research paper about disaster preparedness

Facing Hazards and Disasters: Understanding Human Dimensions (2006)

Chapter: 4 research on disaster response and recovery, 4 research on disaster response and recovery.

T his chapter and the preceding one use the conceptual model presented in Chapter 1 (see Figure 1.1 ) as a guide to understanding societal response to hazards and disasters. As specified in that model, Chapter 3 discusses three sets of pre-disaster activities that have the potential to reduce disaster losses: hazard mitigation practices, emergency preparedness practices, and pre-disaster planning for post-disaster recovery. This chapter focuses on National Earthquake Hazards Reduction Program (NEHRP) contributions to social science knowledge concerning those dimensions of the model that are related to post-disaster response and recovery activities. As in Chapter 3 , discussions are organized around research findings regarding different units of analysis, including individuals, households, groups and organizations, social networks, and communities. The chapter also highlights trends, controversies, and issues that warrant further investigation. The contents of this chapter are linked to key themes discussed elsewhere in this report, including the conceptualization and measurement of societal vulnerability and resilience, the importance of taking diversity into account in understanding both response-related activities and recovery processes and outcomes, and linkages between hazard loss reduction and sustainability. Although this review centers primarily on research on natural disasters and to a lesser degree on technological disasters, research findings are also discussed in terms of their implications for understanding and managing emerging homeland security threats.

The discussions that follow seek to address several interrelated questions: What is currently known about post-disaster response and recovery,

and to what extent is that knowledge traceable to NEHRP-sponsored research activities? What gaps exist in that knowledge? What further research—both disciplinary and interdisciplinary—is needed to fill those gaps?

RESEARCH ON DISASTER RESPONSE

Emergency response encompasses a range of measures aimed at protecting life and property and coping with the social disruption that disasters produce. As noted in Chapter 3 , emergency response activities can be categorized usefully as expedient mitigation actions (e.g., clearing debris from channels when floods threaten, containing earthquake-induced fires and hazardous materials releases before they can cause additional harm) and population protection actions (e.g., warning, evacuation and other self-protective actions, search and rescue, the provision of emergency medical care and shelter; Tierney et al., 2001). Another common conceptual distinction in the literature on disaster response (Dynes et al., 1981) contrasts agent-generated demands , or the types of losses and forms of disruption that disasters create, and response-generated demands , such as the need for situation assessment, crisis communication and coordination, and response management. Paralleling preparedness measures, disaster response activities take place at various units of analysis, from individuals and households, to organizations, communities, and intergovernmental systems. This section does not attempt to deal exhaustively with the topic of emergency response activities, which is the most-studied of all phases of hazard and disaster management. Rather, it highlights key themes in the literature, with an emphasis on NEHRP-based findings that are especially relevant in light of newly recognized human-induced threats.

Public Response: Warning Response, Evacuation, and Other Self-Protective Actions

The decision processes and behaviors involved in public responses to disaster warnings are among the best-studied topics in the research literature. Over nearly three decades, NEHRP has been a major sponsor of this body of research. As noted in Chapter 3 , warning response research overlaps to some degree with more general risk communication research. For example, both literatures emphasize the importance of considering source, message, channel, and receiver effects on the warning process. While this discussion centers mainly on responses to official warning information, it should be noted that self-protective decision-making processes are also initiated in the absence of formal warnings—for example, in response to cues that people perceive as signaling impending danger and in disasters that occur without warning. Previous research suggests that the basic deci-

sion processes involved in self-protective action are similar across different types of disaster events, although the challenges posed and the problems that may develop can be agent specific.

As in other areas discussed here, empirical studies on warning response and self-protective behavior in different types of disasters and emergencies have led to the development of broadly generalizable explanatory models. One such model, the protective action decision model, developed by Perry, Lindell, and their colleagues (see, for example, Lindell and Perry, 2004), draws heavily on Turner and Killian’s (1987) emergent norm theory of collective behavior. According to that theory, groups faced with the potential need to act under conditions of uncertainty (or potential danger) engage in interaction in an attempt to develop a collective definition of the situation they face and a set of new norms that can guide their subsequent action. 1 Thus, when warnings and protective instructions are disseminated, those who receive warnings interact with one another in an effort to determine collectively whether the warning is authentic, whether it applies to them, whether they are indeed personally in danger, whether they can reduce their vulnerability through action, whether action is possible, and when they should act. These collective determinations are shaped in turn by such factors as (1) the characteristics of warning recipients , including their prior experience with the hazard in question or with similar emergencies, as well as their prior preparedness efforts; (2) situational factors , including the presence of perceptual cues signaling danger; and (3) the social contexts in which decisions are made—for example, contacts among family members, coworkers, neighborhood residents, or others present in the setting, as well as the strength of preexisting social ties. Through interaction and under the influence of these kinds of factors, individuals and groups develop new norms that serve as guidelines for action.

Conceptualizing warning response as a form of collective behavior that is guided by emergent norms brings several issues to the fore. One is that far from being automatic or governed by official orders, behavior undertaken in response to warnings is the product of interaction and deliberation among members of affected groups—activities that are typically accompanied by a search for additional confirmatory information. Circumstances that complicate the deliberation process, such as conflicting warning information that individuals and groups may receive, difficulties in getting in touch with others whose views are considered important for the decision-making process, or disagreements among group members about any aspect of the

threat situation, invariably lead to additional efforts to communicate and confirm the information and lengthen the period between when a warning is issued and when groups actually respond.

Another implication of the emergent norm approach to protective action decision making is the recognition that groups may collectively define an emergency situation in ways that are at variance from official views. This is essentially what occurs in the shadow evacuation phenomenon, which has been documented in several emergency situations, including the Three Mile Island nuclear plant accident (Zeigler et al., 1981). While authorities may not issue a warning for a particular geographic area or group of people, or may even tell them they are safe, groups may still collectively decide that they are at risk or that the situation is fluid and confusing enough that they should take self-protective action despite official pronouncements.

The behavior of occupants of the World Trade Center during the September 11, 2001 terrorist attack illustrates the importance of collectively developed definitions. Groups of people in Tower 2 of the World Trade Center decided that they should evacuate the building after seeing and hearing about what was happening in Tower 1 and after speaking with coworkers and loved ones, even when official announcements and other building occupants indicated that they should not do so. Others decided to remain in the tower or, perhaps more accurately, they decided to delay evacuating until receiving additional information clarifying the extent to which they were in danger. Journalistic accounts suggest that decisions were shaped in part by what people could see taking place in Tower 1, conversations with others outside the towers who had additional relevant information, and directives received from those in positions of authority in tenant firms. In that highly confusing and time-constrained situation, emergent norms guiding the behavior of occupants of the second tower meant the difference between life and death when the second plane struck (NIST, 2005).

The large body of research that exists regarding decision making under threat conditions points to the need to consider a wide range of individual, group, situational, and resource-related factors that facilitate and inhibit self-protective action. Qualitatively based decision-tree models developed by Gladwin et al. (2001) demonstrate the complexity of self-protective decisions. As illustrated by their work on hurricane evacuation, a number of different factors contribute to decisions on whether or not to evacuate. Such factors range from perceptions of risk and personal safety with respect to a threatened disaster, to the extent of knowledge about specific areas at risk, to constraining factors such as the presence of pets in the home that require care, lack of a suitable place to go, counterarguments by other family members, fears of looting (shown by the literature to be unjustified; see, for example, Fischer, 1998), and fear that the evacuation process may

be more dangerous than staying home and riding out a hurricane. Warning recipients may decide that they should wait before evacuating, ultimately missing the opportunity to escape, or they may decide to shelter in-place after concluding that their homes are strong enough to resist hurricane forces despite what they are told by authorities.

In their research on Hurricane Andrew, Gladwin and Peacock describe some of the many factors that complicate the evacuation process for endangered populations (1997:54):

Except under extreme circumstances, households cannot be compelled to evacuate or to remain where they are, much less to prepare themselves for the threat. Even under extraordinary conditions many households have to be individually located and assisted or forced to comply. Segments of a population may fail to receive, ignore, or discount official requests and orders. Still others may not have the resources or wherewithal to comply. Much will depend upon the source of the information, the consistency of the message received from multiple sources, the nature of the information conveyed, as well as the household’s ability to perceive the danger, make decisions, and act accordingly. Disputes, competition, and the lack of coordination among local, state, and federal governmental agencies and between those agencies and privately controlled media can add confusion. Businesses and governmental agencies that refuse to release their employees and suspend normal activities can add still further to the confusion and noncompliance.

The normalcy bias adds other complications to the warning response process. While popular notions of crisis response behaviors seem to assume that people react automatically to messages signaling impending danger—for example, by fleeing in panic—the reality is quite different. People typically “normalize” unusual situations and persist in their everyday activities even when urged to act differently. As noted earlier, people will not act on threat information unless they perceive a personal risk to themselves. Simply knowing that a threat exists—even if that threat is described as imminent—is insufficient to motivate self-protective action. Nor can people be expected to act if warning-related guidance is not specific enough to provide them with a blueprint for what to do or if they do not believe they have the resources required to follow the guidance. One practical implication of research on warnings is that rather than being concerned about panicking the public with warning information, or about communicating too much information, authorities should instead be seeking better ways to penetrate the normalcy bias, persuade people that they should be concerned about an impending danger, provide directives that are detailed enough to follow during an emergency, and encourage pre-disaster response planning so that people have thought through what to do prior to being required to act.

Other Important Findings Regarding the Evacuation Process

As noted earlier, evacuation behavior has long been recognized as the reflection of social-level factors and collective deliberation. Decades ago, Drabek (1983) established that households constitute the basic deliberative units for evacuation decision making in community-wide disasters and that the decisions that are ultimately made tend to be consistent with pre-disaster household authority patterns. For example, gender-related concerns often enter into evacuation decision making. Women tend to be more risk-averse and more inclined to want to follow evacuation orders, while males are less inclined to do so (for an extensive discussion of gender differences in vulnerability, risk perception, and responses to disasters, see Fothergill, 1998). In arriving at decisions regarding evacuation, households take official orders into account, but they weigh those orders in light of their own priorities, other information sources, and their past experiences. Information received from media sources and from family and friends, along with confirmatory data actively sought by those at risk, generally has a greater impact on evacuation decisions than information provided by public officials (Dow and Cutter, 1998, 2000).

Recent research also suggests that family evacuation patterns are undergoing change. For example, even though families decide together to evacuate and wish to stay together, they increasingly tend to use more than one vehicle to evacuate—perhaps because they want to take more of their possessions with them, make sure their valuable vehicles are protected, or return to their homes at different times (Dow and Cutter, 2002). Other social influences also play a role. Neighborhood residents may be more willing to evacuate or, conversely, more inclined to delay the decision to evacuate if they see their neighbors doing so. Rather than becoming more vigilant, communities that are struck repeatedly by disasters such as hurricanes and floods may develop “disaster subcultures,” such as groups that see no reason to heed evacuation orders since sheltering in-place has been effective in previous events.

NEHRP-sponsored research has shown that different racial, ethnic, income, and special needs groups respond in different ways to warning information and evacuation orders, in part because of the unique characteristics of these groups, the manner in which they receive information during crises, and their varying responses to different information sources. For example, members of some minority groups tend to have large extended families, making contacting family members and deliberating on alternative courses of action a more complicated process. Lower-income groups, inner-city residents, and elderly persons are more likely to have to rely on public transportation, rather than personal vehicles, in order to evacuate. Lower-income and minority populations, who tend to have larger families, may

also be reluctant to impose on friends and relatives for shelter. Lack of financial resources may leave less-well-off segments of the population less able to afford to take time off from work when disasters threaten, to travel long distances to avoid danger, or to pay for emergency lodging. Socially isolated individuals, such as elderly persons living alone, may lack the social support that is required to carry out self-protective actions. Members of minority groups may find majority spokespersons and official institutions less credible and believable than members of the white majority, turning instead to other sources, such as their informal social networks. Those who rely on non-English-speaking mass media for news may receive less complete warning information, or may receive warnings later than those who are tuned into mainstream media sources (Aguirre et al., 1991; Perry and Lindell, 1991; Lindell and Perry, 1992, 2004; Klinenberg, 2002; for more extensive discussions, see Tierney et al., 2001).

Hurricane Katrina vividly revealed the manner in which social factors such as those discussed above influence evacuation decisions and actions. In many respects, the Katrina experience validated what social science research had already shown with respect to evacuation behavior. Those who stayed behind did so for different reasons—all of which have been discussed in past research. Some at-risk residents lacked resources, such as automobiles and financial resources that would have enabled them to escape the city. Based on their past experiences with hurricanes like Betsey and Camille, others considered themselves not at risk and decided it was not necessary to evacuate. Still others, particularly elderly residents, felt so attached to their homes that they refused to leave even when transportation was offered.

This is not to imply that evacuation-related problems stemmed solely from individual decisions. Katrina also revealed the crucial significance of evacuation planning, effective warnings, and government leadership in facilitating evacuations. Planning efforts in New Orleans were rudimentary at best, clear evacuation orders were given too late, and the hurricane rendered evacuation resources useless once the city began to flood.

With respect to other patterns of evacuation behavior when they do evacuate, most people prefer to stay with relatives or friends, rather than using public shelters. Shelter use is generally limited to people who feel they have no other options—for example, those who have no close friends and relatives to take them in and cannot afford the price of lodging. Many people avoid public shelters or elect to stay in their homes because shelters do not allow pets. Following earthquakes, some victims, particularly Latinos in the United States who have experienced or learned about highly damaging earthquakes in their countries of origin, avoid indoor shelter of all types, preferring instead to sleep outdoors (Tierney, 1988; Phillips, 1993; Simile, 1995).

Disaster warnings involving “near misses,” as well as concerns about the possible impact of elevated color-coded homeland security warnings,

raise the question of whether warnings that do not materialize can induce a “cry-wolf” effect, resulting in lowered attention to and compliance with future warnings. The disaster literature shows little support for the cry-wolf hypothesis. For example, Dow and Cutter (1998) studied South Carolina residents who had been warned of impending hurricanes that ultimately struck North Carolina. Earlier false alarms did not influence residents’ decisions on whether to evacuate; that is, there was little behavioral evidence for a cry-wolf effect. However, false alarms did result in a decrease in confidence in official warning sources, as opposed to other sources of information on which people relied in making evacuation decisions—certainly not the outcome officials would have intended. Studies also suggest that it is advisable to clarify for the public why forecasts and warnings were uncertain or incorrect. Based on an extensive review of the warning literature, Sorensen (2000:121) concluded that “[t]he likelihood of people responding to a warning is not diminished by what has come to be labeled the ‘cry-wolf’ syndrome if the basis for the false alarm is understood [emphasis added].” Along those same lines, Atwood and Major (1998) argue that if officials explain reasons for false alarms, that information can increase public awareness and make people more likely to respond to subsequent hazard advisories.

PUBLIC RESPONSE

Dispelling myths about crisis-related behavior: panic and social breakdown.

Numerous individual studies and research syntheses have contrasted commonsense ideas about how people respond during crises with empirical data on actual behavior. Among the most important myths addressed in these analyses is the notion that panic and social disorganization are common responses to imminent threats and to actual disaster events (Quarantelli and Dynes, 1972; Johnson, 1987; Clarke, 2002). True panic, defined as highly individualistic flight behavior that is nonsocial in nature, undertaken without regard to social norms and relationships, is extremely rare prior to and during extreme events of all types. Panic takes place under specific conditions that are almost never present in disaster situations. Panic only occurs when individuals feel completely isolated and when both social bonds and measures to promote safety break down to such a degree that individuals feel totally on their own in seeking safety. Panic results from a breakdown in the ongoing social order—a breakdown that Clarke (2003:128) describes as having moral, network, and cognitive dimensions:

There is a moral failure, so that people pursue their self interest regardless

of rules of duty and obligation to others. There is a network failure, so that the resources that people can normally draw on in times of crisis are no longer there. There is a cognitive failure, in which someone’s understanding of how they are connected to others is cast aside.

Failures on this scale almost never occur during disasters. Panic reactions are rare in part because social bonds remain intact and extremely resilient even under conditions of severe danger (Johnson, 1987; Johnson et al., 1994; Feinberg and Johnson, 2001).

Panic persists in public and media discourses on disasters, in part because those discourses conflate a wide range of other behaviors with panic. Often, people are described as panicking because they experience feelings of intense fear, even though fright and panic are conceptually and behaviorally distinct. Another behavioral pattern that is sometimes labeled panic involves intensified rumors and information seeking, which are common patterns among publics attempting to make sense of confusing and potentially dangerous situations. Under conditions of uncertainty, people make more frequent use of both informal ties and official information sources, as they seek to collectively define threats and decide what actions to take. Such activities are a normal extension of everyday information-seeking practices (Turner, 1994). They are not indicators of panic.

The phenomenon of shadow evacuation, discussed earlier, is also frequently confused with panic. Such evacuations take place because people who are not defined by authorities as in danger nevertheless determine that they are—perhaps because they have received conflicting or confusing information or because they are geographically close to areas considered at risk (Tierney et al., 2001). Collective demands for antibiotics by those considered not at risk for anthrax, “runs” on stores to obtain self-protective items, and the so-called worried-well phenomenon are other forms of collective behavior that reflect the same sociobehavioral processes that drive shadow evacuations: emergent norms that define certain individuals and groups as in danger, even though authorities do not consider them at risk; confusion about the magnitude of the risk; a collectively defined need to act; and in some cases, an unwillingness to rely on official sources for self-protective advice. These types of behaviors, which constitute interesting subjects for research in their own right, are not examples of panic.

Research also indicates that panic and other problematic behaviors are linked in important ways to the manner in which institutions manage risk and disaster. Such behaviors are more likely to emerge when those who are in danger come to believe that crisis management measures are ineffective, suggesting that enhancing public understanding of and trust in preparedness measures and in organizations charged with managing disasters can lessen the likelihood of panic. With respect to homeland security threats, some researchers have argued that the best way to “vaccinate” the public

against the emergence of panic in situations involving weapons of mass destruction is to provide timely and accurate information about impending threats and to actively include the public in pre-crisis preparedness efforts (Glass and Shoch-Spana, 2002).

Blaming the public for panicking during emergencies serves to diffuse responsibility from professionals whose duty it is to protect the public, such as emergency managers, fire and public safety officials, and those responsible for the design, construction, and safe operation of buildings and other structures (Sime, 1999). The empirical record bears out the fact that to the extent panic does occur during emergencies, such behavior can be traced in large measure to environmental factors such as overcrowding, failure to provide adequate egress routes, and breakdowns in communications, rather than to some inherent human impulse to stampede with complete disregard for others. Any potential for panic and other problematic behaviors that may exist can, in other words, be mitigated through appropriate design, regulatory, management, and communications strategies.

As discussed elsewhere in this report, looting and violence are also exceedingly rare in disaster situations. Here again, empirical evidence of what people actually do during and following disasters contradicts what many officials and much of the public believe. Beliefs concerning looting are based not on evidence but rather on assumptions—for example, that social control breaks down during disasters and that lawlessness and violence inevitably result when the social order is disrupted. Such beliefs fail to take into account the fact that powerful norms emerge during disasters that foster prosocial behavior—so much so that lawless behavior actually declines in disaster situations. Signs erected following disasters saying, “We shoot to kill looters” are not so much evidence that looting is occurring as they are evidence that community consensus condemns looting.

The myth of disaster looting can be contrasted with the reality of looting during episodes of civil disorder such as the riots of the 1960s and the 1992 Los Angeles unrest. During episodes of civil unrest, looting is done publicly, in groups, quite often in plain sight of law enforcement officials. Taking goods and damaging businesses are the hallmarks of modern “commodity riots.” New norms also emerge during these types of crises, but unlike the prosocial norms that develop in disasters, norms governing behavior during civil unrest permit and actually encourage lawbreaking. Under these circumstances, otherwise law-abiding citizens allow themselves to take part in looting behavior (Dynes and Quarantelli, 1968; Quarantelli and Dynes, 1970).

Looting and damaging property can also become normative in situations that do not involve civil unrest—for example, in victory celebrations following sports events. Once again, in such cases, norms and traditions governing behavior in crowd celebrations encourage destructive activities

(Rosenfeld, 1997). The behavior of participants in these destructive crowd celebrations again bears no resemblance to that of disaster victims.

In the aftermath of Hurricane Katrina, social scientists had no problem understanding why episodes of looting might have been more widespread in that event than in the vast majority of U.S. disasters. Looting has occurred on a widespread basis following other disasters, although such cases have been rare. Residents of St. Croix engaged in extensive looting behavior following Hurricane Hugo, and this particular episode sheds light on why some Katrina victims might have felt justified in looting. Hurricane Hugo produced massive damage on St. Croix, and government agencies were rendered helpless. Essentially trapped on the island, residents had no idea when help would arrive. Instead, they felt entirely on their own following Hugo. The tourist-based St. Croix economy was characterized by stark social class differences, and crime and corruption had been high prior to the hurricane. Under these circumstances, looting for survival was seen as justified, and patterns of collective behavior developed that were not unlike those seen during episodes of civil unrest. Even law enforcement personnel joined in the looting (Quarantelli, 2006; Rodriguez et al., forthcoming).

Despite their similarities, the parallels between New Orleans and St. Croix should not be overstated. It is now clear that looting and violent behavior were far less common than initially reported and that rumors concerning shootings, rapes, and murders were groundless. The media employed the “looting frame” extensively while downplaying far more numerous examples of selflessness and altruism. In hindsight, it now appears that many reports involving looting and social breakdown were based on stereotyped images of poor minority community residents (Tierney et al., forthcoming).

Extensive research also indicates that despite longstanding evidence, beliefs about disaster-related looting and lawlessness remain quite common, and these beliefs can influence the behavior of both community residents and authorities. For example, those who are at risk may decide not to evacuate and instead stay in their homes to protect their property from looters (Fischer, 1998). Concern regarding looting and lawlessness may cause government officials to make highly questionable and even counterproductive decisions. Following Hurricane Katrina, for example, based largely on rumors and exaggerated media reports, rescue efforts were halted because of fears for the safety of rescue workers, and Louisiana’s governor issued a “shoot-to-kill” order to quash looting. These decisions likely resulted in additional loss of life and also interfered with citizen efforts to aid one another. Interestingly, recent historical accounts indicate that similar decisions were made following other large-scale disasters, such as the 1871 Chicago fire, the 1900 Galveston hurricane, and the 1906 San Francisco earthquake and firestorm. In all three cases, armed force was used to stop

looting, and immigrant groups and the poor were scapegoated for their putative “crimes” (Fradkin, 2005). Along with Katrina, these events caution against making decisions on the basis of mythical beliefs and rumors.

As is the case with the panic myth, attributing the causes of looting behavior to individual motivations and impulses serves to deflect attention from the ways in which institutional failures can create insurmountable problems for disaster victims. When disasters occur, communications, disaster management, and service delivery systems should remain sufficiently robust that victims will not feel isolated and afraid or conclude that needed assistance will never arrive. More to the point, victims of disasters should not be scapegoated when institutions show themselves to be entirely incapable of providing even rudimentary forms of assistance—which was exactly what occurred with respect to Hurricane Katrina.

Patterns of Collective Mobilization in Disaster-Stricken Areas: Prosocial and Helping Behavior

In contrast to the panicky and lawless behavior that is often attributed to disaster-stricken populations, public behavior during earthquakes and other major community emergencies is overwhelmingly adaptive, prosocial, and aimed at promoting the safety of others and the restoration of ongoing community life. The predominance of prosocial behavior (and, conversely, a decline in antisocial behavior) in disaster situations is one of the most longstanding and robust research findings in the disaster literature. Research conducted with NEHRP sponsorship has provided an even better understanding of the processes involved in adaptive collective mobilization during disasters.

Helping Behavior and Disaster Volunteers. Helping behavior in disasters takes various forms, ranging from spontaneous and informal efforts to provide assistance to more organized emergent group activity, and finally to more formalized organizational arrangements. With respect to spontaneously developing and informal helping networks, disaster victims are assisted first by others in the immediate vicinity and surrounding area and only later by official public safety personnel. In a discussion on search and rescue activities following earthquakes, for example, Noji observes (1997:162)

In Southern Italy in 1980, 90 percent of the survivors of an earthquake were extricated by untrained, uninjured survivors who used their bare hands and simple tools such as shovels and axes…. Following the 1976 Tangshan earthquake, about 200,000 to 300,000 entrapped people crawled out of the debris on their own and went on to rescue others…. They became the backbone of the rescue teams, and it was to their credit that more than 80 percent of those buried under the debris were rescued.

Thus, lifesaving efforts in a stricken community rely heavily on the capabilities of relatively uninjured survivors, including untrained volunteers, as well as those of local firefighters and other relevant personnel.

The spontaneous provision of assistance is facilitated by the fact that when crises occur, they take place in the context of ongoing community life and daily routines—that is, they affect not isolated individuals but rather people who are embedded in networks of social relationships. When a massive gasoline explosion destroyed a neighborhood in Guadalajara, Mexico, in 1992, for example, survivors searched for and rescued their loved ones and neighbors. Indeed, they were best suited to do so, because they were the ones who knew who lived in different households and where those individuals probably were at the time of the disaster (Aguirre et al., 1995). Similarly, crowds and gatherings of all types are typically comprised of smaller groupings—couples, families, groups of friends—that become a source of support and aid when emergencies occur.

As the emergency period following a disaster lengthens, unofficial helping behavior begins to take on a more structured form with the development of emergent groups—newly formed entities that become involved in crisis-related activities (Stallings and Quarantelli, 1985; Saunders and Kreps, 1987). Emergent groups perform many different types of activities in disasters, from sandbagging to prevent flooding, to searching for and rescuing victims and providing for other basic needs, to post-disaster cleanup and the informal provision of recovery assistance to victims. Such groupings form both because of the strength of altruistic norms that develop during disasters and because of emerging collective definitions that victims’ needs are not being met—whether official agencies share those views or not. While emergent groups are in many ways essential for the effectiveness of crisis response activities, their activities may be seen as unnecessary or even disruptive by formal crisis response agencies. In the aftermath of the attack on the World Trade Center, for example, numerous groups emerged to offer every conceivable type of assistance to victims and emergency responders. Some were incorporated into official crisis management activities, while others were labeled “rogue volunteers” by official agencies (Halford and Nolan, 2002; Kendra and Wachtendorf, 2002). 2

Disaster-related volunteering also takes place within more formalized organizational structures, both in existing organizations that mobilize in response to disasters and through organizations such as the Red Cross,

which has a federal mandate to respond in presidentially declared disasters and relies primarily on volunteers in its provision of disaster services. Some forms of volunteering have been institutionalized in the United States through the development of the National Voluntary Organizations Active in Disaster (NVOAD) organization. NVOAD, a large federation of religious, public service, and other groups, has organizational affiliates in 49 states, the District of Columbia, Puerto Rico, and U.S. territories. National-level NVOAD affiliates include organizations such as the Salvation Army, Church World Service, Church of the Brethren Disaster Response, and dozens of others that provide disaster services. Organizations such as the Red Cross and the NVOAD federation thus provide an infrastructure that can support very extensive volunteer mobilization. That infrastructure will likely form the basis for organized volunteering in future homeland security emergencies, just as it does in major disasters.

Helping behavior is very widespread after disasters, particularly large and damaging ones. For example, NEHRP-sponsored research indicates that in the three weeks following the 1985 earthquake in Mexico City, an estimated 1.7 to 2.1 million residents of that city were involved in providing volunteer aid. Activities in which volunteers engaged after that disaster included searching for and rescuing victims trapped under rubble, donating blood and supplies, inspecting building damage, collecting funds, providing medical care and psychological counseling, and providing food and shelter to victims (Wenger and James, 1994). In other research on post-earthquake volunteering, also funded by NEHRP, O’Brien and Mileti (1992) found that more than half of the population in San Francisco and Santa Cruz counties provided assistance to their fellow victims after the 1989 Loma Prieta earthquake—help that ranged from assisting with search and rescue and debris removal activities to offering food, water, and shelter to those in need. Thus, the volunteer sector responding to disasters typically constitutes a very large proportion of the population of affected regions, as well as volunteers converging from other locations.

Social science research, much of it conducted under NEHRP auspices, highlights a number of other points regarding post-disaster helping behavior. One such insight is that helping behavior in many ways mirrors roles and responsibilities people assume during nondisaster times. For example, when people provide assistance during disasters and other emergencies, their involvement is typically consistent with gender role expectations (Wenger and James, 1994; Feinberg and Johnson, 2001). Research also indicates that mass convergence of volunteers and donations can create significant management problems and undue burdens on disaster-stricken communities. In their eagerness to provide assistance, people may “overrespond” to disaster sites, creating congestion and putting themselves and others at risk or insisting on providing resources that are in fact not needed. After disas-

ters, communities typically experience major difficulties in dealing with unwanted and unneeded donations (Neal, 1990).

Research on public behavior during disasters has major implications for homeland security policies and practices. The research literature provides support for the inclusion of the voluntary sector and community-based organizations in preparedness and response efforts. Initiatives that aim at encouraging public involvement in homeland security efforts of all types are clearly needed. The literature also provides extensive evidence that members of the public are in fact the true “first responders” in major disasters. In using that term to refer to fire, police, and other public safety organizations, current homeland security discourse fails to recognize that community residents themselves constitute the front-line responders in any major emergency

One implication of this line of research is that planning and management models that fail to recognize the role of victims and volunteers in responding to all types of extreme events will leave responders unprepared for what will actually occur during disasters—for example, that, as research consistently shows, community residents will be the first to search for victims, provide emergency aid, and transport victims to health care facilities in emergencies of all types. 3 Such plans will also fail to take advantage of the public’s crucial skills, resources, and expertise. For this reason, experts on human-induced threats such as bioterrorism stress the value of public engagement and involvement in planning for homeland security emergencies (Working Group on “Governance Dilemmas” in Bioterrorism Response, 2004).

These research findings have significant policy implications. To date, Department of Homeland Security initiatives have focused almost exclusively on providing equipment and training for uniformed responders, as opposed to community residents. Recently, however, DHS has begun placing more emphasis on its Citizen Corps component, which is designed to mobilize the skills and talents of the public when disasters strike. Public involvement in Citizen Corps and Community Emergency Response Team (CERT) activities have expanded considerably since the terrorist attacks of

9/11—a sign that many community residents around the nation wish to play an active role in responding to future disasters. The need for community-based preparedness and response initiatives is more evident than ever follow-ing the Katrina disaster.

Organizational, Governmental, and Network Responses. The importance of observing disaster response operations while they are ongoing or as soon as possible after disaster impact has long been a hallmark of the disaster research field. The quick-response tradition in disaster research, which has been a part of the field since its inception, developed out of a recognition that data on disaster response activities are perishable and that information collected from organizations after the passage of time is likely to be distorted and incomplete (Quarantelli, 1987, 2002). NEHRP funds, provided through grant supplements, Small Grants for Exploratory Research (SGER) awards, Earthquake Engineering Research Institute (EERI) reconnaissance missions, earthquake center reconnaissance funding, and small grants such as those provided by the Natural Hazards Research and Applications Information Center, have supported the collection of perishable data and enabled social science researchers to mobilize rapidly following major earthquakes and other disasters.

NEHRP provided substantial support for the collection of data on organizational and community responses in a number of earthquake events, including the 1987 Whittier Narrows, 1989 Loma Prieta, and 1994 Northridge earthquakes (see, for example, Tierney, 1988, 1994; EERI, 1995), as well as major earthquakes outside the United States such as the 1985 Mexico City, 1986 San Salvador, and 1988 Armenia events. More recently, NEHRP funds were used to support rapid-response research on the September 11, 2001 terrorist attacks and Hurricanes Katrina and Rita. Many of those studies focused on organizational issues in both the public and private sectors. (For a compilation of NEHRP-sponsored quick-response findings on the events of September 11, see Natural Hazards Research and Applications Information Center, 2003).

In many cases, quick-response research on disaster impacts and organizational and governmental response has led to subsequent in-depth studies on response-related issues identified during the post-impact reconnaissance phase. Following major events such as Loma Prieta, Northridge, and Kobe, insights from initial reconnaissance studies have formed the basis for broader research initiatives. Recent efforts have focused on ways to better take advantage of reconnaissance opportunities and to identify topics for longer-term study. A new plan has been developed to better coordinate and integrate both reconnaissance and longer-term research activities carried out with NEHRP support. That planning activity, outlined in the report The Plan to Coordinate NEHRP Post-earthquake Investigations (Holzer et

al., 2003), encompasses both reconnaissance and more systematic research activities in the earth sciences, engineering, and social sciences.

Through both initial quick-response activities and longer-term studies, NEHRP research has added to the knowledge base on how organizations cope with crises. Studies have focused on a variety of topics. A partial list of those topics includes organizational and group activities associated with the post-disaster search and rescue process (Aguirre et al., 1995); intergovernmental coordination during the response period following major disaster events (Nigg, 1998); expected and improvised organizational forms that characterize the disaster response milieu (Kreps, 1985, 1989b); strategies used by local government organizations to enhance interorganizational coordination following disasters (Drabek, 2003); and response activities undertaken by specific types of organizations, such as those in the volunteer and nonprofit sector (Neal, 1990) and tourism-oriented enterprises (Drabek, 1994).

Focusing specifically at the interorganizational level of analysis, NEHRP research has also highlighted the significance and mix of planned and improvised networks in disaster response. It has long been recognized that post-disaster response activities involve the formation of new (or emergent) networks of organizations. Indeed, one distinguishing feature of major crisis events is the prominence and proliferation of network forms of organization during the response period. Emergent multiorganizational networks (EMON) constitute new organizational interrelationships that reflect collective efforts to manage crisis events. Such networks are typically heterogeneous, consisting of existing organizations with pre-designated crisis management responsibilities, other organizations that may not have been included in prior planning but become involved in crisis response activities because those involved believe they have some contribution to make, and emergent groups. EMONs tend to be very large in major disaster events, encompassing hundreds and even thousands of interacting entities. As crisis conditions change and additional resources converge, EMON structures evolve, new organizations join the network, and new relationships form. What is often incorrectly described as disaster-generated “chaos” is more accurately seen as the understandable confusion that results when mobilization takes place on such a massive scale and when organizations and groups that may be unfamiliar with one another attempt to communicate, negotiate, and coordinate their activities under extreme pressure. (For more detailed discussions on EMONs in disasters, including the 2001 World Trade Center attack, see Drabek, 1985, 2003; Tierney, 2003; Tierney and Trainor, 2004.)

This is not to say that response activities always go smoothly. The disaster literature, organizational after-action reports, and official investigations contain numerous examples of problems that develop as inter-

organizational and intergovernmental networks attempt to address disaster-related challenges. Such problems include the following: failure to recognize the magnitude and seriousness of an event; delayed and insufficient responses; confusion regarding authorities and responsibilities, often resulting in major “turf battles;” resource shortages and misdirection of existing resources; poor organizational, interorganizational, and public communications; failures in intergovernmental coordination; failures in leadership and vision; inequities in the provision of disaster assistance; and organizational practices and cultures that permit and even encourage risky behavior. Hurricane Katrina became a national scandal because of the sheer scale on which these organizational pathologies manifested. However, Katrina was by no means atypical. In one form or another and at varying levels of severity, such pathologies are ever-present in the landscape of disaster response (for examples, see U.S. President’s Commission on the Accident at Three Mile Island, 1979; Perrow, 1984; Shrivastava, 1987; Sagan, 1993; National Academy of Public Administration, 1993; Vaughan, 1996, 1999; Peacock et al., 1997; Klinenberg, 2002; Select Bipartisan Committee to Investigate the Preparations for and Response to Hurricane Katrina, 2006; White House, 2006).

Management Considerations in Disaster Response

U.S. disaster researchers have identified two contrasting approaches to disaster response management, commonly termed the “command-and-control” and the “emergent human resources,” or “problem-solving,” models. The command-and-control model equates preparedness and response activities with military exercises. It assumes that (1) government agencies and other responders must be prepared to take over management and control in disaster situations, both because they are uniquely qualified to do so and because members of the public will be overwhelmed and will likely engage in various types of problematic behavior, such as panic; (2) disaster response activities are best carried out through centralized direction, control, and decision making; and (3) for response activities to be effective, a single person is ideally in charge, and relations among responding entities are arranged hierarchically.

In contrast, the emergent human resources, or problem-solving, model is based on the assumption that communities and societies are resilient and resourceful and that even in areas that are very hard hit by disasters, considerable local response capacity is likely to remain. Another underlying assumption is that preparedness strategies should build on existing community institutions and support systems—for example by pre-identifying existing groups, organizations, and institutions that are capable of assuming leadership when a disaster strikes. Again, this approach argues against

highly specialized approaches that tend to result in “stovepiped” rather than well-integrated preparedness and response efforts. The model also recognizes that when a disaster occurs, responding entities must be flexible if they are to be effective and that flexibility is best achieved through a decentralized response structure that seeks to solve problems as they arise, as opposed to top-down decision making. (For more extensive discussions of these two models and their implications, see Dynes, 1993, 1994; Kreps and Bosworth, forthcoming.)

Empirical research, much of which has been carried out with NEHRP support, finds essentially no support for the command-and-control model either as a heuristic device for conceptualizing the disaster management process or as a strategy employed in actual disasters. Instead, as suggested in the discussion above on EMONs, disaster response activities in the United States correspond much more closely to the emergent resources or problem-solving model. More specifically, such responses are characterized by decentralized, rather than centralized, decision making; by collaborative relationships among organizations and levels of government, rather than hierarchical ones; and, perhaps most important, by considerable emergence—that is, the often rapid appearance of novel and unplanned-for activities, roles, groups, and relationships. Other hallmarks of disaster responses include their fluidity and hence the fast pace at which decisions must be made; the predominance of the EMON as the organizational form most involved in carrying out response activities; the wide array of improvisational strategies that are employed to deal with problems as they manifest themselves; and the importance of local knowledge and situation-specific information in gauging appropriate response strategies. (For empirical research supporting these points, see Drabek et al., 1982; Stallings and Quarantelli, 1985; Kreps, 1985, 1989b; Bosworth and Kreps, 1986; Kreps and Bosworth, 1993; Aguirre et al., 1995; Drabek and McEntire, 2002; Waugh and Sylves, 2002; Webb, 2002; Drabek, 2003; Tierney, 2003; Tierney and Trainor, 2004; Wachtendorf, 2004.)

NEW WAYS OF FRAMING DISASTER MANAGEMENT CHALLENGES: DEALING WITH COMPLEXITY AND ACCOMMODATING EMERGENCE

Advancements brought about through NEHRP research include new frameworks for conceptualizing responses to extreme events. In Shared Risk: Complex Systems in Seismic Response , a NEHRP-supported comparative study of organized responses to 11 different earthquake events, Comfort argues that the major challenge facing response systems is to use information in ways that enhance organizational and interorganizational learning and develop ways of “integrating both technical and organiza-

tional components in a socio-technical system to support timely, informed collective action” (Comfort, 1999:14). Accordingly, effective responses depend on the ability of organizations to simultaneously sustain structure and allow for flexibility in the face of rapidly changing disaster conditions and unexpected demands. Response networks must also be able to accommodate processes of self-organization —that is, organized action by volunteers and emergent groups. This approach again contrasts with command-and-control notions of how major crises are managed (Comfort, 1999:263-264):

A socio-technical approach requires a shift in the conception of response systems as reactive, command-and-control driven systems to one of inquiring systems , activated by processes of inquiry, validation, and creative self-organization…. Combining technical with organizational systems appropriately enables communities to face complex events more effectively by monitoring changing conditions and adapting its performance accordingly, increasing the efficiency of its use of limited resources. It links human capacity to learn with the technical means to support that capacity in complex, dynamic environments [emphasis added].

Similarly, research stressing the importance of EMONs as the predominant organizational form during crisis response periods points to the importance of improving strategies for network management and of developing better methods to take advantage of emergent structures and activities during disasters. Planning and management approaches must, in other words, support rather than interfere with the open and dynamic qualities of disaster response activities. Indicators of improved capacity to manage emergent networks could include the diversity of organizations and community sectors involved in pre-crisis planning; plans and agreements facilitating the incorporation of the voluntary sector and emergent citizen groups into response activities; plans and tools enabling the rapid expansion of crisis communication and information-sharing networks during disasters to include new organizations; and protocols, such as mutual aid agreements, making it possible for new actors to more easily join response networks (Tierney and Trainor, 2004).

In the wake of the Katrina disaster, the need for disaster management by command-and-control-oriented entities has once again achieved prominence. For example, calls have increased for greater involvement on the part of the military in domestic disaster management. Such recommendations are not new. Giving a larger role in disaster management to the military was an idea that was considered—and rejected—following Hurricane Andrew (National Academy of Public Administration, 1993). Post-Katrina debates on needed policy and programmatic changes will likely continue to focus on how to most effectively deploy military assets while ensuring that disaster management remains the responsibility of civilian institutions.

Additional Considerations: Do Responses to Natural, Technological, and Human-Induced Events Differ?

One issue that has come to the fore with the emergence of terrorism as a major threat involves the extent to which findings from the field of disaster research can predict responses to human-induced extreme events. Although some take the position that terrorism and bioterrorism constitute such unique threats that behavioral and organizational responses in such events will differ from what has been documented for other types of extreme events, others contend that this assumption is not borne out by social science disaster research.

The preponderance of evidence seems to suggest that there is more similarity than difference in response behaviors across different types of disaster agents. Regarding the potential for panic, for example, there is no empirical evidence that panic was a problem during the influenza pandemic of 1918, among populations under attack during World War II (Janis, 1951), in catastrophic structure fires and crowd crushes (Johnson, 1987; Johnson et al., 1994; Feinberg and Johnson, 2001), or in the Chernobyl nuclear disaster (Medvedev, 1990). Nor was panic a factor in the 1993 bombing of the World Trade Center (Aguirre et al., 1998), the 1995 Tokyo subway sarin attack (Murakami, 2000), or the terrorist attacks of September 11, 2001 (NIST, 2005; National Commission on Terrorist Attacks upon the United States, 2004). The failure to find significant evidence of panic across a wide range of crisis events is a testimony to the resilience of social relationships and normative practices, even under conditions of extreme peril.

