Making sense of organizational change: Is hindsight really 20/20?
Bibliographic record
Abstract
Summary This qualitative study explores the conceptual links between 2 different approaches to managerial cognition, sensemaking and cognitive bias, in the context of organizational change. A longitudinal case study utilizing both real‐time assessments and retrospective sensemaking data from interviews with 26 hospital employees at 3 points in time was undertaken. Patterns related to individuals' retrospective accounts and real‐time assessments were identified and used to construct 4 prototypical narratives. Data analysis revealed that organizational change was not a markedly negative experience for most informants, which is contrary to the prevailing theme in the literature. This and other findings are discussed in terms of sensemaking and cognitive bias. This study makes 2 contributions to our understanding of how individual's experience and make sense of organizational change over time as (a) little is known about how the process of change unfolds over time at the individual level and (b) extant research has not investigated the extent to which individuals' retrospective sensemaking about organizational change reflects or diverges from their real‐time assessments over the course of the change. More broadly, the study provides insights and focused advice for management researchers regarding the use of retrospective data to understand individuals' perceptions of situations that have already occurred.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".