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A temporal social resilience framework of communities to disasters in Australia

A temporal social resilience framework of communities to disasters in Australia Despite the current interest in and need for studies in the conceptualization and measurement of social resilience to hazards and disasters, there remains significant research gaps within this area. This study seeks to fill one such gap via the provision of an innovative unified framework of social resilience across three disaster phases (i.e., pre-disaster, response and recovery) using a quantitative research method. We utilized the survey results from the New South Wales StateEmergency Servicevolunteerstovalidateaconceptualization framework that aimed to enhance social resilience across all disaster phases. This study had shown the positive correlation between identified indicators and social resilience but varying in impact strength depending on disaster phase. Keywords: Disaster resilience, Social resilience, Climate disaster, Quantitative research, Online questionnaire, Disaster phases, Social resilience framework, Temporal framework resilience Introduction Populations are generally growing and, in many areas, Over the past few decades, and particularly since the settlement patterns (Morley et al., 2012) have exacerbated 1970s, the frequency of natural disasters per year has the potential effect of these disaster events on the popula- been increasing worldwide (Pollach, 2014). In addition tion (Joerin et al., 2012). Thus, the frequency of disasters is to an increase in the number of potential natural disas- increasing (Pollach, 2014), populations are increasing and ters, there has also been an overall increase in the inten- the number of individuals living in vulnerable locations is sity of these events recorded globally by the Center for increasing. The culmination of these factors has resulted in Research on the Epidemiology of Disaster (CRED) via greater risk to communities and their economies, environ- the International Disaster Database. The frequency and ment and infrastructure, as well as the well-being of indi- impact of natural disasters is on an upward trajectory viduals within these communities. and, considering the future influence of climate change Australia is of interest when studying the socio-economic and population growth, is projected to continue to increase. aspects of natural disasters owing to the concentration of Among these increases in natural disasters, the incidences people living in flood-prone areas (Morley et al., 2012). of climate-related disasters have progressively increased Between 1990 and 2014, 74% of Australian disasters were over the last few decades (Leaning and Guha-Sapir, 2013). flood- or storm-related (UNISDR, 2015). These disasters, Nellemann et al. (2008) predict that this incidence of which are climate-related, have a severe negative effect on climate-related natural disasters will only continue to the communities, economies, environments and infrastruc- increase, while the number of geophysical disasters has ture wheretheyoccur. TheAustralia Business Roundtable remained stable. forDisasterResilienceand SaferCommunities report esti- According to CRED and the United Nations International mates that when social impacts (i.e., mental health and Strategy forDisasterReduction report (UNISDR, 2015), chronic disease related to disaster impact) are included increases in these climate-related disasters will result in a alongside critical infrastructure, disasters are estimated to greater impact on human lives, well-being and property. have a financial impact of $33 billion per year by 2050 in Australia in real terms (Insurance Australia Group Limited, 2016). Limiting this impact by enhancing resilience is thus * Correspondence: khalili.sanaz@gmail.com; sanaz.khalili@sydney.edu.au 1 a critical economic and social issue. Project Management, School of Civil Engineering, Building J05, University of Sydney, Camperdown, NSW 2006, Australia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 2 of 9 Therefore, it is necessary to minimize the risks related spatial scales of resilience, as well as the attributes of to potential disasters and enhance the capacity of these hazard-affected bodies. Beccari (2016) conducted a com- communities to be able to cope with any future impacts. parative analysis of 106 different published methods includ- Broadly speaking, the ability of individuals and communi- ing Bijan et al.’s (2014) (Khazai et al., 2014) ontology of 55 ties to cope with disturbances or changes and to maintain methods and the EMBRACE project (Birkman et al., 2012), adaptive behavior is termed resilience. Building resilience to which incorporated 32 different frameworks across a broad natural hazards requires increasing the capacity to cope range of indicators. Despite a solid foundation of literature with the event and its aftermath, as well as increasing the addressing social resilience to disasters and various ap- capacity to learn about hazards and risks, change behavior, proaches for its measurement, there are currently no stud- transform institutions and adapt to a changing environment ies that address social resilience using quantitative (Maguire and Cartwright, 2008). Therefore, it is imperative measures across the emergency management temporal that the determinants of disaster resilience are identified stages or key areas of operation. and measured so that issues may be addressed and capaci- These emergency management stages in Australia are ties improved (Klein et al., 2003,Cutteretal., 2008). termed prevention, preparedness, response and recovery Short-term emergency responses to flood disasters are (Council of Australian Governments, 2011). For the present usually the focus of studies; however, addressing social study, the two pre-disaster phases, prevention and pre- resilience factors for people who are exposed to flood paredness, are grouped into a single preparedness phase disasters should be addressed (Weldegebriel and Amphune, and cover the entire time period prior to the disaster. The 2017). The assessment of disaster resilience using indicators response phase begins once the community has been influ- can be a key element in the planning and management of encedbyacrisisevent.Thisphase is oftenmarkedbyaco- extreme events by providing a tool to identify priorities for ordinated effort to ensure that the needs of those involved improvement and to monitor change. In the past two in the disaster are met, such as search and rescue efforts. decades, and since the publication of the Social Vulnerabil- The nature of the response efforts, however, is determined ity Index (Cutter et al., 2003), therehas been adramaticin- by the immediate and most pressing needs of those crease in the number of studies that have aimed to provide impacted. Finally, the recovery phase occurs after the methodology to measure the various aspects of disaster immediate needs of the community are met and the disas- risk, resilience or vulnerability (Beccari, 2016). ter is no longer presenting further impact to the commu- There is, however, no standard method; approaches can nity (Waugh and Streib, 2006). This phase seeks to restore be top-down or bottom-up, qualitative or quantitative, use the impacted individuals and communities to a state in primary or secondary data and can be designed for scales which recovery is no longer occurring. ranging from local through to national (National Research Khalili et al. (2015) provided a social resilience framework Council, 2015). The focus of the assessment may be on re- across these three phases of disaster; however, their study is silience (e.g. Cutter et al. 2010), vulnerability (e.g. Cutter et limited because of its focus on qualitative interviews with al. 2003) or risk (e.g. United Nations University Institute for subject matter experts. Hinds (1989)arguedthatapplying Environment and Human Security and Alliance Develop- both qualitative and quantitative methods to a research ment Works, 2014) but essentially each method aims to as- problem “increases the ability to rule out rival explanations sess some variation of the capacity within a community to of observed change and reduces skepticism of change-re- withstand and recover from natural hazards. Further, as- lated findings”. Similarly, Hussein (2015) argued that quanti- sessment approaches may also be at different stages of de- tative research can be used to validate qualitative findings. velopment, existing only as a conceptual model (e.g. Norris Therefore, the quantitative approach applied within the et al., 2008; Parsons et al., 2016) or as a conceptual model present study to the qualitative findings from Khalili et al.’s developed into an applied assessment such as Cutter et al.’s (2015) study is essential for the development of a unified so- (2008) disaster resilience of place (DROP) model. The DRP cial resilience framework, which is required to improve model clusters around the following six core dimension: current efforts to address social resilience. Thus, by conduct- ecological, social, economic, institutional, infrastructure ing quantitative research using an online survey with