Similarly, as noted earlier, research findings on challenges related to risk communication and warning the public of impending extreme events are also quite consistent across different types of disaster events. For individuals and groups, there are invariably challenges associated with understanding what self-protective actions are required for different types of emergencies, regardless of their origin.

In all types of disasters, organizations must likewise face a common set of challenges associated with situation assessment, the management of primary and secondary impacts, communicating with one another and with the public, and dealing with response-related demands. The need for more effective communication, coordination, planning, and training transcends hazard type. Although recent government initiatives such as the National Response Plan will result in the incorporation of new organizational actors into response systems for extreme events, most of the same local-, state-, and federal-level organizations will still be involved in managing extreme events of all types, employing common management frameworks such as

the Incident Command System and now the National Incident Management System (NIMS).

Social scientific studies on disasters have long shown that general features of extreme events, such as geographic scope and scale, impact severity, and speed of onset, combined with the overall quality of pre-disaster preparedness, have a greater influence on response patterns than do the specific hazard agents that trigger response activities. Regardless of their origins, very large, near-catastrophic, and catastrophic events all place high levels of stress on response systems.

In sum, social science disaster research finds little justification for the notion that individual, group, and community responses to human-induced extreme events, including those triggered by weapons of mass terror, will differ in important ways from those that have been documented in natural and technological disasters. Instead, research highlights the importance of a variety of general factors that affect the quality and effectiveness of responses to disasters, irrespective of the hazard in question. With respect to warning the public and encouraging self-protective action, for example, warning systems must be well designed and warning messages must meet certain criteria for effectiveness, regardless of what type of warning is issued. Members of the public must receive, understand, and personalize warning information; must understand what actions they need to take in order to protect themselves; and must be able to carry out those actions, again regardless of the peril in question. Community residents must feel that they can trust their leaders and community institutions during crises of all types. For organizations, training and exercises and effective mechanisms for interorganizational communication and coordination are critical for community-wide emergencies of all types. When such criteria are not met, response-related problems can be expected regardless of whether the emergency stems from a naturally occurring event, a technological accident, or an intentional act.

Individual and group responses, as well as organizational response challenges, are thus likely to be consistent across different types of crises. At the same time, however, it is clear that there are significant variations in the behavior of responding institutions (as opposed to individuals, groups, and first responders) according to event type. In most technological disasters, along with the need to help those affected, questions of negligence and liability typically come to the fore, and efforts are made to assign blame and make responsible parties accountable. In terrorist events, damaged areas are always treated as crime scenes, and the response involves intense efforts both to care for victims and to identify and capture the perpetrators. Further, although as noted earlier, scapegoating can occur in disasters of all types, the tendency for both institutions and the public to assign blame to

particular groups may be greater in technological and terrorism-related crises than in natural disasters. 4

Finally, with respect to responses on the part of the public, even though evidence to the contrary is strong, the idea that some future homeland security emergencies could engender responses different from those observed in past natural, technological, and intentional disasters cannot be ruled out entirely. The concluding section of this chapter highlights the need for further research in this area.

Research on Disaster Recovery

Like hazards and disaster research generally, NERHRP-sponsored research has tended to focus much more on preparedness and response than on either mitigation or disaster recovery. This is especially the case with respect to long-term recovery, a topic that despite its importance has received very little emphasis in the literature. However, even though the topic has not been well studied, NEHRP-funded projects have done a great deal to advance social science understanding of disaster recovery. As discussed later in this section, they have also led to the development of decision tools and guidance that can be used to facilitate the recovery process for affected social units.

It is not an exaggeration to say that prior to NEHRP, relatively little was known about disaster recovery processes and outcomes at different levels of analysis. Researchers had concentrated to some degree on analyzing the impacts of a few earthquakes, such as the 1964 Alaska and 1971 San Fernando events, as well as earthquakes and other major disasters outside the United States. Generally speaking, however, research on recovery was quite sparse. Equally important, earlier research oversimplified the recovery process in a variety of ways. First, there was a tendency to equate recovery, which is a social process, with reconstruction, which involves restoration and replacement of the built environment. Second, there was an assumption that disasters and their impacts proceed in a temporal, stage-like fashion, with “recovery” following once “response” activities have

been concluded. 5 Earlier research also underemphasized the extent to which recovery may be experienced differently by different sectors and subpopulations within society. Some of these problems were related to the fact that at a more abstract level, earlier work had not sufficiently explored the concept of recovery itself—for example, whether recovery should be equated with a return to pre-disaster circumstances and social and economic activities, with the creation of a “new normal” that involves some degree of social transformation, or with improvements in community sustainability and long-term disaster loss reduction. Since the inception of NEHRP and in large measure because of NEHRP sponsorship, research has moved in the direction of a more nuanced understanding of recovery processes and outcomes that has not entirely resolved but at least acknowledges many of these issues.

The sections that follow discuss significant contributions to knowledge and practice that have resulted primarily from NEHRP-sponsored work. Those contributions can be seen (somewhat arbitrarily) as falling into four categories: (1) refinements in definitions and conceptions of disaster recovery, along with a critique and reformulation of stage-like models; (2) contributions to the literature on recovery processes and outcomes across different social units; (3) the development of empirically based models to estimate losses, anticipate recovery challenges, and guide decision making; and (4) efforts to link disaster recovery with broader ideas concerning long-term sustainability and environmental management.

Conceptual Clarification. Owing in large measure to NEHRP-sponsored efforts, the disaster field has moved beyond equating recovery with reconstruction or the restoration of the built environment. More usefully, research has moved in the direction of making analytic distinctions among different types of disaster impacts, recovery activities undertaken by and affecting different social units , and recovery outcomes. Although disaster impacts can be positive or negative, research generally tends to focus on various negative impacts occurring at different levels of analysis. As outlined in Chapter 3 , these impacts include effects on the physical and built environment, including residential, commercial, and infrastructure damage as well as disaster-induced damage to the environment; other property losses; deaths and injuries; impacts on social and economic activity; effects at the community level, such as impacts on community cohesiveness and urban

form; and psychological, psychosocial, and political impacts. Such impacts can vary in severity and duration, as well as in the extent to which they are addressed effectively during the recovery process. An emphasis on recovery as a multidimensional concept calls attention to the fact that physical and social impacts, recovery trajectories, and short- and longer-term outcomes in chronological and social time can vary considerably across social units.

Recovery activities constitute measures that are intended to remedy negative disaster impacts, restore social units as much as possible to their pre-disaster levels of functioning, enhance resilience, and ideally, realize other objectives such as the mitigation of future disaster losses and improvements in the built environment, quality of life, and long-term sustainability. 6 Recovery activities include the provision of temporary and replacement housing; the provision of resources (government aid, insurance payment, private donations) to assist households and businesses with replacement of lost goods and with reconstruction; the provision of various forms of aid and assistance to affected government units; the development and implementation of reconstruction and recovery plans in the aftermath of disasters; coping mechanisms developed by households, businesses, and other affected social units; the provision of mental health and other human services to victims; and other activities designed to overcome negative disaster impacts. In some circumstances, recovery activities can also include the adoption of new policies, legislation, and practices designed to reduce the impacts of future disasters.

Recovery processes are significantly influenced by differential societal and group vulnerability; by variations in the range of recovery aid and support that is available; and by the quality and effectiveness of the help that is provided. The available “mix” of recovery activities and post-disaster coping strategies varies across groups, societies, and different types of disasters. For example, insurance is an important component in the reconstruction and recovery process for some societies, some groups within society, and some types of disasters, but not for others.

Recovery outcomes —or the extent to which the recovery activities are judged, either objectively or subjectively, as “complete” or “successful”—also show wide variation across societies, communities, social units, and disaster events. Outcomes can be assessed in both the short and the longer terms, although, as noted earlier, the literature is weak with respect to empirical studies on the outcomes of longer-term disasters. Additionally,

outcomes consist not only of the intended effects of recovery programs and activities, but also of their unintended consequences. For example, the provision of government assistance or insurance payments to homeowners may make it possible for them to rebuild and continue to live in hazardous areas, even though such an outcome was never intended.

Keeping in mind the multidimensional nature of recovery, post-disaster outcomes can be judged as satisfactory along some dimensions, or at particular points in time, but unsatisfactory along others. Outcomes are perceived and experienced differently, when such factors as level of analysis and specific recovery activities of interest are taken into account. With respect to units of aggregation, for example, while a given disaster may have few discernible long-term effects when analyzed at the community level, the same disaster may well be economically, socially, and psychologically catastrophic for hard-hit households and businesses. A community may be considered “recovered” on the basis of objective social or economic indicators, while constituent social units may not be faring as well, in either objective or subjective terms. The degree to which recovery has taken place is thus very much a matter of perspective and social position.

In a related vein, research has also led to a reconsideration of linear conceptions of the recovery process. Past research tended to see disaster events as progressing from the pre-impact period through post-impact emergency response, and later recovery. In a classic work in this genre— Reconstruction Following Disaster (Haas et al., 1977:xxvi), for example—the authors argued that disaster recovery is “ordered, knowable, and predictable.” Recovery was characterized as consisting of four sequential stages that may overlap to some degree: the emergency period; the restoration period; the replacement reconstruction period; and the commemorative, betterment, and developmental reconstruction period. In this and other studies, the beginning of the recovery phase was generally demarcated by the cessation of immediate life saving and emergency care measures, the resumption of activities of daily life (e.g., opening of schools), and the initiation of rebuilding plans and activities. After a period of time, early recovery activities, such as the provision of temporary housing, would give way to longer-term measures that were meant to be permanent. Kates and Pijawka’s (1977) frequently cited four-phase model begins with the emergency period, lasting for a few days up to a few weeks, and encompassing the period when the emergency operations plan (EOP) is put into operation. Next comes the restoration period—when repairs to utilities are made; debris is removed; evacuees return; and commercial, industrial, and residential structures are repaired. The third phase, the reconstruction replacement period, involves rebuilding capital stocks and getting the economy back to pre-disaster levels. This period can take some years. Finally, there is the development phase, when commemorative structures are built, memo-

rial dates are institutionalized in social time, and attempts are made to improve the community.

In another stage-like model focusing on the community level, Alexander (1993) identified three stages in the process of disaster recovery. First, the rehabilitation stage involves the continuing care of victims and frequently is accompanied by the reemergence of preexisting problems at the household or community level. During the temporary reconstruction stage, prefabricated housing or other temporary structures go up, and temporary bracing may be installed for buildings and bridges. Finally, the permanent reconstruction stage was seen as requiring good administration and management to achieve full community recovery.

Later work sees delineations among disaster phases as much less clear, showing, for example, that decisions and actions that affect recovery may be undertaken as early as the first days or even hours after the disaster’s impact—and, importantly, even before a disaster occurs. The idea that recovery proceeds in an orderly, stage-like, and unitary manner has been replaced by a view that recognizes that the path to recovery is often quite uneven. While the concept of disaster phases may be a useful heuristic device for researchers and practitioners, the concept may also mask both how phases overlap and how recovery proceeds differently for different social groups (Neal, 1997). Recovery does not occur at the same pace for all who are affected by disasters or for all types of impacts. With respect to housing, for example, owing to differences in the availability of services and financing as well as other factors, some groups within a disaster-stricken population may remain in “temporary housing” for a very long time—so long, in fact, that those housing arrangements become permanent—while others may move rapidly into replacement housing (Bolin, 1993a). Put another way, as indicated in Chapters 1 and 3 , while stage-like approaches to disasters are framed in terms of chronological time, for those who experience them, disasters unfold in social time.

Researchers studying recovery continue to contend with a legacy of conceptual and measurement difficulties. One such difficulty centers on the question of how the dependent variable should be measured. This problem itself is multifaceted. Should recovery be defined as a return to pre-disaster levels of psychological, social, and economic well-being? As a return to where a community, business, or household would have been were it not for the occurrence of the disaster? The study of disaster recovery also tends to overlap with research on broader processes of social change. Thus, in addition to focusing on what was lost or affected as a consequence of disaster events and on outcomes relative to those impacts, recovery research also focuses on more general post-disaster issues, such as the extent to which disasters influence and interact with ongoing processes of social change, whether disaster impacts can be distinguished from those resulting

from broader social and economic trends, whether disasters simply magnify and accelerate those trends or exert an independent influence, and the extent to which the post-disaster recovery period represents continuity or discontinuity with the past. Seen in this light, the study of recovery can become indistinguishable from the study of longer-term social change affecting communities and societies. While these distinctions are often blurred, it is nevertheless important to differentiate conceptually and empirically between the recovery process, specific recovery outcomes of interest, and the wide range of other changes that might take place following (or as a consequence of) disasters.

Analyzing Impacts and Recovery Across Different Social Units. Following from the discussions above, it is useful to keep in mind several points about research on disaster recovery. First, studies differ in the extent to which they emphasize the objective, physical aspects of recovery—restoration and reconstruction of the built environment—or subjective, psychosocial, and experiential ones. Second, studies generally focus on particular units of analysis and outcomes, such as household, business, economic, or community recovery, rather than on how these different aspects of recovery are interrelated. This is due partly to the fact that researchers tend to specialize in particular types of disaster impacts and aspects of recovery, which has both advantages and disadvantages. While allowing for the development of in-depth research expertise, such specialization has also made it more difficult to formulate more general theories of recovery. Third, the literature is quite uneven. Some aspects of recovery are well understood, while there are others about which very little is known.

Even with these limitations, more general theoretical insights about recovery processes and outcomes have begun to emerge. Key among these is the idea that disaster impacts and recovery can be conceptualized in terms of vulnerability and resilience . As noted in Chapters 2 and 3 , vulnerability is a consequence not only of physical location and the “hazardousness of place,” but also of social location and of societal processes that advantage some groups and individuals while marginalizing others. The notion of vulnerability applies both to the likelihood of experiencing negative impacts from disasters, such as being killed or injured or losing one’s home or job, and to the likelihood of experiencing recovery-related difficulties, such as problems with access to services and other forms of support. Social vulnerability is linked to broader trends within society, such as demographic trends (migration to more hazardous areas, the aging of the U.S. population) and population diversity (race, class, income, and linguistic diversity). Similarly, resilience , or the ability to survive and cope with disaster impacts and rebound after those events, is also determined in large measure by social factors. According to Rose (2004), resilience can be conceptualized

as both inherent and adaptive, where the former term refers to resilience that is based on resources and options for action that are typically available during nondisaster times, and the latter refers to the ability to mobilize resources and create new options following disasters. 7 As discussed in Chapter 6 , resilience stems in part from factors commonly associated with the concept of social capital, such as the extensiveness of social networks, civic engagement, and interpersonal, interorganizational, and institutional trust. (For an influential formulation setting out the vulnerability perspective, see Blaikie et al., 1994). As subsequent discussions show, the concepts of vulnerability and resilience are applicable to individuals, households, groups, organizations, economies, and entire societies affected by disasters. The sections that follow, which are organized according to unit of analysis, discuss psychosocial impacts and recovery; impacts and recovery processes for housing and businesses; economic recovery; and community-level and societal recovery.

Psychological Impacts and Recovery. There is no disagreement among researchers that disasters cause genuine pain and suffering and that they can be deeply distressing for those who experience them. Apart from that consensus, however, there have been many debates and disputes regarding the psychological and psychosocial impacts of disasters. One such debate centers on the extent to which disasters produce clinically significant symptoms of psychological distress and, if so, how long such symptoms last. Researchers have also struggled with the questions of etiology, or the causes of disaster-related psychological reactions. Are such problems the direct result of trauma experienced during disaster, the result of disaster-induced stresses, a reflection of a lack of coping capacity or weak social support networks, a function of preexisting vulnerabilities, or a combination of all these factors? Related concerns center on what constitute appropriate forms of intervention and service delivery strategies for disaster-related psychological problems. Do people who experience problems generally recover on their own, without the need for formally provided assistance, or does such assistance facilitate more rapid and complete recovery? What types of assistance are likely to be most efficacious and for what types of problems?

Research has yielded a wide array of findings on questions involving disaster-related psychological and psychosocial impacts and recovery. Findings tend to differ depending upon disaster type and severity, how disaster victimization is defined and measured, how mental health outcomes are measured, the research methodologies and strategies used (e.g., sampling,

timing, variables of interest), and not inconsequentially, the discipline-based theoretical perspectives employed (Tierney, 2000). With respect to the controversial topic of post-traumatic stress disorder (PTSD), for example, well-designed epidemiological studies have estimated the lifetime prevalence of PTSD at around 5.4 percent in the U.S. population. An important epidemiologic study on the incidence of trauma and the subsequent risk of developing PTSD after various types of traumatic events estimates the risk at about 3.8 percent for natural disasters (Breslau et al., 1998; Kessler and Zhao, 1999). NEHRP-sponsored surveys following recent earthquakes in California found PTSD to be extremely rare among affected populations and not significantly associated with earthquake impacts (Seigel et al., 2000). Other studies show immense variation, with estimates of post-disaster PTSD ranging from very low to greater than 50 percent. Such variations could reflect real differences in the traumatic effects of different events, but it is equally likely that they are the result of methodological, measurement, and theoretical differences among investigators.

One key debate centers on the clinical significance of post-disaster emotional and mental health problems. Research is clear on the point that it is not unusual for disaster victims to experience a series of problems, such as headaches, problems with sleeping and eating, and heightened levels of concern and anxiety, that can vary in severity and duration (Rubonis and Bickman, 1991; Freedy et al., 1994). Perspectives begin to diverge, however, on the extent to which these and other disaster-induced symptoms constitute mental health problems in the clinical sense. In other words, would disaster victims, presenting their symptoms, be considered candidates for mental health counseling or medication if those symptoms were present in a nondisaster context? Do their symptoms correspond to survey based or clinically based measures of what constitutes a “case” for psychiatric diagnostic purposes? Again, as with PTSD, findings differ. While noting that many studies do document a rise in psychological distress following disasters, Shoaf et al. (2004:320) conclude that “those impacts are not of a nature that would significantly increase the rates of diagnosable mental illness.” With respect to severe psychological impacts, these researchers found that suicide rates declined in Los Angeles County following the Northridge earthquake—a continuation of a trend that had already begun before that event. They also note that these findings are consistent with research on suicide following the Kobe earthquake, which showed that the suicide rate in the year following that quake was less than the average rate for the previous 10 years (Shoaf et al., 2004). Yet many researchers and practitioners rightly contend that psychosocial interventions are necessary following disasters, both to address clinically significant symptoms and to prevent more serious psychological sequelae.

There is also the question of whether some types of disasters are more

likely than others to cause negative psychological impacts. Some researchers argue that certain types of technological hazards, such as nuclear threats and chronic exposures to toxic substances, are more pernicious in their effects than natural disasters because they persist longer and create more anxiety among potential victims, and especially because they tend to result in community conflict, causing “corrosive” rather than “therapeutic” communities to develop (Erikson, 1994). Events such as the Oklahoma City bombing, the Columbine school shootings, and the events of September 11, 2001 lead to questions about whether intentional attacks engender psychological reactions that are distinctive and different from those that follow other types of community crisis events. Some studies have suggested that the psychological impacts of terrorist attacks are profound, at least in the short term (North et al., 1999). Other research, focusing specifically on the short-term impacts of the September 11, 2001 terrorist attacks, indicates that the psychological impacts resulting from the events of 9/11 “are consistent with prior estimates of the impact of natural disasters and other terrorist events” (Miller and Heldring, 2004:21). Again, drawing conclusions about the relative influence of agent characteristics—as opposed to other factors—is difficult because studies vary so much in their timing, research designs, methodological approaches, and procedures for defining disaster victimization.

Another set of issues concerns factors associated with risk for poor psychological outcomes. Perilla et al. (2002) suggest that such outcomes can vary as a consequence of both differential exposure and differential vulnerability to extreme events. With respect to differential exposure, factors such as ethnicity and social class can be associated with living in substandard and vulnerable housing, subsequently exposing minorities and poor people to greater losses and disaster-related trauma. Regarding differential vulnerability, minorities and the poor, who are more vulnerable to psychosocial stress during nondisaster times, may also have fewer coping resources upon which to draw following disasters.

In a comprehensive and rigorous review of research on the psychological sequelae of disasters, Fran H. Norris and her colleagues (Norris et al., 2002a,b) carried out a meta-analysis of 20 years of research, based on 160 samples containing more than 60,000 individuals who had experienced 102 different disaster events. These data sets included a range of different types of surveys on both U.S. disaster victims and individuals in other countries, on various subpopulations, and on disasters that differed widely in type and severity. Impacts documented in these studies included symptoms of post-traumatic stress, depression, and anxiety; other forms of nonspecific distress not easily related to specific syndromes such as PTSD; health problems and somatic complaints; problems in living, including secondary stressors such as work-related and financial problems; and “psychosocial resource

loss,” a term that refers to negative effects on coping capacity, self-esteem, feelings of self-efficacy, and other attributes that buffer the effects of stress. According to their interpretation, which was based on accepted methods for rating indicators of psychological distress, the symptoms reported by as many as 39 percent of those studied reached clinically significant levels. However—and this is an important caveat—they found negative psychological effects to be much more prevalent in disasters occurring outside the United States. Generally, symptoms were most severe in the year following disaster events and declined over time.

Norris et al. (2002a, 2002b) classified U.S. disasters as low, moderate, and high in their psychosocial impacts, based on empirical data on post-disaster distress. The Loma Prieta and Northridge earthquakes were seen as having relatively few adverse impacts, and Hurricane Hugo and Three Mile Island were classified as moderate in their effects. Hurricane Andrew, the Exxon oil spill, and the Oklahoma City bombing were classified as severe with respect to their psychological impacts. As these examples suggest, the researchers found no evidence that natural, technological, and human-induced disasters necessarily differ in their effects.

This research review uncovered a number of vulnerability and protective factors that were associated with differential psychological outcomes following disasters. Broadly categorized, those risk factors most consistently shown to be negatively associated with post-disaster psychological well-being include severity of disaster exposure at both the individual and the community levels; being female; being a member of an ethnic minority; low socioeconomic status; experiencing other stressors or chronic stress; having had other mental health problems prior to the disaster; employing inappropriate coping strategies (e.g., withdrawal, avoidance); and reporting problems with both perceived and actual social support.

Overall, these findings are very consistent with perspectives in disaster research that emphasize the relationship between systemically induced vulnerability, negative disaster impacts, lower resilience, and poor recovery outcomes. Recent research situates disasters within the context of other types of stressful events (e.g., death of a loved one or other painful losses) that disproportionately affect those who are most vulnerable and least able to cope. At the same time, studies—many conducted under NEHRP auspices—show how social inequality and vulnerability both amplify the stress that results directly from disasters and complicate the recovery process over the longer term. For example, Fothergill (1996, 1998, 2004) and Enarson and Morrow (1998) have documented the ways in which gender is associated both with the likelihood of becoming a disaster victim and with a variety of subsequent post-disaster stressors. Peacock et al. (1997) and Bolin and Stanford (1998) have shown how pre-disaster conditions such as income disparities and racial and ethnic discrimination contribute both to

disaster losses and to subsequent psychosocial stress and make recovery more difficult for vulnerable groups. Perilla et al. (2002), who studied ethnic differences in post-traumatic stress following Hurricane Andrew, also note that ethnicity can be associated with variations in personality characteristics such as fatalism, which tends to be associated with poor psychosocial outcomes resulting from stressful events, as well as with additional stresses associated with acculturation. 8

Hurricane Katrina represents a critical test case for theories and research on psychosocial vulnerability and resilience. If, as Norris and her collaborators indicate, Hurricane Andrew resulted in relatively high levels of psychosocial distress, what will researchers find with respect to Katrina? For many victims, Katrina appears to contain all of the ingredients necessary to produce negative mental health outcomes: massive, catastrophic impacts; high property losses resulting in financial distress; exposure to traumas such as prolonged physical stress and contact with dead and dying victims; disruption of social networks; massive failures in service delivery systems; continual uncertainty about the future; and residential dislocation on a scale never seen in a U.S. disaster. Over time, research will result in important insights regarding the psychosocial dimensions of truly catastrophic disaster events.

Household Impacts and Recovery. Within the disaster recovery area, households and household recovery have been studied most often, with a significant proportion of that work focusing on post-earthquake recovery issues. Although this line of research predates NEHRP, many later studies have been undertaken with NEHRP support. Studies conducted prior to NEHRP include Bolin’s research on household recovery processes following the Managua earthquake and the Rapid City flood, both of which occurred in 1972 (Bolin, 1976). Drabek and Key and their collaborators had also examined disaster impacts on families and the household recover process (Drabek et al., 1975; Drabek and Key, 1976, 1984). With NEHRP support, Bolin and Bolton studied household recovery following tornadoes in Wichita Falls, Vernon, and Paris, Texas; a hurricane in Hawaii; flooding in Salt Lake City; and the Coalinga earthquake (Bolin, 1982; Bolin and Bolton, 1986). Bolin’s monograph Household and Community Recovery after

Earthquakes was based on research on the 1987 Whittier Narrows and 1989 Loma Prieta events (Bolin, 1993b). Households have also been the focus of more recent studies on the impacts of Hurricane Andrew (Peacock et al., 1997) and the 1994 Northridge earthquake (Bolin and Stanford, 1998). Other NEHRP-sponsored work has focused more specifically on issues that are important for household recovery, such as post-disaster sheltering processes (Phillips, 1993, 1998) and housing impacts and recovery (Comerio, 1997, 1998). As Bolin (1993a:13) observes

[d]isasters can have a multiplicity of effects on a household, including physical losses to property, injury and/or death, loss of job or livelihood, disruption of social and personal relations, relocation of some or all members of a family, physical disruption or transformation of community and neighborhood, and increased household indebtedness.

Accordingly, the literature has explored various dimensions of household impacts and recovery, including direct impacts such as those highlighted by Bolin; changes in the quality and cohesiveness of relationships among household members; post-disaster problems such as conflict and domestic violence; stressors that affect households during the recovery process; and coping strategies employed by households, including the use of both formal and informal sources of post-disaster support and recovery aid.

The literature also points to a number of factors that are associated with differences in short- and longer-term household recovery outcomes. Housing supply is one such factor—as indicated, for example, by housing costs, other real estate market characteristics, and rental vacancy rates Temporary housing options are affected by such factors as the proximity of friends and relatives with whom to stay, although use of this housing option is generally only a short-term strategy. Extended family members may not be able to help if they also are victims (Morrow, 1997). Such problems may be more prevalent in lower-income groups that have few alternative resources and when most members of an extended family live in the same affected community.

Availability of temporary and permanent housing generally is limited by their pre-impact supply in and near the impact area. In the U.S., in situations in which there is an insufficient supply of housing for displaced disaster victims, FEMA provides mobile homes, but even this expedient method of expanding the housing stock takes time. Even when houses are only moderately damaged, loss of housing functionality may be a problem if there is massive disruption of infrastructure. In such cases, tent cities may be necessary if undamaged housing is beyond commuting range (e.g., Homestead, Florida after Hurricane Andrew, as discussed in Peacock et al., 1997).

In the longer term, household recovery is influenced by such factors as household financial resources, the ability to obtain assistance from friends and relatives, insurance coverage, and the mix of housing assistance pro-

grams available to households. Typically, access to and adequacy of recovery resources are inversely related to socioeconomic status. Those with higher incomes are more likely to own their own homes, to be adequately insured, and to have savings and other financial resources on which to draw in order to recover—although disasters can also cause even better-off households to take on additional debt. With respect to formal sources of aid, the assistance process generally favors those who are adept at responding to bureaucratic requirements and who are able to invest time and effort to seek out sources of aid. The aid process also favors those living in more conventional, nuclear family living arrangements, as opposed to extended families or multiple households occupying the same dwelling unit (Morrow, 1997). Recovery may be particularly difficult for single-parent households, especially those headed by women (Enarson and Morrow, 1998; Fothergill, 2004).

The picture that emerges from research on household recovery is not that of a predictable and stage-like process that is common to all households, but rather of a multiplicity of recovery trajectories that are shaped not only by the physical impacts of disaster but also by axes of stratification that include income, race, and ethnicity, as well as such factors as the availability of and access to different forms of monetary aid, other types of assistance, and informal social support—which are themselves associated with stratification and diversity. Disaster severity matters, both because disasters that produce major and widespread impacts can limit recovery options for households and because they tend to be more damaging to the social fabric of the community. As Comerio’s extensive research on housing impacts and issues following earthquakes and other disasters in different societal contexts illustrates, household recovery processes are also shaped by societal-level policy and institutional factors—which themselves have differential impacts (Comerio, 1998). 9

Large-Scale Comparative Research on Household Recovery. Although there is clearly a need for such research, few studies exist that compare household recovery processes and outcomes across communities and disaster events. With NEHRP funding, Frederick Bates and his colleagues carried out what may well be the largest research efforts of this kind: a multicommunity

longitudinal study on household and community impacts and recovery after the 1976 Guatemala earthquake and a cross-national comparative study on household recovery following six different disaster events. The Guatemala study, designed as a quasi-experiment, included households in 26 communities that were carefully selected to reflect differences in the severity of earthquake impacts, size, population composition, and region of the country. That study focused on a broad spectrum of topics, including changes over time in household composition and characteristics; household economic activity; housing characteristics and standards of living; household experiences with relief and reconstruction assistance; and fertility, health, and nutrition. Never replicated for any other type of disaster, the study provided detailed information on these topics, focusing in particular on how different forms of aid provision either facilitated or hampered household recovery (for detailed discussions, see Bates, 1982; Hoover and Bates, 1985; Bates et al., 1979).

The second study carried out by Bates and his colleagues extended methods developed to assess household recovery following the Guatemala earthquake to measure household recovery in disaster-stricken communities in six different countries. The tool used to measure disaster impacts and household recovery across different events and societies, the Domestic Assets Scale, made possible systematic comparisons with respect to one dimension of household recovery—the restoration of household possessions, tools, and technologies (Bates and Peacock, 1992, 1993).

Vulnerability, Resilience, and Household Recovery. Like the other aspects of recovery discussed here, what happens to households during and after disasters can be conceptualized in terms of vulnerability and resilience. With respect to vulnerability, social location is associated with the severity of disaster impacts for households. Poverty often forces people to live in substandard or highly vulnerable housing—manufactured housing is one example—leaving them more vulnerable to death, injury, and homelessness. As discussed in Chapter 3 with respect to disaster preparedness, factors such as income, education, and homeownership influence the ability of households to mitigate and prepare for disasters. Social-structural factors also affect the extent to which families can accumulate assets in order to achieve higher levels of safety, as well as their recovery options and access to resources after disasters strike—for example the forms of recovery assistance for which they are eligible. Households are thus differentially exposed to disasters, differentially vulnerable during the recovery period, and diverse in terms of both inherent and adaptive resilience.

ECONOMIC AND BUSINESS IMPACTS AND RECOVERY: THE CHALLENGE OF ASSESSING DISASTER LOSSES

As discussed in Chapter 3 , assessing how much disasters cost the nation and its communities has proven to be a major challenge. A National Research Council (NRC, 1999c) study concluded that such calculations are difficult in part because different agencies and entities calculate costs and losses differently. Moreover, no universally accepted standards exist for calculating economic impacts resulting from disasters, and there is no single agency responsible for keeping track of disaster losses. For any given disaster event, assessments of economic impacts may vary widely depending on which statistics are used—for example, direct or insured losses versus total losses.

NEHRP-sponsored research has addressed these problems to some degree. For example, as part of the NEHRP-sponsored “Second Assessment of Research on Natural Hazards,” researchers attempted to estimates losses, costs, and other impacts from a wide array of natural and technological hazards. 10 For the 20 year period 1975–1994, they estimated that dollar losses from disasters amounted to $.5 billion per week, with climatological hazards accounting for about 80 percent of those losses; since 1989, losses have totaled $1 billion per week (Mileti, 1999a). Through work undertaken as part of the Second Assessment, data on losses from natural hazard events from the mid-1970s to 2000 are now available at the county level in geocoded form for the entire United States through the Spatial Hazard Events and Losses Database for the United States (SHELDUS). This data collection and database development effort has made it possible to analyze different types of losses, at different scales, using different metrics, and to assess locations in terms of their hazard proneness and loss histories. (For discussions of the data used in the SHELDUS database and associated challenges see Cutter, 2001.) What is still lacking is a national program to continue systematically collecting and analyzing impact and loss data.

Studies on economic impacts and recovery from earthquakes and other disasters can be classified according to the units of analysis on which they focus. Most research concerns economic losses and recovery at the community or, more frequently, the regional level. A smaller set of studies has analyzed economic impacts and recovery at the firm or facility level. There is even less research documenting national-level and macroeconomic impacts.

Community-Level and Regional Studies

Studies on the economics of natural disasters at the community and regional levels of analysis differ significantly in methods, topics of interest, and conclusions. Some researchers, such as Rossi et al. (1978) and Friesema et al. (1979) have argued that at least in the United States, natural disasters have no discernible social or economic effects at the community level and that nondisaster-related trends have a far more significant influence on long-term outcomes than disasters themselves. This position has also been argued at the macroeconomic level, with respect to other developed and developing countries (Albala-Bertrand, 1993). 11 Dacy and Kunreuther (1969:168) even argued (although more than 30 years ago) that “a disaster may actually turn out to be a blessing in disguise” because disasters create reconstruction booms and allow community improvements to be made rapidly, rather than gradually. However, most research contradicts the idea that disasters constitute economic windfalls, emphasizing instead that economic gains that may be realized at one level (e.g., the community, particular economic sectors) typically constitute losses at another (e.g., the national tax base). One analyst has called the idea that disasters are beneficial economically “one of the most widely held misbeliefs in economics” (DeVoe, 1997:188).

Other researchers take the position that post-disaster economic and social conditions are generally consistent with pre-disaster trends, although disasters may amplify those changes (Bates and Peacock, 1993). Disasters may further marginalize firms and sectors of the economy that were already in decline, or they may speed up processes that were already under way prior to their occurrence. For example, Homestead Air Force Base was already slated for closure before Hurricane Andrew despite ongoing efforts to keep the base opened. When Andrew occurred, the base sustained damage and was closed for good. The closure affected businesses that had depended on the base and helped lead to the exodus of many middle-class families from the area, which in turn affected tax revenues in the impact region. These changes would have taken place eventually, but they were accelerated by Hurricane Andrew.

Related research has analyzed the distributive effects of earthquakes and other disasters. In an early formulation, Cochrane (1975) observed that lower-income groups consistently bear a disproportionate share of disaster losses, relative to higher-income groups. This theme continues to be promi-

nent in the disaster literature; the notion that disasters create economic “winners and losers” has been borne out for both households and businesses (Peacock et al., 1997:Chapter 11; Tierney and Webb, forthcoming).

Another prominent research emphasis at the community and regional levels of analysis has grown out of the need to characterize and quantify the economic impacts of disasters (as well as other impacts) in order to be better able to plan for and mitigate those impacts. A considerable amount of NEHRP research on economic impacts and recovery has been driven by concern about the potentially severe economic consequences of major earthquakes, particularly those that could occur in highly populated urban areas. That concern is reflected in a number of NRC reports (1989, 1992, 1999c) on projected losses and potential economic impacts. Within the private sector, the insurance industry has also committed significant resources in an effort to better anticipate the magnitude of insured losses in future disaster events. (For new developments in research on the management of catastrophic insurance risk, see Grossi et al., 2004.)

Stimulated in large measure by NEHRP funding, new tools have been developed for both pre-disaster estimation of potential losses and post-disaster impact assessments, particularly for earthquakes. HAZUS, the national loss estimation methodology, which was originally developed for earthquakes and which has now been extended to flood and wind hazards, was formulated under FEMA’s supervision with NEHRP funding. NEHRP funds have also supported the development of newer and more sophisticated modeling approaches through research undertaken at earthquake centers sponsored by the National Science Foundation (NSF).

The framework for estimating losses from natural hazards was initially laid out more than 20 years ago in publications such as Petak and Atkisson’s Natural Hazard Risk Assessment and Public Policy (1982) and in applied studies such as the PEPPER (Pre-Earthquake Planning for Post-Earthquake Rebuilding) project (Spangle, 1987), which analyzed potential earthquake impacts and post-disaster recovery strategies for Los Angeles. According to the logic developed in these and other early studies (see, for example, NRC, 1989) and later through extensive NEHRP research, loss estimation consists of the analysis of scenario or probabilistic models that include data on hazards; exposures , or characteristics of the built environment at risk, including buildings and infrastructural systems; fragilities , or estimates of damage likelihood as a function of one or more parameters, such as earthquake shaking intensity; direct losses , such as deaths, injuries, and costs associated with damage; and indirect losses and ripple effects that result from disasters. Within this framework, recent research has focused on further refining loss models and reducing uncertainties associated with both the components of loss estimation models and their interrelationships (for

representative work, see theme issue in Earthquake Spectra, 1997; Tierney et al., 1999; Okuyama and Chang, 2004).

This line of research has led both to advances in basic science knowledge and to a wide range of research applications. At the basic science level, loss modeling research—particularly studies supported through NEHRP—has helped distinguish and clarify relationships among such factors as physical damage, direct economic loss, business interruption effects, and indirect losses and ripple effects. For example, it is now more possible than ever before to disaggregate and analyze separately different types of economic effects and to understand how particular types of damage (e.g., damage to electrical power or transportation systems) contribute to overall economic losses. This research has shed light on factors that contribute to the resilience of regional economies, both during normal times and in response to sudden shocks. It has also shown how the application of newer economic modeling techniques, such as computable general equilibrium modeling and agent-based modeling, constitute improvements over more traditional input-output modeling, particularly for the study of extreme events (for discussions, see Rose et al., 2004; Chang, 2005; Rose and Liao, 2005). Econometric modeling provides another promising approach at both the micro and the regional levels (see West and Lenze, 1994), but this potential remains largely untapped.

At the applications level, loss estimation tools and products have proven useful for raising public awareness of the likely impacts of disaster events and for enhancing community preparedness efforts and mitigation programs. They have also made it possible to assess mitigation alternatives, not only in light of the extent to which those measures reduce damage, but also in terms of their economic costs and benefits. When applied in the disaster context, rapid economic loss estimates have also formed the basis for requests for federal disaster assistance. For the insurance industry, loss models provide important tools to improve risk management decision making, particularly with regard to catastrophic risks.

As noted earlier, loss modeling originally was driven by the need to better understand the economic impacts of earthquakes. In addition to economic losses, earthquake loss models are increasingly taking into account other societal impacts such as deaths, injuries, and residential displacement, as well as secondary effects such as earthquake-induced fires. The methodological approach developed to study earthquakes was first extended to other natural hazards and is now being used increasingly to assess potential impacts from terrorism. The nation is now better able to address the issue of terrorism-related losses because of the investments that had been made earlier for earthquakes and other natural hazards. Significantly, when the Department of Homeland Security decided in 2003 to begin funding

university-based “centers of excellence” for terrorism research, the first topic that was selected for funding was risk and economic modeling for terrorist attacks in the United States. 12 Many of the investigators associated with that center had previously worked on loss modeling for earthquakes.

Business and Facility-Level Impacts and Recovery. Most research on recovery processes and outcomes has focused on households and communities. Prior to the 1990s, most research on the economic aspects of disasters focused not on individual businesses but rather on community-wide and regional impacts. Almost nothing was known about how private sector organizations are affected by and recover from disasters. Since then, a small number of studies have focused on business firms or, in some cases, commercial facilities, as units of analysis. Much of this work, including studies on large, representative samples of businesses, has been carried out with NEHRP support. Business impacts and recovery have been assessed following the Whittier Narrows, Loma Prieta, Northridge, and Kobe earthquakes; the 1993 Midwest floods; Hurricane Andrew; and other flood and hurricane events (for representative studies and findings, see Dahlhamer, 1998; Chang, 2000; Webb et al., 2000; Alesch et al., 2001). Long-term business recovery has been studied in the context of only two disaster events—the Loma Prieta earthquake and Hurricane Andrew (Webb et al., 2003).

These studies have shown that disasters disrupt business operations through a variety of mechanisms. Direct physical damage to buildings, equipment, vehicles, and inventories has obvious effects on business operation. It might be less obvious that disruption of infrastructure such as water/sewer, electric power, fuel (i.e., natural gas), transportation, and telecommunications frequently forces businesses to shut down in the aftermath of a disaster (Alesch et al., 1993; Tierney and Nigg, 1995; Tierney, 1997a, b; Webb et al., 2000). For example, Tierney (1997b) reported that extensive electrical power service interruption after the 1993 Midwest floods caused a large number of business closures in Des Moines, Iowa, even though the physical damage was confined to a relatively small area.

Other negative disaster effects include population dislocation, losses in discretionary income among those victims who remain in the impact area—which can weaken market demand for many products and services—and competitive pressure from large outside businesses. These kinds of impacts can cause small local businesses to experience major difficulties recovering from the aftermath of a disaster (Alesch et al., 2001). Indeed, such factors

can produce business failures long after the precipitating event, especially if the community was already in economic decline before the disaster occurred (Bates and Peacock, 1993; Webb et al., 2003).

It is difficult to generalize on the basis of so few studies, particularly when the issues involved and the methodological challenges are so complex. However, studies to date have uncovered a few consistent patterns with respect to business impacts and recovery. First, studies show that most businesses do recover, and do so relatively quickly. In other words, typical businesses affected by disasters show a good deal of resilience in the face of major disruption.

