The determinants affecting the intention of urban residents to prepare for flood risk in China

In the context of global warming and China's disaster response patterns, it is critical to understand how to promote the effectiveness of household flood protection measures among the public. In this study, we developed a comprehensive theoretical framework based on protection motivation theory (PMT) to identify the main determinants that influence urban residents' intention to prepare for flood risk. In addition to the fundamental factors in PMT, this framework also considered the influence of individual coping attitudes and social context. We selected urban residents in flood-prone areas of Henan Province as the study population as well as collected 857 valid questionnaires through an online survey. Firstly, the results showed that both threat perception and coping appraisal of flood risk are effective in increasing residents' intention to prepare. Secondly, negative risk-coping attitudes reduced people's intention to prepare. If people do not perceive preparedness actions as absolutely necessary, they will postpone them or shift their focus to public flood protection measures. In addition, analysis of affective pathways revealed that negative emotion (worry) was primarily influenced by perceptions of flood consequences and was not significantly related to perceptions of likelihood. The analysis of trust mechanisms showed that higher levels of trust in public flood protection reduced people's perceptions of flood risk, thereby hindering their intention to prepare for flood risk. Finally, we found that the positive influence of social norms on preparedness intentions makes it appropriate to focus on the power of social mobilization. The findings will provide theoretical references for government departments to design further policy measures to improve integrated flood risk management in China.

1 Introduction

China has a long history of flooding (Jiang et al., 2005). However, in China's long-term fight against floods, the phenomenon of post-disaster emergency relief over pre-disaster emergency preparedness has emerged. For example, in the case of the flooding disaster event¹ that occurred in China's Henan Province in July 2021 due to an extraordinary rainstorm, it was found during the rescue process that life jackets, medical first-aid kits and emergency lights were much-needed supplies for the affected people, which fully exposed the lack of emergency preparedness of the residents. In addition, according to Swiss Re, the current catastrophe insurance coverage in China is only 10 %, which results in most of the flood losses being borne by the residents and the government. The phenomenon of poverty due to disasters has become an urgent problem in China today. However, at the same time, it creates an opportunity for China to implement an integrated flood risk management strategy (van Herk et al., 2015).

Several studies provide evidence that advance implementation of flood protection measures by individuals or households can be effective in reducing flood losses (Grothmann and Reusswig, 2006; Kreibich and Thieken, 2008; Bubeck et al., 2012a; Poussin et al., 2014, 2015; Botzen et al., 2019). Although studies have shown that implementing flood protection measures is cost-effective in many cases (Kreibich et al., 2011), people living in floodplains are not adequately prepared for potential flood events. For example, a survey of coastal residents in several US states conducted by Meyer et al. (2014) showed that only 25 % of residents surveyed had taken protective measures prior to the arrival of Hurricane Sandy. Therefore, researchers have conducted numerous behavioral studies in an attempt to understand and influence individual decisions related to flood management (Owusu et al., 2015; Osberghaus, 2015; Liu et al., 2018). However, to date, there has been little research on the impact of key factors on residents' flood risk decisions in China. Understanding the key factors influencing the residents' motivation for flood protection in China is essential given the increasing risks associated with flooding and the changing policy environment that is devolving more responsibility to communities and the public. Besides, unlike the bottom-up model of risk governance in west Asian and other Asian countries (Shi et al., 2013), China currently adopts a top-down model of national disaster response (Ge et al., 2021).

Given this context, the following questions arise: what are the determinants that influence urban residents to implement flooding precautions in the context of China's top-down disaster response model, and how do these determinants interact with each other? To address these questions, this study examined the preparedness intentions of residents in flood-prone areas of Henan Province and explored the mechanisms driving residents' preparedness intentions in a structured framework. Understanding these key factors will help governments design further policy measures to improve communication with residents and flood risk management.

The remainder of this article is structured as follows. Section 2 discusses the theoretical background of flood protection actions as well as the integration model and key hypotheses. Section 3 illustrates the research methodology, including measured variables, the research sample and data collection procedure issues. Section 4 presents the results in terms of descriptive statistics and a structural equation model. Section 5 discusses the research results. In Sect. 6, we conclude this research and introduce its limitations.

2 Theoretical background and conceptual framework

2.1 Theoretical background

Many factors influence the intentions of individuals to implement flood protection actions, including perceptions of flood risk, experience, attitudes and socio-demographics (Weyrich et al., 2020). Therefore, it is difficult to identify the drivers that influence individuals to implement flood protection measures. Even so, models of human behavior provide a simplified representation of the main driving forces and resulting actions involved in certain contexts. These models have shown effectiveness in understanding, predicting and influencing factors in human behavior (Martin et al., 2017). To date, researchers have developed several theories to study the factors that influence the public's implementation of flood mitigation measures. However, the use of a single theory is often incomplete. Meanwhile, many of the factors that are excluded may also play a role in behavior. Therefore, the development of a comprehensive and integrated psychosocial model is particularly important for studying the public's flood mitigation behavior.