New and community competence, and conceptualizes resilience South Wales (NSW) State Emergency Services (SES) volun- as achangeableprocess that is dependentuponpre-existing teers from locations throughout NSW who had experienced conditions and the severity, duration and time between di- significant flooding events, the present study attempted to sasters, as well as a range of additional external factors validate the resilience frameworkinthe studybyKhalili et (Cutter et al., 2008). Working from a geographical perspec- al. (2015). tive, Zhou et al. (2010) developed a notable approach to re- silience within local community contexts via a model of Social resilience and indicators disaster resilience of ‘loss-response’ of location based on Resilience is a broadly used concept that is applied across the following three distinct dimensions: the temporal and a range of disciplines including engineering, psychology, Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 3 of 9 science, sociology and economics. Initially, resilience was of resilience to best develop a unified social resilience derived from the Latin word resilio, meaning ‘to jump framework. Such indicators—defined herein as parameters back’ (Klein et al., 2003). As a broad encompassing topic, for assessing the social resilience of a community—include the present study begins by setting parameters on the community demographics, participation, learning pro- dimensions of resilience, which are conceptualized via the cesses related to disaster, and leadership (Miller et al., four dimensions of organizational, technical, social and 2010). These indicators represent variable aspects of com- economical (Bruneau et al., 2003). Of these dimensions, munities, i.e., they vary the phases of a single disaster, but the present study focuses on the social component of also over time across different disasters. By advancing the resilience as it relates to disasters. Specifically, social resili- understanding of the indicators associated with social ence—defined as the ability of communities to withstand resilience, the present study provides implications for the external social shocks of a disaster or disaster-related increasing those capacities identified as positive factors of event (Adger, 2000)—occurs through three phases: pre- social resilience or decreasing those identified as negative disaster, disaster response, and disaster recovery. Accord- factors of social resilience. This begins with recognition of ing to Keck and Sakdapolrak (2013), social resilience to these indicators and their impact in disaster social resili- disasters is widely debated, but includes three capacities ence. The purpose of the present study is thus to improve that are the ability to cope with, adapt to and transform the ability of a community to withstand the shock of from disaster shocks to social systems. Social resilience is disasters by improving social resilience. the ability of a community to absorb shocks, recover from Table 1 shows Khalili et al.’s(2015) two-dimensional disturbances and avoid negative and potentially irrevers- framework on social resilience indicators for the three ible effects (Resilience Alliance, 2007). Social resilience is disaster phases as temporal factors. In all, there are 14 important across these phases to protect the community indicators included in the model: against loss by enhancing the capacity of communities during the pre-disaster and response phases, and to im-  community participation – the engagement of prove their capacity to rebuild and return to normal after community members in organizations and the event (Zhou et al., 2010). Although researchers have activities within their community, including provided definitions, albeit often inconsistent, of social resident associations, neighborhood (Perkins and resilience to disasters, “the questions of how social re- Long, 2002) watches, silience can be properly defined, how it can be opera- self-help groups and religious congregations tionalized, measured and analyzed, and how it might be (Paton et al., 2001; Perkins et al., 2002) fostered (or hindered) are far from being settled yet”  education – disaster-related formal and informal (Keck and Sakdapolrak, 2013). training and educational activities within communities The concept and term ‘community’ has various mean- (Paton and Johnston, 2001) ings and applications. It is invariably used to refer to col-  exchange of information – information flow within a lectives of people joined by shared geography, interests community (Rohrmann, 2000) and concerns, or identity (Jewkes and Murcott, 1996).  learning – learning from previous disasters (Zhou et Common definitions of community highlight the exist- al., 2010) ence within a geographical boundary, and engagement  shared information – distributing information within in ongoing social interaction and psychological connec- a community (Ink, 2006) tions to both the surrounding people and place as key  social support – support from the neighborhood components (Christenson and Robinson, 1980). In (Kaniasty and Norris, 1999; Norris et al., 2008) the present study, community refers to a social unit  sense of community – feeling of belonging to a larger than a household whose members share com- community or place (Paton and Johnston, 2001) mon values and live in some physical proximity to  trust – trust in the neighborhood (Enemark, 2006) each other.  demographic information – i.e., age, gender, socio- Disaster impact is a factor of both the scale of the disas- economic status/income, health, history, education, ter and the ability of the community to withstand the cultural/religious belief, or populations with special shock of the disaster. Thus, social resilience reflects the needs (Tobin, 1999; Cutter et al., 2010) preparedness and response of the community and is  improvisation-inventiveness – community creativity dependent on social situations in the communities during and innovation to devise a solution for enhancing both the pre- and post-disaster contexts (Boyce, 2000). resilience (Demchak, 2006; Lalonde, 2011) Disaster response, therefore, both affects and influences  coping style – the ability to manage, adapt to and social resilience (Tobin and Whiteford, 2002). To capture deal with stressful situations (Miller et al., 1999) this cyclical relationship, it is necessary to consider all  leadership – leadership within a community three disaster phases and the associated social indicators (Harland et al., 2005; Hegney et al., 2008) Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 4 of 9 Table 1 Khalili et al.’s(2015) Framework on Social Resilience Indicators Matrix Social Resilience Indicators High Community Participation Community Participation Community Participation Education Exchange information Exchange information Exchange information Shared information Learning Learning Social Support Shared information Shared Support Sense of community Social Support Sense of community Trust Sense of community Trust Trust Medium Demographic information Coordination Community efficacy Low Improvisation inventiveness Coping Style Improvisation inventiveness Coping Style Leadership Coping Style Leadership Leadership Pre-Disaster Response Recovery coordination – community works together hypotheses (Fink, 2002). The relationship examined via community efficacy – community belief in their own the quantitative methods was that existing between so- capabilities of performing and completing jobs cial resilience indicators identified via prior qualitative re- (Moore et al., 2004). search and social resilience data collected from NSW SES volunteers from locations throughout NSW who have ex- The social resilience indicators are presented as low, perienced significant flooding events. This relationship medium or high influence based on the analysis of previ- was examined to generalize the study findings across dif- ous studies and subject matter expert interviews. This ferent disaster environments. The research relied on ro- framework presents Khalili et al.’s(2015) interpretation of bust scientific procedures to ensure reliability and validity the NSW SES subject matter expert perspectives on the during the process of quantifying the previously qualita- relationship between social resilience indicators and im- tive data of social resilience indicators. pact. The present study proposes metrics for and quantita- In adhering to quantitative methodologies, the present tively tests this model as the two-dimensional framework study proceeded as follows: is limited by its reliance on qualitative data. As qualitative research is used to contextualize understandings of a  formulation of hypotheses phenomenon, such as social resilience to disasters, it is  definition of variables not appropriate to derive a framework from such a small  identification of the sample sample size. Generalization is uncommon from qualitative  development of instruments data, yet the framework uses the interview data to propose  data collection a general framework of social resilience.  