Second, some businesses do tend to fare worse than others in the aftermath of disasters; clearly, not all businesses are equally vulnerable or equally resilient. Although findings from individual studies differ, the factors that seem to contribute most to vulnerability include small size; poor pre-disaster financial condition; business type, with wholesale and retail trade appearing to be especially vulnerable, while manufacturing and construction businesses stand to benefit most from disasters; and severity of disaster impacts. With regard to this last-mentioned factor, studies show that negative impacts on businesses include not only direct physical damage, lifeline-related problems, and business interruption, but also more long-lasting operational problems that businesses may experience following disasters, such as employee absenteeism and loss of productivity, earthquake-induced declines in demands for goods and services, and difficulties with shipping or receiving products and supplies.

Third, business recovery is affected by many factors that are outside the control of the individual business owner. For example, businesses located in highly damaged areas may experience recovery difficulties independent of whether or not they experience losses. In this case, recovery is complicated by the fact that disasters disrupt local ecologies on which individual businesses depend. Business recovery processes and outcomes are also linked to community-level decision making. After the Loma Prieta earthquake, for example, the City of Santa Cruz offered extensive support to businesses and used the earthquake as an opportunity to reinvent itself and to revitalize a business district that had fallen short of realizing its potential prior to the disaster (Arnold, 1998). Actions that communities take with respect to land-use, structural mitigation, infrastructure protection, community education, and emergency response planning also affect how businesses and business districts fare during and after disasters.

Fourth, recovery outcomes following disasters are linked to pre-disaster trends and broader market forces. For example, focusing on an important transport facility, the Port of Kobe, Chang (2000) showed that the port’s inability to recover fully after the 1995 earthquake was due in part to losses in one part of the port’s business—trans-shipment cargo—that had already

been declining before the earthquake owing to severe competition from other ports in the region. Similarly, Dahlhamer (1998) found that businesses in the wholesale and retail trade sectors were more vulnerable to experiencing negative economic outcomes following the Northridge earthquake, perhaps because they constitute crowded and highly competitive economic niches and because turnover is high in those sectors during normal times. He also found that firms in industries that had been experiencing growth in the two-year period just before the earthquake were less likely than firms in declining industries to report being worse off following the Northridge event. Such findings are consistent with a more general theme in recovery research discussed earlier—that disasters do not generate change in and of themselves, but rather intensify or accelerate preexisting patterns.

Community Recovery. Although the topic of community recovery is still not well studied, significant progress has been made in understanding both recovery processes and factors that are associated with recovery outcomes for communities. Earlier research indicated that communities rebound well from disasters and that, at the aggregate level and net of other factors, the impacts of disasters are negligible (Friesema et al., 1979; Wright et al., 1979). However, other more recent research suggests that such findings paint an overly simplified and perhaps overly optimistic picture of post-disaster recovery. This may have been due to methodological shortcomings—for example, the tendency to aggregate data and to group together both more damaging disasters and those that did comparatively little damage—or because such studies were based on “typical” disasters in the United States, rather than catastrophic or near-catastrophic ones. 13 In contrast, in a methodologically sophisticated study focusing on a much more severe disaster, the 1995 Kobe event, Chang (2001) analyzed a number of recovery indicators, including measures of economic activity, employment in manufacturing, changes in the spatial distribution of work activities, and differences in recovery indicators among different districts within the city. She found that the earthquake did have lasting and significant negative effects on the City of Kobe. Equally important, poor recovery outcomes were more pronounced in some parts of the city than in others—specifically those areas that had already been experiencing declines. This study provides yet another illustration of how disasters exploit existing vulnerabilities. It also cautions against making blanket statements about disaster impacts and recovery.

Another limitation of earlier work on community recovery was that it provided too little information on what actually happens in communities during the recovery process or what communities can do to ensure more rapid and satisfactory recovery outcomes. Later research, much of which has been undertaken with NEHRP support, has addressed these issues. For example, in Community Recovery from a Major Natural Disaster , Rubin et al. (1985) developed a set of propositions regarding factors that affect community recovery outcomes. That monograph, which was based on case study analyses of recovery following 14 disasters that occurred in the early 1980s, emphasized the importance of three general constructs—personal leadership, knowledge of appropriate recovery actions, and ability to act—as well as the influence of intergovernmental (state and federal) policies and programs. This work highlighted the effects of both government decision making and broader societal policies on community recovery.

Some more recent research has more explicitly incorporated community and population vulnerability as factors affecting community-level recovery. Bolin and Stanford (1998) traced how the post-Northridge recovery experiences of Los Angeles and smaller outlying towns differed as a function of such factors as political expertise and influence, preexisting plans, institutional capacity, involvement of community organizations, and interest group competition. In these diverse communities, the needs of more vulnerable and marginalized groups were sometimes addressed during the recovery process. However, recovery programs ultimately did little to improve the safety of those groups, because they failed to address the root causes of vulnerability (Bolin and Stanford, 1998:216):

[s]ince vulnerability derives from political, economic, and social processes that deny certain people and groups access or entitlements to incomes, housing, health care, political rights, and, in some cases, even food, then post-disaster rebuilding by itself will have little effect on vulnerability.

Societal-Level and Comparative Research on Disaster Recovery. International research on disasters is discussed in greater detail in Chapter 6 . This chapter focuses in a more limited way on what little research exists on disaster impacts and post-disaster change at the societal level. Regarding long-term societal impacts, researchers have generally found that disasters, even very large ones, typically do not in and of themselves result in significant change in the societies they affect. Instead, the broad consensus has been that to the extent disasters do have lasting effects, it is because they interact with other factors to accelerate changes that were already under way. Albala-Bertrand, for example has argued that while disasters can highlight preexisting political conflicts, whether such effects are sustained over time “has little to do with the disaster itself, but with preexisting economic and sociopolitical

conditions” (1993:197). This research found that the potential for such changes was generally greater in developing countries than developed ones, although not great in any case.

With respect to the political impacts of disasters at the societal level, comparing very large disasters that occurred between 1966 and 1980, political scientist Richard Olson found that that major disasters can result in higher levels of political unrest, particularly in developing countries that are already politically unstable (Olson and Drury, 1997). In other research, Olson argues that under certain (and rare) circumstances, disasters can constitute “critical junctures,” or crises that leave distinctive legacies within those societies. The 1972 earthquake in Managua, Nicaragua, was one such case. Following that devastating event, the corrupt and dictatorial Somoza regime took a large share of post-disaster aid for itself and mismanaged the recovery, in the process alienating Nicaraguan elites, the business establishment, and finally the middle class, and paving the way for the Sandanistas to assume power in 1979. The 1985 Mexico City earthquake also affected the political system of that nation by, among other things, helping to weaken the hegemony of the Institutional Revolutionary Party. However, rather than having a direct and independent influence on subsequent political changes, that earthquake interacted with factors and trends that were already beginning to affect Mexican society before it occurred. That disaster, which was not well managed by the ruling government, provided the Mexican people with a sharp contrast between the vibrancy and the capability of civil society and the government’s lack of preparedness. Grass-roots response and recovery efforts also facilitated broader mobilization by groups that had been pressing for change. Although not a “critical juncture” in its own right, the earthquake did play a role in moving the political system in the direction of greater pluralism and strengthened the power of civil society institutions vis-à-vis the state (Olson and Gawronski, 2003).

Such findings assume particular significance in the aftermath of the December 2004 Indian Ocean earthquake and tsunami. The impacts of that catastrophe span at least 12 different nations and a number of semi-autonomous subnational units, each with its own distinctive history, mode of political organization, internal cleavages, and preexisting problems. Research is needed to better understand both recovery processes and outcomes and the longer-term societal effects of this devastating event.

OTHER DISASTER RECOVERY-RELATED ISSUES

Disaster experience and the mitigation of future hazards.

Social science research has also focused in various ways on the question of whether the positive informational effects of disasters constitute learning

experiences for affected social units by encouraging the adoption of mitigation measures and stimulating preparedness activity. While this idea seems intuitively appealing, the literature is in fact quite equivocal with regard to the extent to which disasters actually promote higher levels of safety. On the one hand, at the community and societal levels, there is considerable evidence to suggest that disasters constitute “windows of opportunity” for those seeking to enact loss reduction programs, making it possible to achieve policy victories that would not have been possible prior to those events (Alesch and Petak, 1986). Disasters have the potential to become “focusing events” (Birkland, 1997) that can alter policy agendas through highlighting areas in which current policy has failed, energizing advocates, and raising public awareness. On the other hand, many disasters fail to become focusing events and have no discernible impacts on the adoption and implementation of loss-reduction measures. For example, Burby et al., (1997), who studied communities in five different states, found no relationship between disaster experience and adoption of mitigation measures. Birkland (1997) suggests that these differences are related in part to the extent to which advocacy coalitions exist, are able to turn disaster events to their advantage, and are able to formulate appropriate policy responses.

Further complicating matters, policies adopted in the aftermath of disasters, like other policies, may meet with resistance and be only partially implemented—or implemented in ways that were never intended. While it is possible to point to examples of successful policy adoption and implementation in the aftermath of disasters, such outcomes are by no means inevitable, and when they do occur, they are typically traceable to other factors, not just to disaster events themselves.

Research does suggest that households, businesses, and other entities affected by disasters learn from their experiences and take action to protect themselves from future events. Those who have experienced disasters may, for example, step up their preparedness for future events or be more likely to heed subsequent disaster warnings. At the same time, it is also clear that there is considerable variability in the relationship between experience and behavioral change. While some studies document the positive informational effects of experience, others show no significant impact, and some research even indicates that repeated experiences engender complacency and lack of action (for a review of the literature, see Tierney et al., 2001).

Role of Prices and Markets

Mainstream economic theory, models, and analytical tools (e.g., benefit-cost analysis) assume that markets generally function efficiently and equilibrate. Barring various situations of market failure, prices serve a key role as signals of resource scarcity. In this context, two broad areas of research

needs can be identified. One is the role of prices and markets in pre-disaster mitigation (see also Chapter 3 ). Market-based approaches to reducing disaster risk involve such questions as how prices can serve as better signals of risk taking and risk protection, and the potential for new approaches to risk sharing (e.g., catastrophe bonds). At the same time, better understanding is also needed of market failures in mitigation (e.g., externalities in risk taking and risk protection). The second broad research need concerns markets in post-disaster loss and recovery. Little empirical research has been conducted on the degree to which assumptions of efficient markets actually hold in disasters, especially those having catastrophic impacts, and the degree to which markets are resilient in the face of disasters. Research is also needed on how economic models can capture the adjustment processes and disequilibria that are important as economies recover from disasters, and how economic recovery policies can influence recovery trajectories.

Disaster Recovery and Sustainability

As discussed in more detail in Chapter 6 , which focuses on international research, disaster theory and research have increasingly emphasized the extent to which vulnerability to disasters can be linked to unsustainable development practices. Indeed, the connection between disaster loss reduction and sustainability was a key organizing principle of the NEHRP-sponsored Second Assessment of Research on Natural Hazards. The title of the summary volume for the Second Assessment, Disasters by Design (Mileti, 1999b), was chosen to emphasize the idea that the impacts produced by disasters are the consequence of prior decisions that put people and property at risk. A key organizing assumption for the Second Assessment was the notion that societies and communities “design” the disasters of the future by failing to take hazards into account in development decisions; pursuing other values, such as rapid economic growth, at the expense of safety; failing to take decisive action to mitigate risks to the built environment; and ignoring opportunities to enhance social and economic resilience in the face of disasters. Conversely, communities and societies also have the ability to design safer futures by better integrating hazard reduction into their ongoing policies and practices in areas such as land-use and development planning, building codes and code enforcement, and quality-of-life initiatives.

Just as disasters dramatically highlight failures to address sources of vulnerability, the post-disaster recovery period gives affected communities and societies an opportunity to reassess pre-disaster plans, policies, and programs, remedy their shortcomings, and design a safer future (Berke et al., 1993). The federal government seeks to promote post-disaster mitigation through FEMA’s Hazard Mitigation Grant Program, as well as programs

that seek to reduce repetitive flood losses through relocating flood-prone properties. The need to weave a concern with disaster loss reduction into the fabric of ongoing community life has also guided federal initiatives such as Project Impact, FEMA’s Disaster Resistant Communities program.

Yet the research record suggests that those opportunities are often missed. While it is clear that some disaster-stricken communities do act decisively to reduce future losses, for others the recovery period brings about a return to the status quo ante, marked at most by gains in safety afforded by reconstruction to more stringent building codes. The section above noted that disasters create “windows of opportunity” for loss reduction advocates, in part by highlighting policy failures and temporarily silencing opponents. At the same time, however, research evidence suggests that even under those circumstances, it is extremely difficult to advance sustainability goals in the aftermath of disasters. Changes in land use are particularly difficult to enact, both during nondisaster times and after disasters, despite the fact that such changes can significantly reduce vulnerability. Land use decision making generally occurs at the local level, but local jurisdictions have great difficulty enacting controls on development in the absence of enabling legislation from higher levels of government. Even when land-use and zoning changes and other mitigation measures are seen as desirable following disasters, community leaders may lack the political will to promote such efforts over the long term, allowing opponents to regroup and old patterns to reassert themselves (see, for example, Reddy, 2000; for more detailed discussions on land-use and hazards, see Burby, 1998). Assessing reconstruction following recent U.S. disasters, Platt (1998:51) observed that “[d]espite all the emphasis on mitigation of multiple hazards in recent years, political, social and economic forces conspire to promote rebuilding patterns that set the stage for future catastrophe.” Overall, the research record suggests that while the recovery period should ideally be a time when communities take stock of their loss reduction policies and enact new ones, post-disaster change tends to be incremental at best and post-disaster efforts to promote sustainability are rare.

RESEARCH RECOMMENDATIONS

This chapter closes by making recommendations for future research on disaster response and recovery. As the foregoing discussions have indicated, existing research has raised numerous questions that need to be addressed through future research. This concluding section highlights general areas in which new research is clearly needed, both to test the limits of current social science knowledge and to take into account broad societal changes and issues of disaster severity and scale.

Recommendation 4.1: Future research should focus on further empirical explorations of societal vulnerability and resilience to natural, technological, and willfully caused hazards and disasters.

Discussions of factors associated with differential vulnerability and resilience in the face of disasters appear in many places in this report. What these discussions reveal is that researchers have only begun to explore these two concepts and much work remains to be done. It is clear that vulnerability is produced by a constellation of psychological, attitudinal, physical, social, and economic factors. However, the manner in which these factors operate and interact in the context of disasters is only partially understood. For example, while sufficient evidence exists to indicate that race, gender, and ethnicity are important predictors of hazard vulnerability and disaster-related behavior, research has yet to fully explore such factors, their correlates, and their interactions across different hazard and disaster contexts. In many cases age is associated with vulnerability to disasters (see Ngo, 2001; Anderson, 2005), but other factors such as ethnicity and socioeconomic status have differential effects within particular age groups (Bolin and Klenow, 1988), and the vulnerability of elderly persons may be related not only to age but also to other factors that are correlated with age, such as social isolation, which can cut off older adults from sources of lifesaving aid under disaster conditions (Klinenberg, 2002).

Even less is known about how to conceptualize, measure, and enhance resilience in the face of disasters—whether that concept is applied to the psychological resilience of individuals or to the resilience of households, communities, local and regional economies, or other units of analysis. Resilience can be conceptualized as the ability to survive disasters without significant loss, disruption, and stress, combined with the ability to cope with the consequences of disasters, replace and restore what has been lost, and resume social and economic activity in a timely manner (Bruneau et al., 2003). Other dimensions of resilience include the ability to learn from disaster experience and change accordingly.

The large volume of literature on psychological resilience and coping offers insights into factors that facilitate resilient responses by individual disaster victims. Other work, such as research on “high-reliability organizations,” organizational adaptation and learning under crisis conditions, and organizational effectiveness (Roberts, 1989; La Porte and Consolini, 1998; Comfort, 1999; Drabek, 2003) also offers insights into correlates of resilience at the organizational and interorganizational levels. As suggested in Chapter 6 , the social capital construct and related concepts such as civic engagement and effective collective action are also related to resilience. The challenge is to continue research on the resilience concept while synthesizing theoretical insights from these disparate literatures, with the ultimate objective of developing an empirically grounded

theory of resilience that is generalizable both across different social units and across different types of extreme events.

Recommendation 4.2: Future research should focus on the special requirements associated with responding to and recovering from willful attacks and disease outbreaks.

A better understanding is needed of likely individual, group, and public responses to intentional acts of terrorism, as well as disease outbreaks and epidemics. As indicated in this chapter, there appears to be no strong a priori reason for assuming that responses to natural, technological, or intentionally caused disasters and willful or naturally occurring disease outbreaks will differ. However, research on hazards and disasters also calls attention to factors that could well prove to be important predictors of responses to such occurrences, particularly those involving unique hazards such as chemical, biological, nuclear, and radiological agents. Research on individual and group responses to different types of disasters has highlighted the importance of such factors as familiarity, experience, and perceptual cues; perceptions about the characteristics of hazards (e.g., their dread nature, lethality and other harms); the content, clarity, and consistency of crisis communications; knowledge of appropriate self-protective actions; and feelings of efficacy with respect to carrying out those measures (see, for example, classic work on risk perception, discussed in Slovic, 2000, as well as Lindell and Perry, 2004).

Recent research has also highlighted the importance of emotions in shaping perceptions of risk. Hazards that trigger vivid images of danger and strong emotions may be seen as more likely to occur, and more likely to produce harm, even if their probability is low (Slovic et al., 2004). If willful acts engender powerful emotions, they could potentially also engender unusual responses among threatened populations.

The potential for ambiguity and confusion with respect to public communications may also be greater for homeland security threats and public health hazards such as avian flu than for other hazards. For example, warning systems and protocols are more institutionalized and more widely understood for natural hazards than for homeland security and public health threats. While it is generally recognized that organizations such as the National Hurricane Center and the U.S. Geological Survey constitute reliable sources of information on hurricanes and earthquakes, respectively, members of the public may be less clear regarding responsibilities and authorities with respect to other risks, particularly since such threats and the expertise needed to assess them are so diverse.

These kinds of differences could translate into differences in public perceptions and subsequent responses. Research is needed on the manner in which the distinctive features of particular homeland security and public

health threats, such as those highlighted here, as well as official plans and management strategies, could affect responses during homeland security emergencies.

Recommendation 4.3: Future research should focus on the societal consequences of changes in government organization and in emer gency management legislation, authorities, policies, and plans that have occurred as a result of the terrorist attacks of September 11, 2001, as well as on changes that will almost certainly occur as a result of Hurricane Katrina.

The period since the 2001 terrorist attacks has been marked by major changes in the nation’s emergency management system and its plans and programs. Those changes include the massive government reorganization that accompanied the creation of the Department of Homeland Security (DHS); the transfer of FEMA, formerly an independent agency, into DHS; the shifting of many duties and responsibilities formerly undertaken by FEMA to DHS’s Office of Domestic Preparedness, which was formerly a part of the Justice Department; the development of the National Response Plan, which supercedes the Federal Response Plan; Presidential Homeland Security Directives 5 and 8, which make the use of the National Incident Management System (NIMS) mandatory for all agencies and organizations involved in responding to disasters and also mandate the establishment of new national preparedness goals; and increases in funding for special homeland security-related initiatives, particularly those involving “first responders.” Other changes include a greater emphasis on regionalized approaches to preparedness and response and the growth at the federal, state, and local levels of offices and departments focusing specifically on homeland security issues—entities that in many cases exist alongside “traditional” emergency management agencies. While officially stressing the need for an “all-hazards” approach, government initiatives are concentrating increasingly on preparedness, response, and recovery in the context of willful attacks. These changes, all of which have taken place within a relatively short period of time, represent the largest realignment of emergency management policies and programs in U.S. history.

What is not known at this time—and what warrants significant research—is how these changes will affect the manner in which organizations and government jurisdictions respond during future extreme events. Is the system that is evolving more centralized and more command-and-control oriented than before September 11? If so, what consequences will that have for the way organizations and governmental entities respond? What role will the general public and emergent groups play in such a system? How will NIMS be implemented in future disasters, and to what effect? What new forms will emergent multiorganizational networks assume in future

disasters? Which agencies and levels of government will be most central, and how will shifts in authority and responsibility affect response and recovery efforts? Will the investment in homeland security preparedness translate into more rapid, appropriate, and effective responses to natural and technological disasters, or will the new focus on homeland security lead to an erosion in the competencies required to manage other types of emergencies? A major research initiative is needed to analyze the intended and unintended consequences in social time and space of the massive changes that have taken place in the nation’s emergency management system since September 11, 2001.

These concerns loom even larger in the aftermath of Hurricane Katrina. That disaster revealed significant problems in virtually every aspect of intergovernmental preparedness and response. The inept management of the Katrina disaster was at least in part a consequence of the myopic institutional focus on terrorism that developed in the wake of the September 11, 2001 attacks—a focus that included marginalizing and underfunding FEMA and downplaying the challenges associated with responding to large-scale natural disasters (Tierney, 2006, forthcoming). Katrina is certain to bring about further efforts at reorganizing the nation’s response system, particularly at the federal level. These reorganizations and their consequences merit special attention.

Recommendation 4.4: Research is needed to update current theories and findings on disaster response and recovery in light of chang ing demographic, economic, technological, and social trends such as those highlighted in Chapter 2 and elsewhere in this report.

It is essential to keep knowledge about disaster response and recovery current. The paragraphs above highlight the need for new research on homeland security threats and institutional responses to those threats. Research is also needed to update what is known about disaster response and recovery in light of other forms of social change and to reassess existing theories. Technological change is a case in point. Focusing on only one issue—disaster warnings—the bulk of the research that has been conducted on warning systems and warning responses was carried out prior to the information technology and communications revolutions. With the rise of the Internet and interactive Web-based communication, the proliferation of cellular and other wireless media, and the growing potential for ubiquitous communications, questions arise regarding the applicability of earlier research findings on how members of the public receive, interpret, and act on warnings. Changes in the mass media, including the rise of the 24-hour news cycle and the trend toward “narrowcasting” and now “podcasting” for increasingly specialized audiences, also have implications for the ways in which the public learns about hazards and receives warning-related

information. In many respects, warning systems reflect a preference for “push-oriented” information dissemination approaches. However, current information collection practices are strongly “pull oriented.” These and other trends in communications technology introduce additional complexity into already complex processes associated with issuing and receiving warnings, decision making under uncertainty, and crisis-related collective behavior. New research is needed both to improve theories and models and to serve as the basis for practical guidance.

Much the same can be said with respect to organizations charged with responding during disaster events. Along with being affected by policy and programmatic changes such as those discussed above, crisis-relevant agencies are also being influenced by the digital and communications revolution and by the diffusion of technology in areas such as remote sensing, geographic information science, data fusion, decision support systems, and visualization. In the more than 15 years since Drabek (1991b) wrote Microcomputers and Emergency Management , which focused on the ways in which computers were affecting the work of local emergency management agencies, technological change has been rapid and massive. How such changes are affecting organizational performance and effectiveness in disasters is not well understood and warrants extensive systematic study.

Recommendation 4.5: More research is needed on response and recovery for near-catastrophic and catastrophic disaster events.

Chapter 1 discusses issues of determining thresholds of disastrous conditions. NEHRP-sponsored social science research indicates that, in the main, U.S. communities have shown considerable resilience even in the face of major disasters. Similarly, at the individual level, U.S. disasters have produced a range of negative psychosocial impacts, but such impacts appear to have been neither severe nor long-lasting. While recognizing that disasters disproportionately affect the most vulnerable in U.S. society and acknowledging that recovery is extremely difficult for many, disasters have been less devastating in the United States and other developed societies than in the developing world. Disaster-related death tolls have also been lower by orders of magnitude, and economic losses, although often large in absolute terms, have also been lower relative to the size of the U.S. economy. At least that was the case until Hurricane Katrina, a catastrophic event that has more in common with disasters in the developing world than with the typical U.S. disaster.

The vast majority of empirical studies on which such generalizations are based have not focused on truly catastrophic disasters, and therefore research results may not be “scalable” to such events. Katrina clearly demonstrates that the nation is at risk for events that are so large that they overwhelm response systems and produce almost insurmountable post-

disaster recovery challenges. What kinds of social and economic impacts and outcomes would result from a large earthquake under downtown Los Angeles, a 7.0 earthquake event on the Hayward Fault in the San Francisco Bay area, a repeat of Hurricane Andrew directly striking Miami, or another hurricane landfall in the already devastated Gulf Coast region? What about situations involving multiple disaster impacts, such as the 2004 hurricane season in Florida and multiple disaster events that produce protracted impacts over time, such as the large aftershocks that are now occurring after the Indian Ocean earthquake and tsunami? To move into the realm of worst cases, what about an attack involving weapons of mass destruction, or simultaneous terrorist attacks in different cities around the United States? Such events are not outside the realm of possibility. There is a need to envision the potential social and economic effects of very large disasters, to learn from catastrophic events such as Hurricane Katrina, and to analyze historical and comparative cases for the insights they can provide.

Recommendation 4.6: More cross-societal research is needed on natural, technological, and willfully caused hazards and disasters.

Most of the research discussed in this chapter has focused on studies conducted within the United States, but it is important to recognize that findings from U.S. research cannot be overgeneralized to other societies. Disaster response and recovery challenges are greater by many orders of magnitude in smaller and less developed societies than in larger and more developed ones.

Disaster impacts, disaster responses, and recovery processes and outcomes clearly vary across societies. Although the earthquakes that struck Los Angeles in 1994, Kobe in 1995, and Bam, Iran, in 2003 were roughly equivalent in size, they differed in almost every other way: lives lost, injuries, extent of physical damage, economic impacts, and subsequent response and recovery activities. Research suggests that such cross-societal differences are attributable to many factors, including differences in physical and social vulnerability; governmental and institutional capacity; government priorities with respect to loss reduction; and response and recovery policies and programs (see, for example, Davis and Seitz, 1982; Blaikie et al., 1994; Berke and Beatley, 1997; Olson and Gawronski, 2003). NEHRP has made significant contributions to cross-societal research through initiatives such as the U.S.-Japan research program on urban earthquake hazards, which was launched following the Northridge and Kobe earthquakes, as well as a similar initiative that was developed after the 1999 Turkey and Taiwan earthquakes. In some cases, these initiatives have led to longer-term research partnerships; Chapter 6 contains information on one such collaboration, involving the Texas A&M University Hazard Reduction and Recovery Center and the National Center for Hazards Mitigation at the National

Taiwan University. Significantly more cross-national and comparative research is needed to further document and explain cross-societal variations in response and recovery processes and outcomes across different scales and different disaster events. Disasters such as the Indian Ocean earthquake and tsunami merit intensive study because they allow for rich comparisons at various scales (individuals, households, communities, and institutional and societal levels), providing an opportunity to greatly expand existing social science knowledge.

Recommendation 4.7: Taking into account both existing research and future research needs, sustained efforts should be made with respect to data archiving, sharing, and dissemination.

As noted in detail in Chapter 7 , attention must be paid to issues related to data standardization, data archiving, and data sharing in hazards and disaster research. NEHRP has been a major driving force in the development of databases on response and recovery issues. However, vast proportions of these data have yet to be fully analyzed. For social scientists to be able to fully exploit the data that currently exist, let alone the volume of data that will be collected in the future, specific steps have to be taken to make available and systematically collect, preserve, and disseminate such data appropriately within the research community. As recommended in Chapter 7 , information management strategies must be well coordinated, formally planned, and consistent with federal guidelines governing the protection of information on human subjects. Assuming that these foundations are established, the committee supports the creation of a Disaster Data Archive organized in ways that would encourage broader use of social science data on disaster response and recovery. Contents of this archive would include (but not be limited to) survey instruments; cleaned databases in common formats; code books, coding instructions and other forms of documentation; descriptions of samples and sampling methods; collections of papers containing analyses using those databases; photographs and Internet links (where applicable); and related research materials. Procedures for data archiving and sharing would build on existing protocols set out by organizations such as the Inter-University Consortium for Political and Social Research (e.g., ICPSR, 2005).

The distributed Disaster Data Archive would perform a number of important functions for social science hazards and disaster research and for the nation. The existence of the archive would make it much more likely that existing data sets will be used to their full potential by greatly improving accessibility. The archive would serve as an important tool for undergraduate and graduate education by making data more easily available for course projects, theses, and dissertations. By enabling researchers to access instruments used in previous research and incorporate past survey and

interview items into their own research, the archive should help make social science research on disasters more cumulative and replicable. An archive would also make it easier for newcomers to the field of disaster research to become familiar with existing research and enable researchers to identify gaps in past research and avoid unnecessary duplication. The archive would also serve an important function in preserving data that might otherwise be lost. Finally, such an archive would enable social science disaster research to better respond to agency directives regarding the desirability of data sharing.

For an effort of this kind to succeed, a number of conditions must be met. Funds will be needed to support the development and maintenance of the archive, and researchers must be willing to make their data sets and all relevant documentation available. This second condition is crucial, because the committee is aware of a number of important data sets that are not currently being shared, and the archive cannot succeed without broad researcher support. Challenges related to human subjects review requirements, confidentiality protections, and disclosure risks must be fully explored and addressed. Other issues include challenges associated with the development and enforcement of quality control standards, rules and standards for data sharing, procedures to ensure that proper acknowledgment is given to project sponsors and principal investigators, and questions about long-term management of the archive.

Related to the need for better data archiving, sharing, and dissemination strategies, social scientists must be poised to take advantage of new capabilities for data integration and fusion. Strategies are needed to integrate social science data with other types of data collected by both pervasive in situ and mobile ad hoc sensor networks (Estrin et al., 2003), such as networks that collect data on environmental and ecological changes and disaster impacts. In light of the availability of such a wide array of data, the hazards and disasters research community must recognize that hazards and disaster informatics—the application of information science and technology to disaster research, education, and practice—is an emerging field.

To realize this potential, and with the foundation established through implementing recommendations in Chapter 7 , the committee further supports the creation of a Data Center for Social Science Research on Hazards and Disasters. In addition to maintaining the Disaster Data Archive, this center would conduct research on automated information extraction from data, including the development of efficient and effective methods for storing, querying, and maintaining both qualitative and quantitative data from disparate and heterogeneous sources.

Social science research conducted since the late 1970's has contributed greatly to society's ability to mitigate and adapt to natural, technological, and willful disasters. However, as evidenced by Hurricane Katrina, the Indian Ocean tsunami, the September 11, 2001 terrorist attacks on the United States, and other recent events, hazards and disaster research and its application could be improved greatly. In particular, more studies should be pursued that compare how the characteristics of different types of events—including predictability, forewarning, magnitude, and duration of impact—affect societal vulnerability and response. This book includes more than thirty recommendations for the hazards and disaster community.

READ FREE ONLINE

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

• Open access
• Published: 28 March 2023

Reimagining natural hazards and disaster preparedness: charting a new course for the future

• Krzysztof Goniewicz 1 ,
• Md Nazirul Islam Sarker 2 , 3 &
• Monica Schoch-Spana 4  

BMC Public Health volume  23 , Article number:  581 ( 2023 ) Cite this article

3648 Accesses

2 Citations

2 Altmetric

Metrics details

The world is facing unprecedented challenges from disasters and natural hazards, which are increasing in frequency and intensity due to climate change. Moreover, they can overlap, producing compounded and cascading effects [ 1 , 2 ]. While these events alone and together can be devastating, the good news is that we can take steps to mitigate their impact and prepare for their aftermath. This is where the art of disaster preparedness comes into play.

In this collection, we explore the latest research and best practices for preparing for natural hazards and disasters. We welcome submissions that showcase innovative solutions and strategies for dealing with a range of hazards, including hurricanes, earthquakes, floods, and wildfires.

One of the key messages from the collection is the importance of preparedness; anticipation and preemption are powerful interventions. Disaster preparedness is crucial for health security for several reasons. Disasters and emergencies can place an enormous strain on health infrastructure, including hospitals, clinics, and medical supply chains [ 3 ]. By being prepared, communities can take steps to protect this infrastructure and ensure that it is able to function during and after a disaster. Disaster preparedness can help to ensure that emergency responders are able to provide a timely response to emergencies. This can be critical in situations where every minute counts, such as during a disaster or a terrorist attack [ 4 , 5 , 6 ].

Another point is prevention of disease outbreaks. Disasters can increase the risk of disease outbreaks, particularly in areas where sanitation and hygiene are compromised. Preparedness efforts can help to reduce the risk of outbreaks by ensuring that individuals have access to clean water, adequate sanitation facilities, and proper medical care [ 7 ]. At the same time, readiness for infectious disease outbreaks that can complicate established response measures such as sheltering and evacuation is essential [ 8 ].

Disasters can also have a significant impact on mental health, causing stress, anxiety, and depression [ 9 , 10 ]. Preparedness efforts can include providing support for mental health needs, which can help individuals to cope with the effects of a disaster [ 11 , 12 ]. Overall, disaster preparedness is a critical component of health security. By being prepared, communities can help to reduce the impact of disasters on health and wellbeing, and ensure that individuals have access to the medical care and support they need during and after a disaster.

Another theme that we would like to see emerging from the collection of articles is the need for collaboration and cooperation [ 13 , 14 , 15 ]. In order to prepare for and respond to disasters effectively, individuals, organizations, and governments must work together. This can involve sharing resources and expertise, coordinating efforts, and engaging in community outreach to ensure that everyone is on the same page.

Training for mass casualty incidents and disasters should be conducted regularly and refreshed at intervals. In order to improve society preparedness and resiliency, disaster management competencies should be linked to the overall quality improvement process [ 16 , 17 ].

In order to ensure the appropriate level of knowledge and skills of society for mass casualty incidents and disasters, it is necessary (as a minimum) to design training curriculum that is comprehensive and aligns with international standards that include the roles, responsibilities, functions and resources needed for MCI and disaster preparedness and response [ 18 , 19 ]. The provision of training for mass casualty incidents and disasters by the employer should be mandatory, as should the participation of employees [ 20 , 21 , 22 , 23 ].

This collection aims to provide an up-to-date overview of the latest scientific research and innovative solutions related to preparing for and responding to natural hazards. The collection is highly relevant to researchers, policy makers, and other experts in the field of disaster management, as they offer insights into emerging trends, challenges, and best practices for addressing natural hazards. We are interested in articles that provide practical advice and strategies that can be implemented in real-world situations, such as community outreach programs, emergency response plans, and risk reduction measures. By implementing these strategies, communities can build resilience and better navigate the challenges posed by natural hazards.

We believe this collection is an essential resource for anyone involved in disaster research and management. It aims to provide a comprehensive overview of the latest research and best practices for preparing for and responding to natural hazards. We are interested in submissions that contribute to this goal by presenting practical solutions and strategies that can be implemented in a range of settings. By working together and implementing the strategies outlined in this collection, we can build more resilient communities and navigate natural hazards with confidence based on the latest science.

Data availability

Not applicable.

Sohrabizadeh S, Yousefian S, Bahramzadeh A, Vaziri MH. A systematic review of health sector responses to the coincidence of disasters and COVID-19. BMC Public Health. 2021;21(1):709.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Feng K, Ouyang M, Lin N. Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate. Nat Commun. 2022;13(1):4421.

Nuzzo JB, Meyer D, Snyder M, et al. What makes health systems resilient against infectious disease outbreaks and natural hazards? Results from a scoping review. BMC Public Health. 2019;19(1):1310.

Article   PubMed   PubMed Central   Google Scholar  

Larson RC, Metzger MD, Cahn MF. Responding to emergencies: Lessons learned and the need for analysis. Interfaces. 2006 Dec;36(6):486–501.

Tin D, Barten DG, Goniewicz K, Burkle FM, Ciottone GR. An epidemiological analysis of terrorism-related attacks in eastern Europe from 1970 to 2019. Prehospital and disaster medicine. 2022 Aug;37(4):468–73.

Khorram-Manesh A, Dulebenets MA, Goniewicz K. Implementing public health strategies—the need for educational initiatives: a systematic review. Int J Environ Res Public Health. 2021 May;30(11):5888.

Waring SC, Brown BJ. The threat of communicable diseases following natural disasters: a public health response. Disaster Management & Response. 2005 Apr 1;3(2):41 – 7.

Whytlaw JL, Hutton N, Wie Yusuf JE, et al. Changing vulnerability for hurricane evacuation during a pandemic: issues and anticipated responses in the early days of the COVID-19 pandemic. Int J Disaster Risk Reduct. 2021;61:102386.

Norris FH, Friedman MJ, Watson PJ, Byrne CM, Diaz E, Kaniasty K. 60,000 disaster victims speak: part I. An empirical review of the empirical literature, 1981–2001. Psychiatry. 2002;65(3):207–39.

Article   PubMed   Google Scholar  

Norris FH, Friedman MJ, Watson PJ. 60,000 disaster victims speak: part II. Summary and implications of the disaster mental health research. Psychiatry. 2002;65(3):240–60.

Makwana N. Disaster and its impact on mental health: a narrative review. J family Med Prim care. 2019 Oct;8(10):3090.

Math SB, Nirmala MC, Moirangthem S, Kumar NC. Disaster management: mental health perspective. Indian J Psychol Med. 2015 Jul;37(3):261–71.

Skryabina EA, Betts N, Reedy G, Riley P, Amlôt R. The role of emergency preparedness exercises in the response to a mass casualty terrorist incident: a mixed methods study. International journal of disaster risk reduction. 2020 Jun 1;46:101503.

Khorram-Manesh A, Mortelmans LJ, Robinson Y, Burkle FM, Goniewicz K. Civilian-military collaboration before and during Covid-19 pandemic—A systematic review and a pilot survey among practitioners. Sustainability. 2022 Jan;14(2):624.

Corbin JH, Oyene UE, Manoncourt E, et al. A health promotion approach to emergency management: effective community engagement strategies from five cases. Health Promot Int. 2021;36(Supplement1):i24–i38.

Khorram-Manesh A, Goniewicz K, Phattharapornjaroen P, Gray L, Carlström E, Sundwall A, Hertelendy AJ, Burkle FM. Differences in Ethical Viewpoints among Civilian–Military Populations: A Survey among Practitioners in Two European Countries, Based on a Systematic Literature Review. Sustainability. 2022 Jan 18;14(3):1085.

Goniewicz K, Goniewicz M. Disaster preparedness and professional competence among healthcare providers: Pilot study results.Sustainability. 2020 Jun17;12(12):4931.

Goniewicz K, Goniewicz M, Włoszczak-Szubzda A, Burkle FM, Hertelendy AJ, Al-Wathinani A, Molloy MS, Khorram-Manesh A. The importance of pre-training gap analyses and the identification of competencies and skill requirements of medical personnel for mass casualty incidents and disaster training. BMC Public Health. 2021 Dec;21:1–1.

Bharosa N, Lee J, Janssen M. Challenges and obstacles in sharing and coordinating information during multi-agency disaster response: propositions from field exercises. Inform Syst Front. 2010 Mar;12:49–65.

Burkle FM. Mass casualty management of a large-scale bioterrorist event: an epidemiological approach that shapes triage decisions. Emergency Medicine Clinics. 2002 May 1;20(2):409 – 36.

Loke AY, Guo C, Molassiotis A. Development of disaster nursing education and training programs in the past 20 years (2000–2019): A systematic review. Nurse education today. 2021 Apr 1;99:104809.

Rebmann T, Charney RL, Loux TM, Turner JA, Abbyad YS, Silvestros M. Emergency medical services personnel’s pandemic influenza training received and willingness to work during a future pandemic. Prehospital Emergency Care. 2020 Sep 2;24(5):601-9.

Hughes A, Almeland SK, Leclerc T, Ogura T, Hayashi M, Mills JA, Norton I, Potokar T. Recommendations for burns care in mass casualty incidents: WHO Emergency Medical Teams Technical Working Group on Burns (WHO TWGB) 2017–2020. Burns. 2021 Mar 1;47(2):349 – 70.

Download references

Acknowledgements

Not applicable for this publication.

Author information

Authors and affiliations.

Department of Security Studies, Polish Air Force University, Deblin, Poland

Krzysztof Goniewicz

School of Social Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia

Md Nazirul Islam Sarker

Department of Development Studies, Daffodil International University, Dhaka, Bangladesh

Johns Hopkins University, Baltimore, USA

Monica Schoch-Spana

You can also search for this author in PubMed   Google Scholar

Contributions

All authors participated in the drafting and critical revision of this editorial. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Krzysztof Goniewicz , Md Nazirul Islam Sarker or Monica Schoch-Spana .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

Non-financial: Authors are Editorial Board Members of BMC Public Health journal.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Goniewicz, K., Sarker, M.N.I. & Schoch-Spana, M. Reimagining natural hazards and disaster preparedness: charting a new course for the future. BMC Public Health 23 , 581 (2023). https://doi.org/10.1186/s12889-023-15497-y

Download citation

Received : 28 February 2023

Accepted : 21 March 2023

Published : 28 March 2023

DOI : https://doi.org/10.1186/s12889-023-15497-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

BMC Public Health

ISSN: 1471-2458

• General enquiries: [email protected]

Disaster Prevention and Management: An International Journal

• Submit your paper
• Author guidelines
• Editorial team
• Indexing & metrics
• Calls for papers & news

Before you start

Author responsibilities.

Our goal is to provide you with a professional and courteous experience at each stage of the review and publication process. There are also some responsibilities that sit with you as the author. Our expectation is that you will:

• Respond swiftly to any queries during the publication process.
• Be accountable for all aspects of your work. This includes investigating and resolving any questions about accuracy or  research integrity
• Treat communications between you and the journal editor as confidential until an editorial decision has been made.
• Include  anyone who has made a substantial and meaningful contribution to the submission (anyone else involved in the paper should be listed in the acknowledgements).
• Exclude  anyone who hasn’t contributed to the paper, or who has chosen not to be associated with the research.
• In accordance with COPE’s position statement on AI tools , Large Language Models cannot be credited with authorship as they are incapable of conceptualising a research design without human direction and cannot be accountable for the integrity, originality, and validity of the published work. The author(s) must describe the content created or modified as well as appropriately cite the name and version of the AI tool used; any additional works drawn on by the AI tool should also be appropriately cited and referenced. Standard tools that are used to improve spelling and grammar are not included within the parameters of this guidance. The Editor and Publisher reserve the right to determine whether the use of an AI tool is permissible.
• If your article involves human participants, you must ensure you have considered whether or not you require ethical approval for your research, and include this information as part of your submission. Find out more about informed consent .

