AN ABSTRACT OF THE DISSERTATION OF Alexandra Buylova for the degree of Doctor of Philosophy in Public Policy presented on May 18, 2018. Risk Perceptions and Behavioral Intentions: Responses to the Threat of Cascadia Subduction Zone Earthquakes and Tsunamis. Abstract approved: _____________________________ Lori A. Cramer

This research examined public perceptions of risk, behavioral intentions in the event of the M9

Cascadia Subduction Zone (CSZ) earthquake and tsunami on the Oregon Coast, and factors that

may influence both attitudes and intentions. A household survey was conducted to understand

public opinion in Seaside, Oregon, which is located within the impact radius of the CSZ earthquakes

and tsunamis. Mediation analysis was applied to evaluate relationships between the three levels of

variables to examine ways in which human decision-making occurs under conditions of risk.

Research results found a positive association between risk perception and evacuation behavioral

intention, suggesting that higher risk perception motivates people to be proactive and adopt

recommended actions of immediate evacuation in a local tsunami emergency. In addition, relying on

mediation analysis, the study found that cognitive constructs of response efficacy and self-efficacy

played a mediating role between socio-environmental factors and behavioral intention, supporting

arguments advocated by theories of cognition that attitudes translate outside influences into

behavioral intentions. Older individuals were identified as vulnerable to a local tsunami risk due to

their lower levels of risk perception, self-efficacy, and physical mobility capacities.

©Copyright by Alexandra Buylova May 18, 2018

All Rights Reserved

Risk Perceptions and Behavioral Intentions: Responses to the Threat of Cascadia Subduction Zone Earthquakes and Tsunamis

by

Alexandra Buylova

A DISSERTATION

submitted to

Oregon State University

in partial fulfillment of the requirements for the

degree of

Doctor of Philosophy

Presented May 18, 2018 Commencement June 2018

Doctor of Philosophy dissertation of Alexandra Buylova presented on May 18, 2018

APPROVED: ______________________________________________________________________________ Major Professor, representing School of Public Policy ______________________________________________________________________________ Director of the School of Public Policy ______________________________________________________________________________ Dean of the Graduate School I understand that my dissertation will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request. ______________________________________________________________________________

Alexandra Buylova, Author

ACKNOWLEDGEMENTS

I am grateful for continuous guidance and support of my major advisor Dr. Lori Cramer and my committee members: Dr. Brent Steel, Dr. Drew Gerkey, Dr. Denise Lach, and my Graduate Representative Dr. Marie Harvey. I am also thankful for financial and logistical support for this project and my education to the School of Public Policy, and Sea Grant and NSF grants of Dr. Lori Cramer and Dr. Haizhong Wang. And gods bless my friends!

TABLE OF CONTENTS Page

1 Introduction………………………………………………………………………………...…...1 2 Literature review……………………………………………………………………………...…9 2.1 Behavioral intentions……………………………………………………………...…...9 2.2 Cognitive and socio-environmental factors……………………………………….........13 2.2.1 Risk perception……………………………………………………………...15

2.2.2 Self-efficacy and response efficacy…………………………………………..19 2.2.3 Socio-environmental factors……………………………………………........21 2.2.4 Demographic variables……………………………………………………...28 3 Background and method………………………………………………………………………..32

3.1 Sample………………………………………………………………………….…..…32

3.2 Survey design…………………………………………………………………………35

3.3 Survey distribution………………………………………………………………....….37

3.4 Variables description and operationalization…………………………………………..39

3.4.1 Dependent variables………………………………………………………...39 3.4.2 Independent cognitive variables…………………………………………......42 3.4.3 Independent socio-environmental variables……………………………....…43 3.4.4 Demographic variables…………………………………………………...…50 3.5 Data cleaning……………………………………………………………………...….51 3.6 Analytical procedures……………………………………………………………...….53 4 Results………………………………………………………………………………………….54 4.1 Behavioral intentions descriptive statistics…………………………………………….54 4.2 OLS regression analysis…………………………………………………………....….56 4.3 Mediation analysis…………………………………………………………………….64 4.4 Analysis of variance……………………………………………………………....…...83

5 Discussion………………………………………………………………………………...……88

TABLE OF CONTENTS (Continued) Page

6 Limitations and suggestions for future research…………………………………………….....102

7 Key findings and policy recommendations………………………..…………………… ….…108 8 Conclusion……………………………………………………………………………….…...115 Bibliography……………………………………………………………………………....……117 Appendices………………………………………………………………………………....…..129 Appendix A Survey Cover Letter………………………………………………………130 Appendix B Survey Questionnaire………………………………………….…....……..131

LIST OF FIGURES Figure Page 1.1 Illustration of the Cascadia Subduction Zone extension from Northern California to

Canada.....................................................................................................................................................3 2.1 Research framework, indicating relationships between variables that are addressed in

research questions and hypotheses………………………………………………...………31 3.1 Map of Seaside and Tsunami Wave Arrival Time………………………………..……...…47 4.1 Descriptive statistics of behavioral intentions……………………………………………..55 4.2 Illustration of a total effect of X on Y…………………………………………………......65 4.3 Illustration of a simple diagram of mediation……………………………………...………66

LIST OF TABLES Table Page

2.1 Research questions and research hypotheses……………………………………...……….30 3.1 Sample bias, comparing U.S. Census statistics for Seaside with survey respondents….….....35 3.2 Descriptive statistics of behavioral intentions……………………………………………..40 3.3 Pattern matrix for behavioral intent………………………………………………....…….41 3.4 Factor matrix of perceived severity………………………………………………………..42 3.5 Summary of hazard-relevant knowledge survey responses………………………........……45 3.6 Factor matrix for knowledge confidence……………………………………....…………..45 3.7 Summary of dependent and independent variables……………………………………..…49 3.8 Summary of demographic variables……………………………………………………….51 4.1 OLS regression output for leadership dependent variable (coefficients are standardized).....57 4.2 Pearson’s correlations between independent variables……………………......……………60 4.3 Pearson’s correlations between demographic variables……………………………….........61 4.4 OLS regression output for delay dependent variable (coefficients are standardized)….........62 4.5 Mediation Analysis Results Leadership Dependent Variable……………………......…...…70 4.6 Mediation Analysis Results Delay Dependent Variable……………………………........….71 4.7 Summary of research hypotheses testing………………………………………….…….…82 4.8 Descriptive statistics and Shapiro-Wilk test of normality for dependent variables…..............84 4.9 Results of ANOVA and Kruskall-Wallis tests………………………….……......…....…..…86

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1 Introduction

Potential for more intense natural hazards and disasters is increasing as Earth system

dynamics are changing rapidly and unexpectedly, creating more frequent and intense storms,

wildfires, droughts, and floods (Mileti 1999; EM-DAT(International Disaster Database)). In addition

to these slow-onset and high-frequency events, a substantial danger to human life is inherent in

rapid-onset low-frequency tsunamis, whose impacts on coastal areas have been significant in the past

century. In a recent large-scale event in Chile (1960) a tsunami killed over 1,000 people across Chile,

Japan, and Hawaii. In Alaska (1964) a tsunami killed 117 people; in Sumatra (2004) a tsunami

claimed more than 288,000 lives in a dozen countries; in the Samoa Islands (2009) a tsunami killed

192 people across the affected islands; and in Tohoku-oki (2011) a tsunami left 15,892 dead in Japan

(Apatu et al. 2016). Considering the devastating outcomes of these events, it is important to

understand how people prepare for and respond to tsunami threats to manage exposure to this

natural hazard on individual and organizational levels.

The importance of preparation for effective disaster response, including tsunamis, has been

voiced by the United Nations in the 2015-2030 Sendai Framework for Disaster Risk Reduction.

Reducing tsunami risks in the United States can be difficult due to the lack of information and

experiential knowledge of such events. In the U.S. the most recent tsunamis took place when a Chile

(1960) tsunami reached the Hawaiian coast killing 61 and injuring more than 100 people in the town

of Hilo (Lachman et al. 1961); an Alaska (1964) earthquake and tsunami killed 106 in Alaska, 5

people in Oregon, and 13 people in California; and a Samoa Islands (2009) earthquake and tsunami

claimed 34 lives in the American Samoa (Apatu et al. 2016). As a result, a majority of the available

data on tsunami preparedness and evacuation is based on studies of hurricanes, storms, floods, and

wildfires due to the frequency of such events in the U.S. (Lindell et al. 2011; Lindell and Prater 2007;

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Toledo et al. 2018). However, these events are more predictable and require different preparedness

and response strategies in contrast to rapid-onset tsunamis.

Most often tsunamis are caused by earthquakes along the ocean seafloor. The biggest ones

occur at subduction zones like the Cascadia Subduction Zone (CSZ) along the west coast of the U.S.

and Canada, where an oceanic plate slides beneath a continental plate. Other causes of tsunamis

include underwater volcanic eruptions and landslides. Tsunamis that travel thousands of miles from

their source are called distant tsunamis. The more devastating events, however, are local or near-

field tsunamis. They can approach coastlines within minutes of an earthquake onset, leaving

minimum reaction time for populations in risk zones. By the time a tsunami wave hits the coastline,

it may have a speed of up to 30 mph and a height from 20 to 100 feet, depending on the

characteristics of the shoreline (water depth and shape of the shore) and the degree of fault

movement. Most local tsunamis are composed of a series of waves that can arrive within hours or

even days after the earthquake. The first wave may not be the most destructive (Oregon Department

of Land Conservation and Development 2016). An earthquake and a tsunami along the Cascadia

Subduction Zone would fall into the category of a local tsunami for the west coast of North

America. This research centers around understanding individual perceptions and behavioral

intentions under the risk of CSZ earthquakes and tsunamis on the Oregon coast.

The CSZ is a fault line that extends along the west coast of North America from the

Mendocino Ridge off the coast of northern California to northern Vancouver Island, British

Columbia. A magnitude 9.0+ earthquake (which is considered the worst-case CSZ scenario, or M9

scenario) along the CSZ is predicted to occur in the next 50 years, with different probabilities of the

strength, extent, and location of the earthquake epicenter (Goldfinger et al. 2012). Figure 1.1

illustrates the location of the CSZ relative to the west coast of North America and Oregon – the

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primary place of interest in this research. The map indicates the edge of the subduction zone, where

the Juan de Fuca oceanic plate begins to slide underneath the North American continental plate,

creating a traction that can cause an earthquake.

Figure 1.1: Illustration of the Cascadia Subduction Zone extension from Northern California to Canada; Source: FEMA,

Secretary of State Audit report, January 2018.

A CSZ MW (moment magnitude) 9.0 earthquake is capable of generating a tsunami wave of 8

meters and higher that could inundate the coast in 15-30 minutes after the initial earthquake (Wood

et al. 2010). Earthquake magnitudes are measured on the seismic moment (MW) scale, which is one

of the most common metrics used by modern seismologists to describe the physical strength of an

earthquake. It is different from the Richter magnitude scale or local magnitude (ML) and surface-

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wave magnitude (MS) estimates, which are becoming less popular. Some scientists argue that the

latter methodologies do not take into the account various characteristics and effects of the natural

event. For example, the 1964 Alaska earthquake using two different scales can be described as MS

8.3 and MW 9.2 event (Spence et al. 1989, USGS).

Several possible CSZ tsunami scenarios have been developed by the Oregon Department of

Geology (DOGAMI) for the Oregon coast using percentages of potential variability in the

subduction zone movement. The worst-case or M9 CSZ tsunami will happen in the case of a full

subduction zone slippage. M9 CSZ tsunami scenario was used to generate tsunami inundation maps

for local tsunami preparedness in Oregon coastal communities (Priest et al. 2016). There are several

methodologies used to estimate the probability of earthquakes along the CSZ, with varying degrees

of uncertainty. A probability of 7-11% of CSZ full rupture (M9 scenario) in the next 50 years was

estimated using both a Poisson and time-dependent calculations. According to failure-analysis, CSZ

full rupture will have exceeded approximately 25% of known recurrence intervals by 2060. For a

partial rupture along the southern segment of the CSZ, the probability of the event occurrence rises

to 18% (Poisson distribution) and 32–43% (time-dependent model), while the earthquake will have

exceeded approximately 85% of its known recurrence intervals by 2060 according to failure analysis

(Goldfinger et al. 2012). Full-length CSZ earthquake recurrence interval is estimated at 530 years,

with the last event taking place on January 26, 1700. These probabilities suggest a presence of real

risk for communities along the west coast of North America. According to Wood et al. (2015), the

CSZ tsunami hazard areas in Northern California, Oregon, and Washington contain 94,872

residents, 42,424 employees, 486 public venues, 440 dependent-care facilities, and 2,314 businesses

with a significant customer presence. In addition, continuous development of U.S. coastal regions is

exacerbating the vulnerability of the coast to tsunami impacts (Crossett 2005; Mileti 1999). Potential

5

damages to coastal communities in terms of mortality rates and impacts on infrastructure could be

substantial as a result of CSZ earthquakes and tsunamis.

It is important to remember that in the event of a local tsunami populations at risk are

generally subjected to two hazards: an earthquake and a tsunami. Individuals need to survive the

impacts of an earthquake before they attempt to evacuate outside of a tsunami hazard zone.

Earthquake preparedness and mitigation is not the focus of this research. In this study, earthquake is

regarded as the primary natural warning sign of an upcoming tsunami. However, it is important to

consider that people may be injured by an earthquake before they have a chance to escape from a

tsunami wave. Tsunami evacuation routes can also be severely damaged or completely impassable

due to earthquake impacts. When thinking about tsunami preparedness, one cannot exclude

earthquake impacts from consideration. However, because earthquakes and tsunamis are different

types of events that imply a different type of response, this research mainly focuses on the threat of

local tsunamis.

From an emergency management perspective, natural hazards are understood in terms of

mitigation, preparation, response, and recovery stages. Risk reduction activities that happen during

any of these stages contribute to the overall resilience of a system as they minimize risks to life and

impacts to physical, economic, and social structures. Investing in all stages of natural hazards

emergency management is important. However, the nature of a local tsunami is unique because of

the immediacy and the unpredictability of the event onset. In local tsunamis, many lives are lost in

the first hour of a disaster (Yun and Hamada 2012). Therefore, generating knowledge that can assist

emergency planners in minimizing human mortality during the response stage is essential and is the

primary focus of this study.

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Contributions to risk reduction efforts for a successful tsunami response can include

developments in earthquake early warning systems (EWS), road networks and evacuation routes,

relocation of critical facilities outside of the tsunami inundation zone, construction of tsunami

evacuation towers, promotion of awareness educational programs, and others (Raskin and Wang

2016; Dunn et al. 2016; McCaughey et al. 2017). However, benefits of these activities cannot be fully

understood without measuring and forecasting individual and social behaviors. What actions people

adopt to protect themselves in reaction to and in the risk of natural hazards depends on a range of

cognitive, emotional, social, environmental, and institutional factors, as well as on built and natural

environments (Dunn et al. 2016). For example, building a tsunami evacuation tower may be the most

effective approach to ensure a successful evacuation during a tsunami event in certain locations. Yet,

people may be unaware of tsunami evacuation towers in their communities or they may not trust the

structures to utilize. Therefore, one may consider investing in a different risk reduction strategy or

try to change people’s attitudes toward built structures.

A successful protective response to tsunamis is impossible without active participation of

individuals, involved in making tough decisions. Effective planning for tsunamis requires advances

in engineering and hazard forecasting. However, hard protective structures do not guarantee full

safety (Mimura et al. 2011). In addition, they do not eliminate the importance of understanding how

people evaluate and interpret risks and what actions they take to protect themselves from hazard

impacts. Human behavior in general is difficult to predict and even more so in stressful and chaotic

events like disaster emergencies (Shapira et al. 2018). Individual response to risk involves cognitive

and emotional reactions that are influenced by social, institutional, and environmental contexts.

Because of a variety of possible behaviors that people can adopt in disaster situations, measuring and

7

predicting individual perceptions and behaviors is an integral part of preparedness and risk reduction

efforts (Dunn et al. 2016).

This study is a pre-impact assessment of individual perceptions and behavioral intentions of

the Oregon coastal population in the event of a local M9 CSZ earthquake and tsunami. Only

behavioral intentions could be empirically assessed. It is important to pay attention to the Oregon

coast in the context of this research for several reasons. The population of Oregon’s coastal counties

grew by 73.4% from 1960 to 2008 (Wilson and Fischetti 2010), with the population exposed to

tsunami hazard projected to increase by 3880 households and 6940 residents by 2061 in U.S. Pacific

Northwest (Sleeter et al. 2017). The increase in the number of people vulnerable to CSZ earthquakes

and tsunamis drives the importance of disaster preparedness to prevent injuries and reduce damages

to infrastructure along the coast.

Why do people have certain intentions and what factors may explain variation in their

behavioral intentions and preparation? Answers to these questions could assist in emergency

planning activities, from developing public education and awareness programs to improving

evacuation forecasting models. To address these questions, I offer a critical overview of natural

hazards scholarship and theory on the relationship between how people perceive and interpret risk

and the intentions they form as a consequence of such interpretations. The goal of my research is to

provide an additional insight into human decision-making process under conditions of natural

hazard risks.

The prominence of natural disaster research is underscored by a number of studies that have

examined public perceptions of risk, adoption of protective behaviors and behavioral intentions, and

factors that determine both perceptions and behavior in various emergency contexts including

technological, environmental, and man-made disasters. This research draws on the literature of

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behavior in rapid-onset natural disasters such as earthquakes, tsunamis, and volcanic eruptions. Yet,

because of fewer studies on earthquakes and tsunamis, literature on slow onset natural disasters such

as hurricanes and floods is examined as well. I do not attempt to cover the literatures of risk

perceptions and behavior within contexts of technological and industrial hazards, war and terrorism,

disease outbreaks, and the like. There are continuities within literatures on natural hazards. People’s

behavior in certain non-natural hazard situations (e.g. terrorist attacks or nuclear plan meltdowns)

can resemble human reactions in rapid onset natural disasters (Riad et al. 1999). Yet, these

intersections mostly fall beyond the scope of this research.

The remainder of this manuscript is structured as follows. Chapter 2 provides a

comprehensive review of natural hazards literature about public perceptions and behaviors in

preparation for and during emergencies, focusing on rapid onset disasters such as earthquakes and

tsunamis. Findings from the literature are used as a benchmark for identifying variables of

importance in the analysis of behavioral intentions of individuals located in the CSZ risk area in

Seaside, Oregon. The data for the analysis was collected through a mail-based survey conducted in

the fall of 2017. Chapter 2 also proposes research hypotheses and research questions. Chapter 3

describes location of the study, sampling method, data collection procedures, variable

operationalization, and briefly reviews analytical approaches employed for data analysis. Chapter 4

explains in greater details analytical procedures employed in this research and presents results of data

analysis. Chapter 5 discusses the major research findings. Chapter 6 proposes practical applications

of the results for emergency preparedness. Chapter 7 concludes with research limitations and

suggestions for future work.

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2 Literature Review

2.1 Behavioral intentions

Studies that examined behavior in natural disaster contexts often use terminology of

‘protective responses’ or ‘hazard adjustments’ to refer to people’s behavior in response to risk.

Hazard adjustments were conceptualized as a set of protective actions to reduce one’s vulnerability

to risk (Burton et al. 1993; Gregg et al. 2004). In different disaster settings ‘protective responses’ can

imply different types of behavior, e.g. retrofitting a house under a threat of an earthquake, storing

additional water and food at home in preparation for floods and storms, evacuating outside of

hazard area in the event of hurricanes and wildfires, and escaping to a high ground in the case of

tsunamis.