The theoretical basis for explaining flood mitigation intentions in this study is protection motivation theory (PMT). PMT was first introduced by Rogers in 1975 (Rogers, 1975) (Fig. S1). It was originally used to explain when individuals would take precautions to reduce their health-related risks (Milne et al., 2000). In recent years, PMT has been widely used to explain the risk reduction behavior of residents or farmers against natural hazards (Poussin et al., 2015; van Duinen et al., 2015). According to PMT, an individual's decision to take protective action or not is driven by two main cognitive processes, namely, threat appraisal and coping appraisal (Rogers and Prentice-Dunn, 1997). Threat appraisal includes both variables of perceived likelihood and perceived consequence. Meanwhile, some researchers have defined threat assessment as risk perception (Grothmann and Reusswig, 2006; Bubeck et al., 2018). Coping appraisal consists of three variables: perceived response efficacy, perceived self-efficacy and perceived response cost. Perceived response efficacy is the individual's perceived usefulness of the measure in reducing losses. Perceived self-efficacy refers to an individual's self-assessment of his or her ability to implement protective action measures. The perceived response cost is the individual's expectation of the cost in terms of financial cost and time and effort needed for the protective measures to be implemented (Poussin et al., 2014). In the context of flood risk, the threat appraisal reflects an individual's perception of flood risk, while the coping appraisal reflects the extent to which an individual expects flood protection measures to be effective, easy to implement and not too costly.

In order to improve this theoretical model as much as possible, this study adds both coping-attitude and social-context components to the PMT framework. Considering the difficulty carrying out of the actual survey, the measure of social context was characterized as two variables, social norms and trust in public flood protection. A structured research framework linking the abovementioned factors was used to identify the factors that trigger an individual's intention to implement protection. Meanwhile, the interactions between the factors were also analyzed.

2.2 Integration model

Based on PMT, this study used an integrated approach to examine determinants affecting the intention of residents' flood protection actions in China. The integration model as shown in Fig. 1 is proposed.

Figure 1 Framework of the integration model.

In this study, threat appraisal refers to an individual's perceived level of flood risk, including both perceived likelihood and perceived consequence. Studies have shown that individuals with high threat appraisal are more likely to take protective measures (Grothmann and Reusswig, 2006; Poussin et al., 2014; Weyrich et al., 2020). However, some studies have found that people with high levels of risk perception caused by flood experience and trust do not necessarily take flood precautions (Wachinger et al., 2013). There is no direct relationship between risk perceptions and preparedness (Diakakis et al., 2018). In addition, researchers have found that the nature and extent of people's emotional responses during a disaster event can influence their plans for and actions in the future (Slovic et al., 2005). In a study of affective and cognitive routes to flood preparedness behavior, Terpstra (2011) found that affective mechanisms influence citizens' intention to prepare. Ejeta et al. (2018) found that perceived risk of flooding has a direct effect on negative emotions in a study of flood preparedness among residents of the city of Dire Dawa, Ethiopia. In addition, Papagiannaki et al. (2019) integrated people's worry about the occurrence and consequences of flood into the flood risk prevention model as a mediator variable, and the results also found that worry had a significant positive effect on the intention to implement flood mitigation behaviors. These results are partially contradictory. This may be because this relationship can also be explained by a mismatch between the temporal dynamics of adaptive behaviors and the cross-sectional nature of the data that are usually connected (Seebauer and Babcicky, 2021; Siegrist, 2013; Bubeck et al., 2012b, 2020; Hudson et al., 2019). In this study, the cross-sectional survey was carried out immediately after the flood, and we focused not only on flood risk perception as a direct driver of residents' intention to prepare for disasters but also on the influence of emotion as a mediating factor on residents' intention to prepare for disasters. Therefore, we propose the following hypotheses.

• H1a–H1b. Perceived likelihood and consequence have direct effects on preparedness intention.

• H1c–H1d. Worry mediates the effects of perceived likelihood and consequence on preparedness intention.

Once a certain level of threat is reached, people will consider adaptive strategies to deal with the threat. Before this step is taken, people often consider the benefits of possible actions and assess whether they have the capacity to take them. This process is defined in PMT as coping appraisal. Numerous studies have shown that coping appraisal has a more salient impact on people's intentions to implement flood mitigation measures than threat appraisal (Bubeck et al., 2018; Poussin et al., 2015). Meanwhile, perceptions of high response efficacy, high self-efficacy and low response costs are positively associated with the intention to implement flood mitigation measures (Parker et al., 2009; Kellens et al., 2013). However, focus group interviews conducted by Haney and Mcdonald-Harker (2017) with residents of flood-prone communities in High River, Alberta, found that respondents perceived their coping capacity to be weak and perceived that they lacked resources, which led to inaction in flood risk reduction. Thus, further understanding is needed for the study of individual coping abilities soon after flooding. Based on the above study, the following hypothesis is proposed.

• H2a–H2c. Response efficacy, self-efficacy and response cost have direct effects on preparedness intention.