data analysis. Methods Hypotheses were formulated based on the pre-identified Quantitative research relies on empirical investigation phenomena of disasters and were designed to assess the methods (Given, 2008) that utilizes numerical, quantifiable previous identified impact of social resilience indicators as data to conduct research (Grove and Burns, 2005). Thus, the independent variables on social resilience as the quantitative research seeks to explain phenomena via the dependent variable. The hypotheses were based on the collection of numerical data that are analyzed using statis- association between the dependent variable of social resili- tical methods (Aliaga and Gunderson, 2005). The phenom- ence and the 14 indicators discussed previously. Thus, the ena being analyzed in the present study are climate- present study quantitatively assessed the relationship induced disasters such as floods and storms. The study between social resilience and social resilience indicators, used quantitative methods to verify Khalili et al.’s(2015)so- assuming that every disaster phase has its own individual cial resilience model by the generation of metrics that were indicators that influence social resilience. used to quantify the framework and test the hypotheses by To test these hypotheses, it was necessary to collect data statistical methods. Social resilience was quantified as the on the community perspective of social resilience. NSW dependent variable and the framework indicators were the SES volunteers are ideal for providing this information be- independent variables for the purpose of generalization cause they formed part of the larger population that the from the sample to the greater population. Quantitative present study aimed to generalize, they are members of research not only allows the generalization of results, the local community and generally have an advanced un- but is also considered more objective for testing derstanding of disaster management through their work Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 5 of 9 with the SES. Although the SES is generally considered an Data collection emergency response agency, SES volunteers are active in The NSW SES volunteers were pre-identified as the target providing education, advice, support and other services population and the survey was administered through Sur- throughout all the disaster phases. vey Monkey, an online survey administration website. An online survey of volunteers from NSW locations Online surveys were selected for this research for their ex- who had experienced significant flooding events was pediency, cost and accuracy in collecting and managing conducted with questions designed to solicit quantitative data as well as their ability to collect data anonymously. data for the dependent and independent variables. Ques- For the final survey, SES volunteers received a direct link tions were designed to measure social resilience for each to the online survey in an email on October 21, 2015 from of the three disaster phases using a 5-point Likert scale the SES commissioner. The survey link remained open for from Strongly Disagree (+ 1) to Strongly Agree (+ 5), three months until closing on January 21, 2016. During which provided a measurement of the attitudes of the this time, the survey received a total of 126 responses. respondents on each of the social resilience indicators (Bowling, 2014). In addition to the scaled questions, the Data analysis instrument also included open- and close-ended ques- After the survey was closed, the data was processed, tions that addressed demographic and similar informa- cleaned and analyzed in the context of the research hypoth- tion for developing survey weights. Sample questions eses. After removing incomplete responses (not finished), from the survey instrument are provided in Table 2. non-responses (blank), and “don’tknow” responses, 42 The survey instrument was pilot tested to ensure that were foundtobeincompleteand 84 were foundtobefully it produced reliable and valid measurements and that complete. To analyze the data, each column of the data the questions generated the data required to test the re- matrix was assigned a label based on the social resilience search hypotheses. Validity was tested to ensure control indicator that the data measured. The 5-point Likert scale of any systematic error in data measurement (Norland, ordinal data were treated as interval-level data and were 1990) and to check that the instrument measured what subjected to interval-level data analysis procedures. The it was designed to measure (Bryman and Cramer, 1994; variable frequencies, means and weights were calculated to Kerlinger, 2011). Content validity was ensured via the re- provide descriptive statistics for each of the columns. Cron- liance on expert opinions in the development of the bach’s alpha was calculated to test the internal reliability scaled items (Rattray and Jones, 2007). Specifically, the and was found to be greater than 90%. Pearson’s bivariate instrument pre-testing and piloting stage allowed us to correlation coefficients were then calculated to assess the identify questions that were not clear to the respondents relationship between variables. This included calculating and that could potentially introduce bias into the data. correlations for each of the three disaster phases to assess This resulted in question order changes, the re-wording the relationship between each of the tested social resilience of some questions and the addition or elimination of indicators and the social resilience expectancy. The scale some questions. showninTable 3 was developed to determine the strength of the relationship based on the Pearson’s correlation coeffi- cient R-value. Table 2 Sample Questions from Online Questionnaire Table 4 provides the correlation coefficients for the Community Participation: pre-disaster indicators categorized by strength of out- Pre-disaster: People in my area have participated in local activities, come from very strong to moderate. The pre-disaster events (e.g., festivals, fetes, fairs) or public meeting. shared information indicator showed the highest correl- During disaster: People in my area tried to help each other and make a ation to pre-disaster social resilience (rho = 0.821, p ≤ positive difference to the community. 0.000 at 2-tailed, n =77). Post-disaster: People in my area have been involved in volunteer Table 5 provides the correlation coefficients for the activities intended to benefit the community (e.g., fundraising, clean-up days, etc.) or have contributed money, food or clothing to local causes, during disaster indicators categorized by strength of out- charities, or others. come from strong to weak. The during disaster shared Please rate your agreement with the following statements on a scale of 1 to 5, where: Table 3 Correlation Relationship Strength 1. Strongly Disagree R-Value Strength of Relationship 2. Disagree .00–.19 Very weak 3. Neither Agree nor Disagree .20–.39 Weak 4. Agree .40–.59 Moderate 5. Strongly Agree .60–.79 Strong 6. Don’t Know .80–1.0 Very strong Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 6 of 9 Table 4 Pre-Disaster Phase Correlations Table 6 Post-Disaster Phase Correlations Strength Pre-Disaster Indicators Correlation Strength Post-Disaster Indicators Correlation ** ** Very Strong Pre-Disaster–Shared Information .821 Very Strong After Disaster–Coping Style .844 ** ** Pre-Disaster–Community Participation .815 Strong After Disaster–Shared Information .650 ** ** Strong Pre-Disaster–Leadership .693 After Disaster–Learning .627 ** ** Pre-Disaster–Sense of Community .679 Moderate After Disaster–Improvisation/Inventiveness .581 ** ** Pre-Disaster–Demographic Information .636 After Disaster–Social Support .567 ** ** Pre-Disaster–Education .603 After Disaster–Trust .555 ** ** Moderate Pre-Disaster–Improvisation/Inventiveness .553 After Disaster–Sense of Community .553 ** ** Pre-Disaster–Exchange Information .548 After Disaster–Exchange Information .535 ** ** Pre-Disaster–Coping Style .541 After Disaster–Leadership .506 ** ** Pre-Disaster–Trust .509 After Disaster–Community Efficacy .437 * ** Pre-Disaster–Social Support .492 After Disaster–Community Participation .424 ** **Correlation is significant at the 0.01 level (2-tailed) Pre-Disaster–Learning .471 *Correlation is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed) organizing social resilience indicators by disaster phase and level of impact to verify Khalili et al.’s(2015) frame- information indicator showed the highest correlation to work (see Table 8). The indicators in both Table 1 and during disaster social resilience (rho = 0.668, p ≤ 0.000 at Table 8 are ordered by strength of association with out- 2-tailed, n = 69). comes. As presented in the revised matrix in Table 8, Table 6 provides the correlation coefficients for the several social resilience indicators, such as community post-disaster indicators categorized by strength of out- efficacy and education, were only relevant in a single dis- come from very strong to moderate. The post-disaster aster phase, while other indicators, such as learning, coping style indicator showed the highest correlation to were relevant in two of the three phases, but most indi- post-disaster social resilience (rho = 0.844, p ≤ 0.000 at cators were present across all three disaster phases. Add- 2-tailed, n = 