In terms of personal safety, evacuation from a hazard zone is considered the best protective

behavior in both slow- and rapid-onset natural disasters (Mas et al. 2012, 2013; Wei et al. 2017;

Charnkol and Tanaboriboon 2006; Takabatake et al. 2017; Wei and Lindell 2017). Research on

evacuation behaviors in slow-onset natural disasters (e.g. hurricanes, tornadoes, floods) is prevalent,

recognizing the frequent nature of such events (Dash and Gladwin 2007; Whitehead et al. 2000;

Kang et al. 2007; Baker 1991; Riad et al. 1999; Huang et al. 2016; Huang et al. 2012; Meyer et al. 2018;

Peacock 2003). Generally, during slow-onset disasters there is time for people to assess risks and

weight options of protective actions (e.g. evacuation vs. non-evacuation, reinforcing a home,

contacting/reuniting with family members, etc.). In slow-onset natural hazards, the ability to ensure

one’s physical safety is generally greater, because of the advanced warning that grants an advantage

of time for decision-making, compared to rapid-onset events such as earthquakes and tsunamis

(Dash and Gladwin 2007).

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Immediate evacuation outside of hazard areas within the first minutes after an earthquake

shaking is considered the most effective personal protective response in local tsunamis (Mas et al.

2012, 2013; Wei et al. 2017; Charnkol and Tanaboriboon 2006; Takabatake et al. 2017; Wang et al.

2016). Ground shaking and informal channels of communication are considered the main signs of

an upcoming tsunami (Fraser et al. 2013). Bernard (2005) suggests that waiting for other natural

phenomena such as a rapid drawdown or a sudden rise of the ocean takes longer and requires

people to be in a visual proximity of a coastline, which would prevent people from evacuating in a

timely manner. In addition, official tsunami warning channels are likely to be damaged in an

earthquake (Fraser et al. 2013). The public is encouraged to rely on the natural warning sign of

earthquake shaking as a trigger for immediate evacuation.

However, post-impact tsunami analyses have shown that people tend to wait for tsunami

confirmation before engaging in immediate evacuation in local tsunami events. In the evaluation of

people’s behavior in the 2009 American Samoa tsunami, Lindell et al. (2015) reported that waiting

for tsunami confirmation from peers, authorities, and news media created delays in evacuation

among the local population. In a study of protective responses to a tsunami threat in 2011

earthquake in New Zealand and Japan, Wei et al. (2017) discovered that nearly half (47.7%) of

respondents said that they started to evacuate only when they saw a tsunami wave coming. In a

survey of Okitsu community following the 2011 Great East Japan earthquake and tsunami, Sun et al.

(2013) found that 10.9% of respondents would not evacuate without hearing a tsunami evacuation

order and 6.3% would wait to hear the evacuation order from community warning alarms system in

future tsunami events.

Other common behaviors that prevent people from evacuating outside of a hazard zone in a

timely manner include looking for family members, talking with neighbors, helping others, and

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waiting for assistance from emergency personnel (Shibayama et al. 2013; Lindell and Perry 2012;

Murakami et al. 2012). While based on reports of survivors who may have conjectured the existence

of otherwise unknown behaviors, Yun and Hamada (2012) discovered that the most common

behaviors taken by people who died in the 2011 Great East Japan tsunami were helping others

(22.4%), doing rescue work (13.9%), and finding family or relatives (9.7%). There were reported

cases of parents travelling to collect their children from schools and individuals returning home to

look for elderly relatives, despite the imminent tsunami arrival. Similarly, Lindell et al. (2015)

reported that people delayed their evacuations in the 2009 American Samoa tsunami because they

were locating family members (36.8%), packing an emergency kit (26%), warning others (19.8%),

obtaining additional information from peers (24%), news media (15.6%), and authorities (11.5%),

protecting property (4.2%), and helping others (2.3%). To minimize such behaviors, following a

devastating 2011 Tohoku-oki earthquake and tsunami in Japan that left 5,892 people dead and 2,574

missing, a policy of immediate evacuation, known as tendenko, that discourages contacting family

members and waiting for tsunami confirmation, has been actively promoted in Japan (Mimura et al.

2011).

This study is set in a pre-impact context, therefore only assessment of evacuation behavioral

intentions was possible. This research inquired about people’s likelihood of adopting a range of

behaviors in the event of the M9 CSZ earthquake and tsunami (e.g. evacuating immediately following

ground shaking outside of tsunami inundation zone, contacting loved ones, waiting for tsunami

confirmation, collecting documents, etc.). Knowing people’s behavioral intentions is valuable for

several reasons. The information can be used to populate tsunami evacuation models with more

realistic data. Certainly, one has to be careful drawing conclusions from prediction analyses because

of possible inconsistencies between behavioral intentions and actual behavior. They can occur

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because individuals report erroneous estimates about their intentions due to the lack of experience

and knowledge, variation of intentions over time, and unawareness of impeding or facilitating

factors to intended actions that may arise after intentions are first formed (Kang et al. 2007;

Grothmann and Reusswig 2006). In the evaluation of behavioral responses before and after

Hurricane Lili, Kang et al. (2007) found correlation between behavioral expectations and behavior.

Yet, empirical evaluations of association between intentions and behavior in studies of natural

disasters are rare. However, more importantly, information on behavioral intentions and how they

differ across different groups of individuals can assist in emergency preparedness by influencing

people’s attitudes, promoting more effective protective responses, and tailoring outreach efforts for

vulnerable populations to improve their level of preparedness. Therefore, I ask the following

research question.

Research question 1: What are the individual behavioral intentions within Oregon coastal

population in a response to M9 CSZ earthquake and tsunami scenario?

Certain populations may face greater risks and obstacles to disaster preparedness and

evacuation because of costs associated with those actions, physical abilities, access to information,

and capabilities to interpret and understand the information regarding preparedness guidelines and

evacuation options (Meyer et al. 2018). Identifying groups under risk of not adopting protective

behaviors or not intending to evacuate in the event of a local tsunami, and factors that influence

such decisions, could suggest channels for early intervention strategies that can, for example, create

access to resources necessary for successful preparation and protective actions adoption (Riad et al.

1999).

There is a range of factors that influence individual motivations and propensities to adopt

protective measures in a threat of disasters. Individual evacuation decision-making is a complex

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phenomenon, influenced by cognitive and emotional (perceptions and attitudes) and external -

social, institutional, environmental, and demographic factors (e.g. previous experiences, knowledge,

location, gender, age, etc.) (Eiser et al. 2012). The following sections examine in detail cognitive,

socio-environmental, and demographic factors that influence people’s decision-making and explain

the variation in behavioral intent in emergency hazard situations. I rely on theories of cognition that

argue that human behavior is a product of processes of human thought and perceptions. Human

mental processes are arranged in a hierarchy of cognitions such as perceiving, remembering,

thinking, interpreting, which in turn form behavioral intentions and behavior. These mental

cognitive processes or perceptions, values, attitudes do not form on their own. They are influenced

by a number of factors – environmental influences, social norms and cultures, experiences, etc. The

following review identifies important factors that could explain people’s perceptions and behavioral

intentions.

2.2 Cognitive and socio-environmental factors

Theories of behavior (e.g. Theory of Reasoned Action, Theory of Planned Behavior,

Protection Motivation Theory, and Protective Action Decision Model) argue that psychological

processes such as perceptions and attitudes are the core components of cognition that translate

outside information into behavioral intent and ultimately behavior, serving as mediators between

observable factors and behavior, with Theory of Reasoned Action (Fishbein and Ajzen 1975) being

the first to synthesize the conceptual relationship between attitudes and behavior.

At the beginning of the 20th century, among social psychologists the notion that human

behavior is guided by attitudes was accepted as a given (Ajzen and Fishbein 2005). At that time, the

main approach to understanding the relationship between attitudes and behavior was based on the

notion of a logical consistency, i.e. a certain behavior was considered a logical outcome of a person’s

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attitudes toward that behavior. In the 1930s, with the emergence of more empirical research, the

assumption that attitudes determine behavior started to weaken. The majority of studies that showed

support for the hypothesis had methodological flaws (e.g. LaPiere 1934, Corey 1937). Research

applied mostly arbitrary measures of attitudes and behavior, while attitude-behavior logical

connections were guided primarily by researcher intuition. By the 1960s and 1970s, many scholars

questioned the strength of the relationship and the utility of the attitude constructs (e.g. Blumer

1955; Campbell 1963; Deutscher 1966; Festinger 1964 in Ajzen and Fishbein 2005).

Fishbein and Ajzen (1975; 1977) revived the determinative power of attitudes on behavior

by improving attitude and behavior measurements. They contended that attitude and behavior

measurements must correspond in terms of action, time, target, and context to serve as a basis for

comparison. For example, an individual’s general attitude toward religion, may not be the most

reliable predictor of a specific behavior such as attending a church. Or, person’s voting preference

toward wolf reintroduction would be better determined by attitudes toward wolf reintroduction

rather than beliefs about or attitudes toward wolfs (Bright and Manfredo, 1996). In addition,

Fishbein and Ajzen (1977) proposed that behavior in question could be better explained by the

intention to perform an action rather than an attitude, creating a link in the cognitive process

between attitudes-behavioral intentions-behaviors. Their argument that behavioral intentions may

serve as a moderator between attitudes and behavior was a departure from the traditional view that

attitudes directly influence behavior. Yet, this is now a widely accepted principle and the foundation

of Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB) (Ajzen 1991), Protection

Motivation Theory (PMT) (Rogers 1983), and Protective Action Decision Model (PADM) (Lindell

and Perry 1992, 2003, 2012), the last two being commonly applied in natural hazards research.

15

TRA argues that behavioral intentions can be explained by two cognitive processes: (1) an

attitude toward a behavior (e.g. perception that immediate evacuation outside of a risk zone will

result in a positive outcome of being safe from injuries) and (2) a normative belief or a social

perception regarding the behavior (e.g. perception that significant others think one should or should

not evacuate during a tsunami) (Armitage and Christian 2003). TPB added an additional cognitive

construct to the behavioral model - perceived control over the performance of a behavior or

perception that one is capable of successfully executing the behavior (Montano and Kasprzyk 2015).

In addition, TRA and TPB advocated that personal and situational factors play a role in shaping

people’s beliefs and behaviors.

Protection Motivation Theory (PMT), originally proposed by Rogers (1983), expanded both

TRA and TPB by drawing attention to the influence of risk perceptions on behavioral intentions and

behavior. Originally developed to understand why people engage in unhealthy behaviors to provide

alternatives ways of changing those behaviors, it also found its application in studies of disaster

emergency because of high risk contexts. It offers a framework to examine why people engage or do

not engage in protective behaviors in risky situations. PMT proposed a two dimensional

measurement of risk perception: (1) perceived probability (e.g. perception of how likely a tsunami is

to occur) and (2) perceived severity (e.g. perception of how likely a tsunami is to cause personal and

property damages), in some contexts also referred to as a threat appraisal. In natural hazards

research risk perception plays a central role in explaining people’s decision-making processes in

emergency situations.

2.2.1 Risk perception

Risk is not an objective phenomenon that can be universally measured and ranked. It does

not exist independently of people’s minds and cultures. Risk arises out of an assessment of possible

16

impacts of a disaster and out of uncertainty surrounding those predictions (Sjoberg et al. 2004).

Assessment of risk, which occurs at institutional and individual levels, depends on many factors

including social and cultural beliefs, availability of resources to mitigate and control risks (e.g.

institutional, financial, and human), and physical and geographic facilitators or constraints. An

outcome of the assessment, which results in a certain level of risk perception, influences choices that

people make in regards to mitigation behaviors to address the threat of harm and uncertainty. Risk

perception is an important concept because it helps people to understand the dangers and

uncertainties of life and motivate certain behavioral choices to address those uncertainties (Krimsky

& Golding, 1992; Slovic, 1992; Wynne, 1992).

There are two main approaches that examine how risk perceptions are formed: the

psychometric paradigm and cultural theory. The psychometric paradigm, rooted in the discipline of

psychology, examines how individual cognitive characteristics, personality traits, needs, and

preferences influence formation of risk perceptions (Fischhoff et al. 1978; Goszczynska et al. 1991;

Rogers 1982; Jaeger et al.. 2013; Slovic et al. 1980; Slovic 1992; Slovic and Weber 2002; Lee and

Lemyre 2009; Gierlach et al. 2010). Cultural theory takes a broader view and focuses on how social

context and group norms and values influence what is perceived as dangerous. It argues that risk

perception is a product of culture, history; and ideology. Fear is not just an outcome of a rational

evaluation of existing risk, but a product of social learning (Douglas and Wildavsky 1982; Turner

1979; Tierney 1994). From cultural theory perspective, variation in a hazard severity or a type of

hazard may not necessarily change perceptions of risk. Rather, perceptions would be shaped by an

adherence to a certain worldview that is a product of interaction within a certain social order (Jones

and Faas 2017). Cultural theory is widely accepted within disciplines of anthropology and sociology.

These two approaches to understanding risk perception are not mutually exclusive as individual

17

cognitive processes of decision-making and cultural norms constantly interplay with one another, as

social and environmental factors interact with physiological and psychological makeups of

individuals, reinforcing or undermining different behaviors. This research does not adhere to a

specific paradigm of origins of risk perceptions. However, it examines factors that influence risk

perception that would align with both psychometric and cultural theory paradigms.

Risk perception (or threat appraisal) has been analyzed as a dependent, explanatory, and

mediating variable. It appears in studies of both behavior and behavioral intentions (Bourque et al.

2013). Following other studies that have examined the concept of risk perception in the emergency

context, this research measures risk perception as perceived probability (i.e. a judgement of

likelihood of an event occurrence) and perceived severity (i.e. a judgement of severity and likelihood

of suffering death or injury, property damage, severe disruptions to daily routines, and financial

losses) (Lindell and Hwang 2008; Huang et al. 2012, 2016; Lindell et al. 2016; Wei et al. 2017; Lindell

and Perry 2003; Peacock 2005).

Research on natural disasters often finds a positive correlation between risk perceptions and

adoption of protective responses in both natural and human-made disasters including earthquakes

and tsunamis (Fraser et al. 2016; Lindell and Whitney 2000; Lindell et al. 2009; Spittal et al. 2008;

Tekeli-Yesil et al. 2010; Wei et al. 2017; Apatu et al. 2013; Lindell and Perry 2000), hurricanes (Dash

and Gladwin 2007; Peacock 2003; Wu et al. 2015; Huang et al. 2012, 2016; Lindell and Hwang 2008;

Wachinger et al. 2013; Bird and Howes 2008), volcanic eruptions (Tobin et al. 2011; Perry and Lindell

2008; Johnston et al. 1999), floods and mudslides (Tobin et al. 2011; Grothmann & Reusswig, 2006;

Lin et al. 2008; Siegrist and Gutscher 2008; Lindell and Hwang 2008), and threats of terrorist attacks

(Bourque et al. 2013; Lee and Lemyre 2009). However, the direction and the degree of relationship

between risk perceptions, behavioral intentions, and behavior have also been reported to be

18

inconsistent across studies (e.g. Lindell and Prater 2000; Lindell and Whitney 2000; Paton et al. 2000;

Perry and Lindell 2008). Different research designs and measurements of risk perception contribute

to the variation in effects, suggesting that it is an important but not a sufficient factor that explains

individuals’ adoption of protective behaviors (Lindell and Whitney 2000). In the tsunami context,

Wei et al. (2017) and Lindell et al. (2016) found that higher levels of risk perception significantly

increased the likelihood of households attempting to evacuate in 2011 earthquakes in Christchurch,

New Zealand and Hitachi, Japan, while Lindell et al. (2015) reported that tsunami individuals’

evacuation decisions had a modest correlation with their risk perceptions in a post-impact evaluation

of the 2009 Samoa Islands earthquake and tsunami.

It is logical to assume that high levels of risk perception may lead to risk mitigating

behaviors, e.g. rapid evacuation in a case of an earthquake and tsunami. However, it is important to

be cognizant of the fact that sometimes people choose not to protect themselves even when

exhibiting high levels of perceived threat (Wachinger et al. 2013). Ruiter et al. (2001) argues that high

level of risk perception may cause denial behaviors as a protective mechanism. Consequently,

response to a threat may instead be manifested in a rejection of precautionary actions when

individuals believe that benefits of not performing a protective action overweight the negative

impacts, when they do not understand personal responsibility for taking actions, and when they do

not have resources to change a situation (Wachinger et al. 2013).

Based on the above review of the literature this study hypothesizes that risk perception

serves as a mediator that translates the influence of socio-environmental factors on the behavioral

intent. In addition, I hypothesize that risk perception will have a direct association with behavioral

intent. More specifically, the hypotheses are the following.

19

Hypothesis 1a: Perceived probability mediates the influence of socio-environmental factors on

behavioral intent.

Hypothesis 1b: Perceived probability is positively associated with the behavioral intent to evacuate

outside of tsunami inundation zone in the event of M9 CSZ tsunami.

Hypothesis 1c: Perceived probability is negatively associated with the behavioral intent to engage in

delay and milling behaviors in the event of M9 CSZ tsunami.

Hypothesis 2a: Perceived severity mediates the influence of socio-environmental factors on

behavioral intent.

Hypothesis 2b: Perceived severity is positively associated with the behavioral intent to evacuate

outside of tsunami inundation zone in the event of M9 CSZ tsunami.

Hypothesis 2c: Perceived severity is negatively associated with the behavioral intent to engage in

delay and milling behaviors in the event of M9 CSZ tsunami.

2.2.2 Self-efficacy and response efficacy

In addition to risk perception construct, TPB and PMT also argue that cognitive elements of

response efficacy and self-efficacy influence behavioral intentions. Response efficacy implies

attitudes toward effectiveness and value of a protective action. Self-efficacy is an evaluation of

personal abilities to perform a given action. It refers to respondents’ assessment of their knowledge,

skill, ability, energy, and financial resources in relation to demands of a protective action (Lindell and

Whitney 2000). Response efficacy and self-efficacy together are also referred to as a coping appraisal.

Generally, higher levels of self-efficacy and response efficacy are associated with higher

likelihood of adoption of protective actions. Johnston et al. (2005) showed a connection between

self-efficacy and decisions to prepare in the assessment of tsunami preparedness on the coast of

Washington. In the analysis of community preparedness in a threat of tsunamis in New Zealand,

20

Paton et al. (2008) found that individuals were more likely to prepare in the threat of a tsunami if

they thought that preparation was the response that would ensure their safety. On the other hand,

low self-efficacy (i.e. when the level of resources relative to the degree of threat is deemed

insufficient) can induce avoidance behaviors, even under high appraisal of threat (Abraham et al.,

1994; Rippetoe and Rogers, 1987; Van der Velde and Van der Pligt, 1991).

In this research self-efficacy is measured by assessing people’s confidence in their ability to

protect themselves to reduce chances of severe injuries in the event of the M9 CSZ earthquake and

tsunami. This study examines whether self-efficacy to perform protective actions has an influence on

behavioral intent and whether self-efficacy mediates effects of socio-environmental factors on the

dependent variable. Adhering to the common argument in the literature, I anticipate that individuals

with higher level of self-efficacy will display higher level of evacuation intent. The resulting

hypotheses are:

Hypothesis 3a: Self-efficacy mediates the influence of socio-environmental factors on behavioral

intent.