Attitude is one of the most mentioned factors in the study of human social behavior and has been shown to be an important factor influencing intention and behavior (Fishbein and Ajzen, 2011). It can be defined as an individual's positive or negative evaluative response to a person or thing, which is usually rooted in the individual's beliefs and expressed in the individual's feelings or behavioral tendencies (Eagly and Chaiken, 2005). In general, the more positive a person's attitude toward a particular behavior, the more likely he or she is to engage in that behavior and vice versa. Therefore, when faced with flood risk, if people are more positive about implementing flood prevention measures, the more likely they are to do so. Positive responses are those that prevent damage, such as purchasing insurance (Shao et al., 2019). Negative responses, on the other hand, include such things as denial of the threat, wishful thinking (Grothmann and Reusswig, 2006; Bubeck et al., 2013) and fatalism (Botzen et al., 2019). Bubeck et al. (2018) argued that negative risk response attitudes (fatalism, postponement and low risk aversion) have a negative impact on the implementation of flood mitigation measures. Similarly, according to expected utility theory, protection against risk (in this case flooding) is less valuable for individuals who are less risk-averse (Von Neumann and Morgenstern, 1944). Based on the above studies, this study focuses on the impact of residents' negative risk attitudes. We therefore propose the following hypothesis.

• H3. Attitude has a direct effect on preparedness intention.

So far, we have discussed the impact of “intra-individual” factors on preparedness, but what about the impact of “inter-individual” factors on residents' intention to prepare for floods? We first considered the power of social influence and characterized it as a social norm. Social norms are the social pressures that people feel to act in a certain way (Abrahamse and Steg, 2013). People who are important to the individual may exert pressure to perform the behavior explicitly or indirectly (Cialdini et al., 1990). In behavior theory, a range of norms have been shown to explain behavior and play a role in behavior change. Many social psychologists view social norms as potential influences. The degree to which people sometimes perceive risk may not originate from the risk itself but indirectly influence their production of adaptive behavior or protective actions by shaping the perception of social norms (Lo, 2013). Studies have shown that individuals are more likely to prepare for disasters if neighbors, friends or family members take mitigation measures (e.g., purchase flood insurance) (Kunreuther et al., 1978; Bubeck et al., 2018). Meanwhile, Botzen et al. (2019) also considered norms a driving force for people to prepare and adopt flood risk mitigation measures. We assume that similar relationships exist in our data. Meanwhile, we assume that there are two main sources of such social pressure: informal social networks (neighbors, friends or relatives) and government policies. The hypothesis is as follows.

• H4. Social norm has a direct effect on preparedness intention.

Since most flood control efforts in China rely on public flood control measures, it is also important to assess the impact of this factor on individuals' flood control intentions. Although laypeople lack the expertise needed to calculate the actual level of protection provided by flood protection facilities, they can deduce the likelihood of flooding based on the level of trust inspired by their observations. Grothmann and Reusswig (2006) surveyed citizens in the German city of Cologne and found that those citizens who had more confidence in public flood protection showed lower perceptions of flood risk and took fewer precautionary measures. Meanwhile, Terpstra (2011) also found that the perception of flood risk is reduced by a high level of trust in flood protection facilities, which in turn discourages citizens from planning to prepare for potential flood disasters. This conclusion was also supported by subsequent studies (Wachinger et al., 2013; Buchanan et al., 2019). Papagiannaki et al. (2019) used survey data from a representative sample of Greek households to show that trust in government flood control measures had a negative impact on flood fear, leading to lower levels of preparedness. Based on the above study, we assume the same relationship for our data. Also, we focused on the effect of people's trust in public flood protection on their attitude toward flood protection. We propose the following hypotheses.

• H5a. Attitude mediates the effect of trust in public flood protection on preparedness intention.

• H5b–H5c. Perceived likelihood and perceived consequence mediate the effects of trust in public flood protection on worry and preparedness intention.

• H5d. Trust in public flood protection has a direct effect on preparedness intention.

3 Materials and methods

3.1 Measurements

In order to empirically test the hypotheses, this study used a questionnaire to collect data. To ensure the reliability and validity of the scales in this study, the measurement scales were compiled mainly through research analysis of the existing literature and appropriate revisions in the context of the actual situation in China. Table 1 provided the operational definitions and sources for these constructs. In the survey, respondents were asked to assess their level of agreement with the measured items. All items measured were administered on a five-point Likert scale. For the complete questionnaire, please see Table S1 (in Supplement).

In this study, we focus on five types of adaptation measures, categorizing them into structural, non-structural and risk transfer measures (Poussin et al., 2014; Dillenardt et al., 2021). The structural measures directly studied in this paper are the reinforcement of houses or/and construction of water-retaining walls. Non-structural measures fall into three main categories: low-, medium- and high-cost. Low-cost measures include participation in emergency drills or/and learning about flooding. Medium-cost measures include the preparation of sandbags, life jackets and other emergency supplies. The high-cost measure is moving the shelter to a safe area away from flooding. Finally, the risk transfer measure is the purchase of flood-related insurance.