65). itionally, while many indicators were significant in more Table 7 demonstrates in general (across the three dis- than one phase, they generally held different levels of aster phases) that social resilience indicators have a significance in each phase. ‘Sense of Community,’ for in- moderate to strong positive correlation with social resili- stance, was of high importance during the pre-disaster ence. Shared information had the highest correlation to phase, of low importance during the response phase and social resilience when aggregated across the three phases of medium importance during the recovery phase. with coping style and community participation indicator Table 7 Aggregate (Across All Phases) Correlations each also showing strong positive correlations. Strength Social Resilience Indicators Correlation ** Results and discussion Strong All Phases–Shared Information .742 ** Following the generation and analysis of the data, the in- All Phases–Coping Style .726 dicators were organized into a two-dimensional matrix ** All Phases–Community Participation .668 ** Moderate All Phases–Leadership .592 Table 5 During Disaster Phase Correlations ** All Phases–Coordination .582 Strength During Disaster Indicators Correlation ** All Phases–Exchange Information .578 Strong During Disaster–Shared Information .668** ** All Phases–Improvisation/Inventiveness .556 ** During Disaster–Community Participation .641 ** All Phases–Learning .555 ** Moderate During Disaster–Coping Style .537 ** All Phases–Sense of Community .554 ** During Disaster–Coordination .489 ** All Phases–Social Support .539 ** During Disaster–Trust .438 All Phases–Education .482 ** Weak During Disaster–Exchange Information .365 ** All Phases–Demographic Information .466 ** During Disaster–Social Support .363 ** All Phases–Community Efficacy .456 ** During Disaster–Leadership .349 ** All Phases–Trust .452 ** During Disaster–Sense of Community .331 *Correlation is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed) Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 7 of 9 Table 8 Social Resilience Indicators Matrix Social Resilience Indicators High Shared Information Shared Information Coping Style Community Participation Community Participation Shared Information Leadership Learning Sense of Community Demographic Information Education Medium Improvisation/Inventiveness Coping Style Improvisation/Inventiveness Exchange Information Coordination Social Support Coping Style Trust Trust Trust Sense of Community Social Support Exchange Information Learning Leadership Community efficacy Community Participation Low Exchange Information Social Support Leadership Sense of Community Pre-Disaster Response Recovery Overall, the findings showed that the 14 indicators  Sixth, the research also aligned with that of Harland were all individually associated with social resilience as et al. (2005) in that leadership is an important factor they all had a positive statistically significant correlation for social resilience. The present study showed with social resilience and each indicator had a different that leadership had a moderate impact on social level of impact on social resilience. The data analysis re- resilience overall, with a high impact during the vealed the following seven findings that are important to pre-disaster phase, a low impact in the during the study and for advancing the current state of know- disaster phase and medium impact in the post- ledge on social resilience in disasters: disaster phase. Finally, all pre- and post-disaster social resilience First, all 14 social resilience indicators had a positive indicators had a high or medium impact, indicating correlation with social resilience as viewed by the that community stakeholders should concentrate on SES volunteers sampled in the study. indicators in these phases to improve community Second, aggregated across all three disaster stages, all social resilience. social resilience indicators had a relationship that was categorized as a high or medium impact with shared The survey results and analysis also showed that every information, community participation and coping style phase of disaster had its own individual indicators that exhibiting the greatest impact on social resilience. influenced social resilience. The relationships among indi- Third, in agreeance with Rohrmann (2000) and Ink catorswereall foundtobestatistically significant; therefore, (2006), the social resilience indicator of shared these can be generalized to a broader framework and used information had a significant impact on social to develop policies for improving and maintaining resili- resilience. Shared information was determined to be ence. The extant literature generally approaches social re- the factor having the greatest impact in the silience to disasters as an entire entity; however, refining aggregated data. the indicators for each of the disaster phases individually Fourth, coping style was determined to be the will allow approaches to be more targeted to the factors indicator with the second greatest overall impact, that are of greatest impact and provide meaning to social which aligns with Miller et al.’s(1999)finding resilience throughout all disaster phases. that coping style had a strong influence on social While this study confirmed that all 14 social resilience resilience. indicators shared a temporally assigned positive statisti- Fifth, the data aligns with Paton and Johnston’s cally significant relationship with the different disaster (2001) conclusion that community participation is a phase outcomes, the placement of the social indicators strong indicator of social resilience. The results within the matrix did not perfectly align with that proposed demonstrated that community participation had a by Khalili et al. (2015). As previously stated, qualitative re- high impact on social resilience during the pre- and search designs do not yield the data that are considered post-disaster phases, as well as a medium impact in appropriate for generalization in this manner, which in part the during disaster phase. explains the differences, but also provides justification for Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 8 of 9 presenting the quantitative based model as one that has Abbreviations CRED: Centre for Research on the Epidemiology of Disaster; DROP: Disaster greater reliability. Beyond qualitative-quantitative differ- resilience of place; NSW: New South Wales; SES: State Emergency Service; ences, Khalili et al.’s(2015) data were collected from com- UNISDR: United Nations International Strategy for Disaster Reduction munity leaders and subject matter experts, while the data Acknowledgements in the present study were collected from volunteers in the We thank the SES and council members who provided us with the opportunity communities. Some of the variation in the results can be at- to gather relevant information for our research. tributed to the differing perceptions of these two samples. Funding For example, analysis of the interview data collected from This work did not have any funding. the SES experts indicated that leadership within the com- munity was a low impact social resilience indicator. Their Availability of data and materials Not applicable. view indicated that only leadership in emergency prepared- ness and response organizations was important; however, Authors’ contributions their positional bias led them to view community leadership SK researched and developed the manuscript. MH and PM read and reviewed the final manuscript. All authors read and approved the final manuscript. as being insignificant. However, from a community per- spective, leadership within a community plays a significant Competing interests role in social resilience especially before and after disasters. The authors declare that they have no competing interests. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in Conclusion published maps and institutional affiliations. Thepresent study usedsurveydatatorefine previ- ously postulated frameworks of social resilience, fo- Author details Project Management, School of Civil Engineering, Building J05, University of cusing on social resilience indicators provided by Sydney, Camperdown, NSW 2006, Australia. University of New England, Khalili et al. (2015). Based on the conclusion that all 14 Armidale, Australia. social resilience indicators presented by Khalili et al. Received: 24 January 2018 Accepted: 25 November 2018 (2015) were positively correlated with social resilience, but varying in impact strength depending on disaster phase, the present study found that social resilience was com- References Adger, W.N. 2000. Social and ecological resilience: Are they related? Progress in posed of varied indicators with different levels of effective- Human Geography 24: 347–364. ness during different phases of a disaster. The indicators Aliaga, M., and B. Gunderson. 2005. Interactive statistics. 3rd ed. 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A temporal social resilience framework of communities to disasters in Australia

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Springer Journals
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Copyright © 2018 by The Author(s).