Generative AI usage key principles

• Copywriting any part of an article using a generative AI tool/LLM would not be permissible, including the generation of the abstract or the literature review, for as per Emerald’s authorship criteria, the author(s) must be responsible for the work and accountable for its accuracy, integrity, and validity.
• The generation or reporting of results using a generative AI tool/LLM is not permissible, for as per Emerald’s authorship criteria, the author(s)
• must be responsible for the creation and interpretation of their work and accountable for its accuracy, integrity, and validity.
• The in-text reporting of statistics using a generative AI tool/LLM is not permissible due to concerns over the authenticity, integrity, and validity of the data produced, although the use of such a tool to aid in the analysis of the work would be permissible.
• Copy-editing an article using a generative AI tool/LLM in order to improve its language and readability would be permissible as this mirrors standard tools already employed to improve spelling and grammar, and uses existing author-created material, rather than generating wholly new content, while the author(s) remains responsible for the original work.
• The submission and publication of images created by AI tools or large-scale generative models is not permitted.

Research and publishing ethics

Our editors and employees work hard to ensure the content we publish is ethically sound. To help us achieve that goal, we closely follow the advice laid out in the guidelines and flowcharts on the  COPE (Committee on Publication Ethics) website .

We have also developed our  research and publishing ethics guidelines . If you haven’t already read these, we urge you to do so – they will help you avoid the most common publishing ethics issues.

A few key points:

• Any manuscript you submit to this journal should be original. That means it should not have been published before in its current, or similar, form. Exceptions to this rule are outlined in our  pre-print and conference paper policies .  If any substantial element of your paper has been previously published, you need to declare this to the journal editor upon submission. Please note, the journal editor may use  Crossref Similarity Check  to check on the originality of submissions received. This service compares submissions against a database of 49 million works from 800 scholarly publishers.
• Your work should not have been submitted elsewhere and should not be under consideration by any other publication.
• If you have a conflict of interest, you must declare it upon submission; this allows the editor to decide how they would like to proceed. Read about conflict of interest in our  research and publishing ethics guidelines .
• By submitting your work to Emerald, you are guaranteeing that the work is not in infringement of any existing copyright.

Third party copyright permissions

Prior to article submission,  you need to ensure you’ve applied for, and received,   written permission to use any material in your manuscript that has been created by a third party. Please note, we are unable to publish any article that still has permissions pending. The rights we require are:

• Non-exclusive rights to reproduce the material in the article or book chapter.
• Print and electronic rights.
• Worldwide English-language rights.
• To use the material for the life of the work. That means there should be no time restrictions on its re-use e.g. a one-year licence.

We are a member of the International Association of Scientific, Technical, and Medical Publishers (STM) and participate in the  STM permissions guidelines , a reciprocal free exchange of material with other STM publishers.  In some cases, this may mean that you don’t need permission to re-use content. If so, please highlight this at the submission stage.

Please take a few moments to read our  guide to publishing permissions  to ensure you have met all the requirements, so that we can process your submission without delay.

Open access submissions and information

All our journals currently offer two open access (OA) publishing paths; gold open access and green open access.

If you would like to, or are required to, make the branded publisher PDF (also known as the version of record) freely available immediately upon publication, you can select the gold open access route once your paper is accepted.

If you’ve chosen to publish gold open access, this is the point you will be asked to pay the  APC (article processing charge) . This varies per journal and can be found on our APC price list or on the editorial system at the point of submission. Your article will be published with a  Creative Commons CC BY 4.0 user licence , which outlines how readers can reuse your work.

Alternatively, if you would like to, or are required to, publish open access but your funding doesn’t cover the cost of the APC, you can choose the green open access, or self-archiving, route. As soon as your article is published, you can make the author accepted manuscript (the version accepted for publication) openly available, free from payment and embargo periods.

You can find out more about our open access routes, our APCs and waivers and read our FAQs on our open research page. 

Find out about open

Transparency and Openness Promotion (TOP) guidelines

We are a signatory of the  Transparency and Openness Promotion (TOP) Guidelines , a framework that supports the reproducibility of research through the adoption of transparent research practices. That means we encourage you to:

• Cite and fully reference all data, program code, and other methods in your article.
• Include persistent identifiers, such as a Digital Object Identifier (DOI), in references for datasets and program codes. Persistent identifiers ensure future access to unique published digital objects, such as a piece of text or datasets. Persistent identifiers are assigned to datasets by digital archives, such as institutional repositories and partners in the Data Preservation Alliance for the Social Sciences (Data-PASS).
• Follow appropriate international and national procedures with respect to data protection, rights to privacy and other ethical considerations, whenever you cite data. For further guidance please refer to our  research and publishing ethics guidelines . For an example on how to cite datasets, please refer to the references section below.

Prepare your submission

Manuscript support services.

We are pleased to partner with Editage, a platform that connects you with relevant experts in language support, translation, editing, visuals, consulting, and more. After you’ve agreed a fee, they will work with you to enhance your manuscript and get it submission-ready.

This is an optional service for authors who feel they need a little extra support. It does not guarantee your work will be accepted for review or publication.

Visit Editage

Manuscript requirements

Before you submit your manuscript, it’s important you read and follow the guidelines below. You will also find some useful tips in our  structure your journal submission  how-to guide.

Submit your manuscript

There are a number of key steps you should follow to ensure a smooth and trouble-free submission.

Double check your manuscript

Before submitting your work, it is your responsibility to check that the manuscript is complete, grammatically correct, and without spelling or typographical errors. A few other important points:

• Give the journal aims and scope a final read. Is your manuscript definitely a good fit? If it isn’t, the editor may decline it without peer review.
• Does your manuscript comply with our  research and publishing ethics guidelines ?
• Have you cleared any necessary  publishing permissions ?
• Have you followed all the formatting requirements laid out in these author guidelines?
• If you need to refer to your own work, use wording such as ‘previous research has demonstrated’ not ‘our previous research has demonstrated’.
• If you need to refer to your own, currently unpublished work, don’t include this work in the reference list.
• Any acknowledgments or author biographies should be uploaded as separate files.
• Carry out a final check to ensure that no author names appear anywhere in the manuscript. This includes in figures or captions.
• We expect prospective authors to outline in their cover letter how their article addresses/aligns with the expectations of our  Disaster Studies Manifesto and Accord  as per our  editorial policy .

You will find a helpful submission checklist on the website  Think.Check.Submit .

The submission process

All manuscripts should be submitted through our editorial system by the corresponding author.

The only way to submit to the journal is through the journal’s ScholarOne site as accessed via the Emerald website, and not by email or through any third-party agent/company, journal representative, or website. Submissions should be done directly by the author(s) through the ScholarOne site and not via a third-party proxy on their behalf.

A separate author account is required for each journal you submit to. If this is your first time submitting to this journal, please choose the Create an account or Register now option in the editorial system. If you already have an Emerald login, you are welcome to reuse the existing username and password here.

Please note, the next time you log into the system, you will be asked for your username. This will be the email address you entered when you set up your account.

Don't forget to add your ORCiD ID during the submission process. It will be embedded in your published article, along with a link to the ORCiD registry allowing others to easily match you with your work. Don’t have one yet?

It only takes a few moments to register for a free ORCiD identifier .

Visit the ScholarOne support centre  for further help and guidance.

What you can expect next

You will receive an automated email from the journal editor, confirming your successful submission. It will provide you with a manuscript number, which will be used in all future correspondence about your submission. If you have any reason to suspect the confirmation email you receive might be fraudulent, please contact the journal editor in the first instance.

Post submission

Review and decision process.

Each submission is checked by the editor. At this stage, they may choose to decline or unsubmit your manuscript if it doesn’t fit the journal aims and scope, or they feel the language/manuscript quality is too low.

If they think it might be suitable for the publication, they will send it to at least two independent referees for double blind peer review.  Once these reviewers have provided their feedback, the editor may decide to accept your manuscript, request minor or major revisions, or decline your work.

This journal offers an article transfer service. If the editor decides to decline your manuscript, either before or after peer review, they may offer to transfer it to a more relevant Emerald journal in this field. If you accept, your ScholarOne author account, and the accounts of your co-authors, will automatically transfer to the new journal, along with your manuscript and any accompanying peer review reports. However, you will still need to log in to ScholarOne to complete the submission process using your existing username and password. While accepting a transfer does not guarantee the receiving journal will publish your work, an editor will only suggest a transfer if they feel your article is a good fit with the new title.

While all journals work to different timescales, the goal is that the editor will inform you of their first decision within 60 days.

During this period, we will send you automated updates on the progress of your manuscript via our submission system, or you can log in to check on the current status of your paper.  Each time we contact you, we will quote the manuscript number you were given at the point of submission. If you receive an email that does not match these criteria, it could be fraudulent, please contact the journal editor in the first instance.

Manuscript transfer service

Emerald’s manuscript transfer service takes the pain out of the submission process if your manuscript doesn’t fit your initial journal choice. Our team of expert Editors from participating journals work together to identify alternative journals that better align with your research, ensuring your work finds the ideal publication home it deserves. Our dedicated team is committed to supporting authors like you in finding the right home for your research.

If a journal is participating in the manuscript transfer program, the Editor has the option to recommend your paper for transfer. If a transfer decision is made by the Editor, you will receive an email with the details of the recommended journal and the option to accept or reject the transfer. It’s always down to you as the author to decide if you’d like to accept. If you do accept, your paper and any reviewer reports will automatically be transferred to the recommended journals. Authors will then confirm resubmissions in the new journal’s ScholarOne system.

Our  Manuscript Transfer Service page  has more information on the process.

If your submission is accepted

Open access.

Once your paper is accepted, you will have the opportunity to indicate whether you would like to publish your paper via the gold open access route.

If you’ve chosen to publish gold open access, this is the point you will be asked to pay the APC (article processing charge).  This varies per journal and can be found on our  APC price list  or on the editorial system at the point of submission. Your article will be published with a  Creative Commons CC BY 4.0 user licence , which outlines how readers can reuse your work.

All accepted authors are sent an email with a link to a licence form.  This should be checked for accuracy, for example whether contact and affiliation details are up to date and your name is spelled correctly, and then returned to us electronically. If there is a reason why you can’t assign copyright to us, you should discuss this with your journal content editor. You will find their contact details on the editorial team section above.

Proofing and typesetting

Once we have received your completed licence form, the article will pass directly into the production process. We will carry out editorial checks, copyediting, and typesetting and then return proofs to you (if you are the corresponding author) for your review. This is your opportunity to correct any typographical errors, grammatical errors or incorrect author details. We can’t accept requests to rewrite texts at this stage.

When the page proofs are finalised, the fully typeset and proofed version of record is published online. This is referred to as the  EarlyCite  version. While an EarlyCite article has yet to be assigned to a volume or issue, it does have a digital object identifier (DOI) and is fully citable. It will be compiled into an issue according to the journal’s issue schedule, with papers being added by chronological date of publication.

How to share your paper

Visit our author rights page  to find out how you can reuse and share your work.

To find tips on increasing the visibility of your published paper, read about  how to promote your work .

Correcting inaccuracies in your published paper

Sometimes errors are made during the research, writing and publishing processes. When these issues arise, we have the option of withdrawing the paper or introducing a correction notice. Find out more about our  article withdrawal and correction policies .

Need to make a change to the author list? See our frequently asked questions (FAQs) below.

Frequently asked questions

• Dr JC Gaillard Waipapa Taumata Rau / The University of Auckland - Aotearoa / New Zealand [email protected]
• Dr Emmanuel Raju University of Copenhagen - Denmark [email protected]

Editor Emeritus

• Dr Harry C Wilson University of Bradford - UK

Editorial Assistant

• Aditi Sharan Waipapa Taumata Rau / The University of Auckland - Aotearoa / New Zealand
• Paul Kidd Emerald Publishing - UK [email protected]

Journal Editorial Office (For queries related to pre-acceptance)

• Rehan Ismail Emerald Publishing [email protected]

Supplier Project Manager (For queries related to post-acceptance)

• Preethi Vittal Emerald Publishing [email protected]

Editorial Advisory Board

• David Alexander Institute for Risk and Disaster Reduction, University College London - UK
• Djillali Benouar Bab Ezzouar University - Algeria
• Ksenia Chmutina Loughborough University - UK
• Brian Cook University of Melbourne - Australia
• Zenaida Delica-Willison Advisor on ‘Community Based Disaster Risk Reduction’ - Philippines
• Maureen Fordham Institute for Risk and Disaster Reduction, University College London - UK
• Virginia Garcia-Acosta Centro de Investigaciones y Estudios Superiores en Antropologia Social - Mexico
• Christopher Gomez Kobe University - Japan
• Rohit Jigyasu ICCROM - Italy
• Ilan Kelman University College London - UK
• Faten Kikano Centre d'Étude en Responsabilité Sociale et Écocitoyenneté - Canada
• Sneha Krishnan O.P. Jindal Global University - India
• Emmanuel Luna University of the Philippines - Philippines
• Tania del Mar López Marrero Universidad de Puerto Rico - Puerto Rico
• Victor Marchezini Centro Nacional de Monitoramento e Alertas de Desastres Naturais - Brazil
• Jessica Mercer Secure Futures - UK
• Dewald van Niekerk African Centre for Disaster Studies, North-West University - South Africa
• Martin Oteng-Ababio University of Ghana - Ghana
• Michael Petterson Auckland University of Technology - New Zealand
• Anthony Redmond The University of Manchester - UK
• Manuel Tironi Pontificia Universidad Católica de Chile - Chile
• Ben Wisner University College London (UK) and Oberlin College (USA) - UK
• Xian Xu Fudan University - People's Republic of China

Citation metrics

CiteScore 2023

Further information

CiteScore is a simple way of measuring the citation impact of sources, such as journals.

Calculating the CiteScore is based on the number of citations to documents (articles, reviews, conference papers, book chapters, and data papers) by a journal over four years, divided by the number of the same document types indexed in Scopus and published in those same four years.

For more information and methodology visit the Scopus definition

CiteScore Tracker 2024

(updated monthly)

CiteScore Tracker is calculated in the same way as CiteScore, but for the current year rather than previous, complete years.

The CiteScore Tracker calculation is updated every month, as a current indication of a title's performance.

2023 Impact Factor

The Journal Impact Factor is published each year by Clarivate Analytics. It is a measure of the number of times an average paper in a particular journal is cited during the preceding two years.

For more information and methodology see Clarivate Analytics

5-year Impact Factor (2023)

A base of five years may be more appropriate for journals in certain fields because the body of citations may not be large enough to make reasonable comparisons, or it may take longer than two years to publish and distribute leading to a longer period before others cite the work.

Actual value is intentionally only displayed for the most recent year. Earlier values are available in the Journal Citation Reports from Clarivate Analytics .

Publication timeline

Time to first decision

Time to first decision , expressed in days, the "first decision" occurs when the journal’s editorial team reviews the peer reviewers’ comments and recommendations. Based on this feedback, they decide whether to accept, reject, or request revisions for the manuscript.

Data is taken from submissions between 1st June 2023 and 31st May 2024

Acceptance to publication

Acceptance to publication , expressed in days, is the average time between when the journal’s editorial team decide whether to accept, reject, or request revisions for the manuscript and the date of publication in the journal. 

Data is taken from the previous 12 months (Last updated July 2024)

Acceptance rate

The acceptance rate is a measurement of how many manuscripts a journal accepts for publication compared to the total number of manuscripts submitted expressed as a percentage %

Data is taken from submissions between 1st June 2023 and 31st May 2024 .

This figure is the total amount of downloads for all articles published early cite in the last 12 months

(Last updated: July 2024)

This journal is abstracted and indexed by

• ASFA-2: Ocean Technology, Policy & Non Living Resources Abstracts
• ASFA-3: Aquatic Pollution & Environmental Quality Abstracts
• Cabell's Directory of Publishing Opportunities in Management and Marketing
• Current Contents ® / Social and Behavioral Sciences
• FMS Journal Rating Guide (C)
• Geographical Abstracts: Human Geography
• Health & Safety Science Abstracts
• Journal Citation Reports / Social Sciences Edition
• ReadCube Discover
• Risk Abstracts
• Taylor & Francis Ergonomics Abstracts
• Norwegian Register for Scientific Journals
• The Publication Forum (Finland)

Reviewer information

Peer review process.

This journal engages in a double-anonymous peer review process, which strives to match the expertise of a reviewer with the submitted manuscript. Reviews are completed with evidence of thoughtful engagement with the manuscript, provide constructive feedback, and add value to the overall knowledge and information presented in the manuscript.

The mission of the peer review process is to achieve excellence and rigour in scholarly publications and research.

Our vision is to give voice to professionals in the subject area who contribute unique and diverse scholarly perspectives to the field.

The journal values diverse perspectives from the field and reviewers who provide critical, constructive, and respectful feedback to authors. Reviewers come from a variety of organizations, careers, and backgrounds from around the world.

All invitations to review, abstracts, manuscripts, and reviews should be kept confidential. Reviewers must not share their review or information about the review process with anyone without the agreement of the editors and authors involved, even after publication. This also applies to other reviewers’ “comments to author” which are shared with you on decision.

Resources to guide you through the review process

Discover practical tips and guidance on all aspects of peer review in our reviewers' section. See how being a reviewer could benefit your career, and discover what's involved in shaping a review.

More reviewer information

Virtual Issue: Conversations with Disasters: Deconstructed

Virtual Issue: Conversations with Disasters: Deconstructed The following papers are included in a Special Issue published in collaboration with the ...

Thank you to the 2022 Reviewers of Disaster Prevention and Management

The publishing and editorial teams would like to thank the following, for their invaluable service as 2022 reviewers for this journal. We are very grateful for the contributions made. With their help, the journal has been able to publish such high...

Thank you to the 2021 Reviewers of Disaster Prevention and Management

The publishing and editorial teams would like to thank the following, for their invaluable service as 2021 reviewers for this journal. We are very grateful for the contributions made. With their help, the journal has ...

Disasters: Deconstructed webinar

In this one hour webinar the editors of a Special Issue of Disaster Prevention and Management focusing on the "disrupting the status quo" will discuss with 5 paper authors how disaster risk creation has become the norm within our current political...

Literati awards

Disaster Prevention and Management - Literati Award Winners 2023

We are pleased to announce our 2023 Literati Award winners. Outstanding Paper The Reflective Research Diary: A Tool fo...

Disaster Prevention and Management - Literati Award Winners 2021

We are pleased to announce our 2021 Literati Award winners. Outstanding Paper Research fatigue in COVID-19 p...

Disaster Prevention and Management publishes high-quality research which advances knowledge and practice in the field of disaster risk reduction and management.

Aims and scope

Disaster Prevention and Management (DPM) aims to offer diverse critical perspectives on all dimensions of disasters. We are therefore open to multiple ontologies and epistemological interpretations of disasters. As such, the journal embraces the ethos and objectives of the Disaster Studies Manifesto: Power, Prestige and Forgotten Values which we encourage authors to read. We also hope authors will have reflected on the questions raised in the Disaster Studies Accord: Priorities, Values, and Relationship .

The readership of Disaster Prevention and Management is primarily composed of social scientists, policymakers and practitioners. However, we welcome submissions from other fields of scholarship if they speak to our main audience. We particularly encourage contributions from early career scholars, authors from less affluent countries, and non-native English speakers.

The journal publishes conceptual and theoretical reflections, methodological contributions, and case studies. We also accept commentaries and book review essays (in dialogue with the author of the book reviewed). We further offer the opportunity to publish blogs and policy briefings through the web platform of our publisher Emerald.

In line with the ethos of the Disaster Studies Manifesto: Power, Prestige and Forgotten Values submissions do not necessarily have to conform to the normative structure of academic articles. We publish photo essays and welcome comic strips, or any other creative formats deemed relevant by the editorial team. Therefore, do contact the editors if you are considering such other formats of article. The only other editorial guidelines are that submissions be limited to 7000 words and that the bibliography be formatted after our publisher’s guidelines.

Manuscripts submitted to Disaster Prevention and Management that pass the initial editorial screening are reviewed by two experts with at least one who is aware of the local context if the submission is a case study.

We expect prospective authors to outline in their cover letter how their article addresses/aligns with the expectations of our Disaster Studies Manifesto and Accord as per our editorial policy .

Latest articles

These are the latest articles published in this journal (Last updated: July 2024)

Artefacts of Disaster Risk Reduction: Conceptualizing bottom-up initiatives of climate action in informal settlements

Assessing the accountability mechanisms in the 2015 nepal earthquakes housing reconstruction: a case study of bungamati, lalitpur metropolitan city, nepal, organisational vulnerability: exploring the pathways., top downloaded articles.

These are the most downloaded articles over the last 12 months for this journal (Last updated: July 2024)

Advancing "€œNo Natural Disasters"€ with Care: Risks and Strategies to Address Disasters as Political Phenomena in Conflict Zones

The c-word: how critical cartography, critical gis and critical data studies can repoliticise disaster-related maps, the distribution of cyclone idai's water impacts in beira, mozambique.

These are the top cited articles for this journal, from the last 12 months according to Crossref (Last updated: July 2024)

Trust in Disaster Resilience

The potential for community driven ecosystem-based disaster risk reduction in south asia: a literature review, related journals.

This journal is part of our Public policy & environmental management collection. Explore our Public policy & environmental management subject area to find out more.  

See all related journals

International Journal of Disaster Resilience in the Built Environment

International Journal of Disaster Resilience in the Built Environment is a CIB-encouraged journal that promotes research...

Management of Environmental Quality: An International Journal

Management of Environmental Quality is an international forum designed to examine factors that impact the environment...

International Journal of Energy Sector Management

International Journal of Energy Sector Management is a platform for research on all subjects covering the management of...

Effects of Risk Perception on Disaster Preparedness Toward Typhoons: An Application of the Extended Theory of Planned Behavior

• Open access
• Published: 15 February 2022
• Volume 13 , pages 100–113, ( 2022 )

Cite this article

You have full access to this open access article

• Sai Leung Ng 1  

15k Accesses

24 Citations

Explore all metrics

This study adopted an extended theory of planned behavior to understand how risk perception affected disaster preparedness behavior. An intercept survey (N = 286) was conducted at a typhoon-prone district of Hong Kong, China in 2019, then the data were analyzed using structural equation modeling. The results indicated that risk perception and intention of preparedness were predictors of disaster preparedness behavior. Risk perception significantly affected intention of preparedness and the effect was partially mediated by subjective norm. Risk perception also significantly affected attitude and perceived behavioral control, but attitude and perceived behavioral control were not significantly correlated with intention of preparedness. Not only may this study supplement the existing literature of disaster preparedness toward typhoons, but also it provides insights for the planning and management of natural hazards and disaster risk reduction in Hong Kong.

Similar content being viewed by others

The role of risk perception, prior experience, and sociodemographics in disaster preparedness and emergency response toward typhoons in Hong Kong

A critical review of cognitive and environmental factors of disaster preparedness: research issues and implications from the usage of “awareness (ishiki)” in Japan

How are various natural disasters cognitively represented?: a psychometric study of natural disaster risk perception applying three-mode principal component analysis

Avoid common mistakes on your manuscript.

1 Introduction

Tropical cyclones, also known as typhoons in Asia or hurricanes in North America, refer to intense low-pressure systems observed in tropical and subtropical oceans. Tropical cyclones bring strong winds and torrential rains that may directly result in physical destruction, they may further cause flooding, landslides, and storm surge that lead to sequential impacts on the affected areas. Globally, tropical cyclones are responsible for the largest proportion of mortality and economic loss among various meteorological hazards (Fok and Cheung 2012 ), although a decreasing trend of mortality was observed in the past 70 years (Doocy et al. 2013 ).

Typhoons are the most common natural hazard in subtropical East Asia (Fok and Cheung 2012 ). On average, Hong Kong is affected by approximately five typhoons every year (Hong Kong Observatory 2020 ). In Hong Kong, typhoons have caused the most casualties and damages among various natural hazards (Johnson et al. 2016 ). From 1980 to 2010, on average, the annual mortality and economic damage are 2.6 people and USD 8.1 million, respectively (Fok and Cheung 2012 ).

Hong Kong, like many coastal cities in China and around the world, is predicted to be at risk of climate change (Sundermann et al. 2013 ). Specifically, global warming is expected to increase the frequency and intensity of typhoons in the West Pacific (Webster et al. 2005 ), and Hong Kong has already experienced an increasing trend of extreme typhoons brought by global warming. In the past 40 years, only two signal no. 10 typhoons Footnote 1 occurred from 1980 to 2010. However, there were three signal no. 10 typhoons in the last decade (that is, 2012, 2017, and 2018).

Facing the challenges of typhoons, a considerable number of studies have examined their physical attributes, including number, duration, and intensity (Webster et al. 2005 ; Lam and To 2009 ). These studies help predict the future occurrence of typhoons in Hong Kong so that possible mitigation plans and measures can be formulated. However, the need for proactive strategies and measures of risk reduction aiming to reduce hazard vulnerability in the process of disaster management is equally important (UNISDR 2015 ; Paton 2019 ).

Disaster preparedness refers to activities and measures taken in advance to ensure an effective response to the impact of hazards (Paton 2019 ; Dasgupta et al. 2020 ). Preparedness increases people’s capacity to cope, adapt, respond, and recover when disaster strikes. Consequently, the costs of natural hazard-related disasters can be reduced (Paton 2019 ). Yet, disaster preparedness is one of the weakest links in the risk management system of Hong Kong. One local study indicated that 69% of residents took no precautions even when they were aware of a severe weather warning (Wong and Yan 2002 ). Another local study indicated that only 22.4% of the respondents were prepared for natural hazard-related disasters (Loke et al. 2010 ).

Although these studies are valuable in understanding the situation of personal preparedness toward typhoons in Hong Kong, our knowledge is limited to the description of the phenomena. To promote personal preparedness in society, it is necessary to understand the factors that motivate or inhibit disaster preparedness behavior (Najafi et al. 2017 ). However, the majority of previous studies of disaster preparedness behavior were lack of underpinned theory. Furthermore, vulnerable people deserve more attention as they may need extra assistance during disasters, but very few studies have been conducted to investigate them (Kuran et al. 2020 ).

With the above considerations in mind, this study adopted an extended theory of planned behavior (Ajzen 1991 ) to investigate the disaster preparedness behavior of typhoon-vulnerable people in Hong Kong, using first-hand data from an intercept survey conducted on the streets of Kwun Tong district from December 2018 to May 2019. Not only may this study supplement the body of knowledge on disaster preparedness toward typhoons, but also it provides a reference for the development of effective management of typhoon disasters in Hong Kong and other coastal cities in Asia.

2 Literature Review

The theory of planned behavior and risk perception have been used to explain goal-directed behaviors (Ajzen 2011 ) and disaster preparedness, respectively. To the best of my knowledge, there is no attempt to incorporate risk perception into the theory of planned behavior to understand an individual’s disaster preparedness behavior toward typhoons. To lay the theoretical foundation, the key concepts and related studies are first summarized by reviewing the existing literature of behavioral sciences and hazard management. Along with the formulation of hypotheses, the integration of the theory of planned behavior and risk perception is elaborated.

2.1 Disaster Preparedness Behavior and the Theory of Planned Behavior

Disaster preparedness behavior refers to the personal undertaking of activities or measures before a hazard event in order to mitigate the severity of disaster impacts (Dasgupta et al. 2020 ). Although the connotations of disaster preparedness behavior are varied in time, place, and type of natural hazard (Fung and Loke 2010 ), two common components can be found in the majority of existing literature: preparing an emergency kit and making an emergency plan (Paul and Bhuiyan 2010 ; Kohn et al. 2012 ; Lam et al. 2017 ). The emergency kit usually refers to a package of items for survival, such as clean water, food, and first-aid supplies (Fung and Loke 2010 ). The emergency plan refers to specific procedures for handling sudden or unexpected situations (Bhanumurthy et al. 2015 ). Considering the urban context of Hong Kong, a simple first-aid kit is sufficient for typhoon preparedness (Chan et al. 2016 ). While formal emergency plans may not be necessary (Lam et al. 2017 ), the plan may refer to the consent of family members, for example, going out with an umbrella or simply canceling the trip according to the weather condition.

The performance of disaster preparedness behavior, like other environmental behaviors, is controlled by various factors, but the process is still not well understood (Najifi et al. 2017 ). Therefore, it is preferable to adopt a behavioral model to guide the research (Najafi et al. 2018 ). Many behavioral models have been developed to understand and predict human behavior. Among them, the theory of planned behavior is the most influential and widely used model (Ajzen 2011 ). The theory of planned behavior has two central propositions. First, an individual’s intention is the immediate cause for the performance of a given behavior. Second, intention is determined by three preceding motivational factors, namely attitude, subjective norm, and perceived behavioral control (Ajzen 1991 ).

Intention refers to the voluntary decision to perform a particular behavior or take an action (Sheeran 2002 ). In a meta-analysis that included 422 studies of intention and behavior relations in various contexts, the mean correlation between intention and behavior was 0.53 (Sheeran 2002 ). Another meta-analysis including 206 independent studies reported a mean correlation of 0.43 (McEachan et al. 2011 ). Because the predictive power of intention was usually higher than socio-demographic and other behavioral factors, many studies considered intention as a proxy measure of the actual behavior (for example, Jang et al. 2016 ).

In the theory of planned behavior, attitude is the first construct affecting intention. Attitude refers to the extent to which a person develops a positive or negative perception toward a given behavior (Ajzen 1991 ). Attitude may be categorized as cognitive (that is, beliefs or knowledge about an attitude object), affective (that is, the feelings or emotions toward an object), and behavioral (that is, the way that a person has influenced his or her behavior) (Eagly and Chaiken 2007 ). Significant associations between attitude and intention can be found in various settings and contexts, which is evident from a large number of published works.

The second construct is subjective norm, which reflects a person’s perceptions of how others expect him or her to behave (Ajzen 1991 ). Subjective norm consists of injunctive (that is, how the social network wants this person to behave) and descriptive (that is, the behavior of the social network) components (Daellenbach et al. 2018 ).

The last construct, perceived behavioral control, is the volitional factor in the theory of planned behavior. It incorporates a person’s perception of his or her capacity or control over the behavior (Ajzen 1991 ). Perceived behavioral control consists of internal (that is, self-efficacy; the belief for a person to be capable of performing a given behavior) and external (that is, perceived controllability; the barriers to performing a given behavior) components (Ajzen 2002 ). Manstead and van Eekelen ( 1998 ) indicated that self-efficacy mainly affected intention and perceived controllability influenced behavior, respectively. Therefore, perceived behavioral control affects both behavioral intention and actual behavior (Ajzen 1991 , 2002 ).

In a meta-analysis that included 185 independent studies using the theory of planned behavior to predict human behaviors in various contexts, the mean correlation between intention and attitude was 0.49, that of subjective norm was 0.34, and that of perceived behavioral control was 0.43, respectively (Armitage and Conner 2001 ).

In the context of hazard studies, the theory of planned behavior had been successfully used to explain the behavioral adjustments related to natural hazards (for example, earthquakes (Najafi et al. 2017 ), and typhoons (Dasgupta et al. 2020 )), and threats of anthropogenic origins (for example, terrorist attacks (Tan et al. 2020 )).

Based on the theory of planned behavior, this study formulated five hypotheses:

Hypothesis 1

Intention of typhoon preparedness positively affects disaster preparedness behavior toward typhoons.

Hypothesis 2

Attitude toward typhoon preparedness positively affects intention of typhoon preparedness.

Hypothesis 3

Subjective norm of typhoon preparedness positively affects intention of typhoon preparedness.

Hypothesis 4

Perceived behavioral control of typhoon preparedness positively affects intention of typhoon preparedness.

Hypothesis 5

Perceived behavioral control of typhoon preparedness positively affects disaster preparedness behavior toward typhoons.

2.2 Risk Perception and Disaster Preparedness Behavior

Despite the success of the theory of planned behavior, some researchers, for example, Sommestad et al. ( 2015 ), questioned whether the three variables in the model—attitude, subjective norm, and perceived behavioral control—were sufficient to predict intention. As a response to the challenge, Ajzen ( 1991 ) indicated that the theory of planned behavior was open to the inclusion of additional variables, when they made significant and distinct contributions.

In the context of disaster preparedness behavior, risk perception is central to a large number of previous studies of disaster preparedness (Paul and Bhuiyan 2010 ; Shreve et al. 2016 ). The popularity of risk perception speaks for its potential to extend the theory of planned behavior for predicting disaster preparedness behavior. Existing literature indicated that humans adopted preparedness measures and behaviors only when they perceived that they were under the threat of a disaster (Lazo et al. 2015 ).

Risk perception refers to personal judgment about the uncertainty associated with the disaster (Paul and Bhuiyan 2010 ; Bourque et al. 2012 ). It is not the objective reality but a subjective evaluation of risk (Xu et al. 2016 ). Most researchers adopted a three-factor model of risk perception: (1) perceived likelihood (that is, the probability of a disaster to occur); (2) perceived severity (that is, the potential damage caused by the disaster); and (3) perceived susceptibility (that is the individual’s constitutional vulnerability to a hazard) (Brewer et al. 2007 ; Shreve et al. 2016 ).

A meta-analysis of several empirical studies of risk perception reported significant associations between risk perception and risk-taking behavior; the overall weighted effect size was −0.70 (Cooper and Faseruk 2011 ). Another meta-analysis of 34 risk perception studies also reported significant correlations between risk perception and behavior; the correlation ranged from 0.16 to 0.26 (Brewer et al. 2007 ). Bourque et al. ( 2012 ) indicated that risk perception was a necessary predictor of preparedness, but it might not be a sufficient predictor.

Based on the findings in the literature, two hypotheses are formulated:

Hypothesis 6

Risk perception of typhoons positively affects disaster preparedness behavior.

Hypothesis 7

Risk perception of typhoons positively affects intention of typhoon preparedness.

2.3 Risk Perception and the Theory of Planned Behavior

Previous studies also reported relations between risk perception and a person’s attitude, subjective norm, and perceived behavioral control of natural hazards, prompting the possibility that these variables mediated the effects of risk perception on disaster preparedness behavior.

First, relations between risk perception and attitude have long been identified by previous literature of natural hazards (for example, Marti et al. 2017 ). The Risk Perception Attitude framework describes the effects of risk perception on behaviors that form different attitude scenarios (Rimal and Real 2003 ). The Risk Perception Attitude framework recently was applied in risk management (for example, Liu-Lastres et al. 2019 ).

Second, both risk perception and subjective norm are socially and culturally shaped by society (Najafi et al. 2017 ). While risk perception provides values or meanings for the potential disaster (McIvor and Paton 2007 ), the internalization of these values forms subjective norms (Khalil et al. 2014 ).

Third, an association is believed to exist between risk perception and perceived behavioral control because both internal and external controls of behavior are related to people’s perception of the context of the issue (Liu-Lastres et al. 2019 ).

Based on the findings in the literature, three hypotheses are formulated:

Hypothesis 8

Risk perception of typhoons positively affects attitude toward typhoon preparedness.

Hypothesis 9

Risk perception of typhoons positively affects subjective norm of typhoon preparedness.

Hypothesis 10

Risk perception of typhoons positively affects perceived behavioral control of typhoon preparedness.

Combining the above observations, this study proposed a conceptual framework that extended the theory of planned behavior by adding risk perception as a new variable for predicting disaster preparedness behavior of typhoon vulnerable people in Hong Kong (Fig. 1 ).

Conceptual framework used in this study

This study conducted an intercept survey at a typhoon-prone district of Hong Kong. The survey period was from December 2018 to May 2019, before the start of a typhoon season, so that the respondents expressed general opinions toward typhoon preparedness without the interference of recent typhoon events.

3.1 Questionnaire

A structured questionnaire was developed according to the conceptual framework presented in Fig. 1 . Excluding the question items to determine the socio-demographic characteristics of respondents, there were 17 items that belonged to six sections (Table 1 ). All instrument items were adopted from previous literature and modified to fit the current research context. Except for the question items of attitude, which used a 5-point bipolar semantic differential scale ranging from −2 to 2, all questions were set with a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Two independent experts in the field were invited to review the questionnaire and a pilot survey was conducted for the revision of ambiguous or unclear wording.

The measurement items of attitude, subjective norm, perceived behavioral control, and intention of disaster preparedness were adapted from Ajzen ( 1991 ), Najafi et al. ( 2017 ), Daellenbach et al. ( 2018 ), Tan et al. ( 2020 ), and Ng ( 2021 ). Attitude was measured by asking respondents to rate three pairs of adjectives: ineffective–effective, useless–useful, harmful–beneficial. Three dimensions of subjective norm were measured: family or friends; people who are important to the respondent, and social pressure. Three items were used to measure perceived behavioral control: confident to do, up to the respondent, and easy to do. Intention was measured using three items: expect to do, plan to do, and will do. The measurement items of risk perception were adopted from Brewer et al. ( 2007 ), Paul and Bhuiyan ( 2010 ), and Miceli et al. ( 2008 ). Disaster preparedness behavior was measured by asking whether respondents prepared a first-aid kit and made an emergency plan before the arrival of imminent typhoons. Previous studies commonly considered these two items as the principal constituents of the operationalization of personal preparedness (Paul and Bhuiyan 2010 ; Kohn et al. 2012 ; Lam et al. 2017 ). Five items of demographic variables (that is, gender, age, education, income, and housing type) of respondents were asked to obtain their background information.

3.2 Data Collection

The research was based on face-to-face interviews conducted on the streets in Kwun Tong. Potential streets, located within a distance of two blocks to the coastline and highly affected by strong winds and heavy rainstorms during typhoons, were first identified from the map, then their suitability for conducting the interview was checked in the field.

There were two reasons for conducting the survey in Kwun Tong. First, Kwun Tong is a typhoon-prone district in Hong Kong. Kwun Tong has high exposure to the impacts of typhoons because of its location and aspects. It also experiences multiple impacts caused by typhoons, including storm surges and landslides because of the local topography and geology (Johnson et al. 2016 ). Therefore, Kwun Tong residents are generally more exposed to hazards than residents of other districts in Hong Kong. Second, Kwun Tong residents may represent a typhoon vulnerable community, as Kwun Tong is the poorest district in Hong Kong. Its median monthly household income is HK$15,960, significantly lower than the median of Hong Kong as a whole (that is, HK$20,500) (Census and Statistics Department 2011 ). Kwun Tong also has the highest population density (57,530 persons per km 2 ) and the largest number of households (227,168) among all Hong Kong districts (Census and Statistics Department 2016 ).

To approach suitable respondents for this study, a street intercept survey was believed to be more effective than conventional methods of population survey in Hong Kong because of three reasons: (1) Hong Kong’s urban fabric is dominated by high-rise buildings guarded by security checkpoints that deny unsolicited visits (Lo et al. 2017 ); (2) respondents generally feel more comfortable with interviews in public areas than in the household units (Lo and Jim 2012 ); and (3) the interviewers can select and check suitable samples before engaging in the interview.

Pedestrians who were visually impaired or illiterate were not invited. Appropriate pedestrians who were Kwun Tong residents above the legal age of 18 and were capable of communication were invited to attend face-to-face interviews. After gaining the consent of suitable respondents, the interviewer engaged with them to complete the questionnaire. Each interview took approximately 15–20 minutes to complete.

To enhance the variability of samples, interviews were conducted on both weekdays and weekends. Furthermore, only one individual was selected from a group of pedestrians. This street survey naturally excluded those people who did not appear on the street for various reasons, such as mobility problems and family roles. However, this did not create a sampling bias because less mobile people are usually less exposed to typhoons.

A total of 300 participants were successfully interviewed. The sample size was comparable to many social surveys in Hong Kong (for example, Wong and Yan 2002 ) and hazard studies in foreign countries (for example, Shapira et al. 2018 ).

3.3 Statistical Analysis

All questionnaire data were checked for completeness and unresponsive results were removed. Descriptive statistics were used to categorize the socio-demographic characteristics of respondents.

The normality of numerical data was checked and skewed data were log-transformed. Multivariate outliers were identified by calculating the Mahalanobis Distance ( p < 0.01). Eventually, a total of 286 cases were secured for statistical analysis. The sample size was considered effective for structural equation modeling (Kline 2010 ). The reliability of the data was tested using Cronbach’s alpha. The common method bias of the data was evaluated by the Common Latent Factor method. The above analyses were performed using IBM SPSS Statistics 26.0.

The structural equation modeling was performed to evaluate the theoretical model (that is, the extended theory of planned behavior in this study) based on its consistency with actual data. It allows the examination of causal relations among multiple variables of different levels in a single analysis (Kline 2010 ). The procedure of structural equation modeling consists of two steps: (1) confirmatory factor analysis assesses the validity of the measurement model by testing the relationships between latent variables and the corresponding items; and (2) path analysis tests the structural model by determining the correlations between latent variables. Based on the results of path analysis, the indirect effects of risk perception on intention and disaster preparedness behavior (that is, the mediations via the constructs of the theory of planned behavior) were assessed using 95% confidence intervals from 2000 bootstrap samples. The structural equation modeling was performed using IBM SPSS AMOS 26.

This section first provides a summary of descriptive statistics. After presenting the results of statistical analysis, the effects of risk perception, attitude, social norm, and perceived behavior control on intention of preparedness and disaster preparedness behavior are elaborated.

4.1 Descriptive Statistics

The socio-demographic characteristics of the respondents are outlined in Table 2 . The numbers of male (49%) and female (51%) respondents were almost equal. Of the respondents, 57% were youth between the ages of 18–35. The seniors (3.5%) were very few. In terms of education, 63.3% had received a qualification from a university or college. More than two-thirds of the respondents (68.5%) reported a gross monthly household income in the range of HK$5000 to $39,999; 3.5% and 28% of the respondents monthly earned < HK$4999 and > HK$40,000, respectively. Over half of the respondents (59.1%) lived in public housing, and the rest lived in either private housing (30.8%) or other types of housing (10.1%).