Before the main survey, a pre-test was conducted to ensure the logical consistency and ease of understanding of the designed questionnaire in July 2021. Firstly, some of the questions in the questionnaire were adjusted and amended based on the opinions of relevant experts in this field. Then we selected the subjects through WeChat (a social software platform like Facebook). Using snowball sampling, these people provided us with further contacts (Weyrich et al., 2020). Finally, we conducted online or telephone semi-structured interviews with 40 contacts who had different education levels, ages and life backgrounds. A content analysis was implemented based on the responses of the contacts. The analysis included further categorization and merging and deletion of questions. In order to further improve comprehensibility, the wording was modified according to the way of thinking of the respondents. The modified constructions and their measurement items are shown in Table S2.

3.2 Sample

The ample size needs to be calculated before conducting the survey. According to Chin (1998), the sample size of the questionnaire is determined by the number of variables studied and the corresponding measurement items, and the sample size should be at least 10 times the total number of measurement items. In this present study, the total number of items measured was 44. Therefore, the sample size should be more than 440. Considering the response rate and invalidation, the sample size for this study was 1000.

Henan Province is a flood-prone area in China. Spanning four major river basins: the Huai River, Yangtze River, Yellow River and Hai River; it has a well-developed water system and a dense river network (Liu et al., 2018). In recent decades, flooding has occurred almost every year in the region due to a significant and continuous increase in precipitation and its uneven spatial and temporal distribution. According to incomplete statistics, there were 1152 floods in Henan Province in 1950–2004. The cumulative death toll exceeded 20 000, and the direct economic loss was about USD 3.5 billion (Liu et al., 2017). Therefore, this paper took Henan Province as the study area and selected residents with a stable income for the online survey, which was conducted from mid-August to late September 2021. The distribution of the sample is shown in Fig. 2.

Figure 2 Location of the survey sample.

3.3 Data collection

The impact of COVID-19 prevention and control policy and frequent rainfall weather made it quite difficult to implement the field research. Therefore, we chose a professional online questionnaire platform called WenJuanXing (http://www.wjx.cn, last access: 23 May 2022) to conduct online surveys. In addition to overcoming the above two problems, online surveys also have the advantages of saving survey time and costs, reducing data entry errors, and reaching a wider group of people (S. Wang et al., 2019). In China, researchers commonly use this questionnaire platform to conduct online surveys, which has over 28.7 million registered members (Zhai et al., 2020). Firstly, the designed questionnaire was uploaded to the platform, and then the platform generated an online questionnaire and a URL (uniform resource locator) link of the online questionnaire. Respondents can access the questionnaire through this URL link. With the help of a computer program, this platform randomly selected 1000 eligible people from its member roster as potential respondents and sent them the URL link to answer the survey.

To better motivate respondents to participate in the survey and increase the response rate of the questionnaire, we assured respondents that their responses were strictly anonymous and confidential and they would be paid CNY 9.50 as remuneration upon finishing the questionnaire survey. A total of 857 questionnaires were finally obtained after eliminating those with missing main variables and those with a response time of less than 180 s.

Table 1 Operational definitions and sources for constructs.

3.4 Research method

This study used the structural equation modeling (SEM) method to analyze the survey data. It is a multivariate statistical analysis technique that is used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis, and it is used to analyze the structural relationship between measured variables and latent constructs. This method is preferred by researchers because it estimates the multiple as well as interrelated dependence in a single analysis and represents such relationships in the form of causal models, path diagrams, etc. Currently, this method is widely used in the social sciences. SEM is divided into two methods: covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM) (Haenlein and Kaplan, 2004). In this study, PLS-SEM is chosen for analysis because it performs more efficiently and clearly in estimating complex models involving multiple constructs compared to CB-SEM (Hair et al., 2017). In addition, its assumptions about the distribution of variables and error terms are less stringent (Zhang, 2007).

SEM consists of two models: the measurement model and the structural model. The measurement model focuses on the relationship between the measured items and the constructs. The structural model focuses on the relationship between the constructs. In the following data analysis, the reliability and validity of the variables were assessed by measurement models with the help of SmartPLS 3.0 and SPSS 22 software packages, while the hypothesized relationships between the constructs were tested by structural models.

This study assessed the reliability of the measurement model using two indicators, Cronbach's alpha values and composite reliability (CR). The critical value for both indicators is 0.7 (Fornell and Larcker, 1981). For the validity evaluation of the measurement model, the factor loadings of the measurement items and the average variance extracted (AVE) of the latent variables were used to assess the convergent validity, where the critical value of the factor-loading value is 0.5 and the critical value of AVE is 0.5 (Fornell and Larcker, 1981). The Fornell–Larcker criterion was used to evaluate the discriminant validity. When the square root of the AVE values of the constructs is greater than the correlation coefficient between the construct and any other constructs, the discriminant validity of the construct is acceptable (Fornell and Larcker, 1981). Meanwhile, the heterotrait–monotrait (HTMT) ratio of correlation was selected as an auxiliary criterion for the determination of discriminant validity (Henseler et al., 2015). Kline (2016) suggested that the HTMT ratio should be lower than 0.85.

