Navigating Organisational Change: Middle Managers’ Sensemaking Practices in a Malaysian Organisation
Bibliographic record
Abstract
Sensemaking is critical for middle managers navigating organisational change, yet research on their sensemaking practices remains limited, particularly in the Malaysian context. This study examines how middle managers in a Malaysian organisation interpret and respond to change, drawing on the Communicative Constitution of Organisations The Montreal School (CCO TMS) theory. Using a qualitative approach, semi-structured interviews were conducted with 30 middle managers to explore their sensemaking strategies. The findings reveal seven key sensemaking practices: adopting a big-picture mindset, demonstrating empathy, reflecting on emotions, relying on Company Approved Procedure guidelines, engaging in storytelling, participating in change intervention programs, and utilizing internal communication channels. These practices enable middle managers to bridge the gap between senior leadership’s strategic vision and employees’ operational realities, fostering alignment and reducing resistance. The study highlights the crucial role of middle managers in facilitating successful change initiatives and underscores the importance of equipping them with communication and sensemaking resources. Organisations should prioritize structured communication strategies and leadership support mechanisms to enhance middle managers’ effectiveness in guiding teams through change.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".