The survey reported high levels of attitude (mean = 3.78 ± 0.646, out of 5 marks), subjective norm (mean = 3.48 ± 0.694, out of 5 marks), perceived behavioral control (mean = 3.60 ± 0.590, out of 5 marks), risk perception (mean = 3.73 ± 0.637, out of 5 marks), and intention of preparedness (mean = 3.54 ± 0.692, out of 5 marks). However, the level of disaster preparedness behavior (mean = 2.59 ± 0.869, out of 5 marks) was comparatively low (Table 3 ). Relatively low levels of preparedness were also reported by a few local studies, for example, Chan et al. ( 2016 ) reported that 49.4% of the respondents had a first-aid kit, and 57.4% prepared non-perishable food and drinking water; Fung and Loke ( 2010 ) reported that 60.6% of the respondents kept a first-aid kit at home.

4.2 Testing the Extended Theory of Planned Behavior

After items SN3 and Risk3 had been deleted, all constructs achieved satisfactory reliability as the Cronbach’s α values were higher than the accepted value of 0.7. The measurement model was assessed by confirmatory factor analysis and the results are also included in Table 3 . The construct validity of all constructs was acceptable as the values of loading were higher than the accepted value of 0.6. The composite reliability of all constructs was excellent as the values of construct reliability were higher than the accepted value of 0.6. Discriminant validity was achieved as the values of average variances extracted were higher than the accepted value of 0.50, and all the correlations between constructs were lower than the square roots of the values of average variances extracted.

The confirmatory factor analysis generated various indices of fit to reflect the fit between the measurement model and the data set. Important indices of fit are stated as follows: Chi-square to degree of freedom (χ 2 /df) = 1.87, comparative fit index (CFI) = 0.965, Tucker-Lewis index (TLI) = 0.949, goodness of fit index (GFI) = 0.940, normed fit index (NFI) = 0.929, incremental fit index (IFI) = 0.965, and root mean square error of approximation (RMSEA) = 0.055. The compliance of indices of fit with recommended values indicated a good fit of the measurement model (Schreiber et al. 2006 ; Hair et al. 2010 ).

The same set of indices of fit was generated for the structural model. The results show that the structural model was a good fit, with χ 2 /df = 2.351, CFI = 0.939, TLI = 0.920, GFI = 0.918, NFI = 0.900, IFI = 0.940, and RMSEA = 0.069. The structural model was a good fit because all indices of fit complied with recommended values.

The path analysis evaluated causal relations among the constructs of the structural model (Fig. 2 ). The correlation between two variables was indicated by the standardized path coefficient. Critical ratio (CR) was calculated to indicate the significance of the path, where significance at 0.05 level if critical ratio > 1.96, and significance at 0.01 level if critical ratio is > 2.576. Hypotheses were tested by evaluating the significances of path coefficients. Therefore, hypotheses 3, 8, 9 and 10 were accepted at the significance level of 0.01, and hypotheses 1, 6, and 7 were accepted at the significance level of 0.05. Hypotheses 2, 4, and 5 were rejected (Table 4 ). The r 2 values were 0.335 and 0.714 for the constructs of behavior and intention, indicating that the structural model explained 33.5% and 71.4% of variances in these two variables, respectively.

Structural paths and path coefficients of the structural equation modeling in this study

4.3 The Theory of Planned Behavior and Disaster Preparedness Behavior

The results of structural equation modeling indicated that intention was a significant predictor of behavior (r = 0.343, CR = 2.485, p < 0.05). Therefore, H1 was accepted. Significant correlations between intention and behavior were reported by previous studies of disaster preparedness behavior (for example, Tan et al. 2020 ). The level of intention (mean = 3.54 ± 0.80) was higher than that of behavior (mean = 2.59 ± 0.96), implying that not all individuals would carry out their intention to perform the behavior (that is, intention-behavior gap). Martins et al. ( 2019 ) indicated that situational facilitators and impediments affected the execution of the decision for disaster preparedness.

Although significant associations between attitude and intention were found in various settings and contexts (Kraus 1995 ), attitude was not significantly correlated with intention (r = −0.060, CR = 0.604, p > 0.05) in this study. Therefore, H2 was rejected. It is probably because attitude does not well explain behavior under extreme conditions (Turaga et al. 2010 ). Glasman and Albarracín ( 2006 ) indicated that attitude was a more reliable predictor of behavior if it was easy to recall and was stable over time. Since disaster is not a matter of daily life, residents may not have stable attitudes toward disaster preparedness.

Among the three basic constructs of theory of planned behavior, subjective norm was the only significant predictor of intention of preparedness (r = 0.483, CR = 3.843, p < 0.01). The acceptance of H3 indicated that society played an important role in a person’s decision to take action (for example, Najafi et al. 2017 ; Tan et al. 2020 ). When the residents were aware of the expectation of preparedness from family, friends, and society, they were more willing to prepare for typhoons. It is because people interact with others (such as friends and family members) to form a social environment that gives meaning (value, benefit, and so on) to the decision for action (Becker et al. 2012 ).

H4 and H5 were rejected because perceived behavioral control was not significantly correlated with intention (r = 0.072, CR = 0.758, p > 0.05) and behavior (r = −0.118, CR = 1.037, p > 0.05), respectively. Similar findings were reported by a few studies of disaster preparedness (for example, Najafi et al. 2017 ; Tan et al. 2020 ). Because the impacts of a disaster are often insurmountable and beyond human imagination, people cannot control the outcome even with preparedness. The low outcome expectancy cuts off the associations between perceived behavioral control, intention, and behavior (Artistico et al. 2014 ). Consequently, people become reluctant to prepare and/or transfer the responsibility of preparedness from themselves to other parties, for instance, the government (Paton 2019 ). Fung and Loke ( 2010 ) reported that nearly half of the surveyed households were confident that the government could manage disastrous situations.

4.4 Risk Perception and Disaster Preparedness

Interestingly, significant correlations were found between risk perception and all studied variables in this study. Risk perception was significantly correlated with disaster preparedness behavior (r = 0.353, CR = 1.980, p < 0.05) and intention (r = 0.406, CR = 1.972, p < 0.05), respectively. Therefore, both H6 and H7 were accepted. Previous studies reported that risk perception was significantly correlated with disaster preparedness behavior and intention in various hazard contexts and settings, for example, landslides (Xu et al. 2016 ), floods (Miceli et al. 2008 ), earthquakes (Becker et al. 2012 ), and hurricanes (Martins et al. 2019 ).

Risk perception was also a significant predictor of the three constructs of the theory of planned behavior. Risk perception was correlated with attitude (r = 0.717, CR = 7.858, p < 0.01), subjective norm (r = 0.762, CR = 8.502, p < 0.01), and perceived behavior control (r = 0.694, CR = 7.056, p < 0.01), respectively. Therefore, H8, H9, and H10 were accepted. These findings generally are consistent with existing studies of attitude (for example, Marti et al. 2017 ), subjective norm (for example, Najafi et al. 2017 ; Tan et al. 2020 ), and perceived behavioral control (for example, Liu-Lastres et al. 2019 ).

The above findings confirm that risk perception generates a multitude of effects on a person who decides to perform disaster preparedness behavior. Risk perception influences intention of preparedness and disaster preparedness behavior via two types of channels. The first type is the “direct” channels, as indicated by the significant correlations between risk perception, intention, and disaster preparedness behavior. The second type is the “indirect” channels via subjective norm. Table 5 summarizes the important statistics of the indirect effects of risk perception on intention and behavior.

5 Discussion

This study has a few theoretical and practical implications. For theoretical implications, this study confirmed the value of adding risk perception to the theory of planned behavior. The extended theory of planned behavior effectively predicted intention of disaster preparedness and disaster preparedness behavior. As the values of r 2 exceed the threshold of 0.26, the model is considered substantial (Cohen 1988 ). Specifically, the extended theory of planned behavior can explain 33.5% of the variances in behavior, and 71.4% of the variances in intention, respectively, performing better than the original theory of planned behavior. A meta-analysis of 206 independent studies reported that, on average, the theory of planned behavior explained 19.3% of the variances in behavior and 44.3% of the variances in intention, respectively (McEachan et al. 2011 ). Second, this study presented a roadmap to show how risk perception and behavioral variables affected intention of disaster preparedness and disaster preparedness behavior. Although risk perception is believed to generate a multitude of effects on disaster preparedness behavior, the process of how risk perception affects disaster preparedness behavior has not yet been clarified by the existing literature. Whereas Miceli et al. ( 2008 ) indicated that risk perception encompassed both cognitive and affective impacts on a person’s decision on preparedness, Loewenstein et al. ( 2001 ) indicated that risk perception exerted both direct and indirect influences on behavior. This study demonstrated that, apart from the direct effect on intention of preparedness and disaster preparedness behavior, the indirect effects of risk perception was exerted via subjective norm.

This study also offers practical insights that enhance personal and household preparedness toward typhoons. Due to the importance of risk perception for disaster preparedness, educational and promotional programs are always necessary to enhance risk perception and awareness in society (Chan et al. 2016 ). Equally important is to identify and understand the factors that distort risk perception, and hence lead to inappropriate decisions for disaster preparedness behavior. Only when people realize the risks associated with typhoons, they become motivated to prepare accordingly (Lazo et al. 2015 ). Significantly, subjective norm was the only construct of the theory of planned behavior that had a significant correlation with intention of preparedness, highlighting the importance of social influence on a person’s disaster preparedness. While conventional initiatives to promote preparedness target the individual’s decision, they often neglect the social context of that decision (Becker et al. 2012 ). Because people are more likely to adopt preparedness measures if they observe or believe that others have prepared, it is important to cultivate the preparedness culture in local communities.

This study has a few noted limitations. The first limitation is the reporting bias associated with the self-reported questionnaire. What the respondents had reported might not be accurate and objective measures of what they thought and how they behaved. However, validating the opinions collected from the respondents is impossible. Second, although this study had developed the survey protocol that aimed at a good control of data quality, younger and well-educated respondents were over-represented, which might have biased the results. Third, this study had only interviewed respondents from one district in Hong Kong, so the samples did not represent the general population of Hong Kong. Hence, the findings should be interpreted with caution. Fourth, relations identified by the structural equation modeling were limited to statistical inferences and could not be recognized as causation. Nevertheless, these findings cast light on developing research questions and hypotheses that inform future studies. Qualitative methods, such as in-depth interviews, are useful to understand the causal relations between disaster preparedness behavior and its predictors. Despite the above limitations, this study was able to integrate risk perception and the theory of planned behavior into a united model that can be used to predict the disaster preparedness behavior of typhoon vulnerable people in Hong Kong.

6 Conclusion

Facing the challenges brought by typhoons, a robust body of research has explored various options for reducing the risks of typhoon impacts. Social scientists emphasize the importance of personal disaster preparedness for reducing hazard vulnerability in the process of disaster management. This study adopted an extended theory of planned behavior to predict the disaster preparedness behavior of typhoon-vulnerable people in Hong Kong by using the data acquired from an intercept survey. Confirmatory factor analysis affirmed the validity of the model and the final structural equation model adequately fits the data. The results indicated that risk perception directly affected intention of preparedness and disaster preparedness behavior, while generating indirect effects via subjective norm. Although risk perception changed attitude and perceived behavioral control, the changes had no significant effects on intention of preparedness and disaster preparedness behavior. This study demonstrated the value of extending the original theory of planned behavior by adding risk perception as the new variable for predicting personal typhoon preparedness. Educational and promotional programs are necessary to enhance risk perception and cultivate a preparedness culture in society.

A warning signal is hoisted if a tropical cyclone approaches within a distance of 800 km to Hong Kong. Signal no. 10 represents the highest level of typhoon intensity.

Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Process 50(2): 179–211.

Google Scholar  

Ajzen, I. 2002. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology 32(4): 665–683.

Ajzen, I. 2011. The theory of planned behavior: Reactions and reflections. Psychology & Health 26(9): 1113–1127.

Armitage, C.J., and M. Conner. 2001. Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology 40(4): 471–499.

Artistico, D., L. Oliver, S. Dowd, A. Rothenberg, and M. Khalil. 2014. The predictive role of self-efficacy, outcome expectancies, past behavior and attitudes on condom use in a sample of female college students. Journal of European Psychology Students 5(3): 100–107.

Becker, J.S., D. Paton, D.M. Johnston, and K.R. Ronan. 2012. A model of household preparedness for earthquakes: How individuals make meaning of earthquake information and how this influences preparedness. Natural Hazards 64(1): 107–137.

Bhanumurthy, V., G.J. Shankar, K.R.M. Rao, and P.V. Nagamani. 2015. Defining a framework for integration of geospatial technologies for emergency management. Geocarto International 30(9): 963–983.

Bourque, L.B., R. Regan, M.M. Kelley, M.M. Wood, M. Kanp, and D.S. Mileti. 2012. An examination of the effect of perceived risk on preparedness behavior. Environment and Behavior 45(5): 615–649.

Brewer, N., G. Chapman, F.X. Gibbons, M. Gerrard, K. McCaul, and N. Weinstein. 2007. Meta-analysis of the relationship between risk perception and health behavior: The example of vaccination. Health Psychology 26(2): 136–145.

Census and Statistics Department. 2011. 2011 population census: Summary results . Hong Kong: Hong Kong special administrative region government. https://www.statistics.gov.hk/pub/B11200552011XXXXB0100.pdf . Accessed 10 Sept 2021.

Census and Statistics Department. 2016. 2016 population by-census: Main results . Hong Kong: Hong Kong Special Administrative Region Government. https://www.bycensus2016.gov.hk/data/16bc-main-results.pdf . Accessed 10 Sept 2021.

Chan, E., J. Yue, P. Lee, and S. Wang. 2016. Socio-demographic predictors for urban community disaster health risk perception and household based preparedness in a Chinese urban city. PLoS Currents Disasters . https://doi.org/10.1371/currents.dis.287fb7fee6f9f4521af441a236c2d519 .

Article   Google Scholar  

Cohen, J. 1988. Statistical power analysis for the behavioral sciences , 2nd edn. New York: Lawrence Erlbaum Associates.

Cooper, T., and A. Faseruk. 2011. Strategic risk, risk perception and risk behaviour: Meta-analysis. Journal of Financial Management and Analysis 24(2): 20–29.

Daellenbach, K., J. Parkinson, and J. Krisjanous. 2018. Just how prepared are you? An application of marketing segmentation and theory of planned behavior for disaster preparation. Journal of Nonprofit & Public Sector Marketing 30(4): 413–443.

Dasgupta, R., M. Basu, P. Kumar, B.A. Johnson, B.K. Mitra, R. Avtar, and R. Shaw. 2020. A rapid indicator-based assessment of foreign resident preparedness in Japan during Typhoon Hagibis. International Journal of Disaster Risk Reduction 51: Article 101849.

Doocy, S., A. Dick, A. Daniels, and T.D. Kirsch. 2013. The human impact of tropical cyclones: A historical review of events 1980–2009 and systematic literature review. PLOS Currents Disasters . https://doi.org/10.1371/currents.dis.2664354a5571512063ed29d25ffbce74 .

Eagly, A.H., and S. Chaiken. 2007. The advantages of an inclusive definition of attitude. Social Cognition 25(5): 582–602.

Fok, L., and L.T.O. Cheung. 2012. Evaluating the impact reduction strategies for the tropical cyclone hazard in Hong Kong. Asian Geographer 29(2): 121–129.

Fung, O.W.M., and A.Y. Loke. 2010. Disaster preparedness of families with young children in Hong Kong. Scandinavian Journal of Public Health 38(8): 880–888.

Glasman, L.R., and D. Albarracín. 2006. Forming attitudes that predict future behavior: A meta-analysis of the attitude-behavior relation. Psychological Bulletin 132(5): 778–822.

Hair, J., W. Black, J. Babin, and R. Anderson. 2010. Multivariate data analysis , 7th edn. Upper Saddle River: Prentice-Hall.

Hong Kong Observatory. 2020. Tropical cyclones in 2019 . Hong Kong: Hong Kong Observatory. https://www.hko.gov.hk/en/publica/tc/files/TC2019.pdf . Accessed 10 Sept 2021.

Jang, L.J., J.J. Wang, D. Paton, and N.Y. Tsai. 2016. Cross-cultural comparisons between the earthquake preparedness models of Taiwan and New Zealand. Disasters 40(2): 327–345.

Johnson, K., Y. Depietri, and M. Breil. 2016. Multi-hazard risk assessment of two Hong Kong districts. International Journal of Disaster Risk Reduction 19: 311–323.

Khalil, T., K. Atieh, A.U. Mohammad, and F. Bagdadlian. 2014. Examining the social and technical factors influencing school teachers knowledge sharing intentions in a teachers online professional community. Electronic Journal of Knowledge Management 12(3): 157–165.

Kline, R.B. 2010. Principles and practice of structural equation modeling . New York: Guilford.

Kohn, S., J.L. Eaton, S. Feroz, A.A. Bainbridge, J. Hoolachan, and D.J. Barnett. 2012. Personal disaster preparedness: An integrated review of the literature. Disaster Medicine and Public Health Preparedness 6(3): 217–231.

Kraus, S.J. 1995. Attitudes and the prediction of behavior: A meta-analysis of the empirical literature. Personality and Social Psychology Bulletin 21(1): 58–75.

Kuran, C.H.A., C. Morsut, B.I. Kruke, M. Krüger, L. Segnestam, K. Orru, T.O. Nævestad, M. Airola, et al. 2020. Vulnerability and vulnerable groups from an intersectionality perspective. International Journal of Disaster Risk Reduction 50: Article 101826.

Lam, K.M., and P. To. 2009. Statistical analysis of extreme wind speeds in Hong Kong and Macau. HKIE Transactions 16(1): 21–28.

Lam, R.P.K., L.P. Leung, S. Balsari, K.-H. Hsiao, E. Newnham, K. Patrick, P. Pham, and J. Leaning. 2017. Urban disaster preparedness of Hong Kong residents: A territory-wide survey. International Journal of Disaster Risk Reduction 23: 62–69.

Lazo, J.K., A. Bostrom, R.E. Morss, J.L. Demuth, and H. Lazrus. 2015. Factors affecting hurricane evacuation intentions. Risk Analysis 35(10): 1837–1857.

Liu-Lastres, B., A. Schroeder, and L. Pennington-Gray. 2019. Cruise line customers’ responses to risk and crisis communication messages: An application of the risk perception attitude framework. Journal of Travel Research 58(5): 849–865.

Lo, A.Y., and C.Y. Jim. 2012. Citizen attitude and expectation towards greenspace provision in compact urban milieu. Land Use Policy 29(3): 577–586.

Lo, A.Y., J.A. Byrneb, and C.Y. Jim. 2017. How climate change perception is reshaping attitudes towards the functional benefits of urban trees and green space: Lessons from Hong Kong. Urban Forestry & Urban Greening 23: 74–83.

Loewenstein, G.F., E.U. Weber, C.K. Hse, and N. Welch. 2001. Risk as feelings. Psychological Bulletin 127(2): 267–286.

Loke, A.Y., C.K.Y. Lai, and O.W.M. Fung. 2010. At-home disaster preparedness of elderly people in Hong Kong. Geriatrics & Gerontology International 12(3): 524–531.

Manstead, A.S.R., and S.A.M. van Eekelen. 1998. Distinguishing between perceived behavioral control and self-efficacy in the domain of academic intentions and behaviors. Journal of Applied Social Psychology 28(15): 1375–1392.

Marti, M., M. Stauffacher, J. Matthes, and S. Wiemer. 2017. Communicating earthquake preparedness: The influence of induced mood, perceived risk, and gain or loss frames on homeowners’ attitudes toward general precautionary measures for earthquakes. Risk Analysis 38(4): 710–723.

Martins, V.N., J. Nigg, H.M. Louis-Charles, and J.M. Kendra. 2019. Household preparedness in an imminent disaster threat scenario: The case of superstorm Sandy in New York City. International Journal of Disaster Risk Reduction 34: 316–325.

McEachan, R.R.C., M. Conner, N.J. Taylor, and R.J. Lawton. 2011. Prospective prediction of health-related behaviours with the theory of planned behaviour: A meta-analysis. Health Psychology Review 5(2): 97–144.

McIvor, D., and D. Paton. 2007. Preparing for natural hazards: Normative and attitudinal influences. Disaster Prevention and Management 16(1): 79–88.

Miceli, R., I. Sotgiu, and M. Settanni. 2008. Disaster preparedness and perception of flood risk: A study in an alpine valley in Italy. Journal of Environmental Psychology 28(2): 164–173.

Najafi, M., A. Ardalan, A. Akbarisari, A.A. Noorbala, and H. Elmi. 2017. The theory of planned behavior and disaster preparedness. PLOS Currents Disasters 9. http://currents.plos.org/disasters/index.html%3Fp=31955.html . Accessed 31 Jan 2022.

Najafi, M., H. Khankeh, H. Elmi, and N. Pourvakhshoori. 2018. Behavioral, normative and control beliefs about earthquake preparedness: A deductive content analysis study. PLOS Currents Disasters 10. http://currents.plos.org/disasters/index.html%3Fp=32519.html . Accessed 31 Jan 2022.

Ng, S.L. 2021. Would you speak softly in public? An investigation of pro-environmental behavior of outbound tourists in Hong Kong. Current Issues in Tourism 24(22): 3239–3255.

Paton, D. 2019. Disaster risk reduction: Psychological perspectives on preparedness. Australian Journal of Psychology 71: 327–341.

Paul, B., and R. Bhuiyan. 2010. Urban earthquake hazard: Perceived seismic risk and preparedness in Dhaka City Bangladesh. Disasters 34(2): 337–359.

Rimal, R.N., and K. Real. 2003. Perceived risk and efficacy beliefs as motivators of change: Use of the risk perception attitude (RPA) framework to understand health behaviors. Human Communication Research 29(3): 370–399.

Schreiber, J.B., A. Nora, F.K. Stage, E.A. Barlow, and J. King. 2006. Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research 99(6): 323–338.

Shapira, S., L. Aharonson-Daniel, and Y. Bar-Dayan. 2018. Anticipated behavioral response patterns to an earthquake: The role of personal and household characteristics, risk perception, previous experience and preparedness. International Journal of Disaster Risk Reduction 31: 1–8.

Sheeran, P. 2002. Intention-behavior relations: A conceptual and empirical review. European Review of Social Psychology 12(1): 1–36.

Shreve, C., C. Begg, M. Fordham, and A. Müller. 2016. Operationalizing risk perception and preparedness behavior research for a multi-hazard context. Environmental Hazards 15(3): 227–245.

Sommestad, T., H. Karlzén, and J. Hallberg. 2015. The sufficiency of the theory of planned behavior for explaining information security policy compliance. Information & Computer Security 23(2): 200–217.

Sundermann, L., O. Schelske, and P. Hausmann. 2013. Mind the risk: A global ranking of cities under threat from natural disasters . Zurich: Swiss Re. https://www.swissre.com/Library/mind-the-risk-a-global-ranking-of-cities-under-threat-from-natural-disasters.html . Accessed 10 Sept 2021.

Tan, K.-L., J.K.-M. Sia, and K.H.D. Tang. 2020. Examining students’ behavior towards campus security preparedness exercise: The role of perceived risk within the theory of planned behavior. Current Psychology . https://doi.org/10.1007/s12144-020-00951-6 .

Turaga, R.M.R., R.B. Howarth, and M.E. Borsuk. 2010. Pro-environmental behavior: Rational choice meets moral motivation. Annals of the New York Academy of Sciences 1185(1): 211–224.

UNISDR (United Nations International Strategy for Disaster Reduction). 2015. Sendai framework for disaster risk reduction 2015 – 2030 . Geneva: United Nations Office for Disaster Risk Reduction. https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030 . Accessed 10 Sept 2021.

Webster, P.J., G.J. Holland, J.A. Curry, and H.R. Chang. 2005. Changes in tropical cyclone number and intensity in a warming environment. Science 309(5742): 1844–1846.

Wong, T., and Y. Yan. 2002. Perceptions of severe weather warnings in Hong Kong. Meteorological Applications 9(3): 377–382.

Xu, D., L. Peng, C. Su, S. Liu, X. Wang, and T. Chen. 2016. Influences of mass monitoring and mass prevention systems on peasant households’ disaster risk perception in the landslide- threatened Three gorges reservoir area, China. Habitat International 58: 23–33.

Download references

Acknowledgements

The author is grateful to Ms. Joni Fung Mei Wong for organizing the questionnaire survey. The author is also grateful to Ms. Joey Cheuk Yee Chan for carrying out the field interview. Thanks are given to Mr. Andrew Yan To Ng for polishing and editing the manuscript.

Author information

Authors and affiliations.

Graduate Institute of Earth Science, Chinese Culture University, Taipei, 11114, Taiwan, China

Sai Leung Ng

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sai Leung Ng .

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Ng, S.L. Effects of Risk Perception on Disaster Preparedness Toward Typhoons: An Application of the Extended Theory of Planned Behavior. Int J Disaster Risk Sci 13 , 100–113 (2022). https://doi.org/10.1007/s13753-022-00398-2

Download citation

Accepted : 02 February 2022

Published : 15 February 2022

Issue Date : February 2022

DOI : https://doi.org/10.1007/s13753-022-00398-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Disaster preparedness behaviour
• Risk perception
• Structural equation modelling
• Theory of planned behaviour
• Typhoon preparedness
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Int J Environ Res Public Health
• PMC10001636

Disaster Preparedness and Awareness among University Students: A Structural Equation Analysis

Ronik ketankumar patel.

1 Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA

Apurva Pamidimukkala

Sharareh kermanshachi, roya etminani-ghasrodashti.

2 Center for Transportation Equity, Decisions and Dollars (CTEDD), University of Texas at Arlington, Arlington, TX 76019, USA

Associated Data

All the data that support the findings of this study are available from the corresponding author upon reasonable request.

Students have long been among those most emotionally and physically affected by natural or manmade disasters, yet universities and colleges continue to lack effective disaster response and mitigation practices. This research identifies how students’ socio-demographics and disaster preparedness indicators (DPIs) impact their awareness of the dangers of disasters and their ability to survive and cope with the changes that disasters bring. A comprehensive survey was designed and distributed to university students to gain an in-depth understanding of their perceptions of disaster risk reduction factors. A total of 111 responses were received, and the impact of the socio-demographics and DPIs on the students’ disaster awareness and preparedness were evaluated by employing structural equation modeling. The results indicate that the university curriculum impacts the disaster awareness of students while the establishment of university emergency procedures impacts the disaster preparedness of students. The purpose of this research is to enable university stakeholders to identify the DPIs that are important to the students so that they can upgrade their programs and design effective DRR courses. It will also aid policymakers in redesigning effective emergency preparedness policies and procedures.

1. Introduction

A disaster is a hazardous event that disrupts the functioning of a society or community and causes human, material, environmental, and economic losses [ 1 ]. The four phases of disaster include mitigation, preparedness, response, and recovery [ 2 , 3 ]. The mitigation and preparedness phases occur before a disaster hits and facilitates realistic predictions of what it will affect. The response phases continue until immediately after the disaster, and the recovery phase extends until the regular operations and activities are again performed at a satisfactory level. Decisions that are made during the mitigating and preparedness phases highly impact the time and effectiveness of the response and recovery phases [ 4 , 5 , 6 ].

The number and severity of natural disasters has increased significantly in recent years [ 7 , 8 ]. There were only about 100 natural disasters reported annually worldwide during the 1980s, and this number has risen to over 300 since 2000. Disasters have impacted both developed and developing countries [ 9 ]. For example, the 2011 earthquake in Japan alone was responsible for the economic loss of USD 221.6 billion. In the United States, during the ten-year period of 2003 to 2013, natural disasters were responsible for damages amounting to USD 1.5 trillion; from 2016 to 2017, the losses were approximately USD 200 billion [ 10 ].

While most are familiar with the disruptions caused by disasters, many are not aware of the negative impacts that they have on students. Disasters affect students by disrupting campus activities, interrupting classes, and damaging school buildings [ 11 ]. In recent years, universities have begun to recognize the value of being prepared for disasters and their associated risks, and students have become more aware of disasters through personal experience, seminars, and the media. Disaster awareness denotes the extent of knowledge about disaster risks, and the factors that lead to disasters influence the actions that could be taken individually or collectively to address exposure and vulnerability to hazards, while disaster preparedness denotes the measures that are taken to prepare for/reduce the effects of disasters [ 6 ]. Despite the increase in awareness, however, many universities and schools still lack adequate planning, response, and mitigation strategies [ 12 ].

According to Tanner and Doberstein [ 13 ], students are the least-considered group of a community when plans are being made for emergency preparedness. Mulilis et al. [ 14 ] found similar results while evaluating tornado preparedness of students, non-students, tenants, and homeowners, and a study that was conducted in China [ 15 ] revealed that more than half of the students did not know basic survival skills, even though they were taught cardiopulmonary resuscitation (CPR). While administering CPR is an important skill, in the face of the increasing number of natural disasters that are occurring worldwide, it seems important for universities and schools to also provide education on other essential rescue skills. Doing so will increase students’ disaster preparedness and enable them to apply their skills during a disaster.

Awareness is important, but students must also be prepared for disasters by being taught the essential rescue skills that can significantly mitigate their effects [ 16 , 17 , 18 ]. These rescue techniques are a vital aspect of disaster education and should be taught by competent professionals [ 15 ]. Universities with nursing and/or medical schools have an advantage, as they have the instruments and professionals to develop and implement effective disaster training and courses [ 19 ]. All educators in institutions of higher learning can successfully develop and offer disaster risk reduction (DRR) courses, however, and their willingness and ingenuity will determine the quality of the courses [ 20 ]. Prepared students are more confident and more likely to use their knowledge of the physical and psychological barriers precipitated by disasters to assist local disaster management agencies [ 21 ].

Even though students are among the most vulnerable groups in the community to natural disasters, they are often overlooked [ 19 , 20 ]. University students are a rare set of individuals with a versatile worldview and exceptional adaptability [ 21 ]. They can learn emergency skills more rapidly and efficiently than the general population since they possess these characteristics. As training sessions might greatly lower the costs of the damages and students can be valuable resources for disaster response, prevention, and mitigation in general, it is strongly suggested to equip students with the right training and education [ 9 , 22 ].

Previous studies have demonstrated that implementing a university disaster preparedness course requires the difficult task of collaborating with local leaders [ 22 ]. Ideally, senior university administrators collaborate with local emergency management agencies to enhance the disaster preparedness of both the university and the community [ 23 ], while providing practical training and strengthening the relationship between them [ 22 ]. The financial capability of a university often plays an important role in the development and delivery of technical disaster education and/or towards the integration of DRR courses with other courses [ 24 ]. Amri et al. [ 20 ] found that most teachers believe that training would help them teach a DRR course more effectively; hence it follows that a collective effort by trained teachers could effectively lead to the successful application of disaster education. Recognizing and awarding effective and experienced distance education teachers may entice others to follow their footsteps [ 25 ].

The literature makes it clear that acquiring knowledge and skills helps students be prepared for disasters. Therefore, it is vital for universities to educate their students about how disasters can impact them and to equip them with the knowledge and skills to mitigate those impacts. Students can be valuable assets for the local community and management agencies during disaster recovery if they are trained and given the necessary tools. Thus, this study aims to fill the void in the literature on this subject by investigating university students’ knowledge and perceptions of disaster preparedness indicators (DPIs). We formulated three specific objectives for this study: (i) to identify the disaster preparedness indicators (DPIs), (ii) to identify the critical components that are associated with the DPIs using factor analysis, (iii) and to develop a structural equation model to evaluate the relationship between students’ socio-demographic characteristics and DPIs on disaster awareness and preparedness. This study’s findings will provide insights for faculty members, academic staff, and university policymakers and will enable them to make changes in existing policies and procedures, reform existing programs, enhance students’ disaster preparedness, and minimize the expensive and deadly impacts of disasters.

Disaster Preparedness Indicators

As the focus of the study is primarily to evaluate the relationship between students’ socio-demographic characteristics and DPIs on disaster awareness and preparedness, the DPIs were identified from the literature. Patel et al. [ 26 ] identified 24 DPIs, and the list of DPIs is presented in Table 1 .

List of DPIs.

Source: Patel et al. [ 26 ].

2. Materials and Methods

As presented in Figure 1 , a four-step research methodology was adopted to fulfill the objectives of this study. In the first step, a comprehensive literature review was conducted to identify the factors that affect disaster preparedness of university students. In the second step, a comprehensive survey was developed from the identified literature, and was reviewed and approved by IRB, before distributing it to the participants. In the third step data analysis was performed on the socio-demographic data of the respondents, and major components were also identified. Finally, structural equation modeling (SEM) was adopted to develop a model to test the effects of sociodemographics on disaster preparedness indicators.

Research Method.

2.1. Data Collection

After reviewing the existing literature on disaster risk reduction, a structured survey was developed. The survey was divided into six sections and consisted of 44 multiple choice and rating-scale questions. The rating-scale type questions employed a 7-point Likert scale. The survey was pilot tested by distributing it among a few Ph.D. students to ensure that the questions were clearly stated, after which the Institutional Review Board of the University of Texas at Arlington (UTA) reviewed and approved it. The survey was distributed to UTA students from different engineering majors via email, with a brief overview of the study. There was no remuneration for taking part in the study.

The questions in the first section pertained to demographics, such as age, gender, ethnicity, level of education, residence (on-campus or off-campus), etc. The second segment had questions about students’ role in disasters to analyze their disaster experiences. The third section examined the students’ views of practical and theoretical disaster education and their willingness to register for such courses. University policies governing emergency preparedness were addressed in the fourth section, which also asked questions pertaining to first aid and the agencies that are responsible for students’ safety during an emergency. The fifth section had questions about the university’s emergency protocols and medical supplies, and the last section asked about disaster education implementation and obstacles to learning about disaster preparedness. The survey questions are provided in Supplementary File S1 .

The survey was distributed through the online platform, Qualtrics to over 300 regular and full-time UTA students older than 18 years from various engineering majors. After two rounds of reminder emails, a total of 111 complete responses were received.

2.2. Statistical Tests

2.2.1. dimensions reduction: principal component analysis.

Principal component analysis (PCA) is a multivariate statistical approach that is used in the field of social science to transform multiple associated factors into a reduced set of factors known as principal components, which account for variability in the original dataset [ 27 ]. It projects the variables that should comprise the latent variables so a model can be developed and tested.

2.2.2. Structural Equation Modeling

Structural equation modeling (SEM) has gained popularity recently for developing multivariate relationships and parsimonious models [ 28 ]. SEM not only validates hypothesized relationships but also provides new relationships between constructs and parameters based on modification indexes. SEM is also efficient in handling complex dependencies and provides flexibilities with sample numbers [ 29 ]. Some researchers have proposed a minimum number of samples (e.g., 100 or 200), while some researchers have 5–10 samples per parameter [ 28 ]. Therefore, based on the type of collected data, the SEM modeling technique was chosen as the best fit.

2.3. Data Analysis

Most of the respondents were Asians below the age of 25 years who came from low-income households (median household income less than $60k). Approximately 65% of the respondents were graduate students; the other 35% were undergraduates. Less than half of the students’ (63%) had either no or very little experience with disasters. The primary types of disasters that students indicated having experienced were earthquakes (25%), thunderstorms (23%), and flooding (20%); hurricanes (5%) and tsunamis (3%) were the least experienced. Table 2 provides detailed descriptive statistics of the participants.

Descriptive statistics of the survey participants.

3.1. Willingness to Take DRR Course

The survey results indicated that 41% of the students were willing to take a DRR course and 74% had not taken a related course prior to attending college. This finding suggests that despite not having had prior education on the subject, most of the students polled were not interested in studying disaster education. An earlier study indicated that knowledge of how to prepare for a disaster ultimately leads to a higher level of disaster preparedness [ 23 ]; therefore, universities should establish awareness programs that impart the importance of disaster education.

3.2. Students’ Perception of Assisting Disaster Management Agencies

The results revealed that 60% of the students were confident that they could assist disaster management agencies during a disaster, which is in line with the outcome of the study [ 23 ] that showed that more than half (59%) of the students would volunteer to participate in the disaster recovery process. Students’ participation in community engagement programs and volunteer activities during a disaster can help both universities and local communities in the recovery process, but their effectiveness is dependent on their having some disaster-related training or experience.

3.3. Students’ Perception of First Aid

Students were asked about their confidence level in providing basic first aid during an emergency, and approximately 39% of them indicated that they were not confident; one-third (29%) of them were neither confident nor unconfident. This finding suggests that many students are not familiar with basic first-aid practices and would not be of much help to themselves or others during a disaster. Universities (ideally, someone from a nursing school) should teach them practical rescue skills and basic medical training and provide opportunities for hands-on experience. Universities without a nursing school or similar department should partner with a local hospital or nursing school to develop a training course.

The survey included questions pertaining to the availability of first aid kits at the students’ university, and the responses revealed that 26% of the undergraduate students and 65% of the graduate students felt that their university had a sufficient number of kits; 53% of the undergraduates and 24% of the graduate students were neutral. This finding obviously suggests a higher awareness of available medical supplies among graduate students.

3.4. Students’ Perception of Who Is Responsible for Their Safety

The survey asked the students to rank the entities that are responsible for their safety during an emergency, and the results are shown in Table 3 . The findings indicated that 76% (important or extremely important) of the students considered themselves to be responsible for their own safety during an emergency. In comparison, 57% believed that government agencies were obligated for their safety, followed by their university (49%), parents (45%), and friends (35%), respectively.

Responsibility for students’ safety.

Rank in order of importance who you feel is responsible for your safety in the case of an emergency: 1 = “not at all important” and 7 = “extremely important”.

3.5. Analysis of Students’ Perception of including DRR Education in the Curriculum

Figure 2 a,b depict the form of education and frequency with which the students felt DRR classes should be offered, respectively. The results revealed that 62% of the students believed that both theoretical and practical DRR education are essential and 38% believed that it should be provided once every year. Accordingly, it is vital that DRR education be conducted either as a part of an existing course or by introducing a new class [ 30 ].

Pie charts showing students’ perception of ( a ) the form of DRR education that needs to be incorporated in a curriculum and ( b ) how often DRR education needs to be offered.

3.6. Analysis of Students’ Perception of Major Barriers to Learning about DRR

The results of the survey revealed that 31% of the students believed that inadequate exposure to practical knowledge is a significant barrier to becoming well educated about DRR, 17% believed that lack of previous disaster experience is a major barrier, and 14% believed that too few disaster preparedness drills is a significant barrier ( Figure 3 ).

Analysis of students’ perception of barriers to effective DRR education.

3.7. Statistical Analysis

PCA with varimax (orthogonal) rotation was performed using SPSS AMOS V.28 software to extract latent factors from all DPIs that were discussed earlier (see Table 1 ). However, DPIs (#2, 3, 5, 6, 7, 8, 9, 10, 18, and 19) were excluded from the analysis due to poor factor loading. The analysis yielded six factors: government/university responsibility, emergency procedures, university curriculum, DRR adoption, disaster preparedness, and disaster awareness. The Kaiser Mayer Olkin (KMO) for the dataset was found to be greater than cutoff point 0.5, indicating that the data are suitable for performing factor analysis. Table 4 depicts the results from the factor analysis.

Factor Analysis Results.

3.7.2. Conceptual Model

Previous studies [ 31 , 32 ] focused on identifying the factors that affect the disaster preparedness of students based on their socio-demographics (gender, race, area of living, and education level). There were six key components (government/university responsibility, emergency procedures, university curriculum, DRR adoption, disaster preparedness, and disaster awareness) that were identified, based on the disaster preparedness indicators that were identified using factor analysis. We hypothesize that students’ socio-demographics also directly influence these key variables and indirectly affect their awareness of and preparedness for disasters. The conceptual framework that was adopted for this study is presented in Figure 4 .

Expected Conceptual Model Before the Analysis.

Structural equation modelling (SEM) was applied to evaluate the effects of different variables on disaster awareness and preparedness. In addition to exploring interdependencies among crucial components, SEM concurrently assesses the direct, indirect, and total effects. The expected conceptual model that was developed based on the factors extracted using factor analysis, is shown in Figure 4 .

These factors behave as observed variables and are considered endogenous variables in path analysis [ 33 ]. The students’ socioeconomic characteristics were targeted as exogenous variables since they influence the key variables but are not affected by other key variables. However, when the authors ran the analysis with the expected conceptual model, they did not find good results. As a result, authors had to remove some of the relationships to better fit the model. Figure 5 presents the verified model after the analysis.

Verified Model After the Analysis.

3.8. SEM Modelling

3.8.1. mediating effects of key variables.

To examine the validity and reliability of the hypothesized model, three model fitness indices were tested to evaluate the difference between the observed and implied variance-covariance matrix. The value of chi-square divided by the degree of freedom (χ²/df = 1.9) indicates whether the data fits the model. If the value of χ²/df is less than 2, it suggests that the model is a good fit [ 34 ]. Secondly, the root mean square error of approximation (RMSEA), which measures the difference between the hypothesized and ideal models, was observed. The value for the hypothesized model was (0.09), which is close to the acceptable value (between 0.05 and 0.08) that indicates that the model is a good fit [ 35 ]. Since the sample size was small, the comparative fit index (CFI) was utilized for verifying the model fitness, as it performs well for a small sampling. The value of CFI was 0.96, which satisfies the minimum criteria of >0.95 for a good model fit [ 36 , 37 , 38 ], as shown in Table 5 below.

Direct effects given in standardized coefficients.

Note: * significance level α = 0.05; ** significance level α = 0.10; ← = influenced by.

3.8.2. Indirect Effects of Socio-Demographics

The survey results suggested that indirect impacts of gender, area of living, race, and education on disaster preparedness and disaster awareness are associated with the direct impacts of socio-demographics on the key variables.