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Environment; Environment, general; Earth Sciences, general; Geography, general; Geoecology/Natural Processes; Natural Hazards; Environmental Science and Engineering
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2197-8670
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10.1186/s40677-018-0114-4
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Abstract

Despite the current interest in and need for studies in the conceptualization and measurement of social resilience to hazards and disasters, there remains significant research gaps within this area. This study seeks to fill one such gap via the provision of an innovative unified framework of social resilience across three disaster phases (i.e., pre-disaster, response and recovery) using a quantitative research method. We utilized the survey results from the New South Wales StateEmergency Servicevolunteerstovalidateaconceptualization framework that aimed to enhance social resilience across all disaster phases. This study had shown the positive correlation between identified indicators and social resilience but varying in impact strength depending on disaster phase. Keywords: Disaster resilience, Social resilience, Climate disaster, Quantitative research, Online questionnaire, Disaster phases, Social resilience framework, Temporal framework resilience Introduction Populations are generally growing and, in many areas, Over the past few decades, and particularly since the settlement patterns (Morley et al., 2012) have exacerbated 1970s, the frequency of natural disasters per year has the potential effect of these disaster events on the popula- been increasing worldwide (Pollach, 2014). In addition tion (Joerin et al., 2012). Thus, the frequency of disasters is to an increase in the number of potential natural disas- increasing (Pollach, 2014), populations are increasing and ters, there has also been an overall increase in the inten- the number of individuals living in vulnerable locations is sity of these events recorded globally by the Center for increasing. The culmination of these factors has resulted in Research on the Epidemiology of Disaster (CRED) via greater risk to communities and their economies, environ- the International Disaster Database. The frequency and ment and infrastructure, as well as the well-being of indi- impact of natural disasters is on an upward trajectory viduals within these communities. and, considering the future influence of climate change Australia is of interest when studying the socio-economic and population growth, is projected to continue to increase. aspects of natural disasters owing to the concentration of Among these increases in natural disasters, the incidences people living in flood-prone areas (Morley et al., 2012). of climate-related disasters have progressively increased Between 1990 and 2014, 74% of Australian disasters were over the last few decades (Leaning and Guha-Sapir, 2013). flood- or storm-related (UNISDR, 2015). These disasters, Nellemann et al. (2008) predict that this incidence of which are climate-related, have a severe negative effect on climate-related natural disasters will only continue to the communities, economies, environments and infrastruc- increase, while the number of geophysical disasters has ture wheretheyoccur. TheAustralia Business Roundtable remained stable. forDisasterResilienceand SaferCommunities report esti- According to CRED and the United Nations International mates that when social impacts (i.e., mental health and Strategy forDisasterReduction report (UNISDR, 2015), chronic disease related to disaster impact) are included increases in these climate-related disasters will result in a alongside critical infrastructure, disasters are estimated to greater impact on human lives, well-being and property. have a financial impact of $33 billion per year by 2050 in Australia in real terms (Insurance Australia Group Limited, 2016). Limiting this impact by enhancing resilience is thus * Correspondence: khalili.sanaz@gmail.com; sanaz.khalili@sydney.edu.au 1 a critical economic and social issue. Project Management, School of Civil Engineering, Building J05, University of Sydney, Camperdown, NSW 2006, Australia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 2 of 9 Therefore, it is necessary to minimize the risks related spatial scales of resilience, as well as the attributes of to potential disasters and enhance the capacity of these hazard-affected bodies. Beccari (2016) conducted a com- communities to be able to cope with any future impacts. parative analysis of 106 different published methods includ- Broadly speaking, the ability of individuals and communi- ing Bijan et al.’s (2014) (Khazai et al., 2014) ontology of 55 ties to cope with disturbances or changes and to maintain methods and the EMBRACE project (Birkman et al., 2012), adaptive behavior is termed resilience. Building resilience to which incorporated 32 different frameworks across a broad natural hazards requires increasing the capacity to cope range of indicators. Despite a solid foundation of literature with the event and its aftermath, as well as increasing the addressing social resilience to disasters and various ap- capacity to learn about hazards and risks, change behavior, proaches for its measurement, there are currently no stud- transform institutions and adapt to a changing environment ies that address social resilience using quantitative (Maguire and Cartwright, 2008). Therefore, it is imperative measures across the emergency management temporal that the determinants of disaster resilience are identified stages or key areas of operation. and measured so that issues may be addressed and capaci- These emergency management stages in Australia are ties improved (Klein et al., 2003,Cutteretal., 2008). termed prevention, preparedness, response and recovery Short-term emergency responses to flood disasters are (Council of Australian Governments, 2011). For the present usually the focus of studies; however, addressing social study, the two pre-disaster phases, prevention and pre- resilience factors for people who are exposed to flood paredness, are grouped into a single preparedness phase disasters should be addressed (Weldegebriel and Amphune, and cover the entire time period prior to the disaster. The 2017). The assessment of disaster resilience using indicators response phase begins once the community has been influ- can be a key element in the planning and management of encedbyacrisisevent.Thisphase is oftenmarkedbyaco- extreme events by providing a tool to identify priorities for ordinated effort to ensure that the needs of those involved improvement and to monitor change. In the past two in the disaster are met, such as search and rescue efforts. decades, and since the publication of the Social Vulnerabil- The nature of the response efforts, however, is determined ity Index (Cutter et al., 2003), therehas been adramaticin- by the immediate and most pressing needs of those crease in the number of studies that have aimed to provide impacted. Finally, the recovery phase occurs after the methodology to measure the various aspects of disaster immediate needs of the community are met and the disas- risk, resilience or vulnerability (Beccari, 2016). ter is no longer presenting further impact to the commu- There is, however, no standard method; approaches can nity (Waugh and Streib, 2006). This phase seeks to restore be top-down or bottom-up, qualitative or quantitative, use the impacted individuals and communities to a state in primary or secondary data and can be designed for scales which recovery is no longer occurring. ranging from local through to national (National Research Khalili et al. (2015) provided a social resilience framework Council, 2015). The focus of the assessment may be on re- across these three phases of disaster; however, their study is silience (e.g. Cutter et al. 2010), vulnerability (e.g. Cutter et limited because of its focus on qualitative interviews with al. 2003) or risk (e.g. United Nations University Institute for subject matter experts. Hinds (1989)arguedthatapplying Environment and Human Security and Alliance Develop- both qualitative and quantitative methods to a research ment Works, 2014) but essentially each method aims to as- problem “increases the ability to rule out rival explanations sess some variation of the capacity within a community to of observed change and reduces skepticism of change-re- withstand and recover from natural hazards. Further, as- lated findings”. Similarly, Hussein (2015) argued that quanti- sessment approaches may also be at different stages of de- tative research can be used to validate qualitative findings. velopment, existing only as a conceptual model (e.g. Norris Therefore, the quantitative approach applied within the et al., 2008; Parsons et al., 2016) or as a conceptual model present study to the qualitative findings from Khalili et al.’s developed into an applied assessment