Hypothesis 3b: Self-efficacy is positively associated with the behavioral intent to evacuate outside

of tsunami inundation zone in the event of M9 CSZ tsunami.

Hypothesis 3c: Self-efficacy is negatively associated with the behavioral intent to engage in delay

and milling behaviors in the event of M9 CSZ tsunami.

Response efficacy is measured by assessing people’s attitudes toward the value of emergency

plans and preparatory actions in increasing their chances of survival in the event of M9 CSZ

earthquake and tsunami. Similar to the assessment of self-efficacy, whether response efficacy has an

influence on behavioral intent and whether it mediates the influence of socio-environmental factors

on behavioral intent are examined. The specific hypotheses are:

21

Hypothesis 4a: Response efficacy mediates the influence of socio-environmental factors on

behavioral intent.

Hypothesis 4b: Response efficacy is positively associated with the behavioral intent to evacuate

outside of tsunami inundation zone in the event of M9 CSZ tsunami.

Hypothesis 4c: Response efficacy is negatively associated with the behavioral intent to engage in

delay behaviors in the event of M9 CSZ tsunami.

2.2.3 Socio-environmental factors

Why do different individuals hold different perceptions and adopt different behavioral

intentions? What influences risk interpretation and what leads people to have intentions to adopt

either protective, risk-taking, or avoidance behaviors under conditions of risk? Lindell and Perry

(2012) argue that the process of adoption of protective actions starts with people’s reaction to and

interpretation of disaster warning signs. In a local tsunami, earthquake and informal communication

channels would be the only indicators of an approaching tsunami (Murakami et al. 2012; Fraser et

al. 2012). Knowledge about natural tsunami warning signs and the difference between warnings for

local and distance tsunamis is highly useful. The main natural sign for local tsunamis is ground

shaking. Others could include unusual wave formations and currents in ocean and estuaries that

expose portions of the ocean floor not normally visible even during low tides (Gregg et al. 2006). In

the event of a distant tsunami, people are encouraged to wait for official government issued

information about status of a tsunami and a possible mandatory evacuation, before evacuating to a

high ground.

In addition to tsunami warning signs, knowledge of tsunami evacuation routes, tsunami safe

places, recommendations for tsunami preparedness and response, and knowledge of extent of

tsunami inundation zones are important factors that influence how people react when a disaster

22

occurs. Studies of earthquakes and tsunamis that report failures by victims to evacuate to tsunami

safe zones, attribute such behaviors to a lack of prior knowledge about tsunamis, natural tsunami

warning signs, tsunami evacuation routes, and tsunami safe places in respective communities (Kurita

et al. 2007; Said et al. 2011; Iemura et al. 2006; Gregg et al. 2006; Okumura et al. 2011). Gregg et al.

(2007) reported that majority of survey participants in a post-tsunami impact assessment in Hilo,

Hawaii expected a tsunami alert via official sources. On the other hand, Fraser et al. (2016) reported

high level of awareness among respondents regarding their location relative to a tsunami hazard. The

evidence suggests that knowledge of tsunami warning messages, tsunami evacuation routes, tsunami

safe places, and other tsunami relevant knowledge can have an influence on behavior in tsunami

emergencies.

Because of the pre-impact nature of this study, it is not possible to assess people’s

interpretation of tsunami warning signs, however, it is possible to inquire about their knowledge of

tsunami warning signs and other tsunami-relevant information. Hazard knowledge has been shown

to be effective in promoting preparation and evacuation response in disasters (Walshe and Nunn

2012; Dudley et al. 2011; McAdoo, 2006; Yogaswara and Yulianto 2008; Said et al. 2011). In addition

non-conclusive evidence exists from a qualitative study of 2009 tsunami in American Samoa that

knowledge of natural tsunami warning signs prompted individuals to evacuate in a timely manner

during the event (Dudley et al. 2011).

Due to resource constraints and the nature of the data collection instrument, instead of

testing people’s actual knowledge, this study examines individual confidence in knowing tsunami

relevant information. More specifically it inquires whether people know the difference between local

and distant tsunamis, whether they understand natural warning signs of tsunamis, whether they

know where to get the information about preparation for tsunamis and about tsunami evacuation

23

routes, and other tsunami-relevant information. This study assumes that tsunami-relevant knowledge

confidence will have a positive influence of behavioral intent. Specific hypotheses are:

Hypothesis 5a: Tsunami-relevant knowledge confidence is positively associated with the behavioral

intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami.

Hypothesis 5b: Tsunami-relevant knowledge confidence is negatively associated with the

behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami.

It is also assumed that tsunami-relevant knowledge confidence will be positively associated

with self-efficacy, response efficacy, perceived probability, and perceived severity. Relationship with

perceived severity is assumed to be negative. Higher confidence in knowing tsunami-relevant

information may reduce perceptions of danger. Related hypotheses are:

Hypothesis 5c: Tsunami-relevant knowledge confidence is positively associated with self-efficacy.

Hypothesis 5d: Tsunami-relevant knowledge confidence is positively associated with response

efficacy.

Hypothesis 5e: Tsunami-relevant knowledge confidence is positively associated with perceived

probability.

Hypothesis 5f: Tsunami-relevant knowledge confidence is negatively associated with perceived

severity.

Efficient and appropriate response to threats involves an ability to discriminate between

different situations, some more dangerous than others. Prior direct experience with hazards can

increase people’s knowledge and ability to recognize threats. This ability depends on how people

interpret previous experience and what information and skills they learn from it, depending on the

recency, frequency, and the degree of damage and casualties experienced personally and by relatives

and friends (Lindell and Perry 2012). Not all experiences ensure a more efficient response in the

24

future. There are examples of individuals with prior hazard experience without major damages,

develop a false sense of security, lower risk perception, and a belief that future events will unlikely

affect them (Becker et al. 2017; Shapira et al. 2018). Similarly, experience of evacuation in a tsunami

warning that was not followed by a tsunami can instill a sense of false security or an intention to wait

for confirmation of tsunamis in future emergencies regardless of their type. The situation is known

as Normalization Bias (Mileti and Fitzpatrick, 1992; Russell et al. 1995; Johnston et al. 1999). In

addition, individuals who previously experienced natural disasters, may develop a sense of security

and confidence in their skills to survive future events. For example, Yun and Hamada (2012) in the

study of Tohoku-Oki earthquake and tsunami showed that the respondents’ confidence in their

ability to ward off the impact of tsunamis was correlated with previous experience with earthquakes

without consequent tsunamis.

Assessment of experience is important because decisions made from experience are

different from choices made from description (Eiser et al. 2012). Previous studies confirm the

importance of hazard specific (and recent) experience in influencing adoption of protective actions.

Charnkol and Tanaboriboon (2006) in a study of tsunami evacuation behavioral intent in Thailand,

showed that respondents with prior experience (e.g. 2004 Indian Ocean tsunami) were three times

more likely to have an intent to evacuate faster and earlier than those without such experiences.

After a community lives through a disaster, the likelihood of adopting emergency preparedness

activities increases (Lindell et al. 2016). In the review of case studies of floods, droughts, earthquakes,

volcanic eruptions, wildfires, and landslides in Europe, Wachinger et al. (2013) summarized that in

majority of studies direct experience with disasters reinforced precautionary behaviors in the future.

In addition, experience has been shown to have both direct and indirect (via risk perception) effects

on hazard adjustment adoption (Lindell and Prater 2000; Lindell and Hwang 2008).

25

Because of the rarity of local tsunamis on the Oregon coast, with the last CSZ tsunami

occurring in 1700, this study examines individual experience with any natural hazard, assuming that

those with greater experience may be more aware of risks and damages that natural hazards can

bring to personal health and property. This study evaluates how influence of previous hazard

experience on behavioral intent is mediated by risk perception, self-efficacy, and response efficacy.

The corresponding hypotheses are:

Hypothesis 6a: Hazard experience is positively associated with the behavioral intent to evacuate

outside of tsunami inundation zone in the event of M9 CSZ tsunami.

Hypothesis 6b: Hazard experience is negatively associated with the behavioral intent to engage in

delay behaviors in the event of M9 CSZ tsunami.

Hypothesis 6c: Hazard experience is positively associated with self-efficacy.

Hypothesis 6d: Hazard experience is positively associated with response efficacy.

Hypothesis 6e: Hazard experience is positively associated with perceived probability.

Hypothesis 6f: Hazard experience is positively associated with perceived severity.

Even though the M9 CSZ earthquake and tsunami is expected to make a large geographic

impact, it is still logical to assume that people’s location relative to the source of risk can make a

difference in their motivation to react to the threat of tsunami. Fraser et al. (2016) reported that

reasons for not evacuating during the local-source earthquake in Wellington, New Zealand included

living at an elevation high enough to be impacted by tsunamis. Responses to their survey revealed

that 95% of survey respondents made a correct assessment regarding their location relative to the

tsunami inundation zone. Other studies of behavior in tsunamis have examined correlation between

coastal distance, risk perception, and evacuation. Looking at the relationship between physical

measures of risk and cognitive measures of risk perception is especially interesting. It allows to

26

determine to what extent personal risk assessment is connected with evaluation of physical reality,

and how well people understand and comprehend their location relative to risk (Lindell and Perry

2012).

Tsunami research that has examined the influence of risk proximity on perceptions and

behavior reported evidence that people living in households closer to coastlines (as a measure of risk

proximity) were more likely to make a decision to evacuate, evacuate earlier and faster, and had

higher level of risk perception (Apatu et al. 2013, 2016; Lindell et al. 2015; Charnkol and

Tanaboriboon, 2006; Wei et al. 2017). This study assessed risk proximity via predicted tsunami wave

arrival time. The assumption is that greater tsunami wave arrival time means a further location from

the coastline, therefore lower risk of being hit by a tsunami wave. Similarly to previous socio-

environmental factors, this study examines the association between risk proximity and behavioral

intent, and whether influence of risk proximity on behavioral intent is mediated by cognitive factors

of risk perception, self-efficacy, and response efficacy. Based on previous studies, this research

assumes that risk proximity will be positively associated with risk perceptions. While there is a lack

of research on the connection between proximity to hazard and self-efficacy and response efficacy, it

is logical to assume that risk proximity could be negatively associated with self-efficacy (being closer

to risk may imply lower confidence in one’s ability to protect themselves) and positively associated

with response efficacy (being closer to risk may imply greater confidence and trust in protective

actions). The associated hypotheses are:

Hypothesis 7a: Risk proximity is positively associated with the behavioral intent to evacuate

outside of tsunami inundation zone in the event of M9 CSZ tsunami.

Hypothesis 7b: Risk proximity is negatively associated with the behavioral intent to engage in delay

behaviors in the event of M9 CSZ tsunami.

27

Hypothesis 7c: Risk proximity is negatively associated with self-efficacy.

Hypothesis 7d: Risk proximity is positively associated with response efficacy.

Hypothesis 7e: Risk proximity is positively associated with perceived probability.

Hypothesis 7f: Risk proximity is positively associated with perceived severity.

Engaging in tsunami preparatory measures in anticipation of the Cascadia earthquakes and

tsunamis can have an influence on people’s confidence in their ability to respond successfully to

tsunamis, confidence in the utility of protective actions and preparation, risk perceptions, and the

intent to evacuate in the case of emergency. Preparation can take different forms: from education to

acquiring skills of an emergency responder. The process of preparation suggests that people who

engage in such behaviors acquire new information and skills, possibly leading to changes in

perceptions and behavioral intent. In the evaluation of responses to the 2009 Samoa tsunami,

Lindell et al. (2015) found that preparation in a form of earthquake hazard awareness meetings

correlated with higher levels of risk perception, yet it did not show correlations with the likelihood

to evacuate to higher ground. This research assumes that greater level of preparedness is associated

with higher level of risk perceptions, self-efficacy, and response efficacy. Similarly to other socio-

environmental factors, this study hypothesizes that preparation will be associated with the behavioral

intent directly and will be mediated by risk perception, self-efficacy, and response efficacy. Specific

hypotheses are:

Hypothesis 8a: Preparation is positively associated with the behavioral intent to evacuate outside of

tsunami inundation zone in the event of M9 CSZ tsunami.

Hypothesis 8b: Preparation is negatively associated with the behavioral intent to engage in delay

behaviors in the event of M9 CSZ tsunami.

Hypothesis 8c: Preparation is positively associated with self-efficacy.

28

Hypothesis 8d: Preparation is positively associated with response efficacy.

Hypothesis 8e: Preparation is positively associated with perceived probability.

Hypothesis 8f: Preparation is positively associated with perceived severity.

2.2.4 Demographic variables

Finally, examining demographic characteristics of the population at risk is an essential part of

any study of behavior in natural hazard. Knowing groups under higher risk for not adopting

protective behaviors or not intending to evacuate in the event of a tsunami, could suggest channels

of reaching out to those individuals to encourage them to evacuate and provide options for early

intervention strategies that can, for example, create access to the resources necessary for successful

evacuation and preparation (Riad et al. 1999). This research examines variation in behavioral intent

among demographic characteristics of individuals and households: gender, age, education, income,

community tenure, homeownership, household size, having difficulty walking, presence of children

and presence of elderly in a household. Depending on a hazard context, influence of demographic

characteristics on perceptions and behavioral intentions can vary, but there are certain common

trends in the association between demographics and behavior in emergency situations.

There is limited and uncertain evidence that females tend to exhibit higher levels of risk

perception in disaster situations (Lindell et al. 2015, Terpstra and Lindell 2012). Also, in post-tsunami

impact studies, distribution of human loss is often skewed towards older populations, with fatality

rates growing with an increase in age. Older populations tend to have restrictions in the ability to

evacuate (e.g. difficulty walking) and tend to stay behind to protect property (Murakami et al. 2012,

Rofi et al. 2006; Sun et al. 2013; Lindell et al. 2015). Yun and Hamada (2015) reported that in the

2011 Tohoku-Oki earthquake and tsunami 63% of the survivors were younger than 39 years of age,

while only 3% were older than 60 years of age. Sun et al. (2013) reported that 64.3% of victims in the

29

Great East Japan Earthquake were over 60 and 45.5% were over 70. Charnkol and Tanaboriboon

(2006) found that respondents between 20 and 40 years of age were more likely to be the first and

the fastest to respond to a disaster and evacuate compare to older persons.

Household size and the presence of elderly in a household may affect household evacuation

decisions as well (Lindell et al. 2015). In disaster research, older populations are typically defined as

65 years and older. Elderly persons may require physical assistance in the evacuation process due to

mobility issues, a reluctance to evacuate, or lack of other necessary resources for successful

evacuation (Wood et al. 2007; Sun et al. 2017; Yun and Hamada 2012, 2015). The presence of

children can also influence the evacuation process as they may also have mobility issues and require

special assistance (Charnkol and Tanaboriboon 2006). Dash and Gladwin (2007) and Apatu et al.

(2016) define the category of children for evacuation purposes as 10 years of age and younger. Wei et

al. (2017) showed a correlation between tsunami evacuation and community tenure, measured in a

number of years lived in a community, where community tenure was expected to influence

knowledge and awareness of the tsunami hazard. They also found that homeowners were more

likely to exhibit higher levels of risk perception compared to non-homeowners. Having education

could provide people with necessary resources to prepare themselves for emergency situations. And

finally, Apatu et al. (2016) in the study of Samoa islands tsunami showed that households reporting

higher incomes had an increased probability of evacuation.

Research question 2: What is the variation in behavioral intent among the following

demographic characteristics: gender, age, formal education, income, community tenure,

homeownership, household size, having difficulty walking, presence of children, and presence of

elderly in a household?

Table 2.1 displays research questions and hypotheses addressed in this study.

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Table 2.1: Research questions and research hypotheses

Research question 1

What are the variations in individual behavioral intentions within Oregon coastal population in a response to M9 CSZ earthquake and tsunami scenario?

Research question 2

What is the variation in behavioral intent within the following demographic characteristics: sex, age, formal education, income, community tenure, homeownership, household size, having difficulty walking, presence of children and presence of elderly in a household?

Hypothesis 1

a) Perceived probability mediates the influence of socio-environmental factors on behavioral intent. b) Perceived probability is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami. c) Perceived probability is negatively associated with the behavioral intent to engage in delay and milling behaviors in the event of M9 CSZ tsunami.

Hypothesis 2

a) Perceived severity mediates the influence of socio-environmental factors on behavioral intent. b) Perceived severity is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami. c) Perceived severity is negatively associated with the behavioral intent to engage in delay and milling behaviors in the event of M9 CSZ tsunami.

Hypothesis 3

a) Self-efficacy mediates the influence of socio-environmental factors on behavioral intent. b) Self-efficacy is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami. c) Self-efficacy is negatively associated with the behavioral intent to engage in delay and milling behaviors in the event of M9 CSZ tsunami.

Hypothesis 4

a) Response efficacy mediates the influence of socio-environmental factors on behavioral intent. b) Response efficacy is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami. c) Response efficacy is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami.

Hypothesis 5

a) Tsunami-relevant knowledge efficacy is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami. b) Tsunami-relevant knowledge efficacy is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami. c) Tsunami-relevant knowledge confidence is positively associated with self-efficacy, response efficacy, and perceived probability. d) Tsunami-relevant knowledge confidence is positively associated with response efficacy. e) Tsunami-relevant knowledge confidence is positively associated with perceived probability. f) Tsunami-relevant knowledge confidence is negatively associated with perceived severity.

Hypothesis 6

a) Hazard experience is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami. b) Hazard experience is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami. c) Hazard experience is positively associated with self-efficacy. d) Hazard experience is positively associated with response efficacy. e) Hazard experience is positively associated with perceived probability. f) Hazard experience is positively associated with perceived severity.

31

Hypothesis 7

a) Risk proximity is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami. b) Risk proximity is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami. c) Risk proximity is negatively associated with self-efficacy. d) Risk proximity is positively associated with response efficacy. e) Risk proximity is positively associated with perceived probability. f) Risk proximity is positively associated with perceived severity.

Hypothesis 8

a) Preparation is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami. b) Preparation is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami. c) Preparation is positively associated with self-efficacy. d) Preparation is positively associated with response efficacy. e) Preparation is positively associated with perceived probability. f) Preparation is positively associated with perceived severity.

Diagram of the research framework, indicating three levels of variables, demographic

characteristics, hypotheses, and research questions is displayed below in Figure 2.1

Figure 2.1: Research framework, indicating relationships between variables that are addressed in research questions and

hypotheses.

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3 Background and Method

A cross-sectional research design was implemented to gauge understanding of public

behavioral intentions and factors associated with behavioral intentions and to address the proposed

research questions and hypotheses. To do so, a structured, self-administered, anonymous household

survey instrument was applied to a randomly selected sample of households in a purposively

targeted community of Seaside, Oregon.

3.1 Sample

Survey questionnaire is a popular method of data collection used to assess public knowledge

and perceptions in studies of natural hazards (Bird 2009; Wei et al. 2017; Lindell et al. 2016; Lindell et

al. 2015; Jon et al. 2016; Fraser et al. 2016; Apatu et al. 2013; Sun et al. 2013; Rofi et al. 2006). It allows

for the possibility to reach large number of people with diverse perceptions and behaviors, which is

useful when trying to understand behaviors of the general public. A large sample also yields an

opportunity to generalize results to a broader population and to use these results in general

evacuation modeling. Survey instruments also allow for comparison of the results to other research

that used similar methodologies. Targeting households is a common method in a survey

methodology because it allows researchers to reach a diverse audience by covering a wide geographic

space within a community, making the sample representative of the vulnerable population. In the

context of natural hazards, in particular, examining behavioral intentions at a household or a family

unit level is central to understanding evacuation decision-making, because families tend to reunite,

search, and account for family members before evacuating together (Fraser et al. 2013).