4. Discussion

As presented in Table 5 , evaluating the relationships between socio-demographic characteristics and key variables revealed that female students are more optimistic about government agencies and universities taking responsibility for their safety during disasters than males, but they have a more negative perception of their university’s disaster curriculum. Gender is not just a factor that evaluates the distinctions between male and female in the aftermath of disasters. Additionally, it concerns how gender power relations are reflected in this situation through living situations, demographic and economic characteristics, behaviors, and attitudes [ 39 ]. Asian students and those living on campus are more positive about the curriculum and expressed willingness to take an exam that covers the material [ 40 ]. Graduate students were more aware than undergraduate students of the emergency procedures and communication channels that were established by their university.

Consideration of the mediating effects of the key variables on disaster preparedness and awareness of students showed that students who are more optimistic about the government or university assuming responsibility for their safety during a disaster and who are aware of university emergency procedures are more likely to be prepared. Those with a positive perception of DRR education in general and their university’s curriculum specifically, including being willing to take an exam at the end of the course, demonstrated a heightened awareness of disasters.

Table 6 presents the indirect effects of sociodemographic on disaster awareness and disaster preparedness. For example, females might not be aware of disasters if they have a negative perception of the university disaster curriculum and are, therefore, unlikely to adopt DRR education. On the other hand, they are better prepared for disasters compared to males if they are optimistic about the university and government assuming responsibility for their safety during disasters. Students living on campus are likely to be less aware of disasters if they are ready to take a course on DRR education and take a test at the end of the course. Graduate students are better prepared for disasters if they are aware of university emergency procedures. Therefore, a university curriculum would help to improve disaster awareness students, which is in line with the results of previous studies [ 41 ].

Indirect effects on the output variables.

Hence, despite the challenges of implementing DRR education as part of the curriculum, it is critical that universities provide practical training so that through practicing rescue skills, the students become more knowledgeable and proficient in how to survive a disaster.

5. Conclusions

The goal of this study was to determine the DPIs and to develop models to identify the disaster preparedness of university students. The disaster preparedness indicators that were identified from the literature belong to six critical components: government/university responsibility, emergency procedures, university curriculum, adoption of disaster risk reduction, disaster preparedness, and disaster awareness. The indicators revealed that the university’s DRR curriculum significantly impacts the students’ level of disaster awareness, and the assumption of the government and university for responsibility of the students’ safety and the establishment of emergency procedures directly influence the students’ level of preparedness. The survey results indicated that the variables not only directly affect students’ disaster preparedness and awareness, but they also mediate the effects of their socio-demographic characteristics. More than half (62%) of the students who participated in the survey believed that both practical and theoretical disaster education are needed for a sound understanding of the survival techniques and rescue skills that are needed during disasters, and 31% considered lack of sufficient practical knowledge a major barrier. The findings of this study can help faculty and academic staff update existing programs and incorporate new ones. It also will allow policymakers to effectively assess the universities’ existing emergency preparedness policies and procedures based on student characteristics

As the sample size of the students that participated in the study was small and the students were only from engineering majors, the findings may not be representative of most students. In the future, more comprehensive studies should be conducted among larger groups of students from a variety of majors in disaster-prone areas in the United States to understand the factors that affect students’ disaster preparedness. Moreover, this study was developed using a self-reported questionnaire and the results are based on perceptions of disaster preparedness and not the actual disaster preparedness of students.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph20054447/s1 , File S1: Disaster Preparedness Survey.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, R.K.P. and S.K.; methodology, R.K.P., A.P. and R.E.-G.; writing—original draft preparation, R.K.P., A.P. and R.E.-G.; writing—review and editing, R.K.P., A.P. and S.K.; supervision, S.K. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of University of Texas at Arlington (2020-0168, 30 January 2020). for studies involving humans.

Informed Consent Statement

Informed consent was obtained from all subjects that were involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• PubMed/Medline
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Disaster preparedness and awareness among university students: a structural equation analysis.

1. Introduction

Disaster preparedness indicators, 2. materials and methods, 2.1. data collection, 2.2. statistical tests, 2.2.1. dimensions reduction: principal component analysis, 2.2.2. structural equation modeling, 2.3. data analysis, 3.1. willingness to take drr course, 3.2. students’ perception of assisting disaster management agencies, 3.3. students’ perception of first aid, 3.4. students’ perception of who is responsible for their safety, 3.5. analysis of students’ perception of including drr education in the curriculum, 3.6. analysis of students’ perception of major barriers to learning about drr, 3.7. statistical analysis, 3.7.2. conceptual model, 3.8. sem modelling, 3.8.1. mediating effects of key variables, 3.8.2. indirect effects of socio-demographics, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• United Nations Office for Disaster Risk Reduction. “Disaster”. 2021. Available online: https://www.undrr.org/terminology/disaster (accessed on 1 December 2021).
• Nipa, T.J.; Kermanshachi, S.; Pamidimukkala, A. Evaluation of Resilience Dimensions on Reconstruction of Highway Infrastructure Projects. J. Leg. Aff. Disput. Resolut. Eng. Constr. 2023 , 15 , 04522057. [ Google Scholar ] [ CrossRef ]
• Behnam, B. Post-Earthquake Fire Analysis in Urban Structures: Risk Management Strategies ; CRC Press: Boca Raton, FL, USA, 2017. [ Google Scholar ]
• Sawalha, I.H. A contemporary perspective on the disaster management cycle. Foresight 2020 , 22 , 469–482. [ Google Scholar ] [ CrossRef ]
• D’Andrea, A.; Grifoni, P.; Ferri, F. FADM: A feasible approach to disaster management. Dev. Policy Rev. 2022 , e12633. [ Google Scholar ] [ CrossRef ]
• Nipa, T.J.; Kermanshachi, S.; Pamidimukkala, A. Identification of Resilience Dimensions in Critical Transportation Infrastructure Networks. J. Leg. Aff. Disput. Resolut. Eng. Constr. 2023 , 15 , 03122001. [ Google Scholar ] [ CrossRef ]
• Safapour, E.; Kermanshachi, S. Identification and categorization of factors affecting duration of post-disaster reconstruction of interdependent transportation systems. In Construction Research Congress 2020: Computer Applications ; American Society of Civil Engineers: Reston, VA, USA, 2020; pp. 1290–1299. [ Google Scholar ]
• Pamidimukkala, A.; Kermanshachi, S.; Safapour, E. Challenges in Post-Disaster Housing Reconstruction: Analysis of Urban vs. Rural Communities. In Creative Construction e-Conference 2020 ; Budapest University of Technology and Economics: Budapest, Hungary, 2020; pp. 103–110. [ Google Scholar ]
• Pamidimukkala, A.; Kermanshachi, S.; Karthick, S. Impact of natural disasters on construction projects: Strategies to prevent cost and schedule overruns in reconstruction projects. In Creative Construction e-Conference 2020 ; Budapest University of Technology and Economics: Budapest, Hungary, 2020; pp. 49–57. [ Google Scholar ]
• Onigbinde, L. The Impacts of Natural Disasters on Educational Attainment: Cross-Country Evidence from Macro Data. Master’s Theses, College of Arts and Sciences, University of San Francisco, San Francisco, CA, USA, 2018. [ Google Scholar ]
• Jaradat, A.; Mziu, H.; Ibrahim, J. Disaster preparedness in universities. Int. J. Comput. Trends Technol. 2015 , 19 , 1–4. [ Google Scholar ] [ CrossRef ]
• Safapour, E.; Kermanshachi, S.; Pamidimukkala, A. Post-disaster recovery in urban and rural communities: Challenges and strategies. Int. J. Disaster Risk Reduct. 2021 , 64 , 102535. [ Google Scholar ] [ CrossRef ]
• Tanner, A.; Doberstein, B. Emergency preparedness amongst university students. Int. J. Disaster Risk Reduct. 2015 , 13 , 409–413. [ Google Scholar ] [ CrossRef ]
• Mulilis, J.P.; Duval, T.S.; Bovalino, K. Tornado preparedness of students, nonstudent renters, and nonstudent owners: Issues of PrE Theory 1. J. Appl. Soc. Psychol. 2000 , 30 , 1310–1329. [ Google Scholar ] [ CrossRef ]
• Tan, Y.; Liao, X.; Su, H.; Li, C.; Xiang, J.; Dong, Z. Disaster preparedness among university students in Guangzhou, China: Assessment of status and demand for disaster education. Disaster Med. Public Health Prep. 2017 , 11 , 310–317. [ Google Scholar ] [ CrossRef ]
• Amri, A. Challenges in Implementing Disaster Risk Reduction Education: Views from the Frontline in Indonesia. Doctoral Dissertation, Macquarie University, Sydney, Australia, 2015. [ Google Scholar ]
• Haynes, K.; Tanner, T.M. Empowering young people and strengthening resilience: Youth-centred participatory video as a tool for climate change adaptation and disaster risk reduction. Child. Geogr. 2015 , 13 , 357–371. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Whittaker, J.; Haynes, K.; Handmer, J.; McLennan, J. Community safety during the 2009 Australian ‘Black Saturday’bushfires: An analysis of household preparedness and response. Int. J. Wildland Fire 2013 , 22 , 841–849. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Edwards, F. Campus Roles in Emergency Management. DRU Workshop 2013 Presentations—Disaster Resistant University Workshop: Linking Mitigation and Resilience. Paper 6. 2013. Available online: https://scholarworks.uno.edu/dru2013/6 (accessed on 13 June 2022).
• Amri, A.; Bird, D.K.; Ronan, K.; Haynes, K.; Towers, B. Disaster risk reduction education in Indonesia: Challenges and recommendations for scaling up. Nat. Hazards Earth Syst. Sci. 2017 , 17 , 595–612. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Khorram-Manesh, A.; Berlin, J.; Roseke, L.L.; Aremyr, J.; Sörensson, J.; Carlström, E. Emergency management and preparedness training for youth (EMPTY): The results of the first Swedish Pilot study. Disaster Med. Public Health Prep. 2018 , 12 , 685–688. [ Google Scholar ] [ CrossRef ]
• Tkachuck, M.A.; Schulenberg, S.E.; Lair, E.C. Natural disaster preparedness in college students: Implications for institutions of higher learning. J. Am. Coll. Health 2018 , 66 , 269–279. [ Google Scholar ] [ CrossRef ]
• Goddard, S. Disaster Preparedness Knowledge, Beliefs, Risk-Perceptions, and Mitigating Factors of Disaster Preparedness Behaviors of Undergraduate Students at a Large Midwest University ; AT Still University of Health Sciences: Kirksville, MI, USA, 2017. [ Google Scholar ]
• Nahayo, L.; Li, L.; Habiyaremye, G.; Richard, M.; Mukanyandwi, V.; Hakorimana, E.; Mupenzi, C. Extent of disaster courses delivery for the risk reduction in Rwanda. Int. J. Disaster Risk Reduct. 2018 , 27 , 127–132. [ Google Scholar ] [ CrossRef ]
• Johnson, V.A.; Ronan, K.R. Classroom responses of New Zealand school teachers following the 2011 Christchurch earthquake. Nat. Hazards 2014 , 72 , 1075–1092. [ Google Scholar ] [ CrossRef ]
• Patel, R.K.; Kermanshachi, S.; Namian, M. A Socioeconomic-Based Analysis of Disaster Preparedness, Awareness and Education. In Creative Construction e-Conference 2020 ; Budapest University of Technology and Economics: Budapest, Hungary, 2020; pp. 76–84. [ Google Scholar ]
• Hladnik, A. Image compression and face recognition: Two image processing applications of principal component analysis. Int. Circ. Graph. Educ. Res. 2013 , 6 , 56–61. [ Google Scholar ]
• Wang, Y.A.; Rhemtulla, M. Power analysis for parameter es-timation in structural equation modeling: A discussion and tutorial. Adv. Methods Pract. Psychol. Sci. 2021 , 4 , 251524592091825. [ Google Scholar ] [ CrossRef ]
• Sambasivan, M.; Deepak, T.J.; Salim, A.N.; Ponniah, V. Analy-sis of delays in Tanzanian construction industry: Transaction cost eco-nomics (TCE) and structural equation modeling (SEM) approach. Eng. Constr. Archit. Manag. 2017 , 24 , 308–325. [ Google Scholar ] [ CrossRef ]
• Luna, E.M. Education and training for disaster risk reduction including climate change adaptation. In The Routledge Handbook of Disaster Risk Reduction Including Climate Change Adaptation ; Routledge: Oxfordshire, UK, 2017; pp. 238–251. [ Google Scholar ]
• Jahan Nipa, T.; Kermanshachi, S.; Patel, R.K.; Tafazzoli, M. Disaster Preparedness Education: Construction Curriculum Requirements to Increase Students’ Preparedness in Pre-and Post-Disaster Activities. In Associated Schools of Construction (ASC) Proceedings of the 56th Annual International Conference 2020 ; EPiC Series in Built Environment; EasyChair Ltd.: Stockport, UK, 2020; Volume 1, pp. 142–151. [ Google Scholar ]
• Jahan Nipa, T.; Kermanshachi, S.; Patel, R.K. Impact of Family Income on Public’s Disaster Preparedness and Adoption of DRR Courses. In Creative Construction e-Conference 2020 ; Budapest University of Technology and Economics: Budapest, Hungary, 2020; pp. 94–102. [ Google Scholar ]
• Cao, X.J.; Xu, Z.; Douma, F. The interactions between e-shopping and traditional in-store shopping: An application of structural equations model. Transportation 2012 , 39 , 957–974. [ Google Scholar ] [ CrossRef ]
• Schermelleh-Engel, K.; Moosbrugger, H.; Müller, H. Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods Psychol. Res. Online 2003 , 8 , 23–74. [ Google Scholar ]
• Cangur, S.; Ercan, I. Comparison of model fit indices used in structural equation modeling under multivariate normality. J. Mod. Appl. Stat. Methods 2015 , 14 , 14. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hooper, D.; Coughlan, J.; Mullen, M. Evaluating model fit: A synthesis of the structural equation modelling literature. In Proceedings of the 7th European Conference on Research Methodology for Business and Management Studies, London, UK, 19–20 June 2008. [ Google Scholar ]
• McDonald, R.P. An index of goodness-of-fit based on noncentrality. J. Classif. 1989 , 6 , 97–103. [ Google Scholar ] [ CrossRef ]
• Weston, R.; Paul, A.G., Jr. A brief guide to structural equation modeling. Couns. Psychol. 2006 , 34 , 719–751. [ Google Scholar ] [ CrossRef ]
• Cvetković, V.M.; Roder, G.; Öcal, A.; Tarolli, P.; Dragićević, S. The role of gender in preparedness and response behaviors towards flood risk in Serbia. Int. J. Environ. Res. Public Health 2018 , 15 , 2761. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Patel, R.K.; Kermanshachi, S.; Jahan Nipa, T. Establishment of a Framework to Measure Disaster Preparedness: Development of Strategies to Enhance Disaster Preparedness Activities. In Creative Construction e-Conference 2020 ; Budapest University of Technology and Economics: Budapest, Hungary, 2020; pp. 22–30. [ Google Scholar ]
• ChungHee, W.; Yoo, J.Y.; Park, J.Y. Experience, awareness and preparedness of disaster among nursing college students. Crisisonomy 2015 , 11 , 19–35. [ Google Scholar ]

Click here to enlarge figure

Share and Cite

Patel, R.K.; Pamidimukkala, A.; Kermanshachi, S.; Etminani-Ghasrodashti, R. Disaster Preparedness and Awareness among University Students: A Structural Equation Analysis. Int. J. Environ. Res. Public Health 2023 , 20 , 4447. https://doi.org/10.3390/ijerph20054447

Patel RK, Pamidimukkala A, Kermanshachi S, Etminani-Ghasrodashti R. Disaster Preparedness and Awareness among University Students: A Structural Equation Analysis. International Journal of Environmental Research and Public Health . 2023; 20(5):4447. https://doi.org/10.3390/ijerph20054447

Patel, Ronik Ketankumar, Apurva Pamidimukkala, Sharareh Kermanshachi, and Roya Etminani-Ghasrodashti. 2023. "Disaster Preparedness and Awareness among University Students: A Structural Equation Analysis" International Journal of Environmental Research and Public Health 20, no. 5: 4447. https://doi.org/10.3390/ijerph20054447

Article Metrics

Article access statistics, supplementary material.

ZIP-Document (ZIP, 171 KiB)

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Disaster management and emerging technologies: a performance-based perspective

Meditari Accountancy Research

ISSN : 2049-372X

Article publication date: 19 August 2021

Issue publication date: 14 July 2022

This paper aims to analyse how emerging technologies (ETs) impact on improving performance in disaster management (DM) processes and, concretely, their impact on the performance according to the different phases of the DM cycle (preparedness, response, recovery and mitigation).

Design/methodology/approach

The methodology is based on a systematic review of the literature. Scopus, ProQuest, EBSCO and Web of Science were used as data sources, and an initial sample of 373 scientific articles was collected. After abstracts and full texts were read and refinements to the search were made, a final corpus of 69 publications was analysed using VOSviewer software for text mining and cluster visualisation.

The results highlight how ETs foster the preparedness and resilience of specific systems when dealing with different phases of the DM cycle. Simulation and disaster risk reduction are the fields of major relevance in the application of ETs to DM.

Originality/value

This paper contributes to the literature by adding the lenses of performance measurement, management and accountability in analysing the impact of ETs on DM. It thus represents a starting point for scholars to develop future research on a rapidly and continuously developing topic.

• Emerging technologies
• Disaster management
• Performance
• Systematic literature review
• Emergency response

Vermiglio, C. , Noto, G. , Rodríguez Bolívar, M.P. and Zarone, V. (2022), "Disaster management and emerging technologies: a performance-based perspective", Meditari Accountancy Research , Vol. 30 No. 4, pp. 1093-1117. https://doi.org/10.1108/MEDAR-02-2021-1206

Emerald Publishing Limited

Copyright © 2021, Carlo Vermiglio, Guido Noto, Manuel Pedro Rodríguez Bolívar and Vincenzo Zarone.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Despite the rising number of catastrophic events occurring in recent years, disaster management (DM) has received little attention from the interdisciplinary accounting community ( Lai et al. , 2014 ; Sargiacomo et al. , 2014 ; Walker, 2014 ; Sciulli, 2018 ; Perkiss and Moerman, 2020 ; Sargiacomo and Walker, 2020 ).

A key aspect of DM theory and practices is related to the information systems used to support decision-making and to measure, manage and report the performance of the whole DM cycle (see, amongst others, Carreño et al. , 2007 ). Information systems have widely supported disaster practitioners in recent decades, providing an increasing volume of data gathered through emerging technologies (ETs), such as big data, Internet of Things (IoT) ( Yang et al. , 2013 ; Shah et al. , 2019 ), machine learning, artificial intelligence (AI), remote sensing, cloud computing, social media communication ( Alexander, 2014 ) and blockchain.

ETs are science-based innovations which provide great transformative potential for an industry, in an “early phase of development” ( Boon and Moors, 2008 ) and can lead to “radical innovations” ( Day and Schoemaker, 2000 ) and/or allow an evolutionary process of technical, institutional and social change; however, they bring risks of uncertainty in terms of network effects, costs and social and ethical concerns ( Halaweh, 2013 ).

All these technologies are spreading their value in a growing variety of domains, effectively contributing to the planning, decision-making, accounting and auditing process of public and private organisations ( Ndou et al. , 2018 ; Bonsón and Bednárová, 2019 ; Lamboglia et al. , 2020 ; Lombardi and Secundo, 2020 ; Rodríguez-Bolívar et al. , 2021 ; Tingey-Holyoak et al. , 2021 ; De Santis and D’Onza, 2021 ; Lombardi et al. , 2021 ).

Indeed, the implementation of digital technologies are becoming increasingly relevant for corporate and performance management ( Oliver, 2018 ; Marrone and Hazelton, 2019 ; Wang et al. , 2020a , 2020b ; Chatterjee et al. , 2021 ; Jun et al. , 2021 ), and especially ETs have demonstrated in the last years to be particularly supportive in fostering these issues in health care ( Spanò and Ginesti, 2021 ), transportation ( Chhabra et al. , 2021 ), manufacturing ( Rezaei et al. , 2017 ) and so on.

With specific regards to DM, extant studies have mainly focused on how technology could support data gathering and visualisation ( Fajardo and Oppus, 2010 ) as well as knowledge management ( Inan et al. , 2018 ; Raman et al. , 2018 ; Oktari et al. , 2020 ). Conversely, literature reviews have focused on how specific technologies influence DM ( Kankanamge et al. , 2019 ), how they support supply chain management ( Ivanov et al ., 2019 ) or how they can be applied to deal with risks in small- and medium-sized enterprises ( Verbano and Venturini, 2013 ).

To date, various streams of research across different disciplines, such as information science, computer science and engineering, have focused on the impact of ETs on disaster and emergency response.

However, to the authors’ knowledge, limited attention has been devoted to understanding how ETs could support performance measurement, management and accountability in the specific setting of DM processes. To fill this gap, this study develops a systematic literature review (SLR) analysing how ETs impact on improving performance in DM, altering and changing DM processes to enhance resilience according to the different phases of the DM cycle (preparedness, response, recovery and mitigation). We used Scopus, ISI Web of Science, ProQuest and EBSCO as the data sources. We selected academic journal articles within the business, management and accounting categories.

The paper is structured as follows. Section 2 presents a theoretical background that links literature on DM, ETs and performance. Section 3 explains the methodology and clarifies the research question, and Section 4 presents the results of the SLR. Finally, the discussion and conclusions are presented.

2. Theoretical background

2.1 an overview of disaster management.

The frequency and magnitude with which natural disasters (earthquakes, floods, landslides, droughts, storms, etc.) have occurred in recent decades are alarming. According to EM-DAT [ 1 ], over the last 20 years, disasters have claimed approximately 1.23 million lives and affected a total of over 4 billion people, leading to US$2.97tn in economic losses worldwide. During the same timeframe, a total of 7,348 disasters related to natural hazards have occurred worldwide.

The concept of disasters is extremely complex and multidimensional in nature; it can be discussed by drawing on several connected fields of research ( Quarantelli, 1998 ).

According to the definition proposed by the United Nations Office for Disaster Risk Reduction, a disaster is:

[…] a serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts [ 2 ].

DM refers to the organisation, planning and application of measures aimed at preparing for, responding to and recovering from disasters. This topic has been widely discussed in the academic literature in recent decades through different perspectives ( Faulkner, 2001 ; Pearce, 2003 ; Lettieri et al. , 2009 ) and, most recently, with a specific focus on how firms ( Kraus et al. , 2020 ; Ferrigno and Cucino, 2021 ) and public institutions ( Steen and Brandsen, 2020 ) have reacted to the COVID-19 pandemic. Social scientists frame disasters from three different perspectives: the hazard , the vulnerability and the holistic view ( Berg and De Majo, 2017 ).

Under the hazard paradigm, disasters are considered extreme physical events with accidental causes and no human or cultural influence on their origin and scope; therefore, DM is mainly focused on post-disaster short-term measures, such as recovery, relief and humanitarian aid for those who need help ( Alexander, 1997 ).

This traditional view has been replaced by the vulnerability paradigm, rooted in development studies, in which disasters are considered the results of natural causes related to the vulnerability of the surrounding social, economic and political environment ( Cutter, 1996 ; McEntire, 2005 ; Buckle, 2005 ; Adger, 2006 ). Natural disasters, rather than being only uncontrollable events, greatly depend on some structural constraints of the population hit by catastrophic events ( Wisner et al. , 2004 ).

Assuming this renewed approach, Gilbert (1998) stated that a “disaster is no longer experienced as a reaction; it can be seen as an action, a result, and more precisely, a social consequence.” This broader perspective sheds light on how human activity, social order and development paths characterise the breadth and severity of natural disasters over time. According to Perry (1998) , “vulnerability is socially produced,” but it “may be also related to the state of technology,” as information systems and ETs play a supportive role and have a key relevance within the various phases of DM ( Von Lubitz et al. , 2008 ).

The increase in the occurrence of natural disasters sheds light on the inadequacy of traditional DM processes and practices around the globe. To tackle the wickedness ( Rittel and Webber, 1973 ; Head and Alford, 2015 ; Pesch and Vermaas, 2020 ) of such problems and reduce their intrinsic complexity, scholars have highlighted the importance of collaborative networks amongst public institutions ( Waugh and Streib, 2006 ; Ansell et al. , 2010 ; Comfort et al. , 2012 ; Kapucu and Garayev, 2016 ), coordination mechanisms to respond and react to the emergence of problems ( Moynihan, 2008 ; Boin et al. , 2013 ; Kuipers et al. , 2015 ), competencies and leadership behaviours ( Rosenthal and Kouzmin, 1997 ; Van Wart and Kapucu, 2011 ) and capacity building and community awareness ( Kitagawa, 2021 ). All these aspects are important in disaster and emergency situations characterised by complexity, urgency and uncertainty ( Kapucu and Van Wart, 2008 ).

The multiple threats posed by disasters suggest the adoption of a holistic view of DM with a more strategic focus on the actions and tools targeted to reduce exposure and vulnerability to disasters ( Berg and De Majo, 2017 ). The holistic view marks a paradigm shift from responsive to proactive management of natural hazards based on the principles of resilience and disaster risk reduction ( Manyena, 2006 ; Demiroz and Haase, 2019 ). The key phases of the DM cycle can be summarised in Figure 1 .

DM requires and generates a huge amount of data coming from different sources, which must be reliable, accurate and real time. Through these data, DM practitioners can gather information on the features, locations and prospective impacts of threats, providing essential inputs for managing all the phases of the disaster cycle in a timely and effective way ( Yu et al. , 2019 ). ETs have, to date, offered opportunities to improve the management of several fields. Table 1 shows the main applications discussed in the academic literature.

These technologies are considered to have a high impact on each of the phases displayed in Table 2 , although all of them are valuable for the whole DM cycle.

2.2 Performance in the disaster management context

Performance is one of the most explored topics by business and public administration scholars in the last half century. It is a broad concept discussed by different streams of literature, which range from the measurement of performance to management accounting and control, behavioural economics and so on ( Moynihan, 2008 ; Ferreira and Otley, 2009 ; Bititci et al. , 2012 ).

The literature usually focuses on performance by adopting three lenses that are strongly connected: performance measurement ( Bititci et al. , 2012 ), performance management ( Ferreira and Otley, 2009 ) and accountability ( Roberts, 1991 ; Gray, 1992 ).

Performance measurement is the activity of collecting data, defining indicators and computing such indicators to evaluate the ability of a certain entity to achieve strategic goals ( Eccles, 1991 ; Hudson et al ., 2001 ).

If performance measurement is concerned with what and how to measure, performance management is instead focused on the utilisation of such information in decision-making processes ( Ferreira and Otley, 2009 ; Bititci et al. , 2012 ). In this sense, performance management could be defined as the process of creating the context for performance ( Lebas, 1995 ). Performance management comprehends the whole process starting from the definition of performance, the identification of related targets and the evaluation ex-post of the results obtained ( Lebas, 1995 ; Ferreira and Otley, 2009 ).

Lastly, performance accountability is a broad concept which covers activities such as reporting performance, communicating the results achieved to stakeholders and the broader community and guaranteeing transparency ( Roberts, 1991 ; Gray, 1992 ). It is a concept which has been widely explored in the literature on both public and private sector organisations ( Kassel, 2008 ; Kaur and Lodhia, 2019 ).

Because of increasingly complex changes in society and the environment, performance studies have rapidly evolved in recent decades. Whilst the first management scholars mainly focused on financial performance, today, the literature agrees that researchers should focus on different performance dimensions, such as social, competitive and environmental ( Kaplan and Norton, 1996 ; Bititci et al. , 2012 ; Khalid et al. , 2019 ). Moreover, the evolution of the discipline has made scholars shift their focus to the inter-organisational level, as in the case of supply chains, strategic alliances or governance networks ( Dekker, 2016 ; Nuti et al. , 2018 ; Dell’ Era et al. , 2020 ; Ferrigno et al. , 2021 ).

DM is amongst the fields of application of management which, more than others, present degrees of social complexity derived from a large set of stakeholders, multiple objectives and goals and the difficulty of measuring many of these because of the high uncertainty given by the unprecedented scenarios characterising every disaster ( Comfort et al. , 2004 ).

The introduction and adoption of ETs are of great support to researchers and practitioners as they cope with the complexities of measuring, managing and reporting performance. According to many authors, information and digital technologies are indeed pivotal to the design and implementation of performance management and accountability systems ( Marr and Neely, 2001 ; Nudurupati and Bititci, 2005 ; Rodríguez-Bolívar et al. , 2006 ; Buys, 2008 ; Marrone and Hazelton, 2019 ; Lombardi and Secundo, 2020 ).

New technologies may support performance in multiple ways. First, they assist in the measurement of performance ( Nudurupati and Bititci, 2005 ; Cockcroft and Russell, 2018 ). Some technologies, such as big data or AI, allow managers to both have access to new sources of information and improve their ability to manage and analyse related data ( Sardi et al. , 2020 ). This may enable the creation of new measures and performance targets. As such, in the case of DM, decision-makers may have access to new forms of information coming from social networks, satellites or sensors.

The second pivotal contribution of ETs is related to the real-time availability of new information, which improves performance management processes ( Marr and Neely, 2001 ; Nudurupati and Bititci, 2005 ). This is of particular interest in the response phase of DM. Having the possibility to promptly react based on real-time reliable information can make a difference in emergency contexts ( Laituri and Kodrich, 2008 ; Ragini et al. , 2018 ; Imran et al. , 2020 ).

Third, ETs as applied to performance have shown great potential for understanding concerns related to reporting and internal and external accountability ( Marrone and Hazelton, 2019 ; Lombardi and Secundo, 2020 ). For example, new forms of data visualisation are being largely used to inform the community about the results achieved by the institutions in charge. What is peculiar in performance accountability in DM is its double directions, i.e. downward in an accountability to the other , in which the focus is on the intrinsic value of the suffering community, and upward in an accounting for itself , in which the focus is on market value ( Sargiacomo et al. , 2014 ).

In light of this theoretical premise, this paper aims at covering a potential gap in understanding how ETs impact on improving performance in DM processes and, concretely, their impact on the performance according to the different phases of the DM cycle (preparedness, response, recovery and mitigation).

3. Data collection and methods

To achieve the research aim, this study conducts an SLR to identify the impact of ETs on performance measurement, management and accountability ( Kraus et al. , 2020 ; Snyder, 2019 ). This methodology has already been applied both in relation to the applications of ETs (i.e. Martinez-Rojas et al. , 2018 ) and to DM (i.e. Lettieri et al. , 2009 ; Akter and Fosso Wamba, 2019 ).

An SLR is a systematic process aimed at defining the research question, identifying relevant studies and evaluating their features, quality and impact on the field. The last phase of an SLR summarises the findings qualitatively and/or quantitatively, reporting evidence to clarify what is and is not known with respect to the object of investigation ( Denyer and Tranfield, 2009 ).

definition of the research questions;

development of the research protocol;

identification of documents for analysis;

development of a coding framework; and

execution of in-depth analyses.

The first phase consisted of defining the research question of the study, which focuses on understanding how ETs contribute to improving DM processes. Consistent with the theme of the special issue, the research question is also explored from the perspective of the emerging issues related to the dimensions of performance and, more specifically, the impact of ETs in terms of management, measurement and accountability within the DM cycle.

In the second phase of the SLR, we define the research protocol to support evidence-based practices and ensure objectivity ( Tranfield et al. , 2003 ). In this phase, the focus of the study, the research strategy, the data sources and the inclusion/exclusion criteria used for the review are specified in accordance with the research question ( Petticrew and Roberts, 2008 ). The background of this study has been created by adopting a wide perspective of analysis, selecting the most relevant articles in the business, management and accounting fields. Later on, we opted for a longitudinal study to collect literature from different scientific databases.

The third phase aims to identify the papers to be added to the literature review, defining the research string to use. We managed to collect research articles via title–abstract–keyword field codes using Boolean operators (AND, OR) as connectors.

Following the parameters, the search strategy was applied in the business, management and accounting areas, referring to the Scopus and JCR lists. A description is reported in Table 2 .

The search query was entered in the ISI Web of Knowledge, Scopus, EBSCO Host and ABI/INFORM (ProQuest) databases, and it allowed us to obtain a total of 101, 172, 184 and 280 articles, respectively, for a total of 737 articles. We first eliminated redundant and non-English articles ( Petticrew and Roberts, 2008 ), which were few and not very significant with respect to the research question. We also restricted the collection to scientific articles only ( Zheng et al. , 2020 ; Lombardi and Secundo, 2020 ) because during the review process, these papers were tested with high-quality standards; the purpose was to ensure the quality of knowledge they provided ( Light and Pillemer, 1984 ).

The timeframe covered the period from 2000 to February 2021. Although few studies have devoted their attention to the potential capabilities and limitations of digital technologies in DM at the end of the last century (amongst others, Wallace and De Balogh, 1985 ; Waugh, 1995 ; Stephenson and Anderson, 1997 ; Barth and Arnold, 1999 ; Chengalur-Smith et al. , 1999 ), the choice of the period was made in light of the growing interest in ETs and their impact in society and the public sector starting from the early 2000s, as confirmed by the academic literature ( Day and Schoemaker, 2000 ; Rotolo et al. , 2015 ).

From a careful reading of the abstracts, we eliminated papers of a specific technical nature, in which the connection between ETs and DM was only mentioned but not developed. Double counting of papers was avoided by including only those that were different across the databases. These processes allowed us to obtain a valid sample of 127 articles. We checked through the full-text articles to further evaluate the quality and eligibility of the studies ( Xiao and Watson, 2019 ). Carrying out a thorough reading of the papers, we selected those relevant to our research question, obtaining a final corpus of 69 papers ( Figure 2 ).

Then, we defined the coding framework, selecting the following parameters: time of publication, distribution of papers amongst journals, author citations and keyword co-occurrence. In this phase, a double analysis was carried out on the final sample: descriptive analysis and clustering. The descriptive analysis aimed to highlight the main characteristics of the articles, indicating their number, evolution over time and distribution amongst journals.

Data analysis was conducted using VOSviewer software ( Van Eck and Waltman, 2017 ). As in other descriptive bibliometric analyses ( Secundo et al. , 2020 ), we analysed keyword co-occurrence and document citations; then, we performed a cluster analysis to capture the focal points and connections between the main topics considered in our study.

We developed the co-occurrence analysis by selecting keywords as a single entity for analysis, as a meaningful description of an article’s content ( Lamboglia et al. , 2020 ) and as an endpoint to add a paper with a minimum number of two occurrences of a keyword. Using this technique, we obtained a twofold visualisation – network and overlay.

The last phase of the SLR aims to carry out a critical and comprehensive analysis of the selected articles. Finally, we clustered the results using VOSviewer. The main findings derived from the SLR are reported in Section 4.

4. Findings

4.1 characteristics of sample selection.

As shown in Figure 3 , the number of articles that investigate the relationship between ETs and DM in accordance with our research question was narrowed until 2016, with an average of three articles per year. The 2018–2020 period seems to be the most prolific, covering almost 65% of the total, with 2019 marking the highest number of publications per year (21).

The descriptive analysis indicates the source titles in which the topic of our research has been mainly discussed. The following table lists the journals with the highest number of published articles concerning the subject of our research question ( Table 3 ).

Source citation indicates that the Journal of Cleaner Production is the source with the highest number of citations for a single article included in the sample ( Papadopoulos et al. , 2017 ), followed by Annals of Operation Research (185), International Journal of Production Economics (138) and Technological Forecasting and Social Change (129).

For article citation counting, we used the Scopus Field-Weighted Citation Impact to compare each paper citation with the average number of citations received by all similar documents over a three-year window. This choice was assumed with the aim of maximising the relevance of our sample, refusing the adoption of an arbitrary cut-off point for citation counting ( Keupp et al. , 2012 ). This way, newer articles were not at a disadvantage compared with older ones. Table 4 lists the top 15 articles with the highest citations within the selected timeframe.

An analysis of documents by country shows that the USA has the highest number of both papers (20) and citations (785), followed by India (14 papers and 379 citations), the UK (13 papers and 632 citations) and France (9 papers and 452 citations). The table also shows the number of citations by source.

4.2 Networking and clustering analysis

Then, we used the VOSviewer algorithm ( Van Eck and Waltman , 2014, 2017 ) to perform the cluster analysis starting from the co-occurrence analysis, which expresses the relatedness of items based on the number of documents in which they occur together. As explained before, our unit of analysis is author keywords, with a threshold of two keywords. We obtained a total of 37 keywords, which fell into four different clusters ( Table 5 and Figure 4 ).

Our analysis includes the overlay visualisation, which is presented in Figure 5 . Keywords in red colour refer to the more recent topics discussed in the academic debate on ETs in DM.

The following paragraph illustrates the findings of each cluster.

4.2.1 Yellow cluster.

Papers included in the yellow cluster are mainly focused on the support that simulation approaches mainly provide to the preparedness phase of DM and to performance measurement.

In the broad management field, the value of simulation is highly recognised when experimentation in the real world is not feasible because of time, cost or ethical constraints ( Davis et al. , 2007 ; Sterman, 2014 ; Noto and Cosenz, 2021 ). These kinds of situations characterise the contexts in which DM operates. In fact, experimenting with a disaster in the real world is never feasible or acceptable. As such, simulated environments are the only way we can discover how DM works and where high leverage points may lie to foster performance.

Simulation in DM studies has been explored in depth by Mishra et al. (2019) , who conducted a literature review of the key approaches adopted by scholars in the field. These authors focused on system dynamics (SD), Monte Carlo simulation (MCS), agent-based modelling (ABM) and discrete event simulation (DES).

MCS has been mainly adopted for risk modelling, SD has been proposed as an effective tool for prevention. ABM has shown effectiveness in considering the behaviour of the multiple agents involved in the DM cycle. Less adopted, according to Mishra et al. ’s (2019) study, was the DES, which is mainly used when modelling for large-scale disasters.

Whilst the literature on performance is already combined with simulation ( Bianchi, 2016 ), with a few exceptions ( Wang et al. , 2020a , 2020b ), the resulting frameworks have not been applied to DM studies. However, in the analysed articles, simulation is mainly examined from the performance point of view. For example, Gul et al. (2020) used DES to assess the preparedness of an emergency department during an earthquake by using length of stay and utilisation of medical staff as measures of performance. Sahebjamnia et al. (2017) used coverage, cost and response time as performance measures in a decision support system for managing humanitarian relief chains. Lee and Lee (2021) focused on disaster response performance in a multi-agent environment. Fan et al. (2021) emphasised how ETs, such as AI algorithms and deep learning architectures, significantly contribute to disaster preparedness at the city level where, through the combination of multiple sources of data (geospatial, sensors, social media, crowdsourcing) and the interactions amongst different entities, the inefficiencies induced by their complex relationships can be easily explored. Moreover, the authors pointed out how temporal information recorded in the Disaster City Digital Twin enables monitoring, analysing and predicting the dynamic structures of the networks involved and their potential effects on the efficiency of relief and response actions.

In all the above-mentioned cases, scenario analysis through simulation was used to explore the preparedness and resilience of a specific system when dealing with different phases of the DM cycle by observing how the measures of performance identified may evolve under different environmental conditions.

4.2.2 Red cluster.

Articles which fall into this cluster are mainly focused on the response phase of DM and provide interesting implications for what concern performance management. In light of our findings, the ETs which mostly support these phases are geospatial data (GIS), volunteered geographic information (VGI), IoT and robotics and automation (RA), such as drones and chatbots. Some scholars clearly described the complementary role of GIS and VGI in the provision of information, which can be helpful in coordinating response tasks amongst volunteer groups and official disaster agencies ( Hung et al. , 2016 ; Contreras et al. , 2016 ; Schumann, 2018 ; Akter and Fosso Wamba, 2019 ; Sharma et al. , 2020 ). Other studies have shown the main challenges (digital divide, lack of resources, poor data quality) associated with their use in emergency response contexts ( Haworth, 2016 ).

RA are effective tools for relief and response operations. To date, unmanned aerial vehicles (UAVs), which are a subcategory of RA, have been used in response to a wide range of disasters that have occurred in the last decade ( Chowdhury et al. , 2017 ; Kim et al. , 2018 ), providing valuable support in searching the victims, mapping the affected zones, making structural inspections, estimating debris and assessing damage.

More recently, UAVs have become of key relevance in supplying emergency commodities in disaster-affected regions. In this regard, some scholars ( Bravo et al. , 2019 ; Zwęgliński, 2020 ) stressed the impact of RA technologies in minimising the time and costs of disaster relief operations.

A further ET used in both the response and recovery phases of DM is IoT ( Shahat et al. , 2020 ), which enables accurate and real-time accountability of resources and personnel allocated to emergency response operations.

Sinha et al. (2019) showed the role of IoT-based solutions in catering to the task requirements of the personnel involved in DM, specifically rescue operations. A critical aspect here is improper resource allocation, which slows down recovery efforts.

Performance measurement seems the main concern of the articles which fall into the red cluster. KPIs are mainly used to calculate the extent to which ETs might reduce time, distance covered, number of lives saved and relief provided. To some extent, ETs enhance the level of accountability of response operations, coping with the lack of visibility of resources available on the disaster scene or dispatched to other places prior to the event ( Yang et al. , 2013 ).

4.2.3 Blue cluster.

This cluster introduced an interesting topic concerning the contribution of data mining, machine learning and social media to performance measurement, management and accountability during disaster events. Data mining and machine learning algorithms are widely recognised tools to support decision making in many areas and, more specifically, along the DM cycle ( Zagorecki et al. , 2013 ).

Machine learning is an umbrella term which sometimes overlaps with other concepts and applications, i.e. deep learning and AI. In any case, our findings show the high relatedness of this ET to the whole DM cycle, specifically to the emergency response phase ( Chaudhuri and Bose, 2020 ).