such as Cutter et al.’s (2015) study is essential for the development of a unified so- (2008) disaster resilience of place (DROP) model. The DRP cial resilience framework, which is required to improve model clusters around the following six core dimension: current efforts to address social resilience. Thus, by conduct- ecological, social, economic, institutional, infrastructure ing quantitative research using an online survey with New and community competence, and conceptualizes resilience South Wales (NSW) State Emergency Services (SES) volun- as achangeableprocess that is dependentuponpre-existing teers from locations throughout NSW who had experienced conditions and the severity, duration and time between di- significant flooding events, the present study attempted to sasters, as well as a range of additional external factors validate the resilience frameworkinthe studybyKhalili et (Cutter et al., 2008). Working from a geographical perspec- al. (2015). tive, Zhou et al. (2010) developed a notable approach to re- silience within local community contexts via a model of Social resilience and indicators disaster resilience of ‘loss-response’ of location based on Resilience is a broadly used concept that is applied across the following three distinct dimensions: the temporal and a range of disciplines including engineering, psychology, Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 3 of 9 science, sociology and economics. Initially, resilience was of resilience to best develop a unified social resilience derived from the Latin word resilio, meaning ‘to jump framework. Such indicators—defined herein as parameters back’ (Klein et al., 2003). As a broad encompassing topic, for assessing the social resilience of a community—include the present study begins by setting parameters on the community demographics, participation, learning pro- dimensions of resilience, which are conceptualized via the cesses related to disaster, and leadership (Miller et al., four dimensions of organizational, technical, social and 2010). These indicators represent variable aspects of com- economical (Bruneau et al., 2003). Of these dimensions, munities, i.e., they vary the phases of a single disaster, but the present study focuses on the social component of also over time across different disasters. By advancing the resilience as it relates to disasters. Specifically, social resili- understanding of the indicators associated with social ence—defined as the ability of communities to withstand resilience, the present study provides implications for the external social shocks of a disaster or disaster-related increasing those capacities identified as positive factors of event (Adger, 2000)—occurs through three phases: pre- social resilience or decreasing those identified as negative disaster, disaster response, and disaster recovery. Accord- factors of social resilience. This begins with recognition of ing to Keck and Sakdapolrak (2013), social resilience to these indicators and their impact in disaster social resili- disasters is widely debated, but includes three capacities ence. The purpose of the present study is thus to improve that are the ability to cope with, adapt to and transform the ability of a community to withstand the shock of from disaster shocks to social systems. Social resilience is disasters by improving social resilience. the ability of a community to absorb shocks, recover from Table 1 shows Khalili et al.’s(2015) two-dimensional disturbances and avoid negative and potentially irrevers- framework on social resilience indicators for the three ible effects (Resilience Alliance, 2007). Social resilience is disaster phases as temporal factors. In all, there are 14 important across these phases to protect the community indicators included in the model: against loss by enhancing the capacity of communities during the pre-disaster and response phases, and to im-  community participation – the engagement of prove their capacity to rebuild and return to normal after community members in organizations and the event (Zhou et al., 2010). Although researchers have activities within their community, including provided definitions, albeit often inconsistent, of social resident associations, neighborhood (Perkins and resilience to disasters, “the questions of how social re- Long, 2002) watches, silience can be properly defined, how it can be opera- self-help groups and religious congregations tionalized, measured and analyzed, and how it might be (Paton et al., 2001; Perkins et al., 2002) fostered (or hindered) are far from being settled yet”  education – disaster-related formal and informal (Keck and Sakdapolrak, 2013). training and educational activities within communities The concept and term ‘community’ has various mean- (Paton and Johnston, 2001) ings and applications. It is invariably used to refer to col-  exchange of information – information flow within a lectives of people joined by shared geography, interests community (Rohrmann, 2000) and concerns, or identity (Jewkes and Murcott, 1996).  learning – learning from previous disasters (Zhou et Common definitions of community highlight the exist- al., 2010) ence within a geographical boundary, and engagement  shared information – distributing information within in ongoing social interaction and psychological connec- a community (Ink, 2006) tions to both the surrounding people and place as key  social support – support from the neighborhood components (Christenson and Robinson, 1980). In (Kaniasty and Norris, 1999; Norris et al., 2008) the present study, community refers to a social unit  sense of community – feeling of belonging to a larger than a household whose members share com- community or place (Paton and Johnston, 2001) mon values and live in some physical proximity to  trust – trust in the neighborhood (Enemark, 2006) each other.  demographic information – i.e., age, gender, socio- Disaster impact is a factor of both the scale of the disas- economic status/income, health, history, education, ter and the ability of the community to withstand the cultural/religious belief, or populations with special shock of the disaster. Thus, social resilience reflects the needs (Tobin, 1999; Cutter et al., 2010) preparedness and response of the community and is  improvisation-inventiveness – community creativity dependent on social situations in the communities during and innovation to devise a solution for enhancing both the pre- and post-disaster contexts (Boyce, 2000). resilience (Demchak, 2006; Lalonde, 2011) Disaster response, therefore, both affects and influences  coping style – the ability to manage, adapt to and social resilience (Tobin and Whiteford, 2002). To capture deal with stressful situations (Miller et al., 1999) this cyclical relationship, it is necessary to consider all  leadership – leadership within a community three disaster phases and the associated social indicators (Harland et al., 2005; Hegney et al., 2008) Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 4 of 9 Table 1 Khalili et al.’s(2015) Framework on Social Resilience Indicators Matrix Social Resilience Indicators High Community Participation Community Participation Community Participation Education Exchange information Exchange information Exchange information Shared information Learning Learning Social Support Shared information Shared Support Sense of community Social Support Sense of community Trust Sense of community Trust Trust Medium Demographic information Coordination Community efficacy Low Improvisation inventiveness Coping Style Improvisation inventiveness Coping Style Leadership Coping Style Leadership Leadership Pre-Disaster Response Recovery coordination – community works together hypotheses (Fink, 2002). The relationship examined via community efficacy – community belief in their own the quantitative methods was that existing between so- capabilities of performing and completing jobs cial resilience indicators identified via prior qualitative re- (Moore et al., 2004). search and social resilience data collected from NSW SES volunteers from locations throughout NSW who have ex- The social resilience indicators are presented as low, perienced significant flooding events. This relationship medium or high influence based on the analysis of previ- was examined to generalize the study findings across dif- ous studies and subject matter expert interviews. This ferent disaster environments. The research relied on ro- framework presents Khalili et al.’s(2015) interpretation of bust scientific procedures to ensure reliability and validity the NSW SES subject matter expert perspectives on the during the process of quantifying the previously qualita- relationship between social resilience indicators and im- tive data of social resilience indicators. pact. The present study proposes metrics for and quantita- In adhering to quantitative methodologies, the present tively tests this model as the two-dimensional framework study proceeded as follows: is limited by its reliance on qualitative data. As qualitative research is used to contextualize understandings of a  formulation of hypotheses phenomenon, such as social resilience to disasters, it is  definition of variables not appropriate to derive a framework from such a small  identification of the sample sample size. Generalization is uncommon from qualitative  development of instruments data, yet the framework uses the interview data to propose  data collection a general framework of social resilience.  