Seaside, Oregon was selected as a representative example of a coastal community in Oregon

with high exposure to CSZ earthquake and tsunami risks. Seaside has a large number of people who

live and work within a local tsunami inundation zone, while its flat geography increases the time

33

required for evacuation outside of the tsunami zone. Seaside is a small town on the central coast of

Clatsop County, Oregon (2010 population: 6,457, U.S. Census Bureau), located in a close proximity

to the Cascadia Subduction Zone. It is situated in an estuary at the confluence of two small rivers

that run parallel to the coast and is surrounded by hills to the south and east. Numerous bridges that

connect the community to tsunami safe zones are critical elements in the effective response to

tsunamis. A number of bridges have been seismically upgraded, but the city expects seven (out of

10) bridges to fail during a CSZ event (Raskin and Wang 2017). Location on the seismic Pacific

basin, fairly flat topography, and the location of the tsunami shelter areas at more than 1.5 km from

the shoreline makes Seaside susceptible and vulnerable to both distant and local tsunamis (Mostafizi

et al. 2017). Currently Seaside evacuation plans assume horizontal evacuation on foot as the official

tsunami evacuation policy (Mostafizi et al. 2017).

In the past, distant tsunamis with origins in Alaska (1952), Chile (1960), and Alaska (1964)

affected Seaside. The Cascadia Subduction Zone earthquake of 1700 was the latest local tsunami

event on the Oregon coast (Priest et al. 2016). In 1964, the tsunami caused 1 fatality in Seaside

(attributed to a heart attack), damaged two bridges and created an estimated damage to the city in

the amount of $41,000 and to private entities in the amount of $235,000 in 1964 dollars. In 2011 as a

result of an earthquake off the coast of Japan an official evacuation warning was issued in Seaside,

triggering mandatory city evacuation. No tsunami arrived in Seaside in 2011, but the Oregon Coast

experienced damages in the amount of $6.7 million (Natural Hazards Mitigation Plan, State of

Oregon, 2015). Today, CSZ earthquakes and tsunamis pose a great risk to the community of Seaside

and its residents (Natural Hazards Mitigation Plan, State of Oregon, 2015).

Considering the characteristics of Seaside, drawing a sample from its population can yield

interesting and useful results for understanding people’s perceptions and behavioral intentions in the

34

face of tsunami risks in other Oregon coastal communities, and perhaps coastal communities of

neighboring states of Washington and California. Communities of Aberdeen, Eureka, Crescent City,

Hoquiam, Ocean Shores, Port Townsend, and the unincorporated portions of Grays Harbor and

Pacific Counties are similar to Seaside in terms of the number of residents, employees, public

venues, dependent-care facilities, and community businesses located in tsunami hazard zones and

therefore exposed to tsunami risks (Wood et al. 2015). Moreover, because physical exposure to

tsunami risk is only one factor that is assumed to influence people’s perceptions and behavioral

intentions, in addition to non-location dependent factors of experience, knowledge, preparation, and

demographic characteristics, results of this study could be applied to other Oregon coastal

communities with more confidence.

To examine the representativeness of the survey sample of the population of Seaside, Table

3.1 compares demographic characteristics of the sample and the population of Seaside. This

information is useful for purposes of being able to apply the results of the study to the population of

Seaside. Within the sample, 45.7% are persons 65 years and older, 63.8% of respondents are home

owners, 56.7% are female, the average household is comprised of two individuals, 43.4% of the

sample have a Bachelor’s degree and higher, and the median income ranges from $40,000-$49,000.

Compared to the overall population of the study area, the survey sample included more persons 65

years and older and individuals with Bachelor’s degree or higher. Persons 65 years and older

represent almost half of the sample population, while in Seaside older persons comprise about 22%

of the city population. Individuals with the Bachelor’s degree of higher also represent almost half the

sample, while in Seaside persons with the Bachelor’s degree of higher comprise about 21% of the

population. The sample is representative of gender, household size and income, but also contains

slightly more respondents who are homeowners.

35

Table 3.1: Sample bias, comparing U.S. Census statistics for Seaside with survey respondents.

Category Census data Seaside Total Sample

Total population (18 and over) 5,163 211

Persons 65 years and older (percent of total population over 18 years) 21.8% 45.7%

Owner occupied housing units (percent of total occupied units) 44.4% 63.8%

Female (18 and over) 52% 56.7%

Average household size 2.21 2.0

Bachelor's degree or higher (population over 25, 2012-2016) 20.9% 43.4%

Median household income (in 2016 dollars), 2012-2016 $36,373 $40,000-49,000

The survey sample over-represents older populations. Therefore during the analysis the

sample is weighted by the age category, using 65 years old as a cut-off point to make the sample

more representative of the population. Weighting cannot eliminate every source of nonresponse

bias. However, random sampling combined with weighting techniques create better conditions for

unbiased results (Meyer et al. 2018).

3.2 Survey design

This research primarily follows Dillman’s et al. (2014) “Internet, phone, mail, and mixed-

mode surveys: the tailored design method” for the design and distribution of the questionnaire.

Designing a questionnaire to produce reliable and valid results is a difficult task. The order and

format of questions, the sequence of the wording and length of the questionnaire, color choices,

layout all factor in participants’ perceptions and therefore their answers. Initial recruiting letters can

also influence decisions to take a survey. Groves (2004) elaborates on written questionnaire

instrument characteristics that can give rise to errors in reporting by respondents. These errors can

36

include: failure to correctly interpret the questions, failure to recall information from the past

required to answer the questions, flawed judgments and estimates, difficulty in formatting answers

due to open ended questions, deliberate misreporting and failure to follow the instructions. Many of

these errors exist because of human cognitive limitations that arise due to various factors, including

internal and external circumstances. For instance, depending on a person’s daily mood survey

responses can vary. One cannot fully control for the above limitations. However, there are

techniques that can reduce shortcomings in survey interpretation, for instance survey visual design

and order and format of questions. They have been applied in this research questionnaire design.

The following paragraph describes the principles that help with a question’s reliability or consistency

(the probability of obtaining the same result if a question is duplicated) and a question’s validity (i.e.,

a question measures what it was intended to measure).

One set of principles focuses on the visual aspect of the survey design. Bird (2009)

recommends sequencing questions in a logical order, allowing for smooth transitions from one idea

to the next, grouping questions by theme and providing a short description of each section. Also,

sufficient space is left between questions. Research shows that spreading survey out on more pages

does not reduce the response rate. However, it helps respondents to organize information on the

page and group related information (Dillman et al. 2014). Cognitive process known as ‘encoding’,

forming memories and attitudes toward the subject of the survey, can be influenced by a decision to

insert certain questions upfront to trigger vivid memories and knowledge to help participants to

answer questions in the remainder of the survey (Groves 2004). This technique was employed in this

study by placing a short description of the M9 Cascadia earthquake and tsunami scenario at the

beginning of the survey. In addition, questions about natural disaster experiences were placed at the

beginning of the survey to trigger respondent memories. Evoking past experiences at the beginning

37

of the questionnaire can help create context, which makes it easier to answer the subsequent

questions.

Another set of principles of sound questionnaires focuses on the design of questions for

soliciting the most accurate and thorough responses. Dillman et al. (2014) recommends to avoid

vague quantifiers (e.g. “often”, “always”), pay attention to scale questions construction (e.g. inclusion

or exclusion of a “zero” point, providing “I don’t know” option, etc.), be consistent with wording

throughout the questionnaire, avoid double-barreled and long questions.

This study developed an 8-page survey questionnaire containing three sections. Section 1

asked participants about their experience with natural disasters, knowledge of tsunamis, tsunami

warning signs, emergency guidelines in the event of a tsunami, and preparation for CSZ tsunamis.

Section 2 solicited opinions about behavioral intentions and factors that may influence behavioral

intentions. Section 3 asked demographic questions. The full questionnaire, including the cover letter,

can be found in the Appendix.

3.3 Survey distribution

The survey data were collected from October-December 2017. The survey was mailed to a

random sample of 951 households in Seaside, Oregon, generated by the commercial company

Marketing Systems Group (MSG). A systematic sampling approach was applied within each

household by asking those with the latest birthday and over 18 years old to take the survey (Fraser et

al. 2016). Out of 951 valid addresses, 211 questionnaires were completed (with 14 questionnaires

completed online), resulting in a 22.2% response rate. The response rate was calculated by

comparing total number of completed questionnaires to the number of valid solicited questionnaires

(Lance and Vandenberg 2009).

38

Before being distributed to a random sample of residents in Seaside, the study questionnaire

was pre-tested on a small group of targeted individuals using paper- and online-versions of the

questionnaire. Participants consisted of graduate and undergraduate students at Oregon State

University and professionals involved in emergency preparedness on the Oregon coast. Five

responses were received from the online version of the questionnaire, allowing to test not only the

content of the survey but also the technical aspects of the online instrument. Five responses were

received from the paper-based version of the questionnaire. Pilot participants were asked to pay

attention to the soundness and clarity of the questions, ease of the visual layout (both paper and

online version), and the content of the survey (applicability and relevance of the questions).

Three waves of the survey using first-class mailings were distributed, starting with a pre-

survey postcard that advertised the project (Wolters et al. 2017). Each two consequent mailings

contained a cover letter explaining the project and participants’ rights, a copy of a questionnaire and

a business postage pre-paid return envelope. The second wave of questionnaires was sent to 813

qualifying addresses, after completed and non-deliverable addresses were removed from the

database. An additional wave with modified addresses was mailed to 167 households (they were

excluded from the second wave). Addresses were modified replacing recipients’ first and last names

to a ‘current resident’ in an attempt to reach more people and solicit more responses. Phone call

reminders were conducted using 286 phone numbers available in the database, during which the

participants were encouraged to respond to the survey. Voicemail was left in a case of non-response.

Out of 286 households that were targeted with the phone calls, 17 households completed the

questionnaire. After all waves of mailing and follow-up phone calls, 672 addresses that did not

return the questionnaire and were not contacted by phone, comprised a sample for in-person visits.

From 672 addresses 11% (98 addresses) were drawn using spatial random sampling technique via

39

ArcGIS software. These 98 addresses were visited in person over a span of two days by the author

of this study. Residents were encouraged to take the survey. A new copy of the survey was provided

if respondents indicated that they did not have one. Out of 98 households that were visited in

person, 8 returned the completed questionnaire.

3.4 Variables description and operationalization

3.4.1 Dependent variables

The behavioral intent dependent variable was measured by asking respondents to indicate

the likelihood (Very unlikely = 1 to Very likely = 5) of engaging in a set of behaviors if they were at

home in the event of a M9 Cascadia earthquake and tsunami (Table 3.2). Behavioral intent items are

the following (for each item N=206): continuing normal routine behavior (M=1.68, s.d. =1.127),

following earthquake behavior guidelines (M=3.93, s.d. =1.147), evacuating to higher ground

immediately after ground shaking (M=4.03, s.d. =1.253), waiting for tsunami confirmation for an

authority/sirens (M=2.88, s.d. =1.418), checking social media, radio, TV for additional

information(M=3.68, s.d. =1.316), contacting loved ones (M=4.1, s.d. =1.195), collecting documents

and other items (M=3.47, s.d. =1.389), travelling to gather family or friends (M=2.66, s.d. =1.365),

talking with neighbors (M=3.24, s.d. =1.365), taking the lead to evacuate and encouraging others

(M=3.57, s.d. =1.250), waiting for family at home (M=2.08, s.d. =1.194), following what friends or

neighbors are doing (M=2.03, s.d. =1.147), trying to help and rescue people (M=3.58, s.d. =1.194),

panicking (M=2.33, s.d. =1.275).

40

Table 3.2: Descriptive statistics of behavioral intentions (numbers are percentages of responses on each behavioral item)

Behavior Very

unlikely Unlikely Neither Likely

Very likely

Mean (s.d.)

Continue normal behavior 63.1 21.4 4.4 6.3 4.9 1.68 (1.127)

n=206

Follow earthquake behavior guidelines

7.3 6.8 5.3 47.1 33.5 3.93 (1.147)

n=206

Evacuate to higher ground immediately

6.8 8.3 11.2 22.8 51.0 4.03 (1.253)

n=206

Wait for tsunami confirmation from authority, sirens

22.8 24.3 9.2 29.6 14.1 2.88 (1.418)

n=206

Check social media, TV 10.2 9.2 5.3 35.0 40.3 3.86 (1.316)

n=206

Contact loved ones 6.8 6.8 5.3 31.6 49.5 4.1 (1.195)

n=206

Collect documents, other items 10.1 24.6 6.8 27.1 31.4 3.47 (1.389)

n=206

Travel to gather family, friends 23.8 30.6 15.0 17.0 13.6 2.66 (1.365)

n=206

Talk with neighbors 18.9 10.7 13.6 40.8 16.0 3.24 (1.365)

n=206

Take the lead to evacuate and encourage others

9.7 12.6 12.6 41.3 23.8 3.57 (1.250)

n=206

Wait for family at home 47.1 18.9 16.5 13.6 3.9 2.08 (1.194)

n=206

Follow friends or neighbors 43.7 27.2 14.6 11.7 2.9 2.03 (1.147)

n=206

Try to help and rescue 8.7 9.7 18.9 39.8 22.8 3.58 (1.194)

n=206

Panic 34.5 28.6 12.1 19.4 5.3 2.33 (1.275)

n=206

Exploratory factor analysis was used to form behavioral intent scales. Factor analysis is often

used to represent latent concepts and for dimension reduction in human dimensions studies (Vaske

2008). In this study maximum likelihood extraction with a promax rotation was used because it is

more rigorous. Items whose communalities were less than 0.3, and items that cross-loaded on

several factors and loaded below 0.5 were removed from the factor analysis. The analysis yielded two

41

factors. The KMO (Kaiser-Meyer-Olkin) measure of sampling adequacy was 0.612 and significant.

Cumulative variance explained was 47.8%. This is an acceptable outcome for small number item

scales.

Two components with eigenvalues ≥1 (1.966 and 1.429) were extracted as a result of factor

analysis as displayed in Table 3.3. Factor loadings greater than 0.5 were chosen as a minimum cut off

(Field 2009). The eigenvalue is a measure of the amount of variance within the observed variable

that a factor explains. Eigenvalue is equivalent to the number of variables that a factor represents.

Therefore, any factor with an eigenvalue ≥1 explains more variance than a single observed variable.

Cronbach’s alphas, the measure of scale reliability, were 0.638 and 0.700, respectively. Cronbach’s

alpha at 0.6 is acceptable for scales with few items.

Table 3.3: Pattern matrix for behavioral intent

Behavioral intent Leadership Delay

Evacuate to high ground immediately after an earthquake 0.600 0.002

Take the lead to evacuate and encourage others 0.777 0.031

Check social media, TV -0.165 0.793

Contact loved ones 0.127 0.633

Collect documents and other items 0.088 0.583

Cronbach’s alpha 0.638 0.700

Two behavioral intent indices were formed from the arithmetic mean of items in each factor

weighted by factor scores (DeVellis 2016): leadership behavioral intent index (M= 2.59, s.d. = 0.74,

N = 206) and delay behavioral intent index (M= 2.56, s.d. = 0.69, N= 206). Leadership behavioral

index represents behavioral intentions of immediate evacuation outside of the tsunami zone

following an earthquake, while delay behavioral index represents behavioral intentions that involve

42

contacting others, checking other sources of information for tsunami confirmation and collecting

household items, i.e. delaying the start of evacuation.

3.4.2 Independent cognitive variables

Perceived severity was measured by asking respondents about the likelihood (Very unlikely =

1 to Very likely = 5) that CSZ earthquakes and tsunamis will: lead to injuries to you or your family

(M=3.85, s.d. =1.041, N =206); create a life-threatening situation for you or your family (M=4.10,

s.d. =0.950, N =206); create a life-threatening situation for people in your town; severely damage or

destroy your home (M=4.26, s.d. =1.020, N =206); destroy or severely damage roads, homes, etc. in

your town; and create a severe financial burden for you or your family (M=4.12, s.d. =1.017, N

=206) (Wei et al. 2017; Lindell et al. 2016; Lindell and Hwang 2008).

Due to kurtosis and skewness, “damage to town” and “damage to people in town” items

were deleted, because they showed high kurtosis of 16 and 11.6, respectively. “Damage to people in

town” item showed skewness level of -3.45. The two items with high kurtosis can be removed

without undermining reliability of the scale due to the other four items still remaining.

Exploratory factor analysis was used to form a perceived severity index. One component

with an eigenvalue ≥1 (2.678) was extracted. Factor loadings greater than 0.5 were chosen as a cut

off. Scale reliability Cronbach’s alpha is 0.830, as displayed in Table 3.4. The KMO measure of

sampling adequacy is 0.753 and significant. Cumulative variance explained is 57.7%, which is

acceptable for a four-item scale.

Table 3.4: Factor matrix of perceived severity

Component matrix Perceived severity

Lead to injuries 0.855

Create life-threatening situations 0.925

Damage or destroy your home 0.684

Create financial burden 0.504

Cronbach’s alpha 0.830

43

The perception of risk index is formed from the arithmetic mean of items weighted by factor

scores (M=3.02, s.d. = 0.62, N =206).

Perceived likelihood was measured by asking respondents about the likelihood (Very unlikely

= 1 to Very likely = 5) that CSZ earthquakes and tsunamis will happen in the next 50 years (M= 3.76,

s.d. = 1.085, M = 206).

Self-efficacy was measured by asking respondents about their confidence (Not at all

confident=1 to Very confident=5), given their personal skills and resources, to effectively protect

themselves and reduce the chance of severe injuries in the event of a M9 Cascadia earthquake and

tsunami (M= 2.83, s.d. = 1.114, M = 206).

Response efficacy was measured by asking respondents about their agreement (Strongly

disagree = 1 to strongly agree = 5) with whether people can increase their chances of surviving a M9

Cascadia earthquake and tsunami if they adopt emergency plans and protective actions (M= 4.14,

s.d. = 0.995, M = 206).

3.4.3 Independent socio-environmental variables

Experience is typically measured by asking respondents to indicate the degree of experience

with disasters under investigation. It is also often measured as records of casualties and damages

experienced by respondents and their family members in similar situations in the past (Jon et al.

2016; Lindell and Hwang 2008; Lindell and Prater 2000). For example, Jon et al. (2016), in a study of

responses to earthquakes in New Zealand and Japan, asked respondents to report their experience

with earthquakes prior to the one being studied by indicating the degree of damage and danger

previous earthquakes posed to their community, personal property, and personal and family health.

Seaside has not experienced severe earthquakes and a tsunamis in the past. Therefore, limiting

measurement of experience to only these events will not create enough variation in the results.

44

Instead, the questionnaire asked respondents to indicate whether they have ever experienced any

type of natural disaster, for instance floods, earthquakes and wildfires (No=1, Not sure=2, Yes=3).

The result was coded as a count variable, the number of disasters a person has experienced in their

lifetime. The range of the number of disasters experienced by respondents in the survey was from 0

to 7 (M= 1.34, s.d. = 1.467, N=206, Range = 0-7).