The key role of social media in DM has been widely recognised in the literature ( Xiao et al. , 2015 ). User generated content (UGC) from disaster-affected areas provides valuable information for emergency response when dealing with DM, as stated by Han et al. (2019) . Nevertheless, this study points out the nature of UGC, which is huge, disordered and continuous. As a consequence, its exploitation has a direct impact on the effectiveness of response actions during disaster events.

On the one hand, the huge amount of data generated by social media – Twitter, Facebook, TikTok and other platforms – provides a big picture of the ongoing disaster situation in terms of location, temporal sequence and entity-related information ( Hoang and Mothe, 2018 ; Singh et al. , 2019 ). On the other hand, the effective use of these tools raises critical issues in terms of text classification, data selection and validation, which are relevant when dealing with unpredictable and catastrophic events. More recently, sentiment analysis, topic modelling and other natural language processing tools have become promising techniques for assessing the reliability and accuracy of data gathered from social media during disasters ( Thekdi and Chatterjee, 2019 ; Karami et al. , 2020 ). These ETs enable situational awareness in disaster response ( Li et al. , 2018 ), especially through the analysis of crowdsourced data provided by the eyewitnesses of disaster events ( Zahra et al. , 2020 ). From a performance-based view, it can be argued that the aforementioned ETs mainly support performance measurement through the real-time data gathered from social media. This result is coherent with our theoretical background. Moreover, social media are largely used by local and national authorities, as they show great potential for improving efficiency and widening the audience of information systems during disasters and for enhancing relations (e.g. improved transparency and accountability) between governments and the community affected by the event ( Wehn and Evers, 2015 ).

4.2.4 Green cluster.

The last cluster obtained from our bibliometric analysis consists of papers which focus on the post-disaster phase (i.e. recovery and mitigation), namely, the humanitarian relief and the related humanitarian supply chain (HSC) logistics. In this regard, the ETs linked with this phase mainly impact on performance management and accountability.

As is well known, humanitarian logistics refers to the mobilisation and management of resources (human and material) through which support for post-disaster response and rehabilitation operations is provided.

HSC management is crucial for the efficiency and effectiveness of DM systems. As observed by Rodríguez-Espíndola et al. (2020) , the “duplication of efforts for data input, multiple formats, lack of control of budgets, absence of accountability, lack of integrity in procurement procedures, absence of a central database, and manual reporting and tracking” affect current DM systems.

The adoption of ETs, such as big data and predictive analytics (BDPA), provides valuable support to overcome the limitations in disaster relief operations. Indeed, scholars agree on the contribution that BDPA can offer when dealing with disasters ( Ragini et al. , 2018 ). Akter and Fosso Wamba (2019) highlighted how BDPA can help address various challenges by providing critical recovery services in disasters. Considering the main properties of BD, such as volume (referring to the amount of data), velocity (referring to the frequency or speed by which data are generated and delivered), veracity (referring to data quality) and value (referring to the benefits from the analysis and use of big data), many authors have underlined how these help improve the visibility, coordination and sustainability of the HSC after a disaster ( Papadopoulos et al. , 2017 ; Dubey et al. , 2018 ; Dubey et al. , 2019 ; Jeble et al. , 2019 ).

The subset of articles which fall into the green cluster gives relevance to some aspects related to both performance management and measurement. Abidi et al. (2014) analysed the state of the art of performance measurement, management and accountability in HSC. They pointed out some factors that determine reluctance to implement performance measurement in the humanitarian sector, such as a short-term perspective of disaster response actions, limited IT capacity and infrastructure and a chaotic environment.

Other scholars have underlined how ETs have enabled officials and non-government organisations involved in disaster relief and rehabilitation operations to reduce information asymmetry ( Dubey et al. , 2018 ) and address the lack of trust amongst agents, volunteers and the affected community using blockchain technology ( Dubey et al. , 2020 ); this has a critical role in enhancing collaboration and quickly building trust amongst various actors engaged in disaster relief operations.

5. Discussion and conclusions

This paper has sought to analyse how ETs impact on improving performance in DM processes, using a SLR as methodology of research and visualizing this impact with the VOSviewer software. The selected articles included in this review use different methodologies and focus on different phases of disasters, technologies and performance perspectives.

In many cases, we observed an inconsistent use of terms. This mainly happens in relation to the DM cycle. As mentioned in the theoretical background, DM can be framed into four phases: mitigation, preparedness, response and recovery. Many of the studies analysed, although focusing on specific phases, broadly refer to DM. This lack of specification poses challenges in the analysis and identification of the relationships between ETs and the DM phases. In some cases, DM is even used as a synonym for emergency management, resulting in a lack of clarity and confusion in the discipline. It is evident that ETs largely contribute to the management of disasters in each phase.

The complexity of DM often makes researchers and practitioners combine different technologies to improve the performance measurement, management and accountability of related activities. Although ETs may all be applied and successfully contribute to the different phases of the DM cycle, our analysis highlights some stronger linkages between some technologies, or features, and specific DM phases.

Many of the technologies considered rely on simulation features, which can be considered as a transversal tool supporting decision-makers at different levels in assessing the preparedness and resilience of a certain system prior to the occurrence of a natural disaster. Simulation enables experimentation with the consequences of a potential disaster in a virtual environment. This experimentation allows us to embrace the disaster risk reduction logic required to effectively tackle natural disasters. As such, simulation could be a valuable tool to improve preparedness. A simulated environment may foster the comprehension of the complex relationships characterizing disasters ex ante; thus, it may support the definition of consistent performance measures applicable to the preparedness phase.

Robotics and IoT are often associated with the improvement of operations in the response phase. ETs, such as drones or sensors, allow people to run activities that are not accessible to humans during disasters. These are valuable tools to monitor and manage performance during the response phases of the DM cycle.

Social media and related analytics tools have been widely used in two ways. On the one hand, they allow decision-makers to have access to a wider range of data sources (e.g. citizens, service users and other people involved in disasters) and to analyse this information through algorithms, such as topic modelling or sentiment analysis; this contribution is thus highly related to performance measurement. On the other hand, such tools foster performance accountability and disclosure towards the community.

In the following table, we highlighted the links between the performance perspectives here considered (measurement, management and accountability) and the main ETs identified by our review of the literature on DM ( Table 6 ).

As emerges from the table above, all these ETs are key to the decision support systems in every DM phase as also emerged from the reviewed papers. However, it is evident that the ability to process the data obtained and to verify their reliability and quality requires much effort. This aspect is probably linked to the lack of performance-related aspects in many of the papers analysed here. In fact, although many of the papers in our sample focus on performance, few of them embrace a theoretical framework based on performance measurement, management or accountability.

In this paper, we attempted to frame existing literature on DM and ETs according to a performance-based perspective to orient future studies and to highlight how and which ET contributes to the different phases of DM cycle.

As a result of this literature review, it emerges that prior research has put emphasis on the usefulness of ETs for preventing and managing disasters as well as to provide channels for reducing the harmful consequences of these disasters. Our systematization of previous literature results may have important implications both for theory and practice. At the theoretical level, the paper provides a framework that links the key performance perspectives and DM phases with the implementation of ETs in the DM field; such a framework may represent a useful reference for studies aimed at deepening related aspect. Moreover, the study highlights that simulation and simulation-based tools allow scholars to explore and test the development of new theories and solutions to analyse performance in DM contexts ( Davis et al. , 2007 ; Mishra et al. , 2019 ). At the practical level, the research suggests to the key involved actors (i.e. public administration, emergency managers, civil protection, experts and other stakeholders) to improve DM performance: analysing the importance of simulation tools to assess their preparedness; examining the ETs successfully used in the different DM phases (thus showing them how to invest in technologies); studying the importance to promote and enable citizens involvement as a new powerful source of data; and examining the need to invest in technologies to improve the ability to process, understand and use for decision-making purposes such data.

Despite its contributions, such as shedding light on the current state of the literature and providing future research directions about the theme addressed, this paper also has some limitations. Although frequently used in SLR, the criteria used to select our source of information – i.e. the exclusive focus on business, management and accounting categories; the exclusive focus on scientific articles in English language – may have excluded some valuable contributions. Future research could thus compare our results with other sources of information such as books and grey literature. Moreover, consistently with prior research, we have mainly analysed the implementation of ETs as “isolated islands.” Nonetheless, future research could analyse integration processes of these ETs for managing all disasters in an efficient manner.

Finally, the study did not consider the question of technological acceptance by the users of the technologies. Verifying whether specific technologies or certain phases of the DM cycle are associated with greater reluctance on users’ side could be interesting.

Disaster risk management cycle. Our elaboration

Selection, screening, eligibility and inclusion process of articles

Documents per year

Network visualization

Overlay visualization

Suitable emerging technologies in the DM field

Search criteria

Top ten journals publishing papers regarding DM

Citation counting. Top 15 cited documents

VOSviewer cluster description

Linking PM and ETs in DM cycle

Centre for Research on the Epidemiology of Disasters – CRED. School of Public Health Université Catholique de Louvain.

http://www.undrr.org/terminology/disaster#:∼:text=A%20serious%20disruption%20of%20the,and%20environmental%20losses%20and%20impacts . Accessed on 1 February 2021.

Abidi , H. , De Leeuw , S. and Klumpp , M. ( 2014 ), “ Humanitarian supply chain performance management: a systematic literature review ”, Supply Chain Management: An International Journal , Vol. 19 Nos 5/6 , pp. 592 - 608 .

Adger , W.N. ( 2006 ), “ Vulnerability ”, Global Environmental Change , Vol. 16 No. 3 , pp. 268 - 281 .

Akter , S. and Fosso Wamba , S. ( 2019 ), “ Big data and disaster management: a systematic review and agenda for future research ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 939 - 959 .

Alexander , D. ( 1997 ), “ The study of natural disasters, 1977–97: some reflections on a changing field of knowledge ”, Disasters , Vol. 21 No. 4 , pp. 284 - 304 .

Alexander , D.E. ( 2014 ), “ Social media in disaster risk reduction and crisis management ”, Science and Engineering Ethics , Vol. 20 No. 3 , pp. 717 - 733 .

Ansell , C. , Boin , A. and Keller , A. ( 2010 ), “ Managing transboundary crises: identifying the building blocks of an effective response system ”, Journal of Contingencies and Crisis Management , Vol. 18 No. 4 , pp. 195 - 207 .

Barcevičius , E. , Cibaitė , G. , Codagnone , C. , Gineikytė , V. , Klimavičiūtė , L. , Liva , G. , … and Vanini , I. ( 2019 ), “ Exploring digital government transformation in the EU-analysis of the state of the art and review of literature ,” Publications Office of the European Union, JRC118857 .

Barth , T.J. and Arnold , E. ( 1999 ), “ Artificial intelligence and administrative discretion: implications for public administration ”, The American Review of Public Administration , Vol. 29 No. 4 , pp. 332 - 351 .

Berg , M. and De Majo , V. ( 2017 ), “ Understanding the global strategy for disaster risk reduction ”, Risk, Hazards and Crisis in Public Policy , Vol. 8 No. 2 , pp. 147 - 167 .

Bianchi , C. ( 2016 ), Dynamic Performance Management , Springer , Berlin Vol. 1 .

Bititci , U. , Garengo , P. , Dörfler , V. and Nudurupati , S. ( 2012 ), “ Performance measurement: challenges for tomorrow ”, International Journal of Management Reviews , Vol. 14 No. 3 , pp. 305 - 327 .

Boin , A. , Ekengren , M. and Rhinard , M. ( 2013 ), The European Union as Crisis Manager: Patterns and Prospects , Cambridge University Press , Cambridge .

Bonsón , E. and Bednárová , M. ( 2019 ), “ Blockchain and its implications for accounting and auditing ”, Meditari Accountancy Research , Vol. 27 No. 5 , pp. 725 - 740 .

Boon , W. and Moors , E. ( 2008 ), “ Exploring emerging technologies using metaphors – a study of orphan drugs and pharmacogenomics ”, Social Science and Medicine , Vol. 66 No. 9 , pp. 1915 - 1927 .

Bravo , R.Z.B. , Leiras , A. and Cyrino Oliveira , F.L. ( 2019 ), “ The use of UAV s in humanitarian relief: an application of POMDP-based methodology for finding victims ”, Production and Operations Management , Vol. 28 No. 2 , pp. 421 - 440 .

Buckle , P. ( 2005 ), “ Disaster: mandated definitions, local knowledge and complexity ”, in Perry , R.W. and Quarantelli , E.L. (Eds), What is a Disaster? New Answers to Old Questions , Xlibris . Philadelphia, PA , pp. 173 ‐ 200 .

Buys , P.W. ( 2008 ), “ The impact of XBRL on the financial reporting supply chain: a South African case study ”, Meditari Accountancy Research , Vol. 16 No. 1 , pp. 43 - 58 .

Carreño , M.L. , Cardona , O.D. and Barbat , A.H. ( 2007 ), “ A disaster risk management performance index ”, Natural Hazards , Vol. 41 No. 1 , pp. 1 - 20 .

Chatterjee , S. , Chaudhuri , R. , Vrontis , D. and Basile , G. ( 2021 ), “ Digital transformation and entrepreneurship process in SMEs of India: a moderating role of adoption of AI-CRM capability and strategic planning ”, Journal of Strategy and Management .

Chaudhuri , N. and Bose , I. ( 2020 ), “ Exploring the role of deep neural networks for post-disaster decision support ”, Decision Support Systems , Vol. 130 , p. 113234 .

Chengalur-Smith , I. , Belardo , S. and Pazer , H. ( 1999 ), “ Adopting a disaster-management-based contingency model to the problem of ad hoc forecasting: toward information technology-based strategies ”, IEEE Transactions on Engineering Management , Vol. 46 No. 2 , pp. 210 - 220 .

Chhabra , D. , Singh , R.K. and Kumar , V. ( 2021 ), “ Developing IT-enabled performance monitoring system for green logistics: a case study ”, International Journal of Productivity and Performance Management .

Chowdhury , S. , Emelogu , A. , Marufuzzaman , M. , Nurre , S.G. and Bian , L. ( 2017 ), “ Drones for disaster response and relief operations: a continuous approximation model ”, International Journal of Production Economics , Vol. 188 , pp. 167 - 184 .

Cockcroft , S. and Russell , M. ( 2018 ), “ Big data opportunities for accounting and finance practice and research ”, Australian Accounting Review , Vol. 28 No. 3 , pp. 323 - 333 .

Comfort , L.K. , Dunn , M. , Johnson , D. , Skertich , R. and Zagorecki , A. ( 2004 ), “ Coordination in complex systems: increasing efficiency in disaster mitigation and response ”, International Journal of Emergency Management , Vol. 2 Nos 1/2 , pp. 62 - 80 .

Comfort , L.K. , Waugh , W. and Cigler , B.A. ( 2012 ), “ Emergency management research and practice in public administration: emergency, evolution, expansion, and future directions ”, Public Administration Review , Vol. 72 No. 4 , pp. 539 - 547 .

Contreras , D. , Blaschke , T. , Tiede , D. and Jilge , M. ( 2016 ), “ Monitoring recovery after earthquakes through the integration of remote sensing, GIS, and ground observations: the case of L’Aquila (Italy) ”, Cartography and Geographic Information Science , Vol. 43 No. 2 , pp. 115 - 133 .

Cutter , S.L. ( 1996 ), “ Vulnerability to environmental hazards ”, Progress in Human Geography , Vol. 20 No. 4 , pp. 529 - 539 .

Davenport , T.H. and Ronanki , R. ( 2018 ). “ Artificial intelligence for the real world ”, Harvard Business Review , Vol. 96 No. 1 , pp. 108 - 116 .

Davis , J.P. , Eisenhardt , K.M. and Bingham , C.B. ( 2007 ), “ Developing theory through simulation methods ”, Academy of Management Review , Vol. 32 No. 2 , pp. 480 - 499 .

Day , G.S. and Schoemaker , P.J. ( 2000 ), “ Avoiding the pitfalls of emerging technologies ”, California Management Review , Vol. 42 No. 2 , pp. 8 - 33 .

De Santis , F. and D’Onza , G. ( 2021 ), “ Big data and data analytics in auditing: in search of legitimacy ”, Meditari Accountancy Research .

Dekker , H.C. ( 2016 ), “ On the boundaries between intrafirm and interfirm management accounting research ”, Management Accounting Research , Vol. 31 , pp. 86 - 99 .

Dell'Era , C. , Di Minin , A. , Ferrigno , G. , Frattini , F. , Landoni , P. and Verganti , R. ( 2020 ), “ Value capture in open innovation processes with radical circles: a qualitative analysis of firms’ collaborations with slow food, Memphis, and free software foundation ”, Technological Forecasting and Social Change , Vol. 158 , p. 120128 .

Demiroz , F. and Haase , T.W. ( 2019 ), “ The concept of resilience: a bibliometric analysis of the emergency and disaster management literature ”, Local Government Studies , Vol. 45 No. 3 , pp. 308 - 327 .

Denyer , D. and Tranfield , D. ( 2009 ), “ Producing a systematic review ”, in Buchanan , D.A. and Bryman , A. (Eds), The Sage Handbook of Organizational Research Methods , Sage , Los Angeles, CA , pp. 671 - 689 .

Dubey , R. , Luo , Z. , Gunasekaran , A. , Akter , S. , Hazen , B.T. and Douglas , M.A. ( 2018 ), “ Big data and predictive analytics in humanitarian supply chains ”, The International Journal of Logistics Management , Vol. 29 No. 2 , pp. 485 - 512 .

Dubey , R. , Gunasekaran , A. , Childe , S.J. , Roubaud , D. , Fosso Wamba , S. and Foropon , C. ( 2019 ), “ Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain ”, International Journal of Production Economics , Vol. 210 , pp. 120 - 136 .

Dubey , R. , Gunasekaran , A. , Bryde , D.J. , Dwivedi , Y.K. and Papadopoulos , T. ( 2020 ), “ Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting ”, International Journal of Production Research , Vol. 58 No. 11 , pp. 3381 - 3398 .

Dumay , J. and Cai , L. ( 2015 ), “ Using content analysis as a research methodology for investigating intellectual capital disclosure ”, Journal of Intellectual Capital , Vol. 16 No. 1 , pp. 121 - 155 .

Eccles , R.G. ( 1991 ), “ The performance measurement manifesto ”, Harvard Business Review , Vol. 69 No. 1 , pp. 131 - 137 .

Fajardo , J.T.B. and Oppus , C.M. ( 2010 ), “ A mobile disaster management system using the android technology ”, WSEAS Transactions on Communications , Vol. 9 No. 6 , pp. 343 - 353 .

Fan , C. , Zhang , C. , Yahja , A. and Mostafavi , A. ( 2021 ), “ Disaster city digital twin: a vision for integrating artificial and human intelligence for disaster management ”, International Journal of Information Management , Vol. 56 , p. 102049 .

Faulkner , B. ( 2001 ), “ Towards a framework for tourism disaster management ”, Tourism Management , Vol. 22 No. 2 , pp. 135 - 147 .

Ferreira , A. and Otley , D. ( 2009 ), “ The design and use of performance management systems: an extended framework for analysis ”, Management Accounting Research , Vol. 20 No. 4 , pp. 263 - 282 .

Ferrigno , G. and Cucino , V. ( 2021 ), “ Innovating and transforming during COVID-19. Insights from Italian firms ”, R&D Management , In Press .

Ferrigno , G. , Dagnino , G.B. and Di Paola , N. ( 2021 ), “ R&D alliance partner attributes and innovation performance: a fuzzy set qualitative comparative analysis ”, Journal of Business and Industrial Marketing , Vol. 36 No. 13 .

Gilbert , C. ( 1998 ), “ Studying disaster: changes in the main conceptual tools ”, Quarantelli , E.L. (Ed.), What is a Disaster? Perspectives on the Question , Routledge , New York, NY , pp. 11 - 18 .

Gray , R. ( 1992 ), “ Accounting and environmentalism: an exploration of the challenge of gently accounting for accountability, transparency and sustainability ”, Accounting, Organizations and Society , Vol. 17 No. 5 , pp. 399 - 425 .

Gul , M. , Fuat Guneri , A. and Gunal , M.M. ( 2020 ), “ Emergency department network under disaster conditions: the case of possible major Istanbul earthquake ”, Journal of the Operational Research Society , Vol. 71 No. 5 , pp. 733 - 747 .

Halaweh , M. ( 2013 ), “ Emerging technology: what is it ”, Journal of Technology Management and Innovation , Vol. 8 No. 3 , pp. 108 - 115 .

Han , S. , Huang , H. , Luo , Z. and Foropon , C. ( 2019 ), “ Harnessing the power of crowdsourcing and internet of things in disaster response ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 1175 - 1190 .

Harrison , S.E. and Johnson , P.A. ( 2016 ), “ Crowdsourcing the disaster management cycle ”, International Journal of Information Systems for Crisis Response and Management (IJISCRAM) , Vol. 8 No. 4 , pp. 17 - 40 .

Haworth , B. ( 2016 ), “ Emergency management perspectives on volunteered geographic information: opportunities, challenges and change ”, Computers, Environment and Urban Systems , Vol. 57 , pp. 189 - 198 .

Head , B.W. and Alford , J. ( 2015 ), “ Wicked problems: implications for public policy and management ”, Administration and Society , Vol. 47 No. 6 , pp. 711 - 739 .

Hoang , T.B.N. and Mothe , J. ( 2018 ), “ Location extraction from tweets ”, Information Processing and Management , Vol. 54 No. 2 , pp. 129 - 144 .

Hudson , M. , Smart , A. and Bourne , M. ( 2001 ), “ Theory and practice in SME performance measurement systems ”, International Journal of Operations & Production Management , Vol. 21 No. 8 , pp. 1096 - 1115 .

Hung , K.C. , Kalantari , M. and Rajabifard , A. ( 2016 ), “ Methods for assessing the credibility of volunteered geographic information in flood response: a case study in Brisbane, Australia ”, Applied Geography , Vol. 68 , pp. 37 - 47 .

Imran , M. , Ofli , F. , Caragea , D. and Torralba , A. ( 2020 ), “ Using AI and social media multimodal content for disaster response and management: opportunities, challenges, and future directions ”, Information Processing and Management , Vol. 57 No. 5 , p. 102261 .

Inan , D.I. , Beydoun , G. and Pradhan , B. ( 2018 ), “ Developing a decision support system for disaster management: case study of an indonesia volcano eruption ”, International Journal of Disaster Risk Reduction , Vol. 31 , pp. 711 - 721 .

Ivanov , D. , Dolgui , A. and Sokolov , B. ( 2019 ), “ The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics ”, International Journal of Production Research , Vol. 57 No. 3 , pp. 829 - 846 .

Jeble , S. , Kumari , S. , Venkatesh , V.G. and Singh , M. ( 2019 ), “ Influence of big data and predictive analytics and social capital on performance of humanitarian supply chain ”, Benchmarking: An International Journal , Vol. 27 No. 2 , pp. 606 - 633 .

Jun , W. , Nasir , M.H. , Yousaf , Z. , Khattak , A. , Yasir , M. , Javed , A. and Shirazi , S.H. ( 2021 ), “ Innovation performance in digital economy: does digital platform capability, improvisation capability and organizational readiness really matter? ”, European Journal of Innovation Management .

Kankanamge , N. , Yigitcanlar , T. , Goonetilleke , A. and Kamruzzaman , M. ( 2019 ), “ Can volunteer crowdsourcing reduce disaster risk? A systematic review of the literature ”, International Journal of Disaster Risk Reduction , Vol. 35 , p. 101097 .

Kaplan , R.S. and Norton , D.P. ( 1996 ), “ Using the balanced scorecard as a strategic management system ”, Harvard Business Review , pp. 75 - 85 .

Kapucu , N. and Garayev , V. ( 2016 ), “ Structure and network performance: horizontal and vertical networks in emergency management ”, Administration and Society , Vol. 48 No. 8 , pp. 931 - 961 .

Kapucu , N. and Van Wart , M. ( 2008 ), “ Making matters worse: an anatomy of leadership failures in managing catastrophic events ”, Administration and Society , Vol. 40 No. 7 , pp. 711 - 740 .

Karami , A. , Shah , V. , Vaezi , R. and Bansal , A. ( 2020 ), “ Twitter speaks: a case of national disaster situational awareness ”, Journal of Information Science , Vol. 46 No. 3 , pp. 313 - 324 .

Kassel , D.S. ( 2008 ), “ Performance, accountability, and the debate over rules ”, Public Administration Review , Vol. 68 No. 2 , pp. 241 - 252 .

Kaur , A. and Lodhia , S.K. ( 2019 ), “ Sustainability accounting, accountability and reporting in the public sector: an overview and suggestions for future research ”, Meditari Accountancy Research , Vol. 27 No. 4 , pp. 498 - 504 .

Keupp , M.M. , Palmié , M. and Gassmann , O. ( 2012 ), “ The strategic management of innovation: a systematic review and paths for future research ”, International Journal of Management Reviews , Vol. 14 No. 4 , pp. 367 - 390 .

Khalid , S. , Beattie , C. , Sands , J. and Hampson , V. ( 2019 ), “ Incorporating the environmental dimension into the balanced scorecard: a case study in health care ”, Meditari Accountancy Research , Vol. 27 No. 4 , pp. 652 - 674 .

Kim , Y.D. , Son , G.J. , Kim , H. , Song , C. and Lee , J.H. ( 2018 ), “ Smart disaster response in vehicular tunnels: technologies for search and rescue applications ”, Sustainability , Vol. 10 No. 7 , p. 2509 .

Kitagawa , K. ( 2021 ), “ Disaster risk reduction activities as learning ”, Natural Hazards , Vol. 105 No. 3 , pp. 3099 - 3118 .

Kraus , S. , Breier , M. and Dasí-Rodríguez , S. ( 2020 ), “ The art of crafting a systematic literature review in entrepreneurship research ”, International Entrepreneurship and Management Journal , Vol. 16 No. 3 , pp. 1 - 20 .

Kuipers , S. , Boin , A. , Bossong , R. and Hegemann , H. ( 2015 ), “ Building joint crisis management capacity? Comparing civil security systems in 22 European countries ”, Risk, Hazards and Crisis in Public Policy , Vol. 6 No. 1 , pp. 1 - 21 .

Lai , A. , Leoni , G. and Stacchezzini , R. ( 2014 ), “ The socializing effects of accounting in flood recovery ”, Critical Perspectives on Accounting , Vol. 25 No. 7 , pp. 579 - 603 .

Laituri , M. and Kodrich , K. ( 2008 ), “ Online disaster response community: people as sensors of high magnitude disasters using internet GIS ”, Sensors , Vol. 8 No. 5 , pp. 3037 - 3055 .

Lamboglia , R. , Lavorato , D. , Scornavacca , E. and Za , S. ( 2020 ), “ Exploring the relationship between audit and technology. A bibliometric analysis ”, Meditari Accountancy Research .

Lebas , M.J. ( 1995 ), “ Performance measurement and performance management ”, International Journal of Production Economics , Vol. 41 Nos 1/3 , pp. 23 - 35 .

Lee , H.R. and Lee , T. ( 2021 ), “ Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response ”, European Journal of Operational Research , Vol. 291 No. 1 , pp. 296 - 308 .

Lettieri , E. , Masella , C. and Radaelli , G. ( 2009 ), “ Disaster management: findings from a systematic review ”, Disaster Prevention and Management: An International Journal , Vol. 18 No. 2 , pp. 117 - 136 .

Li , H. , Caragea , D. , Caragea , C. and Herndon , N. ( 2018 ), “ Disaster response aided by tweet classification with a domain adaptation approach ”, Journal of Contingencies and Crisis Management , Vol. 26 No. 1 , pp. 16 - 27 .

Light , R. and Pillemer , D. ( 1984 ), Summing up: The Science of Reviewing Research , Harvard University Press . Cambridge, MA .

Lombardi , R. and Secundo , G. ( 2020 ), “ The digital transformation of corporate reporting-a systematic literature review and avenues for future research ”, Meditari Accountancy Research .

Lombardi , R. , de Villiers , C. , Moscariello , N. and Pizzo , M. ( 2021 ), “ The disruption of blockchain in auditing – a systematic literature review and an agenda for future research ”, Accounting, Auditing and Accountability Journal .

McEntire , D.A. ( 2005 ), “ Why vulnerability matters: exploring the merit of an inclusive disaster reduction concept ”, Disaster Prevention and Management: An International Journal , Vol. 14 No. 2 , pp. 206 - 222 .

Manyena , S.B. ( 2006 ), “ The concept of resilience revisited ”, Disasters , Vol. 30 No. 4 , pp. 434 - 450 .

Marr , B. and Neely , A. ( 2001 ), “ Organisational performance measurement in the emerging digital age ”, International Journal of Business Performance Management , Vol. 3 Nos 2/3/4 , pp. 191 - 215 .

Marrone , M. and Hazelton , J. ( 2019 ), “ The disruptive and transformative potential of new technologies for accounting, accountants and accountability ”, Meditari Accountancy Research , Vol. 27 No. 5 , pp. 677 - 694 .

Martinez-Rojas , M. , del Carmen Pardo-Ferreira , M. and Rubio-Romero , J.C. ( 2018 ), “ Twitter as a tool for the management and analysis of emergency situations: a systematic literature review ”, International Journal of Information Management , Vol. 43 , pp. 196 - 208 .

Mishra , D. , Kumar , S. and Hassini , E. ( 2019 ), “ Current trends in disaster management simulation modelling research ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 1387 - 1411 .

Moynihan , D.P. ( 2008 ), “ Learning under uncertainty: Networks in crisis management ”, Public Administration Review , Vol. 68 No. 2 , pp. 350 - 365 .

Ndou , V. , Secundo , G. , Dumay , J. and Gjevori , E. ( 2018 ), “ Understanding intellectual Capital disclosure in online media big data: an exploratory case study in a university ”, Meditari Accountancy Research , Vol. 26 No. 3 , pp. 499 - 530 .

Noto , G. and Cosenz , F. ( 2021 ), “ Introducing a strategic perspective in lean thinking applications through system dynamics modelling: the dynamic value stream map ”, Business Process Management Journal , Vol. 27 No. 1 , pp. 306 - 327 .

Nudurupati , S.S. and Bititci , U.S. ( 2005 ), “ Implementation and impact of IT-supported performance measurement systems ”, Production Planning and Control , Vol. 16 No. 2 , pp. 152 - 162 .

Nuti , S. , Noto , G. , Vola , F. and Vainieri , M. ( 2018 ), “ Let's play the patients music: a new generation of performance measurement systems in healthcare ”, Management Decision , Vol. 56 No. 10 , pp. 2252 - 2272 .

Oktari , R.S. , Munadi , K. and Idroes , R. ( 2020 ), “ Knowledge management practices in disaster management: systematic review ”, International Journal of Disaster Risk Reduction .

Oliver , J.J. ( 2018 ), “ Strategic transformations in a disruptive digital environment ”, Strategic Direction , Vol. 34 No. 5 , pp. 5 - 8 .

Papadopoulos , T. , Gunasekaran , A. , Dubey , R. , Altay , N. , Childe , S.J. and Fosso-Wamba , S. ( 2017 ), “ The role of big data in explaining disaster resilience in supply chains for sustainability ”, Journal of Cleaner Production , Vol. 142 , pp. 1108 - 1118 .

Pearce , L. ( 2003 ), “ Disaster management and community planning, and public participation: how to achieve sustainable hazard mitigation ”, Natural Hazards , Vol. 28 Nos 2/3 , pp. 211 - 228 .

Perkiss , S. and Moerman , L. ( 2020 ), “ Hurricane Katrina: exploring justice and fairness as a sociology of common good(s) ”, Critical Perspectives on Accounting , Vols 67/68 , p. 102022 .

Perry , R.W. ( 1998 ), “ Definitions and the development of a theoretical superstructure for disaster research ”, in Quarantelli , E.L. (Ed.), What is a Disaster? Perspectives on the Question , Routledge , New York, NY , pp. 197 ‐ 215 .

Pesch , U. and Vermaas , P.E. ( 2020 ), “ The wickedness of Rittel and Webber’s dilemmas ”, Administration and Society , Vol. 52 No. 6 , pp. 960 - 979 .

Petticrew , M. and Roberts , H. ( 2008 ), Systematic Reviews in the Social Sciences: A Practical Guide , Blackwell Publishing , Malden, MA .

Quarantelli , E.L. (Ed.) ( 1998 ), What is a Disaster? Perspectives on the Question , Routledge , New York, NY .

Ragini , J.R. , Anand , P.R. and Bhaskar , V. ( 2018 ), “ Big data analytics for disaster response and recovery through sentiment analysis ”, International Journal of Information Management , Vol. 42 , pp. 13 - 24 .

Raman , M. , Kuppusamy , M.V. , Dorasamy , M. and Nair , S. ( 2014 ), “ Knowledge management systems and disaster management in Malaysia: an action research approach ”, Journal of Information & Knowledge Management , Vol. 13 No. 1 .

Rezaei , M. , Akbarpour Shirazi , M. and Karimi , B. ( 2017 ), “ IoT-based framework for performance measurement: a real-time supply chain decision alignment ”, Industrial Management and Data Systems , Vol. 117 No. 4 , pp. 688 - 712 .

Rittel , H.W.J. and Webber , M.M. ( 1973 ), “ Dilemmas in a general theory of planning ”, Policy Sciences , Vol. 4 No. 2 , pp. 155 - 169 .

Roberts , J. ( 1991 ), “ The possibilities of accountability ”, Accounting, Organizations and Society , Vol. 16 No. 4 , pp. 355 - 368 .

Rodríguez-Bolívar , M.P. , Pérez , C.C. and Hernández , A.M.L. ( 2006 ), “ Cultural contexts and governmental digital reporting ”, International Review of Administrative Sciences , Vol. 72 No. 2 , pp. 269 - 290 .

Rodríguez-Bolívar , M.P. , Scholl , J. and Reddick , C.G. (Eds) ( 2021 ), “ Blockchain and the public sector: theories ”, Reforms, and Case Studies , Springer .

Rodríguez-Espíndola , O. , Chowdhury , S. , Beltagui , A. and Albores , P. ( 2020 ), “ The potential of emergent disruptive technologies for humanitarian supply chains: the integration of blockchain, artificial intelligence and 3D printing ”, International Journal of Production Research , Vol. 58 No. 15 , pp. 4610 - 4630 .

Rosenthal , U. and Kouzmin , A. ( 1997 ), “ Crises and crisis management: toward comprehensive government decision making ”, Journal of Public Administration Research and Theory , Vol. 7 No. 2 , pp. 277 - 305 .

Rotolo , D. , Hicks , D. and Martin , B.R. ( 2015 ), “ What is an emerging technology? ”, Research Policy , Vol. 44 No. 10 , pp. 1827 - 1843 .

Sahebjamnia , N. , Torabi , S.A. and Mansouri , S.A. ( 2017 ), “ A hybrid decision support system for managing humanitarian relief chains ”, Decision Support Systems , Vol. 95 , pp. 12 - 26 .

Sardi , A. , Sorano , E. , Cantino , V. and Garengo , P. ( 2020 ), “ Big data and performance measurement research: trends, evolution and future opportunities ”, Measuring Business Excellence .

Sargiacomo , M. and Walker , S.P. ( 2020 ), “ Disaster governance and hybrid organizations: accounting, performance challenges and evacuee housing ”, Accounting, Auditing and Accountability Journal , doi: 10.1108/AAAJ-12-2019-4323 .

Sargiacomo , M. , Ianni , L. and Everett , J. ( 2014 ), “ Accounting for suffering: calculative practices in the field of disaster relief ”, Critical Perspectives on Accounting , Vol. 25 No. 7 , pp. 652 - 669 .

Schumann , R.L. III ( 2018 ), “ Ground truthing spatial disaster recovery metrics with participatory mapping in post-Katrina Mississippi ”, Applied Geography , Vol. 99 , pp. 63 - 76 .

Sciulli , N. ( 2018 ), “ Weathering the storm: accountability implications for flood relief and recovery from a local government perspective ”, Financial Accountability and Management , Vol. 34 No. 1 , pp. 30 - 44 .

Secundo , G. , Ndou , V. , Del Vecchio , P. and De Pascale , G. ( 2020 ), “ Sustainable development, intellectual capital and technology policies: a structured literature review and future research agenda ”, Technological Forecasting and Social Change , Vol. 153 , p. 119917 .

Shah , S.A. , Seker , D.Z. , Rathore , M.M. , Hameed , S. , Yahia , S.B. and Draheim , D. ( 2019 ), “ Towards disaster resilient smart cities: can internet of things and big data analytics be the game changers? ”, IEEE Access , Vol. 7 , pp. 91885 - 91903 .

Shahat , E. , Hyun , C.T. and Yeom , C. ( 2020 ), “ Conceptualizing smart disaster governance: an integrative conceptual framework ”, Sustainability , Vol. 12 No. 22 , p. 9536 .

Sharma , S.K. , Misra , S.K. and Singh , J.B. ( 2020 ), “ The role of GIS-enabled mobile applications in disaster management: a case analysis of cyclone Gaja in India ”, International Journal of Information Management , Vol. 51 , p. 102030 .

Singh , J.P. , Dwivedi , Y.K. , Rana , N.P. , Kumar , A. and Kapoor , K.K. ( 2019 ), “ Event classification and location prediction from tweets during disasters ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 737 - 757 .

Sinha , A. , Kumar , P. , Rana , N.P. , Islam , R. and Dwivedi , Y.K. ( 2019 ), “ Impact of internet of things (IoT) in disaster management: a task-technology fit perspective ”, Annals of Operations Research , Vol. 283 Nos 1/2 , pp. 759 - 794 .

Snyder , H. ( 2019 ), “ Literature review as a research methodology: an overview and guidelines ”, Journal of Business Research , Vol. 104 , pp. 333 - 339 .

Spanò , R. and Ginesti , G. ( 2021 ), “ Fostering performance management in healthcare: insights into the role of big data ”, Meditari Accountancy Research .

Steen , T. and Brandsen , T. ( 2020 ), “ Coproduction during and after the COVID‐19 pandemic: will it last? ”, Public Administration Review , Vol. 80 No. 5 , pp. 851 - 855 .

Stephenson , R. and Anderson , P.S. ( 1997 ), “ Disasters and the information technology revolution ”, Disasters , Vol. 21 No. 4 , pp. 305 - 334 .

Sterman , J. ( 2014 ), “ Interactive web‐based simulations for strategy and sustainability: the MIT sloan learning edge management flight simulators, part I ”, System Dynamics Review , Vol. 30 Nos 1/2 , pp. 89 - 121 .

Thekdi , S.A. and Chatterjee , S. ( 2019 ), “ Toward adaptive decision support for assessing infrastructure system resilience using hidden performance measures ”, Journal of Risk Research , Vol. 22 No. 8 , pp. 1020 - 1043 .

Thorpe , R. , Holt , R. , Macpherson , A. and Pittaway , L. ( 2005 ), “ Using knowledge within small and medium-sized firms: a systematic review of the evidence ”, International Journal of Management Reviews , Vol. 7 No. 4 , pp. 257 - 281 .

Tingey-Holyoak , J.L. , Pisaniello , J.D. and Buss , P. ( 2021 ), “ Embedding smart technologies in accounting to meet global irrigation challenges ”, Meditari Accountancy Research .

Toth , C. and Jozkow , G. ( 2016 ), “ Remote sensing platforms and sensors: a survey ”, ISPRS Journal of Photogrammetry and Remote Sensing , Vol. 115 , pp. 22 - 36 .

Tranfield , D. , Denyer , D. and Smart , P. ( 2003 ), “ Towards a methodology for developing evidence-informed management knowledge by means of systematic review ”, British Journal of Management , Vol. 14 No. 3 , pp. 207 - 222 .

Van Eck , N.J. and Waltman , L. ( 2014 ), “ Visualizing bibliometric networks ”, in Ding Y , Rousseau R. and Wolfram D. (Eds), Measuring Scholarly Impact: Methods and Practice , Springer , Cham , pp. 285 - 320 .

Van Eck , N.J. and Waltman , L. ( 2017 ), “ Citation-based clustering of publications using CitNetExplorer and VOSviewer ”, Scientometrics , Vol. 111 No. 2 , pp. 1053 - 1070 .

Van Wart , M. and Kapucu , N. ( 2011 ), “ Crisis management competencies: the case of emergency managers in the USA ”, Public Management Review , Vol. 13 No. 4 , pp. 489 - 511 .

Verbano , C. and Venturini , K. ( 2013 ), “ Managing risks in SMEs: a literature review and research agenda ”, Journal of Technology Management and Innovation , Vol. 8 No. 3 , pp. 186 - 197 .

Von Lubitz , D.K. , Beakley , J.E. and Patricelli , F. ( 2008 ), “ All hazards approach to disaster management: the role of information and knowledge management, Boyd's OODA loop, and network‐centricity ”, Disasters , Vol. 32 No. 4 , pp. 561 - 585 .

Walker , S.P. ( 2014 ), “ Drought, resettlement and accounting ”, Critical Perspectives on Accounting , Vol. 25 No. 7 , pp. 604 - 619 .

Wallace , W.A. and De Balogh , F. ( 1985 ), “ Decision support systems for disaster management ”, Public Administration Review , Vol. 45 , pp. 134 - 146 .

Wang , H. , Feng , J. , Zhang , H. and Li , X. ( 2020a ), “ The effect of digital transformation strategy on performance: the moderating role of cognitive conflict ”, International Journal of Conflict Management , Vol. 31 No. 3 , pp. 441 - 462 .

Wang , L. , Bivona , E. , Yan , H. and Qi , J. ( 2020b ), “ Applying dynamic performance management to public emergency management: an analysis of the Wenchuan earthquake ”, in Bianchi , C. E. , Luis , F. , Luna-Reyes , L.F. and Rich , E. (Eds), Enabling Collaborative Governance through Systems Modeling Methods , Springer International Publishing , Berlin , pp. 263 - 276 .

