A Primer on Herding Cats: 25 Years of Qualitative Studies on Change Recipients' Reactions
Bibliographic record
Abstract
This paper offers a meta-analysis of qualitative studies of individuals’ reactions to organizational change published in high impact factor journals. Through an inductive review of articles published between 1990 and 2014, the authors provide insights into the types of studies conducted, the research methods employed, the objectives stated, and the change consequences reported, at both an individual and organizational level. Additionally, this paper provides a roadmap for readers who wish to undertake a similar initiative. On the basis of their review the authors conclude that most of the change initiatives were planned and initiated from the top-down. In terms of the consequences of change, one interesting observation is that the ratio of adverse to beneficial consequences is reversed between the organization and the individual. Plainly put, the papers included in this review support the idea that organizations gain benefits from change while recipients suffer. Another observation from the findings is that nearly half of the studies looked at recipients’ reactions beyond the purely organizational context and considered their effect on the whole person, whether it be their identity, their sense of self- worth, or their relationships with others. The authors conclude by proposing directions for future research and implications for practitioners.
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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 | 0.198 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.016 | 0.043 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".