The Insider Perspective: Three Emic Studies About Sensemaking and Organizational Change Over Time
Bibliographic record
Abstract
Scholars contend that our understanding of organizational change would benefit from research that more closely represents the lived reality of organizational change.Research on sensemaking is particularly valuable in this vein because of its focus on the individual's experience and understanding.Existing empirical work on sensemaking about organizational change, however, is largely based on retrospective interview data and/or focuses on a narrow window of time during change implementation and thus does not thoroughly consider how individuals' make sense of change over time.This thesis seeks to address this limitation through three complementary studies aimed at understanding how individuals make sense of organizational change over time from an emic (or insider) perspective.All three papers are interpretive process-oriented case studies, which use grounded theory data analysis techniques to analyze interview data for the same 26 hospital employees collected at three points in time over a five year period of organizational change.Paper one is about the interpretation stage of sensemaking.This paper identifies four stable sensemaking lenses and develops two new concepts related to how individuals make sense of organizational change over time: (1) sensemaking constancy and (2) sensemaking scope.Paper two considers sensemaking triggers and explores how individuals' retrospective sensemaking compares to their real-time assessments during change.Findings indicated that in "real-time" the experience of change varied in somewhat stable way between groups but that when "looking back" over the change, change agents tended to view the changes more positively than change recipients.Finally, paper three looks at the enactment stage of the sensemaking process
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".