Public service motivation in the Chinese public and private sectors
Bibliographic record
Abstract
Purpose The purpose of this paper is to statistically test if the public service motivation (PSM) measure operates in the same way across the public and private sectors of a municipal district in China. It also contrasts the relationship between PSM and workplace outcomes across sectors and employee age groups. Design/methodology/approach Survey data from independent samples of public ( n =220) and private ( n =230) sector employees in the Changsha Municipal District of China is used. The analysis tests for invariance across groups, before comparing mean values and regression weights. Findings Only in respect of one PSM dimension do findings show a significant higher mean in the public sector. No significant difference is found on the impact of PSM on employee performance across sectors, while it is in the private sector that PSM has the greater impact on intention to leave. Findings also show no marked impact of age upon outcomes. Research limitations/implications This study provides an initial set of results and further research will need to be undertaken to verify them. The limited sample size and narrow geographical focus, although in line with similar studies on China, means the ability to draw generalisations is limited. The reliance on self-reported measures means issues with common method bias cannot be ignored. Measures were taken during data collection to minimise issues of bias and a set of post-hoc test results are provided. Practical implications The recruitment of employees with higher levels of PSM can be expected to play a role in achieving better outcomes, regardless of sector and age profile. Originality/value The PSM measure has been applied by researchers across various economic sectors. This paper is one of the first to statistically test if the concept and its measure operates in the same way across sectors. The paper contributes to the on-going debate on PSM in the context of China and its relationship with a number of key output variables. Finally, the paper contributes to the emerging debate on changing workforce demographics and their role in shaping outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".