Structural models are designed to reflect the causal pathway relationships between constructs and are the most important element in multivariate studies. This study used the coefficient of determination (R²) to characterize the extent to which the independent variables of the current model explain the variation in the dependent variable (Chin, 1998). It is generally considered that an R² of 0.67 for constructs has high explanatory power, reaching 0.33 indicates moderate explanatory power and reaching 0.19 indicates weak explanatory power. The predictive relevance of the model was assessed using the Stone–Geisser cross-validation method (Geisser, 1974) and was tested by calculating the Q² value through the blindfolding procedure. Q² higher than 0 indicates that the variables in the model have predictive relevance for the constructs, while Q² lower than 0 indicates a lack of predictive relevance (Hair et al., 2021). In addition, this study used the GoF (goodness of fit) index to verify the overall goodness of fit of the model (Tenenhaus et al., 2004), which is calculated as $\mathrm{GoF}=\sqrt{\stackrel{\mathrm{‾}}{\mathrm{communality}}\cdot {\stackrel{\mathrm{‾}}{R}}^{\mathrm{2}}}$, where communality represents the commonality of latent variables. The thresholds of GoF are 0.1, 0.25 and 0.36, which indicate that the model has weak, moderate and strong fitness, respectively (when GoF is less than 0.1, the model is unacceptable) (Wetzels et al., 2009).

4 Results

4.1 Descriptive sample information

Table 2 provides the socio-economic characteristics information of 857 respondents. The number of female respondents (53.12 %) was slightly higher than the number of male respondents (46.88 %). This difference is commonly statistically expected based on population statistics. Meanwhile, the respondents were concentrated between the ages of 20–40 (87.89 %), which can be explained by the fact that children were excluded from the survey. A majority of respondents had a high level of education, with 74.96 % holding an associate or bachelor's degree, followed by a master's or doctoral degree (13.51 %). The distribution of respondents' age and education level shows that the respondents are relatively young and their education levels are relatively high. This may be due to the fact that younger and better-educated people are more likely to be online (S. Wang et al., 2019), more concerned about heavy-rainfall issues and more likely to have a higher intention to answer the questionnaire. About 70.18 % of respondents earn a yearly income below CNY 200 000. However, the percentage of income between CNY 200 000–300 000 is still 17.12 %, which is related to the level of economic development in the regions studied. More than half of the respondents are homeowners (73.5 %), and only 26.5 % of the respondents are renters. In addition, the majority of respondents in this survey are from urban areas and live in areas with more than three floors, which is related to the level of urbanization in the surveyed cities.

Table 2 Socio-economic characteristics of respondents (N= 857).

We conducted a frequency analysis of the five adaptation measures mentioned in this study, as shown in Fig. 3. About 45 % of respondents were unwilling to implement or unsure about implementing structural measures, which was largely in line with our expectations, with urban residents more likely to blame housing developers for the implementation of structural measures. Of the non-structural measures, the low-cost measures were the most attractive, with about 83 % of respondents expressing a clear willingness to participate in emergency drill activities or learn about flooding. In addition, for medium-cost measures such as the purchase of emergency items, nearly 70 % of respondents said they were willing to implement them, a result that far exceeded expectations and should be related to the timing of the survey we chose. People had just experienced a flood caused by a 100-year heavy rainstorm and were impressed by the disastrous consequences of the flood and the shortage of supplies, so most residents began to pay attention to the stockpiling of emergency supplies. At the same time, we also found that 62 % of respondents were unwilling to move or unsure about moving their homes out of the floodplain, which was strongly related to cultural influence. Finally, on the issue of flood insurance, 53 % of the respondents had the intention to purchase it. This survey also revealed that although the current prevalence of catastrophe insurance in China is low, residents had a high willingness to purchase it, which could provide an opportunity for the development and improvement of the flood insurance mechanism in China.

Figure 3 Results of frequency analysis of five adaptive measures. Numbers on the x axis denote the following: 1, strongly unwilling; 2, unwilling; 3, undecided; 4, willing; and 5, strongly willing.

4.2 Common method bias

When all data have the same source, they may have common method bias, which may have an impact on the validity of the study. Since our questionnaires were collected online, the samples were not restricted to a particular group. However, we still used Harman's one-factor test to identify any potential common method bias (Podsakoff and Organ, 1986). The effect of common method bias can be high if a single factor accounts for more than 50 % of the variance (Shiau et al., 2020). Principal component factor analysis in SPSS produced 10 principal components, accounting for 61.55 % of the total variance, with the first (largest) factor accounting for 15.84 % of the variance and thus not exceeding 50 %. Therefore, it can be concluded that there is no serious common method bias in this scale.

Moreover, before the factor analysis, this study first conducted the Kaiser–Meyer–Olkin (KMO) test and Bartlett's test of sphericity on the scale (Table 3). The KMO value was 0.812 (the measurement criterion was 0.5), and the p value of Bartlett's test of sphericity was 0 (the measurement criterion was p < 0.05), which passed the significance test with a significance level of 1 %. This shows that these data are suitable for factor analysis.

