Antecedents and outcome of employee change fatigue and change cynicism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it