Cynical About Change? A Preliminary Meta-Analysis and Future Research Agenda
Bibliographic record
Abstract
Cynicism about organizational change is often considered an important factor that influences employee acceptance of change initiatives. However, cynicism has been compared with several similar constructs with little conceptual or empirical differentiation. To provide a deeper understanding of change cynicism, we compare and contrast it with similar—yet distinct—constructs: organizational trust, resistance to change, and organizational cynicism. We begin with a narrative review of the conceptual distinctions and similarities among these constructs, followed by meta-analysis of the nomological network of change cynicism. In addition, we compare the nomological network of change cynicism with those of organizational cynicism and trust. We discuss the need for a refined conceptualization of cynicism and suggest new areas for future research.
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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 | 0.150 | 0.291 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.037 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".