Table 3 Results of KMO and Bartlett's test of sphericity. Sig. denotes significance.

4.3 Measurement model

The scales used in this study were derived from those used in the relevant literature. These scales were modified according to the needs and usage scenarios of this study. Therefore, the scales used are content-valid.

The results of reliability and validity analysis are summarized in Tables S3–S5. As can be seen from Table S3, Cronbach's alpha values and CR of the nine latent variables were greater than 0.7, indicating good internal consistency reliability. Meanwhile, the loadings of the measured items and the AVE values of the latent variables were greater than 0.5, indicating that the convergent validity was supported. Because response cost (RC) is not a psychological construct, we did not calculate the reliability value of this variable but instead used it as an independent variable for direct analysis. In addition, we kept the first four items according to intention (IN). As shown in Table S4, the square roots of AVE values ranged from 0.711 to 1. The square root of the AVE value for each latent variable was larger than its correlations with other latent variables, thus proving support for discriminant validity. Besides, as presented in Table S5, HTMT values ranged from 0.026 to 0.714, which indicates that the latent variable has good discriminant validity.

4.4 Structural model

According to the evaluation steps of the structural model, it is necessary to first analyze whether there is a multicollinearity problem between the structural model's constructs. Garson (2016) argued that multicollinearity increases the error and makes the significance test of independent variables unreliable. In PLS-SEM, the variance inflation factor (VIF) was used to evaluate the multicollinearity between latent variables. The results showed that all VIF values were lower than 2.1 (the critical value is 3.3.) (Hair et al., 2021). Therefore, it can be determined that there is no multicollinearity problem between the predictor variables of the structural model.

Table 4 shows the results of R², Q² and GoF for PMT and the proposed model. In terms of explained variance, the R² of IN (intention) was 0.381, indicating that the explanatory power of PL (perceived likelihood), PC (perceived consequence), RE (response efficacy), SE (self-efficacy), RC (response cost), AT (attitude), SN (social norm), TR (trust in public flood protection) and W (worry) on the variance of IN was 38.1 %; the R² of W was 0.145, which meant that PL and PC explained 14.5 % of the variation in W; the R² of PC was 0.019, indicating that the explanatory power of TR for the variance of PC was 1.9 %; the R² of PL was 0.018, indicating that the explanatory power of TR for the variance of PL was 1.8 %; the R² of AT was 0.010, indicating that the explanatory power of TR for the variation in AT was 1 %. The Q² values of all endogenous latent variables in this study were greater than 0, which indicated that the model had good predictive power. In addition, the GoF value of the structural model was 0.375, which was greater than 0.36, indicating that the model had a good goodness of fit. In addition, Table 4 showed that the framework had a stronger explanatory power for residents' flood preparedness intentions, in addition to its stronger overall fitting and predictive power, compared to PMT (Y. Wang et al., 2019).

Table 4 Results of GoF, R² and Q².

IN: intention; W: worry; PC: perceived consequence; PL: perceived likelihood; AT: attitude.

4.4.1 Direct-effect analysis

The path coefficient (β), significance level and f-squared effect size (f²) were used to determine the hypothesized relationships of the model. Significance tests for structural equation model path relationships were performed using bias-corrected and accelerated (BCa) bootstrap and two-tailed tests (significance level set at 0.05). The test results of the hypotheses are shown in Fig. 4 and Table S6.

First, the analysis supported the predicted effects of perceived likelihood (H1a – β=0.072, p<0.01, f²=0.015) and perceived consequence (H1b – β=0.171, p<0.001, f²=0.139) on intention. Meanwhile, we also found that perceived consequence was a significant predictor of worry (β=0.383, p<0.001, f²=0.173). However, the analysis rejected the predicted effect of perceived likelihood on worry ($\mathit{\beta }=-\mathrm{0.003}$, not significant (ns)). This suggested that worry only mediated between perceived consequences and preparedness intentions, so H1c ($\mathit{\beta }=-\mathrm{0.000}$, ns) was rejected and H1d (β=0.047, p<0.001) was supported. Besides, the results supported the predicted direct positive effect of worry on intention (β=0.124, p<0.001, f²=0.136).

Second, response efficacy (H2a – β=0.358, p<0.001, f²=0.176) and self-efficacy (H2b – β=0.190, p<0.001, f²=0.153) had significant positive effects on intention. However, response cost (H2c – $\mathit{\beta }=-\mathrm{0.067}$, p<0.05, f²=0.076) had a negative effect on intention.

Third, attitude ($\mathit{\beta }=-\mathrm{0.079}$, p<0.001, f²=0.097) had a significantly negative effect on intention. Thus, H3 was supported. At the same time, there was a significant positive effect of social norms on intention (H4 – β=0.084, p<0.001, f²=0.114).