To assess confidence in tsunami-related knowledge respondents were asked about their

confidence (Not at all confident=1 to Very confident=5) in their knowledge in the following items: the

difference between local and distant tsunami events (M= 3.82, s.d. = 1.065, M = 206); natural

warning signs of tsunamis (M= 3.63, s.d. = 1.139, M = 206); emergency warning messages of

tsunamis (M= 3.99, s.d. = 1.106, M = 206); where to get the information about preparation for

tsunamis (M= 3.79, s.d. = 1.249, M = 206); what to do when there is a warning about a tsunami

(M= 3.98, s.d. = 1.079, M = 206); tsunami evacuation routes from their homes (M= 4.20, s.d. =

1.070, M = 206); tsunami safe places close to their homes (M= 3.92, s.d. = 1.354, M = 206). The

summary of responses to each survey knowledge item are displayed in Table 3.5.

45

Table 3.5: Summary of hazard-relevant knowledge survey responses

Percentage

Items Not at all

confident

A little

confident

Somewhat

confident Confident

Very

confident Mean (s.d.)

Local vs. distant

tsunami 3.9 6.8 23.3 35.4 30.6 3.82 (1.065)

Natural warning

signs 6.3 9.2 24.3 35.4 24.8 3.63 (1.139)

Emergency warning

messages 4.4 7.8 12.1 36.4 39.3 3.99 (1.106)

Sources of

information for

preparation

8.3 9.2 13.1 34.0 35.4 3.79 (1.249)

Government

recommendations 13.1 13.1 21.4 27.7 24.8 3.38 (1.337)

What to do when

there is a warning 3.9 5.8 18.4 32.5 39.3 3.98 (1.079)

Evacuation routes 3.4 5.8 11.2 26.7 52.9 4.20 (1.070)

Tsunami safe places 11.2 5.8 11.2 23.8 48.1 3.92 (1.354)

Exploratory factor analysis examined dimensions of the tsunami-relevant knowledge

confidence scale. One component with an eigenvalue ≥1 (5.427) was extracted as a result. Factor

loadings greater than 0.5 were chosen as a cut off. Cronbach’s alpha is 0.929, as shown in Table 3.6.

KMO measure of sampling adequacy is 0.919 and significant. Cumulative variance explained is

63.4%.

Table 3.6: Factor matrix for knowledge confidence

Component matrix Knowledge perception

Local vs. distant tsunami 0.693

Natural warning signs 0.802

Emergency warning messages 0.826

Sources of information for preparation 0.854

Government recommendations 0.783

What to do when there is a warning 0.906

Evacuation routes 0.778

Tsunami safe places 0.707

Cronbach’s alpha 0.929

46

A knowledge confidence index was formed from the arithmetic mean of items weighted by

factor scores (M=3.05, s.d. = 0.76, N=206).

Proximity or exposure to risk was measured in conjunction with the tsunami wave arrival

time findings by Priest et al. (2016). ArcGIS 10.5 was used to geocode household data from the

survey and then merge the spatial data (i.e., tsunami inundation zone, roads, city boundaries and

tsunami wave arrival time) into GIS layers. Twenty-one respondents out of 211 live outside of M9

CSZ tsunami inundation zone. West coast ocean data portal provided geospatial data of Oregon

tsunami inundation zones for XXL1 scenario (9.0. Magnitude earthquake) developed by Priest et al.

(2013). City of Seaside provided the geospatial data for Seaside city roads and city boundaries.

Department of Geological and Mineral Services (DOGAMI) provided the geospatial data

for CSZ tsunami advance time in the community of Seaside. Geospatial data was set in GCS North

American 1983 HARN geographic coordinate system and NAD 1983 HARN Lambert Conformal

Conic projected coordinate system. Using the Spatial Analyst tool “Extract values to points” in

ArcGIS, each household was assigned a wave arrival time (in minutes) using spatial location of each

household as a reference point (M=25.4, s.d. = 4.11, N = 206, Range = 19.3 to 32.9 minutes). The

resulted map, displayed as Figure 3.1, shows the community of Seaside: city boundaries and streets,

tsunami inundation zone for M9 CSZ scenario, tsunami wave arrival time in different shades of blue,

and households displayed in orange. Black and green circles represent bridges, where black indicated

bridges are expected to fail in the CSZ earthquake, while green indicated retrofitted bridges may

survive in the CSZ earthquake. Wave arrival serves as a proxy for assessment of actual risk. Only

households that are located within the tsunami inundation zone were assigned measure of tsunami

wave arrival time (a characteristic of the wave arrival time data file). Therefore, observations outside

of the inundation zone (N=21) originally had missing data for the wave arrival time. To avoid

47

missing data, these observations were given a value of 34 minutes, because the highest value of the

existing wave arrival data was 33 minutes. Additional values do not negatively impact the analysis.

On the other hand, they support the conceptual idea of risk proximity.

Figure 3.1: Map of Seaside and Tsunami Wave Arrival Time

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Preparedness was measured by asking respondents to mark all the measures that they have

undertaken in the past 2 years: attended a meeting or received written information on how to better

prepare for Cascadia earthquakes and tsunamis (52.2% - Yes, 47.8% - No); prepared an ‘emergency

supply kit’ (stored extra food, water, batteries, etc.) (47.4% - Yes, 52.6% - No); discussed the topic

of preparedness for Cascadia earthquakes and tsunamis with others in the community (47.4% - Yes,

52.6% - No); developed an ‘emergency plan’ with family or friends in order to decide what everyone

would do in the event of Cascadia earthquakes and tsunamis (44.5% - Yes, 55.5% - No); developed a

communication plan (e.g. a plan that establishes how to get in touch with each other if meeting at

home is not possible and phones do not work) (21.1% - Yes, 78.9% - No); attended First Aid or

Cardio-Pulmonary Resuscitation (CPR) training (32.5% - Yes, 67.5% - No); and identified an

emergency contact person outside of the Northwest (27.8% - Yes, 72.2% - No). The preparation

variable was coded as a count variable, the number of measures that a respondent has undertaken

(M = 2.83, s.d. = 2.014, N = 206, Range 0-8). Study followed Dunn et al. (2016) who evaluated

connection between preparedness and perceptions of risks in a survey in Washington, Oregon, and

California regarding people’s perceptions of earthquake hazards and earthquake early warning

systems.

Table 3.7 displays the summary statistics for two dependent variables and both cognitive

constructs and socio-environmental factors. Based on the results, on average respondents are as

likely to have intentions to engage in the leadership behavior (M=2.59) as in the delay behavior

(M=2.56). They also show above average perception of danger (M=3.02) and perceptions of the

likelihood of CSZ tsunami occurrence in the next 50 years (M=3.76). Respondents’ confidence in

being able to protect themselves in the event of the disaster centers on the medium of the scale

(M=2.83), while their belief in the efficacy of preparation is high (M=4.14). In terms of experience,

49

results show that respondents have minimal prior experience with natural disasters, with 1.34

average number of natural disasters experienced in the past. Respondents’ confidence in tsunami-

relevant knowledge is just above the mean (M=3.05). Respondents’ households that are within local

tsunami inundation zone are located within 19.3 to 32.9 minutes of tsunami wave arrival time after

the earthquake. Respondents’ average preparedness is M= 2.83 measures undertaken in the past 2

years.

Table 3.7: Summary of dependent and independent variables

Variable Description Mean (s.d.)

Leadership

behavior

Leadership behavior index based on weighted average

1=very unlikely to 5=very likely

2.59 (0.74)

n=206

Delay behavior Delay behavior index based on weighted average

1=very unlikely to 5=very likely

2.56 (0.69)

n=206

Perceived severity Index based on assessment of 4 risk statements

1=very unlikely to 5=very likely

3.02 (0.62)

N=206

Perceived

likelihood

Perceived likelihood of disaster occurrence in the next 50 years

1=very unlikely to 5=very likely

3.76 (1.085)

N=206

Self-efficacy Confidence in being able to protect yourself in the disaster

1=not at all confident to 5=very confident

2.83 (1.114)

N=206

Response efficacy Preparation can increase chances of surviving the disaster

1=strongly disagree to 5=strongly agree

4.14 (.995)

N=206

Experience Number of natural disasters experienced

[Range: 0-7]

1.34 (1.467)

N=206

Knowledge

confidence

Index based on assessment of 8 knowledge statements

1=not at all confident to 5=very confident

3.05 (0.76)

N=206

Wave arrival M9 Cascadia tsunami wave arrival time

[Range: 19.3 to 32.9 minutes]

25.4 (4.11)

N=206

Preparation Number of preparatory disaster measures already taken

[Range: 0-8]

2.83 (2.014)

N=206

50

3.4.4 Demographic variables

Gender was measured by asking respondents to indicate which sex they identify with

(Female=1, Male=0). Age was measured as a continuous variable by asking respondent to fill in their

age. Household size was measured by asking respondents to indicate the number of people and their

ages who are currently living in their household. Based on the answers, two dummy variables were

constructed, whether there were children of 10 years and younger (Yes=1, No=0) and elderly of 65

years and older (Yes=1, No=0) present in a household. Homeownership was measured by asking

respondents whether they are primary owners=1 of the residence where they received the survey,

whether they are renters or occupiers=2, or other=3. Community tenure was measured as a continuous

variable by asking respondents to indicate how many years they have lived in the community of

Seaside (0 if less than a year). The level of formal education was measured by asking respondents to

indicate the highest year of formal education completed (Less than high school diploma=1 to Graduate

degree or other professional degree=7). Household income was measured by asking respondents to indicate

which category best described their household income before taxes in 2016 (Less than $10,000=1 to

$75,000 or higher=9). Table 3.8 displays summary statistics of demographic variables.

The sample is almost evenly split between female and male respondents. The average age of

respondents is M=60.81. Average household size is M=2.01. There are almost no respondents with

children of 10 years old and younger present in a household. There are more households with the

presence of elderly. The majority of the respondents were homeowners. The average community

tenure of respondents is M=20.79, while the mean income of respondents equals to M=$40,000-

$49,000.

51

Table 3.8: Summary of demographic variables

Variable Description Mean (s.d.)

Gender Female=1, Male=0 0.59 (0.494)

N=206

Age Continuous variable 60.81 (15.18)

N=206

Household size Total number of people living in a household 2.01 (1.055)

N=206

Presence of

children

Presence of children 10 years and younger

Yes=1, No=0

0.09 (0.283)

N=206

Presence of

elderly

Presence of elderly 65 years and older

Yes=1, No=0

0.23 (0.424)

N=206

Homeownership Primary owner=1, Renter=2, Other=3 1.35 (0.537)

N=206

Community

tenure Continuous variable

20.79 (18.388)

N=206

Education Less than high school diploma=1 to Graduate degree or other

professional degree=7

4.87 (1.648)

N=206

Income Less than $10,000=1 to $75,000 or higher=9 5.99 (2.547)

N=206

Difficulty walking Yes=1, No=0 0.27 (0.446)

N=206

3.5 Data cleaning

Several of the sample observations had more than 20% of data missing. Five observations

with missing data were deleted. Two of the five were missing 21 values, two were missing 17 values,

and one was missing 12 values. These responses were deemed significantly incomplete, since the

missing data included important independent and dependent variables such as behavioral intentions,

risk and knowledge perception questions. The deletion led to a working sample of 206 observations.

The sample also had 35 variables with missing values. However, all variables had less than 5 percent

of data missing. Therefore, the missing data was imputed using the median value for ordinal scales

52

and the mean for continuous scales. One of the main reasons for imputing missing values is to be

able to do a mediation analysis explained later in this paper, which calls for no missing values.

Moreover, preserving observations in a small sample gives the results more power. Income has the

most number of missing values—15, representing 7.3% of the sample. If the proportion was more

than 10%, then imputation could cause problems, because it would dilute data, bringing it closer to

the mean.

For variables that are measured on scales from 1-5 using several items within one question,

there is a possibility that respondents are not engaged and answer all questions with the same

answer. I checked answers for knowledge and perceived severity measures by examining standard

deviations of each scale. There were 7 observations with a standard deviation of 0, which means

respondents provided exactly the same answer for all questions. There were also 17 observations in

which standard deviation ranges from 0.25 to 0.4. However, behavioral scale for these observations

displays variation, indicating that these respondents were not necessarily unengaged. There is a

possibility that some scales were not constructed in a way to allow room for variation in answers.

These observations were not deleted, with the sample size preserved at 206 observations.

Outliers – unusual observations – that can cause the most problem on continuous variables

that are characterized by a wide range of values that can qualify as a valid answer. In contrast, scale

questions that are limited to 1-5 range are unlikely to have outliers. In this study, there is one

continuous variable where outliers were possible—community tenure. Outlier values can be defined

as any value beyond 3 standard deviations of the mean (Vaske 2008). Community tenure did not

show a presence of outliers. Therefore, no replacement or deletion was performed.

Examination of skewness and kurtosis allows to check if observations have a normal distribution.

Values over 3 and below -3 were chosen as a cut off. Any value above the absolute value of 3 is

53

considered problematic. Behavioral item, “go towards the ocean,” had a kurtosis of 8.8 and

skewedness just barely over 3. It is acceptable to remove this item from the behavioral intentions

scale because there are many items in that scale to preserve variation in the data. Household size had

kurtosis of 3.47 and presence of children had a kurtosis of 6.7. If they show unusual behavior later,

it could be related to high kurtosis.

The sample was weighted to account for overrepresentation of adults over 65 years old. The

weight of 0.47 was assigned to respondents 65 years and older and a weight of 1.44 was assigned to

respondents of 64 years and younger. During the analysis both weighted and unweighted samples

were compared.

3.6 Analytical procedures

To answer the first research question of the distribution of behavioral intentions in the event

of a local tsunami in the population under the threat of M9 CSZ tsunami, the results section

provides a descriptive statistics table which displays percentage variation among behavioral items.

To evaluate hypotheses 1-8 (i.e. to understand relationships between socio-environmental,

demographic, cognitive, and behavioral intentions variables), Ordinary Least Squares (OLS) linear

regression and mediation analyses were performed. OLS regression was used primarily for an initial

overview of the data. OLS regression that includes all independent variables in one model does not

allow to fully assess the mediating role of the cognitive variables. Therefore, mediation analysis using

PROCESS package in SPSS (Hayes 2012) was utilized to examine the effect of socio-environmental

variables on behavioral intent directly and via cognitive constructs of perceptions. To answer the

third research question of the variation in behavioral intent within the following demographic

characteristics, analysis of variance (ANOVA) was applied. The next chapter 4 talks about analyses

and results.

54

4 Results

4.1 Behavioral intentions descriptive statistics

To answer research question #1, figure 4.1 displays the distribution of behavioral intentions

in the event of a local tsunami in the population under the threat of M9 CSZ tsunami. In the survey

respondents were asked how likely they were to adopt each of the following behaviors if they were

at your home in the event of a M9 Cascadia earthquake and tsunami? (1=very unlikely to 5=very

likely). The graph below combined “very unlikely” and “unlikely” responses into “unlikely” and

“very likely and “likely” into “likely”. According to the graph, the top three behaviors that the

respondents are unlikely to engage in include: continuing normal routine behavior (84.5%),

following friends and neighbors (70.9%), and waiting for family at home (66%).The top three

behaviors that respondents indicate they are likely to engage in include: contacting loved ones

(81.1%), following earthquake behavioral guidelines (80.6%), and checking social media, TV (75.3

%). Relevance and explanation of these results are discussed in detail in chapter 5.

55

Figure 4.1 Descriptive statistics of behavioral intentions

0 10 20 30 40 50 60 70 80 90

Panic

Continue normal behavior

Follow earthquake behavior guidelines

Try to help and rescue

Wait for family at home

Follow friends or neighbors

Wait for tsunami confirmation

Talk with neighbors

Travel to gather family, friends

Check social media, TV

Contact loved ones

Collect documents, other items

Take the lead to evacuate and encourage others

Evacuate to higher ground immediately

Likely Neither Unlikely

56

4.2 OLS regression analysis

To evaluate hypotheses 1-8, OLS regression analysis was performed first, using two

dependent variables: leadership behavior and delay behavior. Linear regression analysis, testing

model specifications that include all independent variables simultaneously, was applied for data

exploratory purposes. Yet, this is not the main technique for hypotheses testing in this research. The

goal of this study is to understand relationships between different levels of variables (i.e. socio-

environmental vs. cognitive vs. behavioral intentions), which are difficult to assess with a simple

linear regression. However, OLS regression results can provide a general understanding of the

association between behavioral intentions and explanatory variables. Regression output for

leadership dependent variable is displayed in Table 4.1. Two sets of regressions, using weighted and

unweighted samples, were conducted. Two model specifications, with and without demographic

variables, were examined. Standardized beta coefficients were reported to compare the effects

among variables.

57

Table 4.1: OLS regression output for leadership dependent variable (coefficients are standardized)

Variable Unweighted 1 Unweighted 2 Weighted 1 Weighted 2

Perceived severity 0.101 0.110 0.137 0.167*

Perceived probability 0.012 0.002 -0.001 -0.015

Self-efficacy 0.064 0.092 0.062 0.082

Response efficacy 0.142 0.147* 0.153* 0.172*

Wave arrival -0.151* -0.157* -0.123 -0.115

Experience -0.041 -0.049 -0.041 -0.061

Preparation 0.178* 0.192* 0.156* 0.182*

Knowledge confidence 0.133 0.084 0.136 0.076

Age 0.040 0.017

Difficulty walking -0.100 -0.062

Household size 0.023 -0.040

Children present 0.096 0.135

Elderly present -0.010 0.005

Ownership 0.060 0.044

Community tenure -0.078 -0.066

Education -0.090 -0.104

Income 0.016 0.080

Gender 0.152* 0.186**

N 206 206 206 206

F-statistic 2.94*** 4.48*** 2.99*** 4.28***

R-squared 0.220 0.154 0.223 0.148

Adjusted R-squared 0.145 0.119 0.148 0.113

* p<.05; ** p<.01; *** p<.001

There is only a slight difference in the outcomes between weighted and unweighted samples.

In the unweighted sample models, wave arrival time, preparation, and gender show a significant

effect on the outcome. In the weighted sample, the effect of perceived severity becomes significant,

while wave arrival time loses its significance. The effect of perceived severity in the weighted sample

is positive and significant, meaning that higher level of perceived risk is associated with higher

likelihood of having an intention to adopt the leadership behavior. This outcome is consistent with

previous empirical findings that shows the importance of risk perception in influencing behavior.

58

The effect of response efficacy in both weighted and unweighted samples is positive and significant,

meaning that higher level of response efficacy is associated with higher likelihood of having an

intention to engage in the leadership behavior. Significance of response efficacy and non-significance

of self-efficacy, shows that the perception of the effectiveness of protective actions is more

influential in leading to immediate evacuation decisions than the perception of one’s ability to do so.

In the unweighted sample the effect of wave arrival time (or risk proximity) is negative and

significant, meaning that increase in wave arrival time (a household is located further away from the

coastline) is associated with lower likelihood of having an intention to engage in the leadership

behavior. This outcome makes logical sense. It could be a reflection of the fact that people living

further away from the coastline have more time to escape the tsunami wave and more time to

engage in other types of behavior before evacuating. Effect of preparation, in both weighted and

unweighted samples, is positive and significant, suggesting that greater level of preparedness is

associated with higher likelihood of having an intention to engage in the leadership behavior. This

outcome shows that those who have done certain preparations (e.g. made a first-aid kit, established a

communication plan with friends and family) are ready to evacuate immediately. Gender is positive

and significant in both weighted and unweighted samples, meaning that being female is associated

with a higher likelihood of having an intention to adopt leadership behavior.

