Orchestra of Change: Building Employee Commitment to Multiple Change Initiatives
Bibliographic record
Abstract
Despite the prevalence of concurrent changes, most research in change management has concentrated on success factors for a single change event and ignores the fact that organizational change is often decomposed into multiple initiatives. This tendency potentially hinders our understanding of employee reactions to simultaneous organizational changes. To address this gap, the paper explores the determinants of employee commitment to multiple change initiatives through the theoretical lens of the Job Demands and Resources theory. A qualitative case study was conducted in a French consulting firm. Drawing on 25 semi-structured interviews, 2 written interviews, over 500 hours of observation, and archival documents, the paper develops a comprehensive resources-and-demands framework to evaluate the impact of bottom-up and top-down changes. Findings reveal that despite accounting for less than 10% of the total change initiatives, the impact of bottom-up change far outweighed that of top-down changes. Bottom-up changes fueled employees’ enthusiasm, enhanced their expertise, strengthened team cohesion, and fostered a sense of belonging. Meanwhile, top-down changes triggered negative employee reactions, including feelings of imposition, uncertainty, and a sense of being “lost”. The paper also advances the commitment to change literature by providing evidence of variations in employee commitment to multiple initiatives. As a complex attitude, employee commitment to change varies depending on the type of change initiative. We describe the dynamics of resources and demands of multiple initiatives on employee commitment to change and call for research that examines the impact of change on employees in a cross-border work setting.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".