Finally, the model supported the predict effects of trust in public flood protection on attitude ($\mathit{\beta }=-\mathrm{0.068}$, p<0.05, f²=0.089), perceived likelihood ($\mathit{\beta }=-\mathrm{0.134}$, p<0.001, f²=0.135) and perceived consequence ($\mathit{\beta }=-\mathrm{0.140}$, p<0.001, f²=0.149) but rejected the predicted effect of trust in public flood protection on intention (H5d – β=0.007, ns). As expected, the effects of trust in public flood protection on attitudes, perceived likelihood and perceived consequences were all negative.

Figure 4 Significance test results of the structural model path coefficient. Only the statistically significant direct effects are reported; n= 857; path significance is as follows: ${}^{***}$ p < 0.001. ${}^{**}$ p < 0.01. ^* p < 0.05. ^ns p > 0.05.

4.4.2 Mediation effect analysis

This study abandoned the traditional Sobel method and instead used the bias-corrected nonparametric percentile bootstrap method (Wen et al., 2012) to test for mediating effects. This is due to studies that found that the bias-corrected nonparametric percentile bootstrap method is more accurate than the Sobel method and has higher test power (Hayes and Scharkow, 2013). According to the mediation test procedure process provided by Wen and Ye (2014), bootstrapping in PLS-SEM was used for the calculation in this study (Table S7).

First, the results supported both the predicted direct effects (β=0.171, p<0.001) and the predicted indirect effects (β=0.047, p<0.001) of perceived consequence on intention, so that worry mediated between perceived consequence and intention. As expected, indirect effects on preparedness intention due to the mediation of worry were found to be positive for perceived consequence. In line with the direction of direct effect, worry therefore acts as a complementary mediator.

Second, the results indicated that attitude mediated the effects of trust in public flood protection on preparedness intention (β=0.005, p<0.05). Perceived consequence mediated the effect of trust in public flood protection on worry ($\mathit{\beta }=-\mathrm{0.054}$, p<0.001) and preparedness intention ($\mathit{\beta }=-\mathrm{0.024}$, p<0.001). Meanwhile, perceived likelihood mediated the effect of trust in public flood protection on preparedness intention ($\mathit{\beta }=-\mathrm{0.010}$, p<0.01). However, the analysis rejected the mediating effect of perceived likelihood on worry (β=0.000, ns), suggesting that H5b was rejected while H5c was supported.

Third, the output indicated that the total effect of trust in public flood protection on intention was not significant ($\mathit{\beta }=-\mathrm{0.036}$, ns). Meanwhile, there was no support for a direct effect of trust in public flood protection on preparedness intention (H5d – β=0.007, ns). Rather, the total indirect effect was significant ($\mathit{\beta }=-\mathrm{0.043}$, p<0.01) – that is, the significant effects of attitude, perceived likelihood, perceived consequence and worry on intention fully mediated the effect of trust in public flood protection on preparedness intention. However, the effects of perceived likelihood, perceived consequence, worry and attitude acted in opposite directions, thus leading to an insignificant total effect of trust in public flood protection on intention.

5 Discussion

To better understand the motivation of urban residents' intention to prepare for flood risk, this study developed a comprehensive theoretical framework based on PMT with a sample of urban residents in flood-prone areas of Henan Province, China. These areas experienced severe pluvial flooding events prior to the survey. The results showed that the framework had a stronger explanatory power for residents' flood preparedness intentions, in addition to its stronger overall fitting and predictive power, compared to PMT. Meanwhile, the findings suggested that the framework is useful in assessing residents' perceptions of flood risk and their intention to adopt risk-reducing behaviors.

Firstly, our study confirmed that risk perceptions about flooding can promote residents' preparedness intentions and worry emotion. The former is consistent with the study of Weyrich et al. (2020), which concluded that the higher the public's threat appraisal of flood risk, the higher the intention to implement flood mitigation measures. The latter further supports the study of Ejeta et al. (2018) in that perceived risk of flooding has a direct effect on negative emotions. However, we also found that the perceived consequences of flooding alone triggered negative emotions among residents, while the perception of the likelihood did not trigger worrying emotions. From Table S8, we found that urban residents generally had higher perceived consequence in relation to flood hazards but lower perceived likelihood of flood hazards. Besides, the perceived consequences had a greater effect on residents' worry than their intention to prepare for flooding. Also perceived consequence has a greater effect on preparedness intention than perceived likelihood. A review of post-disaster health damage showed that high-impact disasters result in more severe health impairments than moderate- or low-impact disasters and that symptoms of health impairments usually diminished over time (Norris et al., 2002). As we conducted a survey of residents' intentions to prepare immediately after the disaster, people were still impressed by the catastrophic consequences of the flood and had a great number of negative emotions. These perceived consequences and this negative emotional state made people more willing to take disaster preparedness measures.