Beta coefficients in the regression output in Table 4.1 may not display their true value

and/or significance because of the possibility of an omitted variable bias or multicollinearity

problems, caused by correlation between variables. Pearson’s correlations between independent

variables are reported in Table 4.2 and between demographic variables are reported in Table 4.3.

Correlations between variables do not invalidate results. In fact, the research hypotheses assume a

presence of association between them, especially between socio-environmental and cognitive

59

variables. For instance, Table 4.2 shows that self-efficacy and knowledge are significantly and

strongly correlated (r = 0.5337, p < 0.001), and so are self-efficacy and preparation (r = 0.4523, p <

0.001). However, presence of correlation makes it more difficult to evaluate variables’ true influence

on the dependent variable when all of them are present in the model. That is why using mediation

analysis that relies on a series of hierarchical regressions could yield more clear results. In addition,

Table 4.3 displays Pearson’s correlations between the demographic variables. The output shows, for

instance, that difficulty walking is associated with age (r = 0.3764, p < 0.05), meaning that older

respondents are more likely to have walking difficulties than younger respondents. Presence of

children in a household and a household size are positively correlated as well (r = 0.6344, p < 0.05).

However, it is important to remember that both variables display high kurtosis and could be

showing unreliable behavior.

60

Table 4.2: Pearson’s correlations between independent variables

Variable Self-efficacy Response

efficacy

Perceived

severity

Perceived

probability Knowledge Experience Wave arrival Preparation

Self-efficacy - - - - - - - -

Response efficacy 0.404*** - - - - - - -

Perceived severity -0.2676*** 0.0091 - - - - - -

Perceived probability -0.0286 0.099 0.2104** - - - - -

Knowledge 0.5337*** 0.2282*** -0.0332 0.0175 - - - -

Experience 0.2247** 0.0483 0.0185 -0.0338 0.2331*** - - -

Wave arrival 0.2725*** 0.0789 -0.3448*** 0.1084 0.1557* 0.0147 - -

Preparation 0.4523*** 0.2217*** 0.0017 0.0769 0.5405*** 0.2583*** 0.1941** -

* p<.05; ** p<.01; *** p<.001

61

Table 4.3: Pearson’s correlations between demographic variables

Variable Age Difficulty

walking

Household

size

Children

present

Elderly

present Ownership

Community

tenure Education Income

Difficulty walking 0.3764**

Household size -0.4096** -0.1509**

Children present -0.4264** -0.1504** 0.6344**

Elderly present 0.2867** 0.0504 0.1259* -0.1705**

Ownership -0.0793 0.1864** -0.1267* 0.0520 -0.2574**

Community tenure 0.2853** 0.0594 0.0328 -0.1529** 0.1905** -0.2383**

Education 0.0200 -0.1057 -0.0947 -0.0913 0.0563 -0.1476** -0.1810**

Income -0.0669 -0.3154** 0.2343** 0.0485 0.1738** -0.3755** -0.0060 0.3077**

Gender -0.0735 0.0245 0.0359 0.0498 -0.0512 0.1311* -0.1081 -0.0284 -0.0692

** p<.05 or better

62

Table 4.4 shows the output of OLS regression analysis for unweighted and weighted models

respectively for delay dependent variable. Compared to leadership behavior regression output, there

are fewer variables that show significant influence on the dependent variable.

Table 4.4: OLS regression output for delay dependent variable (coefficients are standardized)

Variable Unweighted 1 Unweighted 2 Weighted 3 Weighted 4

Perceived severity -0.105 -0.087 -0.120 -0.078

Perceived probability -0.068 -0.055 -0.104 -0.097

Self-efficacy 0.134 0.145 0.060 0.091

Response efficacy 0.066 0.032 0.053 0.012

Wave arrival -0.046 -0.033 -0.007 0.005

Experience -0.086 -0.113 -0.109 -0.150*

Preparation 0.005 0.035 -0.049 -0.007

Knowledge

perception

-0.040 -0.055 0.008 -0.025

Age 0.701 0.009

Difficulty walking -0.067 -0.087

Household size 0.285** 0.286**

Children present -0.097 -0.126

Elderly present -0.075 -0.024

Ownership -0.004 0.009

Community tenure -0.067 -0.074

Education -0.095 -0.081

Income -0.049 -0.052

Gender 0.174* 0.199**

N 206 206 206 206

F-statistic 1.66* 1.17 1.93* 1.24

R-squared 0.138 0.045 0.156 0.048

Adjusted R-squared 0.055 0.007 0.075 0.009

* p<.05; ** p<.01; *** p<.001

In Table 4.4 in the unweighted model household size and gender show significant positive

association with delay intent dependent variable. Interestingly, for both leadership and delay

dependent variables gender shows significant positive influence on the outcome, suggesting that

females are more likely than males to have both leadership and delay intent. The reason that gender

63

variable shows such an outcome, could be reflective of the fact that females are more aware of risks

and the need to act proactively, but they also feel the importance of protecting their families (Shapira

et al. 2018). In this study female gender is also associated with higher level of perceived severity

(r=0.171, p < 0.05) and lower level of self-efficacy (r = -0.142, p < 0.05). Being female is the only

demographic characteristic that is consistently shown to be associated with higher levels of risk

perception and the adoption of protective actions in the natural hazard literature (Lindell et al. 2015;

Terpstra and Lindell 2012; Barberi et al. 2008; Fothergill 1996; Griffin et al. 1999; Lindell and Hwang

2008; Peacock et al. 2005). In the weighted model, household size and gender variables perform

similarly to the unweighted model.

In addition, experience shows significant negative association with the delay intent

dependent variable, meaning that higher number of natural disasters experienced in the past is

associated with lower likelihood of delay intent. This outcome suggest that perhaps experiential

knowledge plays a role in influencing people’s intentions to avoid delay behaviors. However,

experience variable did not show the opposite influence on the leadership intent, suggesting that

respondents who experienced natural disasters in the past are less likely to have a delay intention,

but are not necessarily more likely to have a leadership intention. Relationships between variables are

going to be analyzed in more details in the following section relaying on the mediation analysis

technique. OLS regression analyses show significant influence of some variables on behavioral

intentions, more specifically response efficacy, preparation, wave arrival time, gender, and household

size, supporting findings of previous research. Therefore, this research framework has value and is

going to be analyzed in more details in the following section.

64

4.3 Mediation analysis

To evaluate research hypotheses 1-8 regarding the relationships between socio-

environmental and cognitive perceptions variables and behavioral intent (leadership behavior and

delay behavior), a mediation analysis approach suggested by Preacher and Hayes (2004) was used. A

simple mediation model, the most commonly employed type of a mediation model, demonstrates a

sequence in which independent variable X can affect dependent variable Y indirectly through

mediator variable M and directly without the influence of M. Baron and Kenny (1986) define

mediator as a mechanism through which central independent variable influences the dependent

variable of interest, partially or fully accounting for the effect of a predictor on an outcome. The

impetus for using mediation analysis in this study is that it has the potential for improving

understanding of the mechanisms through which behavioral intent is formed, evaluating whether

cognitive variables partially or fully account for the influence of socio-environmental variables on

the outcome and through which socio-environmental variables have the most influence on the

outcome (Zanocco et al. 2017).

It is important to note that using mediation analysis does not improve the process of

assessing a causal effect. Mediation, as a process, is a causal phenomenon, but a statistical model

cannot prove causality. Especially in simple mediation models it is difficult to avoid key effects from

being confounded by omitted variables (Haynes and Preacher 2014). Yet, mediation statistical

analysis can help illuminate certain alternative explanations of phenomena. It has been chosen for

this study because perceptions and attitudes are hypothesized to be core components of cognition

that translate outside information into behavioral intent, serving as mediators between observable

factors and behavioral intent (Ajzen and Fishbein 2005).

65

OLS regression analyses conducted earlier shows the influence of socio-environmental,

demographic, and cognitive variables on behavioral intent. Yet, a model that contains all variables

does not evaluate the full complexity of the relationship between independent variables and between

independent and dependent variables that exists as suggested by theories of behavior. In addition,

the OLS regression independent variables may not be displaying their true effects and significance

because of possible multicollinearity. Mediation analysis also relies on the OLS estimator to

understand effects of independent variables on the outcome.

Using diagrammatic conventions common to mediation analysis (Preacher and Hayes 2004),

a generalized model of the relationship between socio-environmental factors, cognitive factors, and

behavioral intent is presented in Figure 4.2. and 4.3. The simple relationship between X and Y is

known as the total effect (path c) (see Figure 4.2).

Figure 4.2. Illustration of a total effect of X on Y

Behavioral

intentions

(Y)

c

Socio-

environmental

factors

(X)

66

Figure 4.3: Illustration of a simple diagram of mediation

In Figure 4.2 path c is called the direct effect of X on Y. The direct effect is estimated

through equation (1), where c’1 coefficient is the direct effect of X on Y when controlled for the

mediator. It quantifies how much variations in the levels of X account for variations in Y,

independent of the effect of M on Y, i.e. it represents the effect of X on Y that is unique to X.

Y=iY+c1’ X+b1M+eY (1)

M=iM+a1X+eM (2)

The indirect effect of X on Y is represented as the two paths linking X to Y through M

(paths a and b in Figure 4.3). The indirect effect of X on Y through M is quantified as the product of

a and b paths (a*b). It is estimated as a1b1, meaning the product of the effect of X on M equation (2)

Perceptions

(M)

Behavioral

intentions

(Y)

c’

Socio-

environmental

factors

(X)

Model effects Direct effect of X on Y, while controlling for M -- (c’) Direct effect of M on Y, while controlling for X -- (b)

Indirect effect of X on Y -- (ab)

Model variables Primary independent variable (X) Mediator variable (M) Dependent variable (Y)

67

and the direct effect of M on Y while controlling for X equation (1) (Baron and Kenny 1986, Vaske

2008).

A widely accepted approach developed by Baron and Kenny (1986) argues that for a

mediation to exist three criteria must be met: (1) X significantly predicts Y (i.e. total effect is

significant); (2) X significantly predicts M (path a); and (3) M significantly predicts Y controlling for

X (path b) (Preacher and Hayes 2004). Mediation occurs when the effect of X on Y decreases (effect

c’) with the inclusion of M, either to zero (full mediation) or by a nontrivial amount (partial

mediation). Baron and Kenny (1986) also argue that to claim mediation there should be no

measurement error in the mediator and Y should not cause M, the former being a difficult criterion

to meet considering the latent nature of cognitive variables.

Preacher and Hayes (2004) have argued for the necessity to perform a formal and direct

significance test of the indirect effect (ab) rather than following a series of separate significance tests

that do not involve effect ab, as described by Baron and Kenny (1986). One such direct tests has

been developed by Sobel (1982). The Sobel test provides a direct test of the indirect effects (ab) and

has been argued to be a more statistically rigorous test of the presence of indirect effects. It relies on

unstandardized regression coefficients of paths a and b and the standard errors associated with those

coefficients. However, few researchers have relied on the Sobel test compared to following the

criteria for mediation established by Baron and Kenny (1986), where significance of the indirect

effect is not a required condition for mediation. Preacher and Hayes (2004) argue that another

reason for the unpopularity of the Sobel test lies in the fact that common programs for regression

analysis such as SPSS and SAS would provide all the information needed to conduct the Sobel test

manually, but researchers would not bother to make additional computational efforts.

68

Hayes (2013) developed an alternative to the Sobel test, examining the significance of the

indirect effects using bootstrapped confidence intervals that can be executed in the PROCESS

package in SPSS, making it easier for researchers to implement. Bootstrapping is a nonparametric

approach to effect-size estimation and hypothesis testing that makes no assumption about the shape

of the distribution of the indirect effect (Bollen and Stine, 1990; MacKinnon and Dwyer, 1993;

Stone and Sobel, 1990; Preacher and Hayes 2004). Using Sobel significance test which assumes

normal distribution when testing for mediation is not appropriate and results in underpowered tests

of mediation (MacKinnon et al. 1995). Bootstrapping has been applied to analyses with issues of

asymmetries and nonnormality in the distribution of the data, which is the case with several variables

in the sample of this research. It can also be applied to smaller samples with more confidence

because it produces a test that does not rely on large-sample theory. The indirect effect of X on Y is

considered statistically different from zero when the bootstrap confidence interval does not span

zero (Hayes 2013).

It is important to keep in mind the distinction between terms: mediated effects and indirect

effects, which are sometimes used interchangeably. A claim that mediation exists implies the initial

presence of a total effect between X and Y to be mediated. No such assumption is necessary in the

assessment on the indirect effects. Variable X can influence Y through M, even if there is no direct

relationship between X and Y (Preacher and Hayes 2004). Therefore, following Preacher and Hayes

(2004) a more power strategy to test for mediation, compared to Baron and Kenny’s (1986) steps,

would require satisfying two criteria: (1) there is an effect to be mediated (effect c is different from

zero) and (2) the indirect effect is statistically significant. Yet, even if a mediation effect cannot be

established, it is still important to evaluate the presence of indirect effects, because it allows for a

better understanding of the complexity of the relationship between variables.

69

Tables 4.5 and 4.6 show the output of a mediation analysis following Hayes (2013) method

of bootstrapping for direct testing of the indirect effects (ab) using the PROCESS package in SPSS.

Table 4.5 describes results for the leadership behavior dependent variable (column Y), while Table

4.6 describes the output for the delay behavior dependent variable (column Y). Weighted sample

with robust standard errors was used in the analysis. All the effects displayed in the table are

unstandardized except the indirect (ab) output, which is standardized to allow for comparison of

effects across several independent variables and mediators. PROCESS produces standardized

coefficients only for the indirect effects, therefore other coefficients are recorded using their true

effect.

In Tables 4.5 and 4.6, effect c’ (X>>Y) represents the direct effect of socio-environmental

variables (column X) on the outcome while controlling for mediator variables (column M). Tables

also display the relationship between outside variables and mediators (effect a, X>>M) and between

mediators and outcome variables (effect b, M>>Y). In addition, ab is the indirect effect of the

outside controls on the behavioral intent via the mediator. Effect c is the total effect between X and

Y. To claim mediation, total initial effect must be significant.

Results for demographic variables are not displayed in the tables. However, demographic

characteristics considered in this study were included as controls in the mediation procedure in the

PROCESS tool. Reasons they are not displayed in the output table is because most of them did not

show any significant effect on the outcome and because they primarily serve as control variables and

not the main explanatory variables. This study takes an in-depth look at the variation of perceptions

and behavior among different demographic groups using the analysis of variance (ANOVA).

70

Table 4.5: Mediation Analysis Results Leadership Dependent Variable; SE are HC0, weighted sample

X M Y c’

(X>>Y) SE (c’) a (X>>M) SE (a)

b

(M>>Y) SE (b)

ab

standardized SE (ab)

(c)

Total

Effect

SE (c)

Wave arrival

Self-

efficacy

Leadership

behavior

.0305** .0136 .0656*** .0152 .1501*** .0509 .0547** 0.0044 -.0207 .0128

Knowledge .2085*** .0776 .6248*** .0845 .0489 .0554 .0317 .0380 .2390*** .0683

Preparation .0817*** .0254 .2082*** .0349 .0538 .0523 .0305 .0322 .0929*** .0228

Experience .0139 .0347 .1660*** .0410 .1115** .0500 .0369** .0195 .0325 .0335

Wave arrival

Response

efficacy

-.0235* .0130 .0167 .0171 .1679*** .0550 .0155 .0192 -.0207 .0128

Knowledge .2016*** .0711 .2859*** .0923 .1308** .0534 .0388** .0206 .2390*** .0683

Preparation .0788*** .0235 .1095*** .0341 .128 ** .0520 .0384** .0203 .0929*** .0228

Experience .0273 .0335 .0326 .0420 .1593*** 0.0524 .0103 .0152 .0325 .0335

Wave arrival

Perceived

severity

-.0139 .0134 -.0539*** .0102 .1267 .0832 -.0379 .0271 -.0207 .0128

Knowledge .2417*** .0683 -.0164 .0533 .1646** .0772 -.0028 .0108 .2390*** .0683

Preparation .0944*** .0220 -.0087 .0237 .1672** .0749 -.0040 .0125 .0929*** .0228

Experience .0301 .0330 .0150 .0332 .1566** .0797 .0047 .0127 .0325 .0335

Wave arrival

Perceived

probability

-.0225 * .0131 .0360** .0173 .0496 .0496 .0099 .0127 -.0207 .0128

Knowledge .2367*** .0685 .0767 .1008 .0303 .0486 .0024 .0072 .2390*** .0683

Preparation .0917*** .0227 .0556 .0442 .0224 .0474 .0034 .0098 .0929*** .0228

Experience .0328 .0335 -.0087 .0616 .0388 .0490 -.0007 .0083 .0325 .0335

71

Table 4.6: Mediation Analysis Results Delay Dependent Variable; SE are HC0, weighted sample

X M Y c’

(X>>Y) SE (c) a (X>>M) SE (a) b (M>>Y) SE (b)

ab

standardized SE (ab)

(c)

Total

effect

SE (c)

Wave arrival

Self-

efficacy

Delay

behavior

-.0035 .0127 .0656 *** .0152 .0860* .0491 .0335 .0229 .0021 .0122

Knowledge -.0678 .0754 .6248 *** .0845 .1042 ** .0526 .0723 .0405 -.0026 .0683

Preparation -.0202 .0225 .2082 *** .0349 .0978 ** .0488 .0592** .0345 .0001 .0221

Experience -.0489 * .0283 .1660 *** .0410 .1004 ** .0475 .0355** .0208 -.0322 .0286

Wave arrival

Response

efficacy

.0012 .0122 .0167 .0171 .0584 .0486 .0058 .0094 .0021 .0122

Knowledge -.0203 .0669 .2859*** .0923 .0618 .0484 .0196 .0191 -.0026 .0683

Preparation -.0066 0.0223 .1095*** .0341 .0614 .0500 .0196 .0189 .0001 .0221

Experience -.0342 .0283 .0326 .0420 .0613 .0479 .0043 .0076 -.0322 .0286

Wave arrival

Perceived

severity

-.0070 .0129 -.0539*** .0102 -.1691 * .0929 .0542 .0305 .0021 .0122

Knowledge -.0051 .0670 -.0164 .0533 -.1528* .0867 .0028 .0113 -.0026 .0683

Preparation -.0012 .0219 -.0087 .0237 -.1528* .0863 .0039 .0131 .0001 .0221

Experience -.0300 .0298 .0150 .0332 -.1501 .0861 -.0048 .0140 -.0322 .0286

Wave arrival

Perceived

probability

.0040 .0123 .0360** .0173 -.0524 .0429 -.0112 .0124 .0021 .0122

Knowledge .0012 .0674 .0767 .1008 -.0504 .0419 -.0043 .0083 -.0026 .0683

Preparation .0030 .0221 .0556 .0442 -.0508 .0423 -.0082 .0111 .0001 .0221

Experience -.0327 .0282 -.0087 .0616 -.0509 .0424 .0009 .0091 -.0322 .0286

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The following discussion examines outcomes of the mediation analysis, addressing

previously proposed hypotheses.

Hypothesis 1a: Perceived probability mediates the influence of socio-environmental factors on the

behavioral intent.

For two independent variables (knowledge confidence and preparation) there is an observed

reduction in the association with the leadership behavioral intent when perceived probability is

added to the model, however, there is no significant influence of the independent variables on the

mediator nor mediator on the leadership intent. Following Baron and Kenny (1986) criteria to

establish mediation is not met. In Hayes (2013) direct test of indirect effect, perceived probability

does not show a significant influence of the leadership intent either. Perceived probability does not

mediate the influence of socio-environmental variables on the delay behavioral intent.

