, whereas the change logic of appropriateness increases change fatigue. The authors also found that change fatigue does not have any direct effect on change success, although it maintains an indirect negative effect on change success through change cynicism. Practical implications Along with other practical implications, the authors recommend that change managers help employees understand any logic of consequences that sustain their change initiatives. Additionally, change managers should work to prevent change fatigue from turning into change cynicism, which is the real precursor of reduced change success. Originality/value This study is among the first to show that employees experience change fatigue and change cynicism differently, depending on the reason underlying the change. It is also among the first to show that change fatigue does not affect change success directly but does so through the interplay of change cynicism.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".