We also found that individuals' negative emotions can effectively contribute to their flood preparedness intentions, which is consistent with the findings of Siegrist and Gutscher (2008), who suggest that negative emotions explain why flood victims take more measures than non-victims. We try to provide a reasonable explanation for the phenomenon from the perspective of emotional dysregulation (Squires et al., 2021). It is argued that people's negative emotional state in response to a disaster is a dysregulated state and people will adopt a series of psychological activities or behaviors to release this undesirable state in order to regain emotional balance. It becomes one of the options for residents to take certain protective measures in order to eliminate their concerns about flood risks.

Secondly, our results confirmed H2. Response efficacy and self-efficacy have a facilitating effect on residents' preparedness intentions, while response cost acts as a disincentive. This is consistent with the Parker et al. (2009) study showing that high response efficacy, high self-efficacy and low response costs are positively associated with an individual's intention to take protective action. This also suggests that when a flood control strategy is perceived by residents as having high response efficacy and self-efficacy as well as low response costs, the likelihood of the strategy being implemented will be greatly increased.

Besides, H3, stating that negative risk response attitudes had negative effects on residents' flood preparedness intentions, was confirmed. As Ajzen (1991) mentions in the theory of planned behavior, attitudes toward behavior can effectively predict behavioral intentions. Negative attitudes toward precautionary measures lead to a reduced willingness to protect against flooding. However, the respondents seemed to have overlooked the important fact that flood risk is still a significant threat even when relatively well established public flood protection facilities are in place (Bubeck et al., 2013). Unrealistic ideas and attitudes about individual flood safety are a barrier to preparedness intentions.

Finally, in the study of the influence of social context on residents' intention, it was found that social norms play an effective role in promoting residents' intention to prepare. These include the influence of family, neighbors or friends and government policies. Although the contribution of norms or social networks to residents' intention to prepare has been reported in numerous works in the literature (Kunreuther et al., 1978; Mileti and Darlington, 1997; Bubeck et al., 2018), no plausible explanation has been given. Deutsch and Gerard (1955) argued that social norms can trigger conformity behavior in individuals. This stems from the individual's desire to be liked by others, and there is often an emotional cost to people deviating from group norms. This conformity, triggered by social norms, may be one of the reasons why people adopt disaster preparedness measures. The facilitating effect of social norms on preparedness intentions makes it appropriate to focus on the power of social mobilization. Another social-context factor to consider is public flood protection measures. In exploring the effect of trust mechanisms, contrary to our expectation, trust in public flood protection measures did not have a direct effect on the preparedness intentions of urban residents. The findings also suggested that attitudes, risk perceptions and emotions fully mediated the effect of trust in public flood protection on intention. This is in line with the findings of Terpstra (2011): higher trust reduces people's perceived level of risk and negative feelings, thus reducing the intention to prepare. As Poussin et al. (2014) mentioned, trust in public flood protection brings a sense of security and therefore may be an important reason why residents are reluctant to take preventive measures.

In summary, for intra-individual factors, it was found that perceptions and affective–attitudinal paths jointly influence residents' intention and that affect is largely influenced by perceptions. Research on social context showed that social norms and trust mechanisms were also key factors influencing residents' intention to prepare. Among them, trust in public flood protection plays an important central role. This suggests that effective communication, active social mobilization, and sound policies and regulations are effective measures to increase the public's intention to prepare for floods.

6 Conclusion

Based on PMT, this study comprehensively analyses the factors that influence the willingness of Chinese urban residents to prepare for floods. Firstly, it was found that perceived risk of flooding can effectively promote residents' preparedness intention, and therefore there is a need to raise public awareness of flood protection as well as to establish a proper relationship between citizens and government. Secondly, high response efficacy, high self-efficacy and low response costs are positively correlated with individuals' intention to take flood protective actions. Therefore, government departments need to clearly tell residents what scientific and effective disaster prevention and mitigation measures should be taken in case of extreme flood events exceeding standards. Meanwhile, they should focus publicity on the effectiveness and ease of implementation of the measures. Besides, negative risk response attitudes negatively impacted preparedness intentions. If people do not perceive preparedness actions as absolutely necessary, they may delay implementing flood protection measures (e.g., not purchasing flood protection products or delaying things like reinforcing their houses) or move to relying on public flood protection measures. Therefore, government departments should implement relevant policies to stimulate residents' preparedness behaviors, such as subsidies and incentives that can be offered to households that implement measures, which can also further reduce response costs. Finally, we found that the positive effect of social norms on preparedness intentions makes it appropriate to focus on the power of social mobilization. Government departments should actively express that building resilience to flood risk at the community level requires the participation of all people and encourage the participation of the whole community in risk response in order to increase the resilience of society to risk.

However, there are still some limitations that need to be noted. On the one hand, as with all cross-sectional studies, the conclusive causal inferences drawn from this study are limited. That is, if two variables are correlated, the causal relationship between A and B is ambiguous (Lindell and Hwang, 2008). On the other hand, this study only focused on residents' preparedness intentions and did not extend to behavior. As mentioned by Schifter and Ajzen (1985), after a person has the intention to act, there may be uncertainty about his or her actual actions. Individuals may indefinitely postpone their behaviors due to non-urgency, so further questions are needed to assess the actual behaviors of respondents.