3.1 Data sources
Taiwan is located on the Circum-Pacific seismic zone, which means earthquakes are frequent and often quite strong. This is especially true in eastern Taiwan, an area prone to densely clustered earthquakes and resultant earthquake disasters. According to Verisk Analytics, two M7.1 earthquakes have occurred in Taiwan: the Taichung quake in 1935 and the Zhongpu quake in 1941. These two events claimed the lives of more than 3,000 and 4,500 people, respectively, and caused widespread damage to infrastructure. More recently, there have been two other major earthquakes. They were the Jiji quake on September 21, 1999, which measured magnitude M7.3 and caused 2,415 deaths, and the M6.4 earthquake that struck north of Hualian on February 6, 2018. The latter earthquake damaged mainly older, soft-story buildings, and caused serious casualties in the main areas affected, Hualien City and Sincheng Township (CEOC; NFA). Therefore, this research focuses on these two regions—Hualien City and Sincheng Township—which are urban and suburban, respectively.
Hualien City has a total of six joint district offices, and there are a total of eight villages in Sincheng Township. The samples of urban residents and suburban residents were distributed according to the regional population rate, at a sample ratio of 8 to 2. Based on the population rate ratio, this study conducted data analysis with an effective sample size of 410 respondents, including 330 respondents from the urban area and 80 respondents from the suburban area. We calculated the frequency distribution in the database analysis of descriptive statistics to give an accurate picture of the sample distribution (Fig. 1).
3.2 Selection of model attribution
The purpose of the study was to evaluate and explore the links between community capacity bases, community adaptation, and earthquake disaster management. To achieve this, a few key indicators need to be measured.
(1) Measurement of community awareness
To measure community awareness and community capacity, reference was made to Ma et al. (2021), Zhang et al. (2013), Zhu et al. (2017), Chuang et al. (2016), Hosseini et al. (2014), Wang. et al. (2020), Lai, et al. (2011), Noda, et al. (2019). Under the framework of community awareness and community capacity, it is divided into two dimensions (two indicators for each dimension): disaster prevention education, disaster prevention drills, disaster prevention organizations, and drill cooperation. Among them, education and drills are to strengthen the community's awareness of disaster prevention; organization and cooperation are to enhance the community's disaster prevention capabilities, as shown in Table 1.
(2) Measurement of residents’ adaptive for disaster prevention
To measure disaster prevention, refer to Kusumasari, et al. (2011), Wang, et al. (2020), Ma, et al.(2021), Deng, et al. (2021), Cui, et al. (2015). Community adaptive of disaster prevention is "cope" that include two indicators: Training to become disaster prevention vanguard and disaster relief volunteers. Among them, the definition of disaster Prevention Vanguard is that residents have received disaster prevention education, undergone training and participated in drills. The definition of disaster relief volunteers is that residents had pass the disaster prevention related courses and drills, and pass the examination to obtain a license. Both indicators could enhance community capacities of disaster prevention, as shown in Table 1.
(3) Measures of community organization transformation for disaster prevention
Regarding the measurement of organization transformation, Zhang, et al. (2013), Cui, et al. (2015), Wang, et al. (2020), Ma, et al. (2021) was referred to.
The dimension of community organization transformation is "change" that include two indicators: assemble nongovernment and government forces
for pre-disaster preparedness and to respond for disaster-occurring. Community organization transformation could improve and enhance community abilities to adapt and response to pre and after disaster, see Table 1.
3.3 Research method
Based on a comprehensive overview of the literature, we know that IPA techniques are widely utilized to examine the quality of services, and have been used in multiple disciplines, such as tourism, ecosystem services, food services, education, green practices, management, and health services. IPA methodology allows researchers to assess the discrepancies between the perceived importance and performance of any service, which is especially helpful for the implementation of a government or company’s programming (Zhang & Chan 2016; Addas et al., 2021; Hua & Chen 2019; Boley et al.,2017).
