3.1. Characteristics of Respondents
A total of 339 valid questionnaires were obtained in this study. According to the reliability test of the questionnaire data, the Cronbach’s alpha coefficient reached 0.811, which indicated good consistency of the scale [
48].
Table 2 showed the sociodemographic attributes of the respondents in Shenzhen on four basic aspects: gender, age, education level and monthly income.
Among the 339 respondents, the proportion of male (50.74%) and female (49.26%) were rather close. For each district, the largest gap between male and female was less than 3%, indicating that the sample size was balanced in terms of gender. The majority (approximately 85%) of the respondents aged from 20 to 49 years old, more specifically, the proportions of people in their 20′s, 30′s and 40′s were 44.84%, 28.02% and 11.51%, respectively. With respect to education level, none of the respondents held a master’s degree or higher and high school education (36.28%) accounted for the most. In detail, most respondents in Xixiang District had middle school or below education, while most respondents held high school degree in Shatou District and bachelor’s degree in Nanwan District. Regarding monthly income, 88.79% of respondents made less than 10,000 RMB per month and only 3.54% of the respondents earned more than 20,000 RMB. The distribution of income in the three communities was basically consistent with the overall population. On the whole, the respondents in the three districts were representative of the general population.
3.2. Protective Coping Behaviors in Three Districts
Among the 339 samples, 89 respondents (26.25%) were from Xixiang District, 72 (21.24%) were from Shatou District and 178 (52.51%) were from Nanwan District, which was consistent with the population distribution of the three districts (the population of each district was approximately 66,000, 63,000 and 122,000, respectively, about 1:1:2).
Table 3 showed the descriptive statistics of the respondents’ intentions to take protective coping behaviors in three areas.
As shown in
Table 3, the overall mean score of the 339 respondents’ intentions to take protective coping behaviors in Shenzhen was 3.76 (SD = 1.225) on a scale of 1–5. This means that respondents in Shenzhen had a slightly higher than medium level of coping behavior intention during flood disasters, which further indicated a greater risk of potential flood loss in Shenzhen. Nanwan District had the highest level of coping behavior intention, with mean of 3.85 (SD = 1.176), exceeding the grand mean, which could be explained by being one of the most severe flood-prone areas in Shenzhen. On the contrary, the levels of coping behavior intention of Xixiang District and Shatou District were lower than average, with mean of 3.71 (SD = 1.168) and 3.64 (SD = 1.359), respectively. However, it was found that the
p-values of the pairwise comparisons were all greater than 0.05 (Xixiang District and Shatou District: t(336) = −0.350,
p = 0.727; Xixiang District and Nanwan District: t(336) = −1.307,
p = 0.192; Shatou District and Nanwan District: t(336) = −0.818,
p = 0.414), which indicated that the differences of the levels of protective coping behaviors among the three districts was insignificant.
3.3. Correlation between Factors
The purpose of the correlation analysis between each influential factors and protective coping behaviors was twofold: (a) to verify whether the influential factors in
Table 1 were associated with protective coping behaviors; (b) to find out how these factors were correlated with protective coping behaviors.
As shown in
Table 4, gender, perception of local flooding likelihood and monthly income proved to be unrelated to protective coping behaviors, because the
p-values of these factors were all greater than 0.05 and the correlation coefficients were all less than 0.1. Correlation analysis failed to provide evidence for the impact of the gender and perception of local flooding likelihood on protective coping behaviors of the public. Moreover, monthly income was also considered to be independent from protective coping behaviors through the correlation analysis. This result was similar to the findings of Meyer et al. [
38], which discovered that there was no significant correlation between income and evacuation intention.
Age, education level, flood risk perception, flood experience, knowledge of flood damage, trust in government, worry, insurance willingness, familiarity of self-help measures passed the correlation test, which indicated that changes in these factors will affect protective coping behaviors when people are faced with flood disasters. Among these factors, only age was negatively correlated with protective coping behaviors—that was, the willingness to cope protectively diminished as people got older. Except for age, all the other factors were positively correlated with protective coping behaviors.