Hypothesis 1b: Perceived probability is positively associated with the behavioral intent to evacuate outside

of the tsunami inundation zone in the event of M9 CSZ tsunami.

Mediation analysis output does not show a significant association between perceived

probability and leadership behavioral intent when controlling for four independent variables (path

b), even though the association is positive. Pearson’s correlation between perceived probability and

leadership behavior is also positive, but non-significant (r=0.018, p=0.793). This result is similar to

the OLS regression output, where there were found no association between leadership behavioral

intent and perceived probability. In the interpretation of this and the following hypotheses, where

there is no analysis of a mediation effect, output from the mediation model is prioritized over OLS

regression output. In the OLS regression analysis, the presence of all variables in one regression

model, many of which are highly correlated with each other, is dampening the true effect of

constructs. In the mediation model, even though there is a possibility for the omitted variable bias,

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focus only on one variables prevents multicollinearity and type II error, retaining false null

hypothesis.

Hypothesis 1c: Perceived probability is negatively associated with the behavioral intent to engage in delay

behaviors in the event of a M9 CSZ tsunami.

Mediation analysis output does not show a significant association between perceived

probability and delay behavioral intent when controlling for four independent variables (path b),

even though the association is negative. Pearson’s correlation between perceived probability and

delay behavior is also negative, but non-significant (r=-0.099, p=0.160).Hypothesis 2a: Perceived

severity mediates the influence of socio-environmental factors on the behavioral intent.

For two independent variables (knowledge confidence and preparation) there is an observed

reduction in the association with the leadership behavioral intent when perceived severity is added to

the model. There is also a significant association between M and Y. However, path a is not

significant. Therefore, following the Baron and Kenny (1986) criteria to establish mediation is not

met. In Hayes (2013) direct test of indirect effect, perceived severity does not show a significant

influence of the leadership intent either. Perceived severity does not mediate the influence of socio-

environmental variables on the delay behavioral intent as there is no established significant

association between X and Y (c’ and c) and X and M (a).

Hypothesis 2b: Perceived severity is positively associated with the behavioral intent to evacuate outside of

the tsunami inundation zone in the event of M9 CSZ tsunami.

Mediation analysis output shows a positive significant association between perceived severity

and leadership behavioral intent. Pearson’s correlation between perceived severity and leadership

behavior is also positive and significant (r=0.145, p<0.05).

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Hypothesis 2c: Perceived severity is negatively associated with the behavioral intent to engage in delay

behaviors in the event of M9 CSZ tsunami.

Mediation analysis output shows a significant negative association between perceived

severity and delay behavioral intent when controlling for four wave arrival, knowledge confidence

and preparation (path b). Pearson’s correlation between perceived severity and delay behavior is also

negative and significant on the 90% confidence level (r=-0.133, p<0.1).

Hypothesis 3a: Self-efficacy mediates the influence of socio-environmental factors on the behavioral intent.

Not all conditions to satisfy criteria for mediation according to Baron and Kenny’s (1986)

approach exist for the leadership dependent variable. For knowledge confidence and preparation

variables, there is an observed reduction in the association with the leadership behavioral intent

when self-efficacy is controlled for in the model. There is also a significant association between X

and M (path a) However, path b is not significant. For the wave arrival variable, path c, a, and b are

significant, but there is no established initial effect of X on Y. However, following Hayes (2013)

direct test of indirect effect, wave arrival and experience show positive significant influence on

leadership behavior through self-efficacy, wave arrival showing greater influence compared to

experience.

Similarly, not all conditions to satisfy criteria for mediation according to Baron and Kenny’s

(1986) approach exist for the delay dependent variable. None of the independent variables shows a

significant total association with the delay dependent variable. However, following Hayes (2013)

direct test of indirect effect, preparation and experience show positive significant influence on delay

behavior through self-efficacy, preparation showing greater influence compared to experience.

Hypothesis 3b: Self-efficacy is positively associated with the behavioral intent to evacuate outside of

tsunami inundation zone in the event of M9 CSZ tsunami.

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Mediation analysis output shows positive significant association between self-efficacy and

leadership behavioral intent when controlling for wave arrival and experience. Pearson’s correlation

between self-efficacy and leadership behavior is also positive and significant (r=0.182, p<0.01).

Hypothesis 3c: Self-efficacy is negatively associated with the behavioral intent to engage in delay behaviors

in the event of M9 CSZ tsunami.

Mediation analysis output shows positive significant association between self-efficacy and

delay behavioral intent when controlling for all four independent variables. This result contradicts

the hypothesis. Pearson’s correlation between self-efficacy and leadership behavior is also positive

but non-significant (r=0.072, p=0.310).

Hypothesis 4a: Response efficacy mediates the influence of socio-environmental factors on the behavioral

intent.

Criteria for mediation according to Baron and Kenny’s (1986) approach exist for leadership

dependent variable when looking at the relationship between knowledge confidence and the

outcome, and preparation and the outcome. There is a positive significant total influence of X on Y.

There is an established reduction in the association between X and Y when M is controlled for in

the model. Paths a and b are significant. Response efficacy mediates the influence of knowledge

confidence and preparation on leadership behavioral intent. In addition, following Hayes (2013)

direct test of indirect effect, shows a significant indirect influence of knowledge confidence and

preparation on the outcome via response efficacy. The indirect effect of the two variables is similar

to each other. For delay behavioral intent, not all conditions exist to satisfy criteria for mediation

according to Baron and Kenny’s (1986). None of the independent variables shows a significant total

association with the delay dependent variable. In addition, following Hayes (2013) direct test of

indirect effects, there is no established significant indirect effects.

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Hypothesis 4b: Response efficacy is positively associated with the behavioral intent to evacuate outside of

tsunami inundation zone in the event of M9 CSZ tsunami.

Mediation analysis output shows positive significant association between response efficacy

and leadership behavioral intent when controlling for all independent variables. Pearson’s correlation

between response efficacy and leadership behavior is also positive and significant (r=0.231, p<0.01).

Hypothesis 4c: Response efficacy is negatively associated with the behavioral intent to evacuate outside of

tsunami inundation zone in the event of M9 CSZ tsunami.

Mediation analysis output does not show a negative significant association between response

efficacy and delay behavioral intent. Pearson’s correlation between response efficacy and delay

behavior is positive and non-significant (r=0.037, p=0.599), showing no support for the hypothesis.

Hypothesis 5a: Tsunami-relevant knowledge confidence is positively associated with the behavioral intent

to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami.

Mediation analysis output shows that knowledge confidence is positively and significantly

associated with leadership behavioral intent with and without control of mediator variables.

Pearson’s correlation between knowledge confidence and leadership behavior is also positive and

significant (r=0.209, p<0.01), showing support for the hypothesis.

Hypothesis 5b: Tsunami-relevant knowledge confidence is negatively associated with the behavioral intent

to engage in delay behaviors in the event of M9 CSZ tsunami.

Mediation analysis output shows no support for this hypothesis. Pearson’s correlation

between knowledge confidence and delay behavior is also positive and non-significant (r=0.003,

p=0.971), showing no support for the hypothesis.

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Hypothesis 5c: Tsunami-relevant knowledge confidence is positively associated with self-efficacy.

Unstandardized coefficient of association between knowledge confidence and self-efficacy

from the mediation analysis output is positive and significant (b=0.6248, p<0.01). Pearson’s

correlation (or standardize coefficient) is also positive and significant (r=0.560, p<0.01), showing

support for the hypothesis.

Hypothesis 5d: Tsunami-relevant knowledge confidence is positively associated with response efficacy.

Unstandardized coefficient of association between knowledge confidence and response

efficacy from the mediation analysis output is positive and significant (b=0.2859, p<0.01). Pearson’s

correlation (or standardize coefficient) is also positive and significant (r=0.223, p<0.01), showing

support for the hypothesis.

Hypothesis 5e: Tsunami-relevant knowledge confidence is positively associated with perceived probability.

There is no support for this hypothesis in the mediation analysis output. Pearson’s

correlation (or standardize coefficient) is also non-significant (r=0.043, p=0.538), showing no

support for the hypothesis.

Hypothesis 5f: Tsunami-relevant knowledge confidence is negatively associated with perceived severity.

There is no support for this hypothesis in the mediation analysis output. Pearson’s

correlation (or standardize coefficient) is also non-significant (r=-0.107, p=0.129), showing no

support for the hypothesis.

Hypothesis 6a: Hazard experience is positively associated with the behavioral intent to evacuate outside of

tsunami inundation zone in the event of M9 CSZ tsunami.

Mediation analysis output shows that hazard experience is not significantly associated with

leadership behavioral intent, non-significance holds in models with and without control of mediator

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variables. Pearson’s correlation between hazard experience and leadership behavior is also non-

significant (r=0.052, p=0.458), showing no support for the hypothesis.

Hypothesis 6b: Hazard experience is negatively associated with the behavioral intent to engage in delay

behaviors in the event of M9 CSZ tsunami.

Mediation analysis output shows support for the hypothesis only in the model with the

presence self-efficacy. In that situation, hazard experience shows negative significant association

with delay behavioral intent (b=-0.0489, p<0.1). Pearson’s correlation between hazard experience

and delay behavior is also negative and significant (r=-0.138, p<0.05), showing support for the

hypothesis.

Hypothesis 6c: Hazard experience is positively associated with self-efficacy.

Unstandardized coefficient of association between hazard experience and self-efficacy from

the mediation analysis output is positive and significant (b=0.1660, p<0.01). Pearson’s correlation

(or standardize coefficient) is also positive and significant (r=0.217, p<0.01), showing support for

the hypothesis.

Hypothesis 6d: Hazard experience is positively associated with response efficacy. There is no support for

this hypothesis in the mediation analysis.

Pearson’s correlation (or standardize coefficient) is also non-significant (r=0.087, p=0.218),

showing no support for the hypothesis.

Hypothesis 6e: Hazard experience is positively associated with perceived probability. There is no support

for this hypothesis in the mediation analysis.

Pearson’s correlation (or standardize coefficient) is also non-significant (r=-0.044, p=0.531),

showing support for the hypothesis.

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Hypothesis 6f: Hazard experience is positively associated with perceived severity.

There is no support for this hypothesis in the mediation analysis. Pearson’s correlation (or

standardize coefficient) is also non-significant (r=0.079, p=0.260), showing support for the

hypothesis.

Hypothesis 7a: Risk proximity is positively associated with the behavioral intent to evacuate outside of

tsunami inundation zone in the event of M9 CSZ tsunami.

Mediation analysis output shows no total effect of wave arrival time on the leadership intent.

Wave arrival time has a positive significant association with leadership intent when controlled for

self-efficacy, which does not support the hypothesis because that means that greater tsunami arrival

time is associated with a higher likelihood of having an intention to engage in the leadership

behavior. Wave arrival time is negatively significantly associated with leadership intent when

controlled for response efficacy and perceived probability, which supports the hypothesis. Pearson’s

correlation between wave arrival time and leadership behavior is negative, but non-significant (r=-

0.095, p=0.177), showing no support for the hypothesis.

Hypothesis 7b: Risk proximity is negatively associated with the behavioral intent to engage in delay

behaviors in the event of M9 CSZ tsunami.

Mediation analysis output shows no support for this hypothesis. Pearson’s correlation

between wave arrival time and leadership behavior is negative, but non-significant (r=0.046,

p=0.511), showing no support for the hypothesis.

Hypothesis 7c: Risk proximity is negatively associated with self-efficacy.

Unstandardized coefficient of association between wave arrival time and self-efficacy from

the mediation analysis output is positive and significant (b=0.0656, p<0.01). Pearson’s correlation

(or standardize coefficient) is also positive and significant (r=0.251, p<0.01), showing support for

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the hypothesis. This result suggests that higher wave arrival time (i.e. lower risk proximity) is

associated with higher level of self-efficacy.

Hypothesis 7d: Risk proximity is positively associated with response efficacy.

There is no support for the association between wave arrival time and response efficacy in

the mediation analysis. Pearson’s correlation (or standardize coefficient) is also non-significant

(r=0.076, p=0.292), showing no support for the hypothesis.

Hypothesis 7e: Risk proximity is positively associated with perceived probability.

Unstandardized coefficient of association between wave arrival time and perceived

probability from the mediation analysis output is positive and significant (b=0.0360, p<0.05).

Pearson’s correlation (or standardize coefficient) is also positive and significant (r=0.165, p<0.01),

showing the opposite direction of the relationship from the one assumed in the hypothesis.

Hypothesis 7f: Risk proximity is positively associated with perceived severity.

Unstandardized coefficient of association between wave arrival time and perceived severity

from the mediation analysis output is negative and significant (b=-0.0539, p<0.01). Pearson’s

correlation (or standardize coefficient) is also negative and significant (r=-0.374, p<0.01), showing

support for the hypothesis, that further location of a household from the coastline is associated with

lower level of perceived severity (or closer risk proximity is associated with higher level of perceived

severity).

Hypothesis 8a: Preparation is positively associated with the behavioral intent to evacuate outside of

tsunami inundation zone in the event of M9 CSZ tsunami.

Mediation analysis output shows presence of total significant effect of preparation on the

leadership intent. Preparation is also positively significantly associated with leadership intent when

controlled for all cognitive variables, strongly supporting the hypothesis. Pearson’s correlation

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between preparation and leadership behavior is positive and significant (r=0.251, p<0.01), showing

support for the hypothesis.

Hypothesis 8b: Preparation is negatively associated with the behavioral intent to engage in delay

behaviors in the event of M9 CSZ tsunami.

Mediation analysis output shows no support for this hypothesis. Pearson’s correlation

between wave arrival time and leadership behavior is negative, but non-significant (r=-0.020,

p=0.773), showing no support for the hypothesis.

Hypothesis 8c: Preparation is positively associated with self-efficacy.

Unstandardized coefficient of association between preparation and self-efficacy from the

mediation analysis output is positive and significant (b=0.2082, p<0.01). Pearson’s correlation (or

standardized coefficient) is also positive and significant (r=0.446, p<0.01), showing support for the

hypothesis.

Hypothesis 8d: Preparation is positively associated with response efficacy.

Unstandardized coefficient of association between preparation and self-efficacy from the

mediation analysis output is positive and significant (b=0.1095, p<0.01). Pearson’s correlation (or

standardized coefficient) is also positive and significant (r=0.205, p<0.01), showing support for the

hypothesis.

Hypothesis 8e: Preparation is positively associated with perceived probability.

There is no support for the association between preparation and perceived probability in the

mediation analysis. Pearson’s correlation (or standardized coefficient) is also non-significant

(r=0.097, p=0.167), showing no support for the hypothesis.

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Hypothesis 8e: Preparation is positively associated with perceived severity.

There is no support for the association between preparation and perceived probability in the

mediation analysis. Pearson’s correlation (or standardized coefficient) is also non-significant (r=-

0.034, p=0.628), showing no support for the hypothesis. Table 4.7 displays summary of all

hypotheses and outcomes of the mediation analysis.

Table 4.7: Summary of research hypotheses testing.

# Hypothesis Outcome

Hypothesis 1

a) Perceived probability mediates the influence of socio-environmental factors on behavioral intent b) Perceived probability is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami c) Perceived probability is negatively associated with the behavioral intent to engage in delay and milling behaviors in the event of M9 CSZ tsunami

a) Not supported b) Not supported c) Not supported

Hypothesis 2

a) Perceived severity mediates the influence of socio-environmental factors on behavioral intent b) Perceived severity is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami c) Perceived severity is negatively associated with the behavioral intent to engage in delay and milling behaviors in the event of M9 CSZ tsunami

a) Not supported b) Supported c) Supported

Hypothesis 3

a) Self-efficacy mediates the influence of socio-environmental factors on behavioral intent b) Self-efficacy is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami c) Self-efficacy is negatively associated with the behavioral intent to engage in delay and milling behaviors in the event of M9 CSZ tsunami

a) Leadership: no mediation, but positive indirect effect of wave arrival and experience; Delay: no mediation, but positive indirect effect of preparation and experience b) Supported c) Not supported (relationship is positive)

Hypothesis 4

a) Response efficacy mediates the influence of socio-environmental factors on behavioral intent b) Response efficacy is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami c) Response efficacy is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami

a) Leadership: mediation exist for knowledge and preparation; Delay: no mediation b) Supported c) Not supported

Hypothesis 5

a) Tsunami-relevant knowledge efficacy is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami b) Tsunami-relevant knowledge efficacy is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami

a) Supported b) Not supported c) Supported d) Supported e) Not supported f) Not supported

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c)Tsunami-relevant knowledge confidence is positively associated with self-efficacy d) Tsunami-relevant knowledge confidence is positively associated with response efficacy e) Tsunami-relevant knowledge confidence is positively associated with perceived probability f) Tsunami-relevant knowledge confidence is negatively associated with perceived severity

Hypothesis 6

a) Hazard experience is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami b) Hazard experience is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami c) Hazard experience is positively associated with self-efficacy d) Hazard experience is positively associated with response efficacy e) Hazard experience is positively associated with perceived probability f) Hazard experience is positively associated with perceived severity

a) Not supported b) Supported, when controlled for self-efficacy; no total effect c) Supported d) Not supported e) Not supported f) Not supported

Hypothesis 7

a) Risk proximity is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami b) Risk proximity is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami c) Risk proximity is negatively associated with self-efficacy d) Risk proximity is positively associated with response efficacy e) Risk proximity is positively associated with perceived probability f) Risk proximity is positively associated with perceived severity

a) Supported, when controlled for response efficacy and perceived probability b) Nor supported c) Supported d) Not supported e) Not supported, relationship is negative f) Supported

Hypothesis 8

a) Preparation is positively associated with the behavioral intent to evacuate outside of tsunami inundation zone in the event of M9 CSZ tsunami b) Preparation is negatively associated with the behavioral intent to engage in delay behaviors in the event of M9 CSZ tsunami c) Preparation is positively associated with self-efficacy d) Preparation is positively associated with response efficacy e) Preparation is positively associated with perceived probability f) Preparation is positively associated with perceived severity

a) Supported b) Not supported c) Supported d) Supported e) Not supported f) Not supported

4.4 Analysis of variance

To answer research question 2: what is the variation in behavioral intent among the

following demographic characteristics: gender, age, education, income, community tenure, house

ownership, household size, difficulty walking, presence of children and presence of elderly in a

household, this study employs analysis of variance. The standard test for the analysis of variance is

ANOVA test, which compares variation of means between groups of interests. ANOVA relies on

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assumptions of normal distribution of the population. Table 4.8 displays statistics of mean, standard

deviation, skewness, kurtosis, and Shapiro-Wilk test of normality for leadership and delay dependent

variables.

Table 4.8: Descriptive statistics and Shapiro-Wilk test of normality for dependent variables

Statistics Leadership Delay

Mean 2.59 2.56

Standard deviation 0.74 0.69

Skewness -0.847 -0.925

Kurtosis -0.043 0.309

Shapiro-Wilk test 0.900*** 0.905***

N 206 206

Normality distributed data generally has skewness that is between -0.5 and +0.5 and kurtosis

of 3 (Bulmer 1979). Table 4.8 shows that leadership variable has a skewness of -0.847, which

represents a moderately negatively skewed distribution, i.e. the left tail of the distribution is longer

than the right tail. Delay variable has a skewness of -0.925, which also represents a moderately

negatively skewed distribution, i.e. the left tail of the distribution is longer than the right tail.