data analysis. Methods Hypotheses were formulated based on the pre-identified Quantitative research relies on empirical investigation phenomena of disasters and were designed to assess the methods (Given, 2008) that utilizes numerical, quantifiable previous identified impact of social resilience indicators as data to conduct research (Grove and Burns, 2005). Thus, the independent variables on social resilience as the quantitative research seeks to explain phenomena via the dependent variable. The hypotheses were based on the collection of numerical data that are analyzed using statis- association between the dependent variable of social resili- tical methods (Aliaga and Gunderson, 2005). The phenom- ence and the 14 indicators discussed previously. Thus, the ena being analyzed in the present study are climate- present study quantitatively assessed the relationship induced disasters such as floods and storms. The study between social resilience and social resilience indicators, used quantitative methods to verify Khalili et al.’s(2015)so- assuming that every disaster phase has its own individual cial resilience model by the generation of metrics that were indicators that influence social resilience. used to quantify the framework and test the hypotheses by To test these hypotheses, it was necessary to collect data statistical methods. Social resilience was quantified as the on the community perspective of social resilience. NSW dependent variable and the framework indicators were the SES volunteers are ideal for providing this information be- independent variables for the purpose of generalization cause they formed part of the larger population that the from the sample to the greater population. Quantitative present study aimed to generalize, they are members of research not only allows the generalization of results, the local community and generally have an advanced un- but is also considered more objective for testing derstanding of disaster management through their work Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 5 of 9 with the SES. Although the SES is generally considered an Data collection emergency response agency, SES volunteers are active in The NSW SES volunteers were pre-identified as the target providing education, advice, support and other services population and the survey was administered through Sur- throughout all the disaster phases. vey Monkey, an online survey administration website. An online survey of volunteers from NSW locations Online surveys were selected for this research for their ex- who had experienced significant flooding events was pediency, cost and accuracy in collecting and managing conducted with questions designed to solicit quantitative data as well as their ability to collect data anonymously. data for the dependent and independent variables. Ques- For the final survey, SES volunteers received a direct link tions were designed to measure social resilience for each to the online survey in an email on October 21, 2015 from of the three disaster phases using a 5-point Likert scale the SES commissioner. The survey link remained open for from Strongly Disagree (+ 1) to Strongly Agree (+ 5), three months until closing on January 21, 2016. During which provided a measurement of the attitudes of the this time, the survey received a total of 126 responses. respondents on each of the social resilience indicators (Bowling, 2014). In addition to the scaled questions, the Data analysis instrument also included open- and close-ended ques- After the survey was closed, the data was processed, tions that addressed demographic and similar informa- cleaned and analyzed in the context of the research hypoth- tion for developing survey weights. Sample questions eses. After removing incomplete responses (not finished), from the survey instrument are provided in Table 2. non-responses (blank), and “don’tknow” responses, 42 The survey instrument was pilot tested to ensure that were foundtobeincompleteand 84 were foundtobefully it produced reliable and valid measurements and that complete. To analyze the data, each column of the data the questions generated the data required to test the re- matrix was assigned a label based on the social resilience search hypotheses. Validity was tested to ensure control indicator that the data measured. The 5-point Likert scale of any systematic error in data measurement (Norland, ordinal data were treated as interval-level data and were 1990) and to check that the instrument measured what subjected to interval-level data analysis procedures. The it was designed to measure (Bryman and Cramer, 1994; variable frequencies, means and weights were calculated to Kerlinger, 2011). Content validity was ensured via the re- provide descriptive statistics for each of the columns. Cron- liance on expert opinions in the development of the bach’s alpha was calculated to test the internal reliability scaled items (Rattray and Jones, 2007). Specifically, the and was found to be greater than 90%. Pearson’s bivariate instrument pre-testing and piloting stage allowed us to correlation coefficients were then calculated to assess the identify questions that were not clear to the respondents relationship between variables. This included calculating and that could potentially introduce bias into the data. correlations for each of the three disaster phases to assess This resulted in question order changes, the re-wording the relationship between each of the tested social resilience of some questions and the addition or elimination of indicators and the social resilience expectancy. The scale some questions. showninTable 3 was developed to determine the strength of the relationship based on the Pearson’s correlation coeffi- cient R-value. Table 2 Sample Questions from Online Questionnaire Table 4 provides the correlation coefficients for the Community Participation: pre-disaster indicators categorized by strength of out- Pre-disaster: People in my area have participated in local activities, come from very strong to moderate. The pre-disaster events (e.g., festivals, fetes, fairs) or public meeting. shared information indicator showed the highest correl- During disaster: People in my area tried to help each other and make a ation to pre-disaster social resilience (rho = 0.821, p ≤ positive difference to the community. 0.000 at 2-tailed, n =77). Post-disaster: People in my area have been involved in volunteer Table 5 provides the correlation coefficients for the activities intended to benefit the community (e.g., fundraising, clean-up days, etc.) or have contributed money, food or clothing to local causes, during disaster indicators categorized by strength of out- charities, or others. come from strong to weak. The during disaster shared Please rate your agreement with the following statements on a scale of 1 to 5, where: Table 3 Correlation Relationship Strength 1. Strongly Disagree R-Value Strength of Relationship 2. Disagree .00–.19 Very weak 3. Neither Agree nor Disagree .20–.39 Weak 4. Agree .40–.59 Moderate 5. Strongly Agree .60–.79 Strong 6. Don’t Know .80–1.0 Very strong Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 6 of 9 Table 4 Pre-Disaster Phase Correlations Table 6 Post-Disaster Phase Correlations Strength Pre-Disaster Indicators Correlation Strength Post-Disaster Indicators Correlation ** ** Very Strong Pre-Disaster–Shared Information .821 Very Strong After Disaster–Coping Style .844 ** ** Pre-Disaster–Community Participation .815 Strong After Disaster–Shared Information .650 ** ** Strong Pre-Disaster–Leadership .693 After Disaster–Learning .627 ** ** Pre-Disaster–Sense of Community .679 Moderate After Disaster–Improvisation/Inventiveness .581 ** ** Pre-Disaster–Demographic Information .636 After Disaster–Social Support .567 ** ** Pre-Disaster–Education .603 After Disaster–Trust .555 ** ** Moderate Pre-Disaster–Improvisation/Inventiveness .553 After Disaster–Sense of Community .553 ** ** Pre-Disaster–Exchange Information .548 After Disaster–Exchange Information .535 ** ** Pre-Disaster–Coping Style .541 After Disaster–Leadership .506 ** ** Pre-Disaster–Trust .509 After Disaster–Community Efficacy .437 * ** Pre-Disaster–Social Support .492 After Disaster–Community Participation .424 ** **Correlation is significant at the 0.01 level (2-tailed) Pre-Disaster–Learning .471 *Correlation is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed) organizing social resilience indicators by disaster phase and level of impact to verify Khalili et al.’s(2015) frame- information