What affects the turnover intention of civil servants: Evidence from Bhutan
Bibliographic record
Abstract
Abstract Research utilising self‐determination theory (SDT) and, in particular, the concept of need satisfaction in public organisations has been increasing in recent years. At the same time, most studies are using insights from SDT, and are not really testing them. In fact, we still have limited knowledge on the outcomes of need satisfaction for civil servants. In this study, we aim to understand how need satisfaction affects the intrinsic motivation of civil servants, as well as their intention to leave the organisation in which they currently work. Using original data from 580 civil servants in Bhutan, this study finds that need satisfaction matters for intrinsic motivation and turnover intention. More specifically, this study finds that while need satisfaction has a positive effect on intrinsic motivation, it has a negative effect on turnover intention. Intrinsic motivation also mediates this relationship. To reduce turnover intention, policymakers may need to enhance public sector employees’ need satisfaction and their intrinsic motivation. These findings are consistent with Bhutan's context, in which happiness and human connection and fulfilment are more important than economic values. Points for practitioners Civil servants of Bhutan whose needs are satisfied are more likely to be intrinsically motivated and less likely to express an intention to quit their jobs. To reduce turnover intention, policymakers may need to enhance public sector employees’ need satisfaction and their intrinsic motivation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".