Managing change: role of leadership and diversity management
Bibliographic record
Abstract
Purpose The lived paid work experiences of two women (a European Canadian-born and a South Asian immigrant) demonstrate how low-quality leader–member exchanges and poor diversity management negatively influence employees' health, job satisfaction and retention during a period of major organizational change. Design/methodology/approach This paper combined a narrative case study with auto ethnography to examine the lived paid work experiences of the two female authors and identify common patterns of meaning within the data. Findings The analysis of personalized accounts demonstrate the damaging results of a failed change management initiative when leaders did not follow an organizational change model and used an authoritarian leadership style. Further, the low-quality leader–member exchanges and poor diversity management reduced authors' feelings of inclusion and negatively impacted their emotional and physical health, job satisfaction, and retention. Research limitations/implications New knowledge gained about leader–member exchange and diversity management has implications not only for leaders, but also human service managers. The data represents the authors' two perspectives, constraining generalizability. Larger samples of employees' narratives from diverse cultural/work backgrounds would be valuable to inform organizational change. Practical implications The paper provides practical reasons for leadership training and skill development in change management models. Social implications Given global demographic diversity, the findings are relevant to organizations, highlighting the importance of creating a climate of inclusion for workers' job satisfaction and retention and organizational success. Originality/value While the sample size (n = 2) is very small, using a combination of personal experience methods offered insights into the complexity of leader–member exchange and diversity management from workers' perspectives, and went beyond successful cases, adding value to organizational change research.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".