Exploring the Impact of Servant Leadership on Thriving at Work and Adaptive Performance
Bibliographic record
Abstract
Servant leadership has been found to be associated with a wide range of positive employee work outcomes; however, we have a limited understanding of whether and how it may contribute to employee thriving and adaptability in the workplace.Drawing on Spreitzer and Porath's (2014) integrative model of human growth at work and relevant theories of self-esteem (e.g., sociometer theory), this research examined the influence of servant leadership on employee thriving and adaptive performance, and the potential mediating role of organizationbased self esteem (OBSE) on these relationships.Using a three-wave design, survey data were collected from employees and their supervisors in a medium-sized private sector organization.Results revealed that servant leadership is positively associated with both employee thriving and adaptive performance partly via its effects on OBSE.Furthermore, results indicated that the relationship between servant leadership on OBSE was more pronounced when employees reported lower levels of power distance, signaling that employees lower on power distance may be more responsive to the more personalized approach displayed by servant leaders.Taken together, this research highlights the integral role that servant leadership may play in shaping OBSE, and in turn, fostering employee thriving and adaptability.Further research is needed examining how servant leadership influences well-being in the workplace, including the effects of servant leadership on the well-being and career success of the leaders themselves.like to thank my internal examiner, Dr. Kate Dupré and my external examiner, Dr. Aareni Uruthirapathy, for sharing their time and expertise during my thesis defense and for providing very thoughtful ideas and suggestions to make my dissertation better.I would like to extend my appreciation to my fellow researcher and colleague, Jade Han
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".