| Literature DB >> 33868106 |
Xiaotong Jin1, Yurou Zhao1, Wei Song1, Taiyang Zhao2.
Abstract
In public health emergencies, people are more willing to save money rather than spending it, which is not conductive to economic development and recovery. Due to the absence of relevant research, the internal logic of this phenomenon is not clear. In the context of the COVID-19 pandemic, this study systematically explored whether and why public health emergencies stimulate consumers' preference for saving (vs. spending). We conducted two online surveys and used methods including stepwise regression analysis and bootstrapping to test the hypotheses. The first survey, with 1,511 participants from China in February 2020, indicates that the severity of emergencies has a significant positive impact on the populations' willingness to save (vs. spend). Risk perception plays a mediating role between the severity of emergencies and consumers' saving (vs. spending) willingness. Materialism plays a moderating role between risk perception and an individual's saving (vs. spending) willingness, individuals who are more materialistic have a lower saving (vs. spending) willingness when they perceive the risks of the pandemic. To verify the duration of the above effects, we conducted a follow-up survey consisted of 466 instances in August 2020. It is noteworthy that the above effects are not significant during the post-pandemic period. Thus, spending behavior in public health emergencies can be motived by reducing risk perception and increasing materialism. These findings can provide a valuable inspiration for public health, crisis management, and economic recovery during public health emergencies.Entities:
Keywords: COVID-19; materialism; post-pandemic; risk perception; saving and spending
Year: 2021 PMID: 33868106 PMCID: PMC8047313 DOI: 10.3389/fpsyg.2021.636859
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
Demographic information of sample (N = 1511).
| Gender | Male | 833 | 55.1 | Age | <25 | 704 | 46.6 |
| Female | 678 | 44.9 | 25–40 | 706 | 46.7 | ||
| Income per month | <3000 RMB | 566 | 37.5 | >40 | 101 | 6.7 | |
| 3000–6000 | 543 | 35.9 | Education | High school | 351 | 23.2 | |
| 6000–9000 | 245 | 16.2 | Bachelor's degree | 1020 | 67.5 | ||
| >9000 RMB | 157 | 10.4 | Master's degree | 140 | 9.3 |
Reliability and validity analysis (N = 1511).
| SE | CNP | 0.931 | 0.889 | 0.674 | 0.895 |
| NNP | 0.964 | ||||
| CNC | 0.659 | ||||
| NNC | 0.681 | ||||
| SA | SA1 | 0.766 | 0.818 | 0.602 | 0.805 |
| SA2 | 0.693 | ||||
| SA3 | 0.860 | ||||
| RP | RP1 | 0.815 | 0.838 | 0.633 | 0.836 |
| RP2 | 0.842 | ||||
| RP3 | 0.726 | ||||
| MA | MA1 | 0.731 | 0.872 | 0.462 | 0.871 |
| MA2 | 0.680 | ||||
| MA3 | 0.697 | ||||
| MA4 | 0.645 | ||||
| MA5 | 0.702 | ||||
| MA6 | 0.776 | ||||
| MA7 | 0.605 | ||||
| MA8 | 0.578 | ||||
| Model Fit: χ2/df = 1.092, RMSEA = 0.008, CFI = 0.999, IFI = 0.999,TLI = 0.999 | |||||
C.R., composite reliability; AVE, average variance extracted; RMSEA, root mean square error of approximation; CFI, comparative fit index; IFI, incremental fit index; and TLI, Tucker–Lewis Index.
Correlation and coefficient matrix (N = 1511).
| SE | ||||
| RP | 0.036 | |||
| MA | 0.147 | 0.008 | ||
| SA | 0.036 | 0.082 | −0.006 |
p < 0.01. The diagonal bold numbers are AVE square roots.
Regression test of the mediating effect during pandemic (N = 1511).
| Gender (female =0) | −0.060 | −0.063 | −0.065 | 0.028 | 0.025 |
| Age | 0.109 | 0.106 | 0.104 | 0.040 | 0.037 |
| Education | 0.074 | 0.074 | 0.072 | 0.038 | 0.039 |
| Monthly income | −0.111 | −0.119 | −0.110 | −0.115 | −0.124 |
| SE | 0.051 | 0.047 | 0.053 | ||
| RP | 0.070 | ||||
| 0.026 | 0.029 | 0.034 | 0.011 | 0.014 | |
| 0.024 | 0.026 | 0.030 | 0.008 | 0.010 | |
| 10.187 | 8.936 | 8.733 | 4.198 | 4.178 | |
p < 0.05,
p < 0.01, and
p < 0.001.
Results of mediating effect during pandemic (N = 1511).
| SE->SA | Total effect | 0.082 | 0.042 | 0.001 | 0.163 |
| SE->RP->SA | Indirect effect | 0.006 | 0.004 | 0.001 | 0.016 |
| Direct effect | 0.076 | 0.042 | −0.006 | 0.157 | |
Regression test of mediating effect after pandemic (N = 466).
| Gender (female = 0) | −0.089 | −0.089 | −0.088 | −0.008 | −0.009 |
| Age | −0.131 | −0.130 | −0.136 | 0.047 | 0.048 |
| Education | −0.052 | −0.052 | −0.051 | −0.007 | −0.007 |
| Monthly income | −0.052 | −0.055 | −0.056 | 0.009 | 0.004 |
| SE | 0.013 | 0.010 | 0.022 | ||
| RP | 0.124 | ||||
| 0.034 | 0.034 | 0.049 | 0.003 | 0.003 | |
| 0.026 | 0.024 | 0.037 | −0.006 | −0.008 | |
| 4.052 | 3.250 | 3.974 | 0.293 | 0.274 | |
p < 0.05,
p < 0.01, and
p < 0.001.
Regression test of moderating effect during pandemic (N = 1511).
| Sex | −0.060 | −2.284 | −0.062 | −2.366 | −0.060 | −2.283 |
| Age | 0.109 | 3.999 | 0.106 | 3.873 | 0.104 | 3.800 |
| Monthly income | −0.111 | −3.931 | −0.102 | −3.619 | −0.101 | −3.567 |
| Education | 0.074 | 2.808 | 0.071 | 2.708 | 0.070 | 2.666 |
| RP | 0.072 | 2.839 | 0.074 | 2.886 | ||
| MA | −0.002 | −0.085 | −0.003 | −0.126 | ||
| RP×MA | −0.062 | −2.432 | ||||
| 0.026 | 0.032 | 0.035 | ||||
| Adjust | 0.024 | 0.028 | 0.031 | |||
| 10.187 | 4.032 | 5.914 | ||||
p < 0.05,
p < 0.01, and
p < 0.001.
Result of moderating effect during pandemic (N = 1511).
| MA | SE->RP->SA | 2.304 | 0.011 | 0.002 | 0.028 |
| 3.045 | 0.006 | 0.001 | 0.017 | ||
| 3.786 | 0.001 | −0.005 | 0.011 | ||