Waugh , W.L. ( 1995 ), “ Geographic information systems: the case of disaster management ”, Social Science Computer Review , Vol. 13 No. 4 , pp. 422 - 431 .

Waugh , W.L. and Streib , G. ( 2006 ), “ Collaboration and leadership for effective emergency management ”, Public Administration Review , Vol. 66 No. s1 , pp. 131 - 140 .

Wehn , U. and Evers , J. ( 2015 ), “ The social innovation potential of ICT-enabled citizen observatories to increase eParticipation in local flood risk management ”, Technology in Society , Vol. 42 , pp. 187 - 198 .

Wisner , B. , Blaikie , P. , Cannon , T. and Davis , I. ( 2004 ), At Risk: Natural Hazards People's Vulnerability and Disaster , Routledge , London .

Xiao , Y. and Watson , M. ( 2019 ), “ Guidance on conducting a systematic literature review ”, Journal of Planning Education and Research , Vol. 39 No. 1 , pp. 93 - 112 .

Xiao , Y. , Huang , Q. and Wu , K. ( 2015 ), “ Understanding social media data for disaster management ”, Natural Hazards , Vol. 79 No. 3 , pp. 1663 - 1679 .

Yang , L. , Yang , S.H. and Plotnick , L. ( 2013 ), “ How the internet of things technology enhances emergency response operations ”, Technological Forecasting and Social Change , Vol. 80 No. 9 , pp. 1854 - 1867 .

Yu , M. , Huang , Q. , Qin , H. , Scheele , C. and Yang , C. ( 2019 ), “ Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies ”, International Journal of Digital Earth , Vol. 12 No. 11 , pp. 1230 - 1247 .

Zagorecki , A.T. , Johnson , D.E. and Ristvej , J. ( 2013 ), “ Data mining and machine learning in the context of disaster and crisis management ”, International Journal of Emergency Management , Vol. 9 No. 4 , pp. 351 - 365 .

Zahra , K. , Imran , M. and Ostermann , F.O. ( 2020 ), “ Automatic identification of eyewitness messages on twitter during disasters ”, Information Processing and Management , Vol. 57 No. 1 , p. 102107 .

Zheng , T. , Ardolino , M. , Bacchetti , A. and Perona , M. ( 2020 ), “ The applications of industry 4.0 technologies in manufacturing context: a systematic literature review ”, International Journal of Production Research , Vol. 59 No. 6 .

Zwęgliński , T. ( 2020 ), “ The use of drones in disaster aerial needs reconnaissance and damage assessment–three-dimensional modeling and orthophoto map study ”, Sustainability , Vol. 12 No. 15 , p. 6080 .

Further reading

Choi , T.M. , Wallace , S.W. and Wang , Y. ( 2018 ), “ Big data analytics in operations management ”, Production and Operations Management , Vol. 27 No. 10 , pp. 1868 - 1883 .

Davis , F.D. ( 1989 ), “ Perceived usefulness, perceived ease of use, and user acceptance of information technology ”, MIS Quarterly , Vol. 13 No. 3 , pp. 319 - 340 .

Kanbara , S. , Ozawa , W. , Ishimine , Y. , Ngatu , N.R. , Nakayama , Y. and Nojima , S. ( 2016 ), “ Operational definition of disaster risk-reduction literacy ”, Health Emergency and Disaster Nursing , Vol. 3 No. 1 , pp. 1 - 8 .

Suárez , E. , Calvo-Mora , A. , Roldán , J.L. and Periáñez-Cristóbal , R. ( 2017 ), “ Quantitative research on the EFQM excellence model: a systematic literature review (1991-2015) ”, European Research on Management and Business Economics , Vol. 23 No. 3 , pp. 147 - 156 .

Venkatesh , V. and Bala , H. ( 2008 ), “ Technology acceptance model 3 and a research agenda on interventions ”, Decision Sciences , Vol. 39 No. 2 , pp. 273 - 315 .

Corresponding author

Related articles, all feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Disaster Risk Awareness and Preparedness: A Term Paper

This Term Paper aims to generate necessary data and information to assess the Disaster Risk Management in the areas of Awareness and Preparedness. Specifically, this paper aims to consider the following objectives: 1. To identify the fundamental concepts of disaster 2. To identify some relevant theories about disaster 3. To site some related literatures about disaster To identify some related components and parameters in assessing the level of disaster risk awareness and preparedness of the LGUs.

Related Papers

Fabrizio Boldrini , Fondazione Hallgarten - Franchetti Centro Studi Villa Montesca

This booklet was prepared in the frames of the SEE project www.seeproject.eu It is a report on best practices identified from existing educational projects or actions at European level on disasters’ risk awareness, prevention and preparedness measures for emergencies and self protection. Based on knowledge, expertise and innovation it aims to build a culture of safety and resilience for education. The selection of these elements of excellence was realized by a SWOT matrix analysis and will form the basis of the decision-making process for the establishment of a teaching program for disasters.

International Journal of Advanced Research (IJAR)

IJAR Indexing

This study aimed to determine the awareness of Laguna State Polytechnic University ? Siniloan Campus towards natural disasters? safety precautionary measures and mitigation. The researcher used descriptive research in finding the answers to the problems stated in the study. The study was composed of 384 respondents among the staff, faculty and students of LSPU ? SC who experienced the natural disaster. The research instrument used was structured questionnaire checklist to determine the extent of information about the level of awareness of the staff, faculty and students of LSPU-SC towards natural disaster?s safety precautionary measures and mitigation. Upon the interpretation of the gathered data of the study, it was found out that the result of the said study that Laguna State Polytechnic University-Siniloan Campus was aware on the natural disasters safety precautionary measures and mitigation. The researcher recommends the institution to conduct frequent seminars and trainings about the natural disasters in order for the constituents to be more aware and knowledgeable about the things that must be done before, during and after the occurrence of all the natural disasters that will strike either in school or in home.

Journal of Physics: Conference Series

Lucia Maminta

International Journal of Social Learning

Richard Ampo

Journal of Engineering Management and Competitiveness

MaJa Timovska

Susanna Falsaperla , R. Nave , Gemma Musacchio

Earthquake damage includes non-structural failure, failure of utility systems and, infrastructure, loss of function and other non-structural damage. Occupants, building owners, insurance companies, building inspectors and others, through their use of the buildings, systems and content, can affect the risk of such negative events. Thus, a prerequisite for more effective disaster risk reduction is increased risk awareness amongst people and in the community and state development planning process, the educational curriculum, and media. As knowledge is clearly connected with understanding risks, the perception of natural hazards and risks in the local environment should be developed with the help of education. This paper presents a comparative study of the current educational curriculum on natural hazards within the school systems in four European countries: Italy, Portugal, Spain and Iceland. None of the countries provides courses dedicated to this topic but include it within other sub...

Proceedings of International Structural Engineering and Construction

Ehab Mlybari

The Kingdom of Saudi Arabia has historic profile of multifaceted disasters, including, floods, earthquakes, volcanoes, cyclones, sand storms, rock falls, collapse of structures, epidemics, fire, terrorist acts, stampedes and other complex emergencies. The recurring enormous human and material losses emphasize the need for pursuing modern concept and approach which entail paradigm shift in handling disasters, from &#39;response centric&#39; to &#39;proactive&#39; disaster risk management (DRM) leading to disasterresilient infrastructure, building integrated response capability and achieving enhanced awareness in the societal context. The DRM encompasses all activities related to disaster mitigation, preparedness, emergency response, recovery, rehabilitation and reconstruction. The study synthesizes information obtained on DRM from published literature, personal experiences and interaction with the stakeholders. Besides, questionnaire survey was also conducted to assess the community ...

Proceedings of the Creative Construction e-Conference 2020

Ronik Patel

Kamani Perera

There are many kinds of disasters taking place in the globe such as coastal erosion, tidal waves, floods, soil erosion, increased storm intensity, species migration, fire and explosions which can be damaged to human lives as well as cultural heritage. Cultural heritage is experiencing the arts, heritage and activities that truly represent the stories and people of the past and present. This has taken much attention during the past decade. Less rainfall in the dry season may negatively affect on cultural heritage. It is very important to protect world cultural heritages from natural disasters. A significant number of human lives and cultural heritage destroy due to unavailability of effective early warning dissemination system. Countries should take preventive measures to minimize the damages created by natural disasters. Human actions are causing greater change in climate and that will have a significant adverse impact on the cultural heritage. Many historic cultural sites are likel...

Nextgen Research Publication

Among various kinds of disasters, flooding is unique in the sense that it has a very high degree of predictability, both in the short term, as well as long term. Floods can have devastating consequence and can have effects on the economy, environment and people. Though flood situations cannot be entirely prevented but steps can be taken to prevent or minimize injury, loss and speed the recovery process. Present study is an effort to assess the knowledge regarding disaster preparedness. During the study it has been found that majority of the sample had average knowledge regarding disaster preparedness but they were not prepared to deal with disaster i.e. flood situation.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

• Open access
• Published: 14 August 2024

Overcoming challenges in nursing disaster preparedness and response: an umbrella review

• Abdulellah Al Thobaity 1  

BMC Nursing volume  23 , Article number:  562 ( 2024 ) Cite this article

Metrics details

Disaster nursing plays a vital role in addressing the health needs of vulnerable populations affected by large scale emergencies. However, disaster nursing faces numerous challenges, including preparedness, logistics, education, ethics, recovery and legalities. To enhance healthcare system effectiveness during crises, it is essential to overcome these issues. This umbrella review, conducted using the Joanna Briggs Institute (JBI) methodology, synthesizes data from 24 studies to identify key strategies for improving disaster nursing. The review highlights nine key themes: Education and Training, Research and Development, Policy and Organizational Support, Technological Advancements, Psychological Preparedness and Support, Assessment and Evaluation, Role-Specific Preparedness, Interprofessional Collaboration and Cultural Competence, and Ethics and Decision-Making. The review emphasizes the importance of education, technological advancements, psychological support, and interprofessional collaboration in bolstering disaster nursing preparedness and response efforts. These elements are crucial for enhancing patient outcomes during emergencies and contributing to a more resilient healthcare system. This comprehensive analysis provides valuable insights into the various aspects essential for enhancing disaster nursing. By implementing evidence-based strategies within these nine themes, the nursing profession can enhance its capacity to effectively manage and respond to the complex needs of disaster-affected populations, ultimately improving patient care and outcomes during emergencies.

Peer Review reports

Introduction

Disaster nursing is a specialized field that focuses on the provision of care and support individuals and communities who are affected by emergencies and crises. Disaster Nursing, emphasizes the critical roles of nurses in addressing the health needs of vulnerable populations who has special needs such as elderly and children during disasters [ 1 ]. Competent disaster Nursing is essential to improve the overall effectiveness and efficiency of healthcare systems during times of crisis by ensuring the well-being and resilience of individuals and communities. However, disaster nursing faces major challenges that must be acknowledged and addressed, including preparedness and planning, logistical, and organizational, as well as education, training, recovery and ethical and legal considerations [ 2 , 3 ]. By exploring these challenges and identifying strategies for overcoming them, nursing profession can continue to evolve and enhance the ability to respond to the complex needs of those affected by disasters.

Challenges related to preparedness and planning in disaster nursing encompass various aspects that can hinder effective crisis response in many countries worldwide [ 1 , 4 , 5 ]. These include limitations in the disaster paradigm, inadequacies in the pre-hospital system, lack of coordination and cooperation among stakeholders, insufficient hospital preparedness, scarce resources and capacities, and gaps in patient knowledge [ 6 , 7 ]. Furthermore, challenges in planning for the unpredictable nature of disasters, disparities in emergency nurses’ preparedness, workplace readiness, and the preparedness of colleagues and institutions (including leadership and peers) contribute to the complexity of the issue [ 8 ]. Limited availability of training opportunities, individual preparedness due to lack of prior experience, absence of a comprehensive disaster plan, insufficient disaster training, and unassigned roles in workplace disaster plans further exacerbate the difficulties faced by nursing professionals in the realm of disaster preparedness and planning [ 8 , 9 , 10 , 11 ]. Addressing these challenges is crucial for enhancing the ability of nurses and healthcare institutions to effectively manage and respond to emergencies.

Logistical, organizational, and managerial challenges pose significant obstacles to effective disaster nursing in numerous countries worldwide. Such as Japan ; China and Iran [ 2 , 12 , 13 ] Logistical challenges, such as constructing and operating hospitals in disaster zones and addressing equipment issues, create difficulties in the provision of care [ 2 ]. Staff challenges, including the orientation of personnel in new and challenging environments, further complicate the situation [ 14 ]. Organizational and managerial challenges encompass the development and implementation of appropriate policies, procedures, and support structures, which are essential for enabling nursing professionals to work effectively under extreme conditions [ 2 ]. Adequate support from hospital administration, the promotion of evidence-based practice research, and the use of evaluation tools to assess and improve performance are crucial in overcoming these challenges. Gaps in these areas can hinder the ability of nurses and health care institutions to manage and respond effectively to emergencies, underscoring the need for comprehensive strategies to address logistical, organizational, and managerial challenges in disaster nursing.

Challenges related to education and training in disaster nursing have far-reaching consequences on the ability of nurses to effectively respond to emergencies [ 15 ]. These challenges encompass the defining roles of nurses, the creation and implementation of educational training programs, and the overall education system. Factors such as the lack of disaster educators, insufficient formal education, inadequate nurse training, and limited disaster experience hinder the development of competent and prepared nursing professionals [ 2 ]. Furthermore, challenges in understanding hospital disaster policies and procedures, and the roles of nurses in disaster management, as well as deficiencies in communication and leadership skills, contribute to the problem. Personal evacuation experiences, a scarcity of studies, the lack of specialized journals, inaccessible programs, and gaps in nursing curricula further exacerbate the difficulties faced by nursing professionals. Addressing these educational and training challenges is essential to equip nurses with the knowledge, skills, and confidence required to effectively manage and respond to disasters.

Ethical and legal challenges in disaster nursing present unique obstacles that nursing professionals must navigate while providing care in crisis situations [ 16 ]. These challenges include addressing patient-related issues, such as cultural differences, language barriers, and follow-up concerns [ 17 , 18 ]. Ethical challenges unique to disaster zones and related to the scope and scale of the disaster, along with more general ethical issues, arise in areas such as justice in resource allocation, privacy and confidentiality, beneficence and non- maleficence. Furthermore, determining appropriate triage, setting treatment priorities, working autonomously, and obtaining informed consent can be particularly complex in disaster settings [ 3 , 18 ]. Conflicts and legal issues such as allocating the resources may also emerge, further complicating the delivery of care during emergencies. Addressing these ethical and legal challenges is vital for ensuring that nursing professionals can provide compassionate and effective care while upholding their professional responsibilities and the rights of the patients they serve.

Conducting an umbrella review on overcoming the challenges faced by disaster nursing is crucial for various reasons. First, it allows for a comprehensive and systematic synthesis of evidence from multiple systematic reviews, identifying studies, evidence, and interventions employed to address these challenges, thus mapping the knowledge landscape and progress made. Secondly, it reveals gaps in the literature, highlighting areas for further research and guiding researchers in prioritizing underexplored topics. Thirdly, it offers valuable insights into effective strategies and best practices, informing policymakers, healthcare institutions, and nursing professionals about evidence-based interventions and policies. Additionally, an umbrella review can facilitate interdisciplinary collaboration by revealing shared challenges and solutions across various fields, foster innovation and the development of integrated approaches to disaster nursing, and ultimately enhancing the efficacy and resilience of healthcare systems in responding to emergencies. Hence, the aim of this umbrella review is to explore the strategies that have been implemented in overcoming nursing challenges in disaster preparedness and response.

This umbrella review was conducted following the Joanna Briggs Institute (JBI) methodology for umbrella reviews. The purpose of this review is to synthesize existing systematic reviews related to the challenges in nursing disaster preparedness and response [ 19 ]. Studies were selected for inclusion in this research based on the criteria outlined in Table  1 .

A comprehensive search strategy was developed using relevant keywords and Medical Subject Headings (MeSH) terms, including “nursing,” “disaster preparedness,” “disaster response,” “challenges,” “interventions,” “strategies,” and “effectiveness,” applied to selected databases (PubMed, CINAHL, Scopus, Web of Science, and PsycINFO) and grey literature sources. Handsearching reference lists of included articles further enhanced the search. Duplicates were removed using EndNote reference management software, and titles and abstracts were screened based on eligibility criteria. Potentially eligible full-text articles were assessed for inclusion, and the study selection process was documented using a PRISMA flowchart Fig.  1 . The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram outlines the study selection process for this review.

PRISMA flowchart of study selection process

Initially, 3,223 records were identified from databases and 68 from registers. Before screening, 1,281 duplicate records and 1,050 ineligible records were removed, leaving 960 records for screening. After excluding 858 records, 102 reports were sought for retrieval, resulting in a final inclusion of 24 studies in the review which involve the flowing : Al Thobaity , Plummer , & Williams , 2017 [ 20 ] ; Kalanlar , 2019 [ 21 ] ; Zarea et al. , 2014 [ 2 ]; Jose & Dufrene , 2014 [ 22 ]; Cong Geng , Yiqing Luo , Xianbo Pei , & Xiaoli Chen , 2021 [ 23 ]; Alice Yuen Loke , Chunlan Guo , & Alex Molassiotis , 2021 [ 5 ] Nejadshafiee , Bahaadinbeigy , Kazemi , & Nekoei-Moghadam , 2020 [ 24 ]; Karin Hugelius & Adolfsson , 2019 [ 25 ]; Veenema , Lavin , Bender , Thornton , & Schneider-Firestone , 2019 [ 26 ]; Labrague et al. , 2018 [ 27 ] Yousefi , Larijani , Golitaleb , & Sahebi , 2019 [ 28 ] ; Varghese et al. , 2021 [ 29 ]; Kalanlar, 2022 [ 30 ] ; Said & Chiang , 2020 [ 31 ]; Pourvakhshoori , Norouzi , Ahmadi , Hosseini , & Khankeh , 2017 [ 32 ]; Hutton , Veenema , & Gebbie , 2016 [ 33 ]; Su et al. , 2022 [ 34 ]; Firouzkouhi , Kako , Abdollahimohammad , Balouchi , & Farzi , 2021 [ 35 ]; Tas & Cakir , 2022 [ 36 ]; Lin , Tao , Feng , Gao , & Mashino , 2022 [ 37 ]; Fithriyyah, Alda, & Haryani, 2023 [ 38 ]; Songwathana & Timalsina, 2021 [ 39 ] and Kimin, Nurachmah, Lestari, & Gayatri, 2022 [ 40 ] Putra , Kamil , Yuswardi , & Satria , 2022 [ 41 ]. The essential information such as: authors, publication year, type of review and key strategies were extracted and summarised in Table  2 .

Data synthesis

In this umbrella review, a single investigator conducted the thematic analysis using a thorough and systematic approach. The process began with familiarization through detailed reading and note-taking, followed by manual coding to identify key concepts. Preliminary themes were developed by grouping similar codes and refined iteratively for coherence. To enhance credibility, feedback was sought from a senior qualitative researcher. Detailed documentation of the process ensured transparency, while reflexive notes and discussions with the senior researcher mitigated potential bias. This approach ensured rigorous and transparent theme identification, enhancing the findings’ reliability and validity. Data from selected studies were synthesized to create a narrative synthesis, organized by strategies for improving disaster nursing. These strategies were summarized into nine key themes: (1) Education and Training; (2) Research and Development; (3) Policy and Organizational Support; (4) Technological Advancements; (5) Psychological Preparedness and Support; (6) Assessment and Evaluation; (7) Role-Specific Preparedness; (8) Interprofessional Collaboration and Cultural Competence; and (9) Ethics and Decision-Making. This approach allowed for a comprehensive analysis of the various aspects of disaster nursing enhancement.

This umbrella review aims to explore and emphasize the diverse strategies implemented to address nursing challenges in disaster preparedness and response. By synthesizing findings from the included studies, the discussion is organized into the nine key themes previously mentioned. Through a narrative synthesis of these themes, the review provides a comprehensive understanding of the various approaches used to enhance disaster nursing. Examining these strategies is intended to inform future research, policy, and practice, ultimately leading to improved disaster preparedness and response, better patient care, and enhanced outcomes during emergencies.

Education and training

Improving disaster nursing locally and worldwide requires a multifaceted approach, starting with enhancing nurses’ understanding of core competency domains [ 10 ]. Integrating these domains into training and disaster drills helps reinforce practical skills, ensuring efficient and effective responses in real-life disaster situations [ 10 , 22 ]. Expanding undergraduate and graduate disaster nursing education on national and international levels creates a well-prepared workforce capable of addressing diverse challenges in disaster management [ 21 , 22 , 23 ]. Effective training programs can address existing gaps in education by providing ongoing professional development opportunities for nurses. Establishing dedicated organizational units within healthcare systems to prepare for and respond to disasters by educating healthcare providers, including nurses, enhances disaster preparedness by encouraging collaboration and resource sharing. Moreover, a focused approach to improving education and training in disaster nursing is crucial worldwide [ 5 , 21 , 23 , 42 ]. Developing educational content for disaster nursing requires a tailored approach that considers the unique needs and challenges of the field. This includes accounting for various types of disasters, impacted healthcare settings, and the diverse roles that nurses play in disaster situations. By addressing these distinct aspects, educational materials can better equip nurses with the skills and knowledge needed to respond to emergencies and deliver high-quality patient care in disaster preparedness and response contexts [ 3 , 42 ]. Lastly, incorporating interprofessional education promotes teamwork, communication, and coordination among different healthcare providers, ultimately contributing to enhanced disaster preparedness worldwide [ 43 ].

Research and development in disaster nursing

Research and development (R&D) are critical for advancing disaster nursing. They generate evidence-based knowledge that guides clinical practice [ 5 , 44 ]. By involving nurses in research focused on competencies, studies become more relevant and applicable, as they are rooted in real-world experiences [ 44 ]. It is essential to optimize resource allocation in order to be more efficient and effective for both disaster preparedness and response [ 5 ]. Rigorous research, combined with addressing limitations in study design and methods, enhances the quality of the evidence base, which then informs best practices in disaster nursing [ 5 , 44 ]. One area of research with significant potential is the application of simulation in disaster care. High-level studies in this field can reveal innovative training methods, improving nurses’ readiness and performance during crises [ 21 , 23 ]. Additionally, exploring practical approaches in areas such as psychosocial support, holistic health assessments, disaster nurse management, and minimizing distress for deployed nurses can contribute to comprehensive and integrated strategies. These strategies ultimately promote optimal patient care and nurse well-being during disaster response efforts.

Policy and organizational support

Policy and organizational support are crucial in strengthening disaster nursing by fostering collaboration among nursing staff, health care organizations, and governments. Key strategies include formalizing relationships between nursing staff and disaster organizations, which is essential for seamless communication and coordination during large scale emergencies [ 21 ]. Implementing robust hospital policies that promote disaster preparedness through regular drills and training can significantly enhance the readiness of healthcare facilities [ 26 ]. Investing in comprehensive disaster nursing education programs at both national and international levels addresses global nursing shortages and ensures that nurses are adequately prepared for disaster response [ 24 ]. Offering competitive salary packages, particularly in low- and middle-income countries, can improve nurse retention rates and maintain a skilled workforce capable of effective disaster management [ 27 ]. These strategies not only improve disaster response outcomes but also enhance hospital preparedness and the overall resilience of the healthcare system.

Technological advancements in disaster nursing

The integration of technological advancements presents a significant opportunity to revolutionize disaster nursing, impacting education, access to specialized care, and the efficiency of healthcare response during emergencies. As highlighted in the literature, incorporating innovative educational technologies like virtual reality and e-learning platforms can significantly improve disaster nursing training [ 23 , 34 ]. These technologies offer immersive and engaging learning experiences, allowing nurses to practice critical skills in simulated disaster scenarios without real-world risks. This is particularly crucial given the need for continuous improvement in training for diverse disaster situations [ 34 ]. Furthermore, telenursing emerges as a promising solution to address the shortage of specialized nurses in disaster-stricken areas [ 37 ]. By leveraging telecommunication technologies, experienced nurses can provide remote consultations, triage, and support to frontline healthcare workers, ensuring timely and specialized care for disaster victims. Mobile health applications and electronic health records can further enhance disaster response by streamlining communication and decision-making during crises [ 37 ]. These technologies facilitate real-time data sharing, patient tracking, and resource allocation, ultimately leading to a more coordinated and effective response.

Realizing the full potential of these technological advancements requires a collaborative effort. Nursing educators must embrace and integrate these technologies into their curricula, while healthcare organizations need to invest in the necessary infrastructure and training for their staff. Researchers play a crucial role in evaluating the effectiveness of these technologies and identifying best practices for their implementation in disaster settings. By fostering collaboration and innovation, we can leverage technological advancements to enhance disaster nursing preparedness and response, ultimately improving patient outcomes and saving lives.

Psychological preparedness and support

Psychological preparedness and support play a vital role in disaster nursing, contributing to the well-being and resilience of healthcare professionals and impacted communities. Implementing strategies like the HOPE model, proactive psychological interventions, flexible support, and including mental health provisions in disaster preparedness plans can effectively address nurses’ emotional and psychological needs during emergencies. The HOPE model for disaster nursing is a framework emphasizing holistic health assessment, immediate response, professional adaptation, and recovery [ 25 ]. Studies have highlighted the importance of psychological preparedness, emphasizing the need for proactive psychological interventions and mental health provisions in preparedness plans due to the mental health impact of the COVID-19 pandemic on nurses [ 29 ]. It is essential to improve nurses’ psychological preparedness and prioritize education to enhance their ability to respond effectively to disasters [ 31 ]. Some scholars emphasize the need for targeted training that incorporates psychological support [ 32 , 35 ], while others discuss strategies to address the complexities of disaster contexts, including psychological readiness [ 39 ]. By prioritizing psychological preparedness and support, healthcare organizations and policymakers can equip nurses to better handle challenges during disasters, ultimately resulting in enhanced patient care and a more robust healthcare system.

Assessment and evaluation

Assessment and evaluation play a crucial role in disaster nursing, offering key insights into the preparedness and abilities of the nursing workforce. By broadening the scope of existing scales, creating comprehensive assessment tools, and emphasizing improvements in nurses’ psychological preparedness, knowledge, and skills, healthcare organizations and educators can gain a deeper understanding of the strengths and weaknesses in current disaster nursing practices. For instance [ 27 ], systematically reviewed literature to gauge nurses’ preparedness for disaster response, identifying gaps and areas for improvement. Similarly [ 28 ], conducted a systematic review and meta-analysis to assess the knowledge, attitudes, and performance of Iranian nurses regarding disaster preparedness, highlighting key areas needing enhancement. Furthermore [ 29 ], explored the mental health outcomes of nurses globally during the COVID-19 pandemic, underscoring the importance of psychological preparedness. Additionally [ 26 ], assessed nurse readiness for radiation emergencies and nuclear events, providing critical insights into preparedness gaps and specific roles and responsibilities. These studies collectively underscore the necessity for rigorous assessment and evaluation frameworks in disaster nursing, enabling the implementation of targeted interventions to boost nurses’ capacity to deliver effective care during disasters, thereby fostering a more resilient and responsive healthcare system.

Role-specific preparedness

Role-specific preparedness is vital in disaster nursing, ensuring that nurses possess the required knowledge and skills to effectively manage diverse emergencies, such as radiation and nuclear events [ 20 ]. underscore the importance of identifying core competency domains through a scoping review to enhance disaster nursing. Similarly, [ 21 ] highlights the challenges and opportunities within disaster nursing education in Turkey, emphasizing the need for integrative training approaches [ 2 ]. Focus on the unique roles of nurses in disaster management in Iran, advocating for role-specific training tailored to regional needs [ 22 ]. Argue for incorporating disaster preparedness competencies into the undergraduate nursing curriculum, suggesting that suitable instruction methods are crucial for effective education. Moreover [ 23 ], map the application of simulation in disaster nursing education, demonstrating that simulation-based training can significantly enhance nurses’ preparedness for handling radiation and nuclear emergencies. By incorporating these findings into educational and training programs, healthcare organizations and policymakers can better equip nurses to deliver specialized care during such critical events, leading to a more efficient and coordinated healthcare response.

Interprofessional collaboration and cultural competence

Interprofessional collaboration and cultural competence are crucial for effective disaster nursing, fostering a comprehensive and inclusive approach to emergency response. Interprofessional collaboration involves coordinated efforts among different healthcare professions, enhancing communication, reducing redundancies, and ensuring a more efficient and cohesive response to emergencies. By integrating cultural competence into disaster relief planning and public health research, and by educating and training nurses in both interprofessional collaboration and cultural competence, healthcare professionals’ ability to work cooperatively with diverse populations during emergencies is significantly enhanced. This dual focus not only improves therapeutic relations but also ensures that all aspects of patient care are addressed effectively in a multidisciplinary context. Training in these areas is essential, as it enhances disaster response capabilities. Encouraging cultural understanding and fostering interprofessional collaboration ensure that disaster nursing practices are more adaptable and responsive to the distinct needs of various communities. These practices ultimately lead to better emergency management and care outcomes. Studies emphasize the importance of these elements in improving disaster response. Hugelius and Adolfsson, through their systematic review of real-life experiences, highlight the necessity of interprofessional collaboration, while Lin et al. propose a framework for cultural competence in disaster nursing [ 25 , 37 ]. These findings underscore the critical role that targeted training in cultural competence and interprofessional collaboration plays in effective disaster response.

Ethics and decision-making

Ethics and decision-making are fundamental components of disaster nursing, guiding healthcare professionals as they navigate the complexities and challenges that emerge during emergencies. By recognizing potential ethical dilemmas, pinpointing factors that encourage ethical decision-making, devising strategies for implementing ethics, and evaluating the impact of ethical practices in disaster settings, healthcare organizations and educators can better prepare nurses to make well-informed and morally responsible choices under pressure. Integrating ethics into nursing education, institutional policies, and disaster preparedness plans empowers nurses to maintain ethical standards and provide empathetic care, even amid the most demanding situations. Nurses prepare for and respond to emergencies, disasters, conflicts, epidemics, pandemics, social crises, and conditions of scarce resources. The safety of those who receive care and services is a responsibility shared by individual nurses and the leaders of health systems and organizations. This involves assessing risks and developing, implementing, and resourcing plans to mitigate these. Several studies underscore the importance of ethics and decision-making in disaster nursing. For instance, a model for disaster nursing was developed through a systematic review of real-life experiences, highlighting the ethical challenges faced by nurses during disaster response. Their findings emphasize the need for robust ethical frameworks and support systems to guide nurses in making difficult decisions [ 25 ]. Similarly, core competencies in disaster nursing, which include ethical decision-making as a crucial domain, were identified. It is suggested that integrating ethical training into disaster preparedness programs can enhance nurses’ ability to handle ethical dilemmas effectively [ 20 ].They suggest that integrating ethical training into disaster preparedness programs can enhance nurses’ ability to handle ethical dilemmas effectively.

This umbrella review examines strategies to tackle nursing challenges in disaster preparedness and response, consolidating the findings into nine key themes: Education and Training, Research and Development, Policy and Organizational Support, Technological Advancements, Psychological Preparedness and Support, Assessment and Evaluation, Role-Specific Preparedness, Interprofessional Collaboration and Cultural Competence, and Ethics and Decision-Making. To enhance disaster nursing, Education and Training should emphasize core competency domains and integrate them into curricula and drills, while Research and Development should be nurse-centric, improving resource allocation and evidence quality. Policy and organizational support should encourage collaboration among nursing staff, healthcare organizations, and governments, reinforcing hospital policies and addressing global nursing shortages. Technological advancements, such as virtual reality and e-learning, hold the potential to transform disaster nursing education. Psychological preparedness and support are essential for nurses’ well-being and resilience, and assessment and evaluation frameworks are crucial for identifying gaps and areas for improvement. Role-specific preparedness equips nurses with the necessary knowledge and skills for various emergencies. Interprofessional collaboration and cultural competence promote a comprehensive and inclusive approach to emergency response, and ethics and decision-making guide healthcare professionals in navigating complexities during disasters. This review aims to inform future research, policy, and practice, ultimately enhancing disaster preparedness and response, patient care, and outcomes during emergencies.

Data availability

No datasets were generated or analysed during the current study.

Powers R, Daily E. International disaster nursing: Cambridge University Press; 2010.

Zarea K, Beiranvand S, Sheini-Jaberi P, Nikbakht-Nasrabadi A. Disaster nursing in Iran: challenges and opportunities. Australasian Emerg Nurs J. 2014;17(4):190–6.

Article   Google Scholar  

Al Harthi M, Al Thobaity A, Al Ahmari W, Almalki M. Challenges for nurses in disaster management: a scoping review. Risk Manage Healthc Policy. 2020:2627–34.

Alharazi A, Al Thobaity A. From planning to execution: delving into the Crucial Role and challenges of HEPPUs in Hospital Emergency Management. Int J Disaster Risk Sci. 2023;14(5):858–67.

Loke AY, Guo C, Molassiotis A. Development of disaster nursing education and training programs in the past 20 years (2000–2019): a systematic review. Nurse Educ Today. 2021;99:104809.

Article   PubMed   Google Scholar  

Feizolahzadeh S, Vaezi A, Mirzaei M, Khankeh H, Taheriniya A, Vafaeenasab M, et al. Barriers and facilitators to provide continuity of care to dischargeable patients in disasters: a qualitative study. Injury. 2019;50(4):869–76.

Aliakbari F, Hammad K, Bahrami M, Aein F. Ethical and legal challenges associated with disaster nursing. Nurs Ethics. 2015;22(4):493–503.

Lim GH, Lim BL, Vasu A. Survey of factors affecting health care workers’ perception towards institutional and individual disaster preparedness. Prehosp Disaster Med. 2013;28(4):353–8.

VanDevanter N, Raveis VH, Kovner CT, McCollum M, Keller R. Challenges and resources for nurses participating in a Hurricane Sandy hospital evacuation. J Nurs Scholarsh. 2017;49(6):635–43.

Al Thobaity A, Williams B, Plummer V. A new scale for disaster nursing core competencies: development and psychometric testing. Australasian Emerg Nurs J. 2016;19(1):11–9.

Arbon P, Cusack L, Ranse J, Shaban RZ, Considine J, Kako M, et al. Exploring staff willingness to attend work during a disaster: a study of nurses employed in four Australian emergency departments. Australasian Emerg Nurs J. 2013;16(3):103–9.

Kayama M, Akiyama T, Ohashi A, Horikoshi N, Kido Y, Murakata T, et al. Experiences of municipal public health nurses following Japan’s earthquake, tsunami, and nuclear disaster. Public Health Nurs. 2014;31(6):517–25.

Li Y, Li S, Chen S, Xie X, Song Y, Jin Z, et al. Disaster nursing experiences of Chinese nurses responding to the Sichuan Ya’an earthquake. Int Nurs Rev. 2017;64(2):309–17.

Article   CAS   PubMed   Google Scholar  

Alpert EA, Weiser G, Kobliner D, Mashiach E, Bader T, Tal-Or E, et al. Challenges in implementing international standards for the field hospital emergency department in a disaster zone: the Israeli experience. J Emerg Med. 2018;55(5):682–7.

Hamdi A, Al Thobaity A. Enhancing Disaster Triage competencies through Simulation-based training: an interventional study among undergraduate nursing students. Sustainability. 2023;15(21):15513.

Al Nhdi N, Al Asmari H, Al Thobaity A. Investigating indicators of waiting time and length of Stay in Emergency Departments. Open Access Emerg Med. 2021:311–8.

Alomrani M. Disaster nursing education: a qualitative case study to determine the key concepts for national education in the Kingdom of Saudi Arabia. (KSA): University of York; 2021.

Google Scholar  

Brinjee D, Al Thobaity A, Al Ahmari W, Almalki M. Current issues facing disaster nursing education in Saudi Arabia. 2021.

Aromataris E, Fernandez RS, Godfrey C, Holly C, Khalil H, Tungpunkom P. Methodology for JBI umbrella reviews. 2014.

Al Thobaity, Plummer V, Williams B. What are the most common domains of the core competencies of disaster nursing? A scoping review. Int Emerg Nurs. 2017;31:64–71.

Kalanlar B. The challenges and opportunities in disaster nursing education in Turkey. J Trauma Nursing| JTN. 2019;26(3):164–70.

Jose MM, Dufrene C. Educational competencies and technologies for disaster preparedness in undergraduate nursing education: an integrative review. Nurse Educ Today. 2014;34(4):543–51.

Geng C, Luo Y, Pei X, Chen X. Simulation in disaster nursing education: a scoping review. Nurse Educ Today. 2021;107:105119.

Nejadshafiee M, Bahaadinbeigy K, Kazemi M, Nekoei-Moghadam M. Telenursing in incidents and disasters: a systematic review of the literature. J Emerg Nurs. 2020;46(5):611–22.

Hugelius K, Adolfsson A. The HOPE model for disaster nursing–A systematic literature review. Int Emerg Nurs. 2019;45:1–9.

Veenema TG, Lavin RP, Bender A, Thornton CP, Schneider-Firestone S. National nurse readiness for radiation emergencies and nuclear events: a systematic review of the literature. Nurs Outlook. 2019;67(1):54–88.

Labrague L, Hammad K, Gloe D, McEnroe-Petitte D, Fronda D, Obeidat A, et al. Disaster preparedness among nurses: a systematic review of literature. Int Nurs Rev. 2018;65(1):41–53.

Yousefi K, Larijani HA, Golitaleb M, Sahebi A. Knowledge, attitude and performance associated with disaster preparedness in Iranian nurses: a systematic review and meta-analysis. Adv J Emerg Med. 2019;3(4).

Varghese A, George G, Kondaguli SV, Naser AY, Khakha DC, Chatterji R. Decline in the mental health of nurses across the globe during COVID-19: a systematic review and meta-analysis. J Global Health. 2021;11.

Kalanlar B. Psychometric properties of disaster preparedness tools in nurses: a systematic literature review. Prehosp Disaster Med. 2022:1–6.

Said NB, Chiang VC. The knowledge, skill competencies, and psychological preparedness of nurses for disasters: a systematic review. Int Emerg Nurs. 2020;48:100806.

Pourvakhshoori N, Norouzi K, Ahmadi F, Hosseini M, Khankeh H. Nursing in disasters: a review of existing models. Int Emerg Nurs. 2017;31:58–63.

Hutton A, Veenema TG, Gebbie K. Review of the International Council of Nurses (ICN) framework of disaster nursing competencies. Prehosp Disaster Med. 2016;31(6):680–3.

Su Y, Wu XV, Ogawa N, Yuki M, Hu Y, Yang Y. Nursing skills required across natural and man-made disasters: a scoping review. J Adv Nurs. 2022;78(10):3141–58.

Article   PubMed   PubMed Central   Google Scholar  

Firouzkouhi M, Kako M, Abdollahimohammad A, Balouchi A, Farzi J. Nurses’ roles in nursing disaster model: a systematic scoping review. Iran J Public Health. 2021;50(5):879.

PubMed   PubMed Central   Google Scholar  

Tas F, Cakir M. Nurses’ knowledge levels and preparedness for disasters: A systematic review. Int J Disaster Risk Reduct. 2022:103230.

Lin T, Tao Y, Feng X, Gao Y, Mashino S. Cultural competence for disaster nursing: a scoping review of the Chinese and English literature. Int J Disaster Risk Reduct. 2022:103188.

Fithriyyah YN, Alda AK, Haryani H. Trends and ethical issues in nursing during disasters: a systematic review. Nurs Ethics. 2023:09697330231155602.

Songwathana P, Timalsina R. Disaster preparedness among nurses of developing countries: an integrative review. Int Emerg Nurs. 2021;55:100955.

Kimin A, Nurachmah E, Lestari F, Gayatri D. Factors affecting nurses’ ability to provide effective care in a disaster response: A review. Journal of Public Health Research. 2022;11(2):jphr. 2021.732.

Putra A, Kamil H, Yuswardi Y, Satria B. Knowledge and practice among public health nurses in disaster response phase. J Liaquat Univ Med Health Sci. 2022;21(02):89–96.

Brinjee D, Al Thobaity A, Almalki M, Alahmari W. Identify the disaster nursing training and education needs for nurses in Taif City, Saudi Arabia. Risk Manage Healthc Policy. 2021:2301–10.

Murray B, Judge D, Morris T, Opsahl A. Interprofessional education: a disaster response simulation activity for military medics, nursing, & paramedic science students. Nurse Educ Pract. 2019;39:67–72.

Hugelius K. Disaster nursing research: a scoping review of the nature, content, and trends of studies published during 2011–2020. Int Emerg Nurs. 2021;59:101107.

Download references

Acknowledgements

The auther would like to encourage the deanship of graduate studies and scientific research, Taif University for funding this study.

This research received no external funding.

Author information

Authors and affiliations.

Medical Surgical Nursing, College of Nursing, Taif Unoversity, P. O. Box 11099, Taif, 21944, Saudi Arabia

Abdulellah Al Thobaity

You can also search for this author in PubMed   Google Scholar

Contributions

This umbrella review, conducted by a single author; AA.

Corresponding author

Correspondence to Abdulellah Al Thobaity .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Informed consent, institutional review board statement, competing interests.

The author declares no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Al Thobaity, A. Overcoming challenges in nursing disaster preparedness and response: an umbrella review. BMC Nurs 23 , 562 (2024). https://doi.org/10.1186/s12912-024-02226-y

Download citation

Received : 03 June 2024

Accepted : 05 August 2024

Published : 14 August 2024

DOI : https://doi.org/10.1186/s12912-024-02226-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Keyword disaster nursing
• Preparedness strategies
• Healthcare system resilience
• Interprofessional collaboration
• Emergency response education

BMC Nursing

ISSN: 1472-6955

• General enquiries: [email protected]