indicator showed the highest correlation to work (see Table 8). The indicators in both Table 1 and during disaster social resilience (rho = 0.668, p ≤ 0.000 at Table 8 are ordered by strength of association with out- 2-tailed, n = 69). comes. As presented in the revised matrix in Table 8, Table 6 provides the correlation coefficients for the several social resilience indicators, such as community post-disaster indicators categorized by strength of out- efficacy and education, were only relevant in a single dis- come from very strong to moderate. The post-disaster aster phase, while other indicators, such as learning, coping style indicator showed the highest correlation to were relevant in two of the three phases, but most indi- post-disaster social resilience (rho = 0.844, p ≤ 0.000 at cators were present across all three disaster phases. Add- 2-tailed, n = 65). itionally, while many indicators were significant in more Table 7 demonstrates in general (across the three dis- than one phase, they generally held different levels of aster phases) that social resilience indicators have a significance in each phase. ‘Sense of Community,’ for in- moderate to strong positive correlation with social resili- stance, was of high importance during the pre-disaster ence. Shared information had the highest correlation to phase, of low importance during the response phase and social resilience when aggregated across the three phases of medium importance during the recovery phase. with coping style and community participation indicator Table 7 Aggregate (Across All Phases) Correlations each also showing strong positive correlations. Strength Social Resilience Indicators Correlation ** Results and discussion Strong All Phases–Shared Information .742 ** Following the generation and analysis of the data, the in- All Phases–Coping Style .726 dicators were organized into a two-dimensional matrix ** All Phases–Community Participation .668 ** Moderate All Phases–Leadership .592 Table 5 During Disaster Phase Correlations ** All Phases–Coordination .582 Strength During Disaster Indicators Correlation ** All Phases–Exchange Information .578 Strong During Disaster–Shared Information .668** ** All Phases–Improvisation/Inventiveness .556 ** During Disaster–Community Participation .641 ** All Phases–Learning .555 ** Moderate During Disaster–Coping Style .537 ** All Phases–Sense of Community .554 ** During Disaster–Coordination .489 ** All Phases–Social Support .539 ** During Disaster–Trust .438 All Phases–Education .482 ** Weak During Disaster–Exchange Information .365 ** All Phases–Demographic Information .466 ** During Disaster–Social Support .363 ** All Phases–Community Efficacy .456 ** During Disaster–Leadership .349 ** All Phases–Trust .452 ** During Disaster–Sense of Community .331 *Correlation is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed) Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 7 of 9 Table 8 Social Resilience Indicators Matrix Social Resilience Indicators High Shared Information Shared Information Coping Style Community Participation Community Participation Shared Information Leadership Learning Sense of Community Demographic Information Education Medium Improvisation/Inventiveness Coping Style Improvisation/Inventiveness Exchange Information Coordination Social Support Coping Style Trust Trust Trust Sense of Community Social Support Exchange Information Learning Leadership Community efficacy Community Participation Low Exchange Information Social Support Leadership Sense of Community Pre-Disaster Response Recovery Overall, the findings showed that the 14 indicators  Sixth, the research also aligned with that of Harland were all individually associated with social resilience as et al. (2005) in that leadership is an important factor they all had a positive statistically significant correlation for social resilience. The present study showed with social resilience and each indicator had a different that leadership had a moderate impact on social level of impact on social resilience. The data analysis re- resilience overall, with a high impact during the vealed the following seven findings that are important to pre-disaster phase, a low impact in the during the study and for advancing the current state of know- disaster phase and medium impact in the post- ledge on social resilience in disasters: disaster phase. Finally, all pre- and post-disaster social resilience First, all 14 social resilience indicators had a positive indicators had a high or medium impact, indicating correlation with social resilience as viewed by the that community stakeholders should concentrate on SES volunteers sampled in the study. indicators in these phases to improve community Second, aggregated across all three disaster stages, all social resilience. social resilience indicators had a relationship that was categorized as a high or medium impact with shared The survey results and analysis also showed that every information, community participation and coping style phase of disaster had its own individual indicators that exhibiting the greatest impact on social resilience. influenced social resilience. The relationships among indi- Third, in agreeance with Rohrmann (2000) and Ink catorswereall foundtobestatistically significant; therefore, (2006), the social resilience indicator of shared these can be generalized to a broader framework and used information had a significant impact on social to develop policies for improving and maintaining resili- resilience. Shared information was determined to be ence. The extant literature generally approaches social re- the factor having the greatest impact in the silience to disasters as an entire entity; however, refining aggregated data. the indicators for each of the disaster phases individually Fourth, coping style was determined to be the will allow approaches to be more targeted to the factors indicator with the second greatest overall impact, that are of greatest impact and provide meaning to social which aligns with Miller et al.’s(1999)finding resilience throughout all disaster phases. that coping style had a strong influence on social While this study confirmed that all 14 social resilience resilience. indicators shared a temporally assigned positive statisti- Fifth, the data aligns with Paton and Johnston’s cally significant relationship with the different disaster (2001) conclusion that community participation is a phase outcomes, the placement of the social indicators strong indicator of social resilience. The results within the matrix did not perfectly align with that proposed demonstrated that community participation had a by Khalili et al. (2015). As previously stated, qualitative re- high impact on social resilience during the pre- and search designs do not yield the data that are considered post-disaster phases, as well as a medium impact in appropriate for generalization in this manner, which in part the during disaster phase. explains the differences, but also provides justification for Khalili et al. Geoenvironmental Disasters (2018) 5:23 Page 8 of 9 presenting the quantitative based model as one that has Abbreviations CRED: Centre for Research on the Epidemiology of Disaster; DROP: Disaster greater reliability. Beyond qualitative-quantitative differ- resilience of place; NSW: New South Wales; SES: State Emergency Service; ences, Khalili et al.’s(2015) data were collected from com- UNISDR: United Nations International Strategy for Disaster Reduction munity leaders and subject matter experts, while the data Acknowledgements in the present study were collected from volunteers in the We thank the SES and council members who provided us with the opportunity communities. Some of the variation in the results can be at- to gather relevant information for our research. tributed to the differing perceptions of these two samples. Funding For example, analysis of the interview data collected from This work did not have any funding. the SES experts indicated that leadership within the com- munity was a low impact social resilience indicator. Their Availability of data and materials Not applicable. view indicated that only leadership in emergency prepared- ness and response organizations was important; however, Authors’ contributions their positional bias led them to view community leadership SK researched and developed the manuscript. MH and PM read and reviewed the final manuscript. All authors read and approved the final manuscript. as being insignificant. However, from a community per- spective, leadership within a community plays a significant Competing interests role in social resilience especially before and after disasters. The authors declare that they have no competing interests. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in Conclusion published maps and institutional affiliations. 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Geoenvironmental DisastersSpringer Journals

Published: Dec 18, 2018

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