Overall, one of the main advantages of the IPA method is that it utilizes importance and performance to reveal satisfaction levels (Spangenberg et al., 2015; Addas et al., 2021). Ratings for the importance and performance of management functions are crucial in determining management strategies. Facets of management with high importance but low performance ratings require a relatively high prioritization in decision-making, to enhance stakeholder satisfaction levels (Spangenberg et al., 2015; Addas et al., 2021).
In the present study, we applied the IPA to evaluate the community residents’ assessments of the importance of CEDM, as well and their satisfaction with the government's performance of DM implementation. We combined findings from previous CBDM literature with information gleaned from interviews with experts, to arrive at eight key indicators of environmental disaster adaptive capacity and community-based disaster management, with these indicators further divided into four attributes. The stakeholders were asked to score each indicator according to two aspects; namely, "importance" and "performance." Finally, we took the averages of all the data collected in the research survey according to the forgoing classification, and converted the averages into a coordinate value (Z-score) with a standardized value, with “importance” as the horizontal axis and
“performance” as the vertical axis. Therefore, the coordinate values can be drawn as plane coordinates. From the coordinates, the relative importance of each indicator in the considered disaster management project and the respondents’ satisfaction with the government's implementation of disaster management can be obtained (Zhang & Chan 2016).
The IPA chart and its four quadrants are defined as follows. The first quadrant (“Concentrate here”) indicates the interviewee thinks this indicator is very important and the government needs to pay serious attention to it, however, the interviewee is not satisfied with the government’s performance, which means the government needs to work harder to improve this indicator. The second quadrant (“Keep up the work”) means the respondent thinks this indicator is very important and is also satisfied with the government's DM performance, which means the government ought to continue to maintain this indicator. The third quadrant (“Low priority”) indicates the interviewee considers this indicator to be of comparatively lesser importance, and is not satisfied with the government’s performance. Indicators in this quadrant can be used by the government when formulating secondary or ancillary implementation projects The fourth quadrant (“Possible overkill”) signifies the interviewee believes this indicator is unimportant, but also believes the government attaches too much importance to it and over-implements it. In other words, the perception is that the government overspends on this indicator, in terms of energy and funds, despite the indicator’s inability to help residents bolster the disaster prevention capabilities of their communities (Duke & Persia 1996; Zhang & Chan 2016) (Fig. 2).
3.4 Research design
We developed a CDM index based on prior empirical studies focusing on CB and DM perceptions and indicators. In the initial planning stage, focus group discussions were held with experts to gather various stakeholders’ perspectives on topics including disaster prevention education (Zhang et al., 2013; Zhu & Zhang 2017), disaster preparedness and response training (Chuang & Yen 2016; Zhu & Zhang 2017; Hosseini et al., 2014), disaster awareness and adaptation (Wang et al., 2020; Lai 2011), and cooperative disaster programs with governments (Noda et al., 2019; Zhang et al., 2013). Second, we decided to focus on elements enhancing of disaster response capabilities; namely, disaster adaptation (Kusumasari & Alam 2011; Wang et al., 2020), disaster prevention skills (Wang et al., 2020; Cui 2018), and assemblages of NGOs and government (Zhang et al., 2013; Cui 2018; Wang et al., 2020). Finally, we combined CBDM, community resilience, and community capital principles to formulate the content of interview questions to pose to experts, who included local government officials, architects, and chairpersons of Community Management committees. From the results of these interviews with experts, 8 CDM indicators were decided upon (Table 1). A formal questionnaire design was then developed, based on these 8 indicators and the results of the community disaster management and pre-test questionnaire survey. We subsequently began carrying out our questionnaire surveys, which took the form of face-to-face interviews prefaced by verbal description of our research theme and objectives: to understand the interviewees’ views on community disaster management and their awareness and perceptions of disaster prevention in their communities.
In our study, the community residents’ disaster prevention management preferences and perceptions were measured through their responses to questionnaire items focused on four attributes, so the survey questionnaire served as the method of data collection (Vaske 2008). The questionnaire included the following three sections: 1) Status of community disaster prevention organization and disaster management; 2) Community residents' awareness of the importance and performance (IPA) of earthquake disaster management; and 3) Socio-economic demographics.
