| Literature DB >> 34415940 |
Ai-Xiang Zheng1,2.
Abstract
New-generation migrant workers in Chinese cities are struggling with a lack of urban resources, such as capital, skills, and relationships. To cope with the pressure of these resource constraints, new-generation migrant workers obtain urban development opportunities through resource bricolage. Based on a questionnaire survey of 365 new-generation migrant workers, we used a multiple regression analysis to study the mechanism underlying the effects of resource bricolage on the city integration of new-generation migrant workers. There were four findings: (1) resource bricolage had a significant positive effect on career growth and city integration; (2) career growth had a mediation effect on the relationship between resource bricolage and city integration; (3) environmental dynamism had a positive moderating effect on the relationship between resource bricolage and city integration for new-generation migrant workers; and (4) resource bricolage and environmental dynamism had a moderating effect on city integration through the mediation effect of career growth. The results suggest that resource bricolage promotes the career growth of new-generation migrant workers and further promotes their city integration, and that the environmental dynamism faced by workers is the external condition for promoting integration through resource bricolage. The study emphasizes the importance of resource bricolage in new-generation migrant workers' city integration.Entities:
Mesh:
Year: 2021 PMID: 34415940 PMCID: PMC8378738 DOI: 10.1371/journal.pone.0256332
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Theoretical model of the mechanisms of resource bricolage on urban integration.
Sample characteristics (N = 309).
| Classification |
| Percentage | Classification |
| Percentage | ||
|---|---|---|---|---|---|---|---|
|
| Male | 213 | 68.9 |
| Below 2,000 | 11 | 3.6 |
| Female | 96 | 31.1 | 2,000–3,000 | 37 | 12.0 | ||
|
| 16–20 | 24 | 7.8 | 3,000–4,000 | 74 | 23.9 | |
| 21–25 | 71 | 23.0 | 4,000–5,000 | 63 | 20.4 | ||
| 26–30 | 55 | 17.8 | 5,000–6,000 | 63 | 20.4 | ||
| 31–35 | 59 | 19.1 | 6,000–7,000 | 36 | 11.7 | ||
| 36–40 | 84 | 27.2 | 7,000–8,000 | 14 | 4.5 | ||
| 40–41 | 16 | 5.2 | 8,000–9,000 | 11 | 3.6 | ||
|
| Primary school and below | 3 | 1.0 | 9,000 or above | 12 | 3.3 | |
| Middle school | 71 | 23.0 |
| Married | 257 | 83.2 | |
| High school | 155 | 50.2 | Unmarried | 44 | 14.2 | ||
| College | 75 | 24.3 | Divorced | 8 | 2.6 | ||
| Above college | 5 | 1.6 | Total | 309 | 100% |
Means, standard deviations, and correlation matrix.
| Variable | Mean | Standard deviation | Gender | Age | Education level | Resource bricolage | Career growth | Environmental dynamism | Urban integration |
|---|---|---|---|---|---|---|---|---|---|
| Gender | 0.689 | 0.464 | 1 | — | — | — | — | — | — |
| Age | 3.504 | 1.427 | 0.194 | 1 | — | — | — | — | — |
| Education level | 3.025 | 0.76 | −0.208 | −0.362 | 1 | — | — | — | — |
| Resource bricolage | 3.547 | 0.555 | −0.195 | −0.108 | 0.013 | (0.822) | — | — | — |
| Career growth | 3.288 | 0.607 | −0.059 | −0.052 | −0.035 | 0.592 | (0.856) | — | — |
| Environmental dynamism | 3.521 | 0.593 | −0.020 | 0.037 | −0.087 | 0.527 | 0.576 | (0.720) | — |
| Urban integration | 3.475 | 0.493 | 0.102 | 0.238 | −0.104 | 0.344 | 0.435 | 0.363 | (0.740) |
The diagonal of the correlation coefficient is the arithmetic square root of the AVE (average variance extracted).
* p < 0.05
** p < 0.01.
Reliability and validity results.
| Variable | CR | Cronbach’s α | KMO |
|---|---|---|---|
| Resource bricolage | 0.943 | 0.943 | 0.929 |
| Career growth | 0.976 | 0.956 | 0.945 |
| Environmental dynamism | 0.798 | 0.776 | 0.749 |
| Urban integration | 0.947 | 0.865 | 0.860 |
CR = composite reliability; KMO = Kaiser–Meyer–Olkin value.
Regression analysis results.
| Urban integration | Career growth | Urban integration | Urban integration | Career growth | Model | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9 | |||
| Gender | 0.057 | 0.132 | 0.053 | 0.085 | 0.114 | 0.118 | 0.121 | 0.034 | 0.112 | ||
| Age | 0.223 | 0.260 | −0.011 | 0.253 | 0.264 | 0.246 | 0.255 | −0.023 | 0.261 | ||
| Education level | −0.011 | 0.013 | −0.036 | 0.022 | 0.025 | 0.026 | 0.042 | 0.002 | 0.041 | ||
| Resource bricolage | 0.397 | 0.602 | 0.191 | 0.275 | 0.266 | 0.393 | 0.159 | ||||
| Environmental dynamism | 0.225 | 0.191 | 0.327 | 0.102 | |||||||
| Resource bricolage × Environmental dynamism | 0.139 | 0.155 | 0.097 | ||||||||
| Career growth | 0.453 | 0.343 | 0.272 | ||||||||
| F | 6.497 | 20.293 | 41.803 | 27.223 | 24.331 | 19.809 | 18.030 | 44.649 | 18.677 | ||
| R2 | 0.060 | 0.211 | 0.355 | 0.264 | 0.286 | 0.246 | 0.264 | 0.470 | 0.303 | ||
* p < 0.1
** p < 0.01
*** p < 0.001.