| Literature DB >> 35223756 |
Wei Liu1, Yaoping Liu2.
Abstract
In the past, different researchers have conducted studies on incentives and how they are linked to employee motivation, influencing emerging economies. This study addresses two gaps as outlined in previous studies. One research gap exists in examining employee loyalty and employee engagement in relation to the business cycle. The other gap is observed in the recommendation that future researchers use different moderators between incentives, the health of employees, and job performance with population health. This focus was explored in the present study by identifying the responses of hospitals and physicians to the business cycle to examine the impact of incentives on job performance and health of workers in public and private sector hospitals in Shandong, Eastern China. Data were collected in the form of questionnaires that consisted of close-ended questions. These questionnaires were then filled out by 171 doctors and 149 nurses working in both public and private sectors in Shandong, Eastern China. The results showed that there is a relation between different variables. Some variables have more impact on other variables such as transformational leadership, which has a significant impact on the job performance and business cycle, whereas monetary incentives also impact job performance and population health, but this impact was lower than that of transformational leadership in terms of how job performance influences emerging economies.Entities:
Keywords: business cycle; economies; employee incentives; health performance; job performance; patient satisfaction; population health; service quality
Mesh:
Year: 2022 PMID: 35223756 PMCID: PMC8866177 DOI: 10.3389/fpubh.2021.778101
Source DB: PubMed Journal: Front Public Health ISSN: 2296-2565
Figure 1Conceptual framework.
Figure 2Normality plot of JPM.
Reliability analysis.
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| ELM | 320 | 2.67 | 1.00 | 3.67 | 2.15 | 0.030 | 0.537 | 0.289 | 0.077 | 0.136 | −0.098 | 0.272 |
| TSLM | 320 | 4.00 | 1.00 | 5.00 | 2.52 | 0.047 | 0.853 | 0.729 | 0.402 | 0.136 | −0.228 | 0.272 |
| MIM | 320 | 4.00 | 1.00 | 5.00 | 2.82 | 0.040 | 0.717 | 0.515 | 0.200 | 0.136 | 0.061 | 0.272 |
| JPM | 320 | 3.13 | 1.13 | 4.25 | 2.49 | 0.033 | 0.591 | 0.350 | 0.238 | 0.136 | 0.402 | 0.272 |
| Valid N (listwise) | 320 | |||||||||||
Min, Minimum; Max, Maximum; SE, Standard error; SD, Standard deviation; N, Total sample.
Correlation matrix.
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| MIM | Pearson correlation | 1 | 0.059 | 0.307 | 0.180 |
| Sig. (2-tailed) | 0.290 | 0.000 | 0.001 | ||
| N | 320 | 320 | 320 | 320 | |
| ELM | Pearson correlation | 0.059 | 1 | 0.323 | 0.240 |
| Sig. (2-tailed) | 0.290 | 0.000 | 0.000 | ||
| N | 320 | 320 | 320 | 320 | |
| TSLM | Pearson correlation | 0.307 | 0.323 | 1 | 0.222 |
| Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | ||
| N | 320 | 320 | 320 | 320 | |
| JPM | Pearson correlation | 0.180 | 0.240 | 0.222 | 1 |
| Sig. (2-tailed) | 0.001 | 0.000 | 0.000 | ||
| N | 320 | 320 | 320 | 320 |
Correlation is significant at the 0.01 level (2-tailed).
Tests of normality.
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| JPM | Doctor | 0.085 | 171 | 0.004 | 0.972 | 171 | 0.002 |
| Nurse | 0.062 | 149 | 0.200 | 0.984 | 149 | 0.088 | |
| ELM | Doctor | 0.136 | 171 | 0.000 | 0.975 | 171 | 0.003 |
| Nurse | 0.080 | 149 | 0.020 | 0.983 | 149 | 0.056 | |
| TSLM | Doctor | 0.126 | 171 | 0.000 | 0.961 | 171 | 0.000 |
| Nurse | 0.131 | 149 | 0.000 | 0.973 | 149 | 0.006 | |
| MIM | Doctor | 0.147 | 171 | 0.000 | 0.962 | 171 | 0.000 |
| Nurse | 0.140 | 149 | 0.000 | 0.974 | 149 | 0.006 | |
This is a lower bound of the true significance.
Lilliefors significance correction.
Levene's test of equality of error variances.
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| 2.779 | 13 | 306 | 0.001 |
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
Design: Intercept + MIM + ELM + MIM .
Figure 3Normality plot of JPM.
Figure 4CFA model.
Model summary.
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| 1 | 0.311 | 0.097 | 0.088 | 0.56512 | 1.774 |
Predictors: (Constant), TSLM, MIM, ELM.
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Coefficients.
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| 1 | (Constant) | 1.513 | 0.177 | 8.543 | 0.000 | |||
| MIM | 0.109 | 0.046 | 0.132 | 2.346 | 0.020 | 0.904 | 1.106 | |
| ELM | 0.213 | 0.062 | 0.193 | 3.419 | 0.001 | 0.894 | 1.119 | |
| TSLM | 0.083 | 0.041 | 0.119 | 2.010 | 0.045 | 0.812 | 1.231 | |
Dependent variable: JPM.
ANOVA.
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| 1 | Regression | 10.791 | 3 | 3.597 | 11.264 | 0.000 |
| Residual | 100.918 | 316 | 0.319 | |||
| Total | 111.709 | 319 | ||||
Dependent variable: JPM.
Predictors: (Constant), TSLM, MIM, ELM.