Figure 2 intuitively showed the correlations between each factor and protective coping behaviors, from which we could tell the ranking of the degree of correlation. In terms of the correlation degree, the five factors that had the greatest influences on protective coping behaviors were: trust in government (r = 0.403), flood experience (r = 0.334), familiarity of self-help measures (r = 0.235), knowledge of flood damage (r = 0.180), insurance willingness (r = 0.175). The correlation coefficients of trust in government and flood experience were greater than 0.3, indicating that they were the key factors affecting coping behaviors of the public. Additionally, the impact of other factors on protective coping behaviors was not neglectable. The correlations with familiarity of self-help measures, knowledge of flood damage, insurance willingness, flood risk perception, worry and education level were 0.235, 0.180, 0.175, 0.156, 0.148 and 0.115, respectively. A weak correlation between flood risk perception and protective coping behaviors was found in this study.
3.4. Influential Factors of Protective Coping Behaviors
(1) Linear regression model
Regression analysis was carried out to explore the linear relationships between the influential factors and protective coping behaviors of the residents in Shenzhen. Initially, a multiple regression model which contained 9 influential factors was established with a significance level of 0.05.
Factors that passed the correlation test (age, education level, flood risk perception, flood experience, knowledge of flood damage, trust in government, worry, insurance willingness and familiarity of self-help measures) were considered to be closely related to protective coping behaviors. Therefore, all these factors were included in the initial multiple linear regression model. The results showed that this model was significant (F(9, 329) = 11.459, p = 0.000) to predict protective coping behaviors. The adjusted R-square was 0.218, which means 21.8% variation in the dependent variable (protective coping behaviors) could be explained by the regression.
As shown in
Table 5, age (
p = 0.010), flood experience (
p = 0.002) and trust in government (
p = 0.000) were regarded as main influential factors of protective coping behaviors in this model, because the significance level of these three factors were all less than 0.05. However, the other variables (education, flood risk perception, knowledge of flood damage, worry, insurance willingness and familiarity of self-help measures) were insignificant in predicting protective coping behaviors. Nevertheless, the influence of these factors on protective coping behaviors was still worth discussing.
The regression coefficient (B) measured the linear effect level of independent variables on coping behaviors. The regression coefficients of trust in government, 0.330, was the highest in this model, followed by flood experience, which was 0.172. The regression coefficient of age was negative (B = −0.162), which was consistent with the results from the correlation analysis. Among the insignificant factors, the regression coefficients of worry (B = 0.084) and familiarity of self-help measures (B = 0.093) were nonignorable.
To summarize, through the multiple linear regression model, three main influential factors of protective coping behaviors, age, flood experience and trust in government, were identified. Therefore, when studying coping behaviors of the public, these three factors should be taken into account. Even though the other factors were found to be insignificant in this linear regression, it did not mean that they had no effect on coping behaviors. In the next section, the interaction effects of these variables were studied, instead of simply excluding them from the linear model.
(2) Linear regression model with interaction
Although the linear regression model in
Section 3.3 only identified three major influential factors of protective coping behaviors, this model ignored the interaction effects of these factors. It did not necessarily suggest that insignificant variables should be eliminated from the linear model, and they might potentially interact with other variables to jointly influence coping behaviors. Therefore, this study attempted to explore all possible linear models with interactions. An interaction term was the product of two different variables, and only the interaction between monthly income and insurance willingness were presented.
Compared to the initial linear regression model above, the new model considering interaction effect improved the goodness of fit (adjusted R-square = 0.2261), indicating 0.81% more of the variability in the protective coping behaviors could be explained by including the interaction between monthly income and insurance willingness in the model. As shown in
Table 6, the interaction between monthly income and insurance willingness was significant (
p = 0.027). This indicated that monthly income and insurance willingness jointly affected the protective coping behaviors, even though neither of them was significant in the previous model. This finding agreed with the significant correlation coefficient between monthly income and insurance willingness (as shown in
Table 4) which suggested that these two factors were closely associated.
In order to further explore the interaction effect between monthly income and insurance willingness, other factors in the model, age, trust in government and flood experience, were fixed as constant. The values of age (mode = 2), trust in government (mode = 3), flood experience (mode = 5) were set to be their modes. As demonstrated in
Figure 3, when the monthly income of the respondents was less than 5000 RMB, their intentions to cope protectively decreased as their insurance willingness increased. On the contrary, when monthly income was more than 5000 RMB, there was a positive relationship between protective coping behaviors and insurance willingness. The higher the income level was, the greater the positive effect of insurance intention on protective coping behaviors. In other words, the willingness to take coping behaviors of high-income individuals was stronger when they had higher insurance willingness, while that of low-income people was stronger when they had lower insurance willingness.