Bibliographic record
Abstract
Purpose – Change excessiveness is argued as a critical contextual aspect of change management. The purpose of this paper is to identify three major dimensions to change excessiveness: change frequency, extent, and impact. A three-factor structure is proposed to broaden the emerging study on the contextual aspects of change. Its pertinence is proposed in addressing healthcare employees’ exhaustion, change-related uncertainty, and support for change. Design/methodology/approach – Using questionnaires, a first pilot sample (n=131) was recruited to test the psychometric properties and validity of the three-factor structure, while controlling for affectivity. Structural equation modeling techniques following a two-step approach were used on a second sample (n=363). First a confirmatory assessment of the three-factor structure of excessive change is tested. Second, a full mediation effect of excessive change, as a second-order latent factor, regrouping change frequency, impact and extent as first-order factors, was modeled to predict a tripartite conception of change-related reactions: exhaustion, uncertainty, and support for change. Findings – The excessive change three-factor structure is validated, while showing its superiority over alternative models. The fully mediated model is confirmed. Therefore, the significant added effects of change frequency, impact, and extent are positively related to emotional exhaustion and cognitive uncertainty, while negatively related with behavioral support for change. Originality/value – This study contributes by proposing a three-factor structure to excessive change assessment based on previous and independent findings in the literature. It also contributes in modeling the added effect of change frequency, extent, and impact in the full mediation relationship of change excessiveness on a tripartite reactions to change in healthcare management settings.
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.
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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".