In the second section of questionnaire, which pertained to IPA, urban and suburban residents rated their perceptions of both the importance and performance of the 8 indicators on a Likert five-point scale, where a value of 1 represented "extremely unimportant/highly dissatisfied" while a value of 5 represented
"extremely important/highly satisfied". The Likert scale was developed by Rensis Likert in 1932. As a psychologist interested in measuring people's views or attitudes towards various things, he developed the five-point scale bearing his name to explore people's preferences for and satisfaction with the implementation of various projects. The Likert scale is frequently used in questionnaire design, and is, in fact, currently the measurement instrument most widely used by survey researchers (Miller 1991). In the present study, we selected the following indicators, which encompassed multiple dimensions for residents completing the IPA survey including the following: 1) The community residents' awareness of disaster prevention for earthquake events; 2) The community residents' perception of environmental risk; 3) The community residents' adaptive capacity in case of disaster; and 4) The community organizations' transformation. Based on the literature review and interviews with experts, these indicators are assumed to contribute to helping communities establish sustainable CEDM and enhancing their capacity to adapt to disasters. The urban and suburban residents’ evaluations of these indicators are presented in Table 4 and will be discussed at length in the sections following it.
Table 1
Definition and measurement of the indicators and attribution in CEDM
No.
|
Attribution
|
Indicators of the CEDM
|
Attribution of the CEDM
|
Literature
|
Definition of the attribute in CEDM
|
1
|
Awareness
|
Risk
Regular disaster prevention education and propaganda
|
Disaster Prevention Education
(Enhancing Risk Perception)
|
Ma, Z.; Guo, S., Deng, X., Xu, D.,(2021);
Zhang X., Yi L., Zhao D., (2013); Zhu, T. T. & Zhang Y. J., (2017)
|
2
|
Risk
Regular disaster prevention drills
|
Disaster Preparedness and Response Training (Enhancing Risk Perception)
|
Ma, Z.; Guo, S., Deng, X., Xu, D.,(2021); Chuang, S. C. & Yen C, J., (2016); Zhu, T. T. & Zhang Y. J., (2017); Hosseini, K., et al., (2014)
|
3
|
Learn
Regularly join a disaster prevention organization
|
Disaster awareness and adaptation (Improve Disaster Awareness Ability)
|
Ma, Z.; Guo, S., Deng, X., Xu, D.,(2021); Wang Y. C., Lin S.W. and Lee C. H. (2020);
Lai, Y. H., (2011)
|
4
|
Adaptive
|
Learn
Cooperative drills with local governments
|
Cooperative disaster program with government (Improve Disaster Awareness Ability)
|
Noda T., et al., (2019); Zhang X., Yi L., Zhao D., (2013)
|
5
|
Cope
Disaster Prevention Vanguard
(i.e. have received disaster prevention education, undergone training and participated in drills)
|
Disaster adaptation (Enhancing Disaster Response Capabilities)
|
Kusumasari, B. & Alam, Q., (2011); Wang Y. C., Lin S.W. and Lee C. H. (2020)
|
6
|
Cope
Training to become disaster relief volunteers
|
Disaster prevention skills (Enhancing Disaster Response Capabilities)
|
Ma, Z.; Guo, S., Deng, X., Xu, D.,(2021);Wang Y. C., Lin S.W. and Lee C. H. (2020); Cui, K. et al., (2015)
|
7
|
Transformation
|
Change
Assemble nongovernment and government resources for pre-disaster preparedness
|
Assemble NGOs and Government (Transformation)
|
Zhang X., Yi L., Zhao D., (2013); Cui, K. et al., (2015); Wang Y. C., Lin S.W. and Lee C. H. (2020)
|
8
|
Change
Assemble nongovernment and government resources to respond when disasters occur
|
Assemble NGOs and Government (Transformation)
|
Ma, Z.; Guo, S., Deng, X., Xu, D.,(2021); Zhang X., Yi L., Zhao D., (2013); Cui, K. et al., (2015); Wang Y. C., Lin S.W. and Lee C. H. (2020)
|