Leadership and delay behavior have kurtosis of 0.309 and -0.043 respectively, both deviating from

kurtosis of a normal distribution. Shapiro-Wilk’s test, which is significantly positive for both

leadership and delay behavior, indicates that there is a deviation from a normal distribution in

observations, but it does not violate the assumption of normality that ANOVA t-test relies on.

However, for purposes of robustness, this study provides output for both ANOVA t-test and

Kruskall-Wallis test.

Kruskall-Wallis test is a non-parametric test that does not assume normal distribution of

residuals. In the Kruskall-Wallis test the null hypothesis is that the medians of groups are statistically

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the same. Therefore, rejection of the null means that there is a statistical difference between medians

of at least two group, if there are more than two groups in the analysis. In general ANOVA is

considered a robust test even if there is violation of its assumptions. This study relies on ANOVA t-

test and F-test to address the research question, however, Kruskall-Wallis Chi-squares (i.e. test of a

null hypothesis) are provided in Table 4.9 for comparison. For two variables “difficulty walking” and

“children present” there is a difference between t-test and Kruskall-Wallis tests, which indicates that

there may be some deviation from normality in the distribution of the observations.

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Table 4.9: Results of ANOVA and Kruskall-Wallis tests

Variable

Leadership (1-5 scale) mean

Delay (1-5 scale) mean

Gender (n=206)

Female Male

t-test (F-test) Chi-square (Kruskall-Wallis)

2.71 2.42

7.895** 7.113**

2.65 2.43

5.293* 7.189**

Age (n=206)

(1) 24-40 (2) 41-64 (3) 65-95

t-test (F-test) Chi-square (Kruskall-Wallis)

Tukey Post Hoc

2.84 2.69 2.44

4.181* 10.899**

(1-3) = p<0.05

2.76 2.59 2.48 1.639 5.932 N/A

Education (n=206)

Less than Bachelor Bachelor

Graduate degree t-test (F-test)

Chi-square (Kruskall-Wallis)

2.58 2.7 2.53 0.683 2.166

2.59 2.51 2.53 0.301 0.313

Income (n=206)

Below 40,000 40,000-75,000 Above 75,000 t-test (F-test)

Chi-square (Kruskall-Wallis)

2.59 2.57 2.64 0.136 0.483

2.58 2.54 2.55 0.046 0.302

Community tenure (n=206)

0-10 years 11-30 years 31-76 years

t-test (F-test) Chi-square (Kruskall-Wallis)

2.61 2.65 2.49 0.701 1.008

2.56 2.55 2.57 0.010 0.107

Homeownership (n=206)

Primary Renter Other

t-test (F-test) Chi-square (Kruskall-Wallis)

2.60 2.55 2.84 0.446 0.653

2.55 2.60 2.41 0.297 0.966

Household size (n=206)

1 person 2 people

3 and more t-test (F-test)

Chi-square (Kruskall-Wallis) Tukey Post Hoc

2.50 2.56 2.81 2.420 5.529 N/A

2.48 2.47 2.88

5.936** 13.242***

(1-3) = p<0.01;(2-3) = p<0.01

Difficulty walking (n=206)

Yes No

t-test (F-test) Chi-square (Kruskall-Wallis)

2.39 2.67

6.100* 6.903**

2.43 2.61 2.839 5.490*

Children present (n=206)

Yes No

t-test (F-test) Chi-square (Kruskall-Wallis)

2.93 2.56

3.990* 3.986*

2.85 2.53 3.517 4.813*

Elderly present (n=206)

Yes No

t-test (F-test) Chi-square (Kruskall-Wallis)

2.56 2.61 0.146 0.301

2.53 2.57 0.135 0.011

*p<0.05; **p<0.01; ***p<0.001

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Table 4.9 displays ANOVA analysis outcomes. There were distinct differences between

genders with respect to the mean levels of leadership behavioral intent, t=7.895, p<0.01 and of delay

behavioral intent, t=5.293, p<0.05. Females were statistically more likely to have an intent to engage

in both leadership and delay behaviors compared to men. Kruskall-Wallis test confirms the statistical

difference among the groups, Chi-square=7.113, p<0.01 for the leadership behavioral intent and

Chi-square=7.189, p<0.01 for the delay behavioral intent. There was a significant difference among

age groups in the mean levels of leadership behavioral intent, F=4.181, p<0.05, where 24-40 age

group was statistically more likely to have an intent to engage in the leadership behavior compared

to 65-95 age group. Kruskall-Wallis test confirms the statistical difference among the groups, Chi-

square=10.899, p<0.01 for the leadership behavioral intent. There was a significant difference

among household of different sizes in the mean levels of delay behavioral intent, F=5.936, p<0.01,

where a household of three and more people was statistically more likely to have an intent to engage

in the delay behavior compared to a household of 2 people and compared to a household of 1

person. Kruskall-Wallis test confirms the statistical difference among the groups, Chi-

square=13.242, p<0.001 for the delay behavioral intent. There was a significant difference between

respondents with and without walking difficulty with respect to the mean level of the leadership

behavioral intent, t=6.100, p<0.05. Kruskall-Wallis test confirms the statistical difference among the

groups, Chi-square=6.903, p<0.01 for the leadership behavioral intent. Finally, there was a

significant difference among household with and without children in the mean levels of leadership

behavioral intent, t=3.990, p<0.05, where households with children were statistically more likely to

have an intent to engage in the leadership behavior compared to households without children.

Kruskall-Wallis test confirms the statistical difference among the groups, Chi-square=3.986, p<0.05

for the leadership behavioral intent.

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5 Discussion

The goal of this research was to understand how populations at risk of CSZ earthquakes and

tsunamis along the Oregon coast would respond in the event of the disaster and what factors may

explain variation in their behavioral intentions. To do two research questions were asked and several

hypotheses proposed, which will be evaluated in detail in this chapter, connecting the discussion

with the existing natural hazard literature and the goals of this study.

The first research question asked about variation in people’s behavioral intentions in the

event of M9 CSZ earthquake and tsunami. The second research question asked about differences in

people’s behavioral intentions with respect to gender, age, formal education, income, community

tenure, home ownership, household size, walking difficulty, presence of children, and presence of

elderly in the household.

In rapid-onset natural disasters such as earthquakes and tsunamis, evacuation from a risk

area is considered the best protective action within the first minutes of a disaster onset (Mas et al.

2012, 2013; Wei et al. 2017; Charnkol and Tanaboriboon 2006; Takabatake et al. 2017; Wei and

Lindell 2017). However, previous research demonstrates that in emergency situations people tend to

engage in a variety of behaviors that delay the start of their immediate evacuation, for example

searching for tsunami confirmation information (Lindell et al. 2015; Wei et al. 2017), looking for

family members, talking with neighbors, packing an emergency kit and other things around the

house, helping others, and waiting for assistance from emergency personnel (Shibayama et al. 2013;

Lindell and Perry 2012; Yun and Hamada 2012; Lindell et al. 2015; Murakami et al. 2012; . Jon et al.

2016). Delay and milling behaviors are unavoidable as local tsunamis can happen unexpectedly at

any hour during the day, likely catching people unprepared in different locations and separated from

family and friends.

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Even though empirical literature that reveals the connection between behavioral intentions

and behavior in natural disasters is limited, the link is possible (Kang et al. 2007). Moreover,

understanding people’s intentions during hypothetical scenarios and having information about how

people think about their future actions provide a valuable perspective on their current level of

preparedness. This information can inform efforts to change people’s beliefs and attitudes toward

tsunami protective actions and preparedness, and other projects such as construction of evacuation

routes and shelters, improving emergency warning systems, targeting vulnerable populations, and

other programs.

Analysis of behavioral intentions’ distribution revealed the following results. The top three

behaviors that the respondents said they would very unlikely adopt in the event of M9 CSZ

earthquakes and tsunamis included continuing normal routine behavior (63.1%), waiting for family

at home (47.1%), and following friends and neighbors (43.7%). The top three behaviors that

respondents indicated they would very likely assume included evacuating to higher ground

immediately following the earthquake (51%), contacting loved ones (49.5%), and checking social

media, TV (40.3%).

On one hand, it is encouraging to see that 63.1% of respondents indicated that they were

very unlikely to continue normal routine behavior in the event of CSZ earthquake and tsunami

(21.4% responding with “unlikely”). This result conveys that respondents are aware of the risks of

the potential disaster and intend to respond to it by changing their routine behaviors. From a

methodological perspective, this outcome also suggests that respondents pay attention to survey

questions, because this is a logically consistent response. It is likely that their answers to the rest of

behavioral intentions scale reflect their true intentions. On the other hand, 15.6% of respondents (32

participants) who were either uncertain or said they were likely or very likely to continue normal

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behavior also scored on the lower end of the spectrum on self-efficacy, which could be a reflection

of a fatalist mentality due to reduced ability to protect oneself in a risk situation. In addition, 16 of

32 participants belong to the eldest age category of 65-95, suggesting that older populations are

more likely to have an intention to continue normal behavior in an emergency situation. Lindell et al.

(2016) reported that during 2011 earthquakes in Hitachi, Japan and Christchurch, New Zealand

older residents were more likely to continue normal activities or stay behind to protect their

property.

From a tsunami response perspective, it is encouraging to see that 22.8% of respondents

answered that they were very unlikely and 24.3% said that they were unlikely to wait for tsunami

confirmation. In local tsunami events, the earthquake itself is the only necessary indicator of an

upcoming tsunami that the population at risk must follow (Fraser et al. 2013, Bernard 2005). Waiting

for and relying on additional tsunami warnings from any sources is discouraged because channels of

communication are likely disabled during an earthquake. Waiting reduces time available for

evacuation. Therefore, it is also troubling to see that 29.6% of respondents indicated that they would

likely wait for tsunami confirmation, while 14.1% said they would be very likely to do so. These

results could be an outcome of an unawareness on the part of survey participants that behavioral

questions referred to a local tsunami event. Also, respondents may not know the difference between

local and distant tsunami events and the warning signs for each type of event, even though 35.4% of

respondents reported that they were confident and 30.6% said they were very confident in knowing

the difference between local and distant tsunamis. It is possible that confidence in knowing

information does not match with actual knowledge. Residents of the Oregon coast are familiar with

distant tsunami warnings because of the relative frequency of distant tsunami threats (e.g. latest

tsunami watch was announced in January 2018 as a result of 7.9 M earthquake in the Gulf of

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Alaska). There could be an expectation among coastal populations that a warning for local tsunamis

would come via the same channels. The fact that 35.0% of survey respondents indicated that they

would likely and 40.3% would very likely to check social media and TV in the event of the Cascadia

earthquake and tsunami suggests that respondents are not sure about the extent of damages during

the CSZ scenario, during which it is highly unlikely that phones and electrical services would be

functioning. Finally, 63.1% of respondents indicated that they were either very unlikely or unlikely to

panic in the event of the disaster. This result supports empirical findings in hazard literature that

argue that a decision-making process in emergency situations more often involves a reflective

process of interpreting available information and evaluating alternatives for actions. Inefficient and

tragic outcomes are generally a result of inadequate information and resources than faults in

cognition (Lindell and Perry 2012).

This study did not explicitly focus on the analysis of people’s behavioral intentions or

preparation for impacts of the earthquake. However, results indicated that 80.6% of respondents

would either likely or very likely follow earthquake behavioral guidelines of sheltering under the

table. Although covering under a table inside is a recommended action in earthquake emergencies,

effectiveness of protective actions depends on a seismic performance of particular types of

buildings. While post-earthquake assessments show that many people die while attempting to escape

outside, running outside could be the only chance of surviving if one is located in a non-seismically

resistant building. Previous research on behavioral responses to earthquakes revealed a range of

behaviors that people tend to engage in, suggesting that availability and access to information on the

safest earthquake behavior by specific locations is important (Alexander and Magni 2013; Goltz et al.

1992; Lindell et al. 2016).

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There are contradicting results within behavioral intentions outcomes, which could be

indicative of a poor question construction and the uncertainty in intentions on the part of survey

participants. The most popular behaviors respondents indicated they would adopt included

evacuating to higher ground immediately following the earthquake (very likely - 51%, likely –

22.8%), contacting loved ones (very likely - 49.5%, likely – 31.6%), and checking social media, TV

(very likely - 40.3%; likely – 40.3%).

Factor analysis of the behavioral intent scale revealed two distinct behavioral indices: the

leadership behavioral intent, which included two behavioral intentions of evacuating to higher

ground immediately and taking the lead to evacuate and encourage others; and the delay behavioral

intent, which included intentions of checking social media, TV, contacting loved ones, and collecting

documents and other items. Conceptually, the two indices represent different types of behavior, but

not mutually exclusive behaviors of evacuation vs. non-evacuation. The leadership index captures

behavioral intent of immediate evacuation following earthquake shaking, while the delay index

represents intentions of waiting for tsunami confirmation, desires to reunite with family and friends

and to help others before evacuating.

ANOVA results demonstrated that on average women indicate a higher likelihood of having

an intention to adopt leadership behavior compared to men, while also indicating a higher likelihood

of having an intention to engage in delay behaviors. The reason that females score higher than males

on both behavioral intentions could be because females are more aware of risks and the need to act

proactively, but they also feel the importance of protecting their families. In this study female gender

is also associated with higher level of perceived severity (r=0.171, p < 0.05) and lower level of self-

efficacy (r = -0.142, p < 0.05). Being female is the only demographic characteristic that is

consistently shown to be associated with higher levels of risk perception and the adoption of

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protective actions in the natural hazard literature (Lindell et al. 2015; Terpstra and Lindell 2012;

Barberi et al. 2008; Fothergill 1996; Griffin et al. 1999; Lindell and Hwang 2008; Peacock et al. 2005).

According to Rivers’ (1982) argument on sex discrimination in disasters, it is worth paying

attention to how gender differences in social role expectations result in different levels of exposure

to risk and mortality rates during disaster events between females and males. For instance, the

reason that females scored higher on both leadership and delay behavioral intentions scales could be

indicative of the fact that females are more likely to be aware of official emergency guidelines,

because they feel responsible for safety of their children and other family members (that being a

stronger social role for females than males). At the same time, females may also feel a stronger need

to protect and help others, mobilizing instincts of social attachment, leading to delay in evacuation

(Shapira et al. 2018).

An ANOVA test also showed that there were distinct differences between survey

respondents who live in households with and without children under the age of 10 with respect to

the mean level of likelihood of having an intention to adopt leadership behavior. At the same time

individuals living in households with three or more people scored higher on the mean level of

likelihood of adopting delay behaviors. There is a strong correlation between household size and

presence of children in a household (r = 0.634, p < 0.01). It is possible that individuals in

households with children are more aware of recommended protective responses (similar to the

argument with respect to female gender), therefore they are more inclined to evacuate immediately

to ensure safety of children. At the same time, respondents in households with higher numbers of

people might also want to account for their cohabitants before evacuating, having an intent to

engage in delay behaviors. In addition, Kruskall-Wallis test shows statistically significant difference

between survey respondents who live in households with and without children under the age of 10

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with respect to the mean level of likelihood of having an intention to adopt leadership behavior,

suggesting that individuals living in households with children scored higher on the mean level of

likelihood of delay behaviors as well, suggesting that perhaps parents have intentions to find and

account for their children before evacuating to high ground.

Mean differences in the levels of behavioral likelihood with respect to age were consistent

with previous empirical findings in the disaster literature. The highest mortality rates in natural

disasters are generally observed among older populations (Murakami et al. 2012; Rofi et al. 2006; Sun

et al. 2013; Yun and Hamada 2015). In this study, there was a significant difference in mean levels of

the leadership behavioral intent likelihood among age groups, where the 24-40 age group was more

likely to have an intent to engage in the leadership behavior compared to the 65-95 age group. Older

respondents also demonstrated a lower level of confidence in tsunami-relevant knowledge (r = -

0.165, p < 0.05), a lower self-efficacy (r = -0.268, p < 0.001), and reported fewer items from the list

of preparatory measures compared to younger participants (r = -0.186, p < 0.01). Low levels of self-

efficacy and preparation can induce avoidance behaviors (e.g. intention to not evacuate), which puts

older residents at higher risks of injuries during a disaster (Abraham et al., 1994; Rippetoe and

Rogers, 1987; Van der Velde and Van der Pligt, 1991). Age also displays positive and significant

correlation with walking difficulty (r = 0.376, p < 0.01). Respondents reporting a walking difficulty

demonstrate a lower mean level of likelihood to have the leadership behavioral intent, compared to

respondents who report having no walking difficulties. It is reasonable to conclude that older

respondents are more likely to have walking difficulties compared to younger respondents and they

are also less likely to have an intention to evacuate immediately after the earthquake. This finding

identifies a specific vulnerable population group with elevated risks to the effects of a CSZ

earthquake and tsunami.

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This research also examined a series of hypotheses designed to understand factors that

influence behavioral intentions and channels through which these elements operate. More

specifically, it is argued that perceptions and attitudes translate socio-environmental observable

factors into behavioral intentions, serving as mediators between outside influences and behavioral

intentions. Four cognitive constructs (perceived probability, perceived severity of disaster outcomes,

self-efficacy to perform protective actions, and efficacy of response actions) were evaluated. Four

socio-environmental factors, that were hypothesized to influence behavioral intentions, included

tsunami wave arrival time or risk proximity, confidence in knowing tsunami-relevant information,

preparedness measures already taken, and previous experience with natural hazards. Mediation

analysis utilized bootstrapping confidence intervals for significance analysis of indirect effects

advocated by Hayes (2013) as the primary method of hypotheses’ assessment.

Risk perception (perceived severity and perceived probability) has been shown to be one of

the most important cognitive factors that influences people’s behavior in different types of disaster

situations, including earthquakes and tsunamis (Fraser et al. 2016; Lindell and Whitney 2000; Lindell

et al. 2009; Spittal et al. 2008; Tekeli-Yesil et al. 2010; Wei et al. 2017; Apatu et al. 2013; Lindell and

Perry 2000, 2012). Previous findings attest that higher level of risk perception motivates people to

adopt protective actions and be more proactive in terms of preparation for and in reaction to risks

of natural hazards. On the other hand, low risk perception may lead to delay and avoidance

behaviors. Outcomes of the mediation analysis partially support these arguments.

Perceived severity (i.e. perceptions about dangers to health, property and financial resources)

displayed a positive significant association with the leadership behavioral intention and a negative

significant association with the delay behavioral intention. This outcome suggests that a higher level

of perception that CSZ earthquakes and tsunamis would result in negative consequences to health

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and property, is associated with higher likelihood of having intentions of immediate evacuation, and

lower likelihood of having intentions to engage in delay and milling behaviors. However, the analysis

did not show support for theoretical claims of a mediating role of risk perception. Neither perceived

probability nor perceived severity met either Baron and Kenny’s (1986) criteria for mediation or

Hayes’s (2013) direct test of indirect effects. Certainly, other studies have previously found that

direction and degree of relationship between risk perception and behavioral intent and behavior can

be inconsistent (e.g. Lindell and Prater 2000; Lindell and Whitney 2000; Paton et al. 2000; Perry and

Lindell 2008). Different research designs and measurements of risk perception can contribute to the

variation in outcomes, suggesting that risk perception is an important but not a sufficient factor that

explains households’ adoption of protective behaviors (Lindell and Whitney 2000).

Even though risk perception did not display a mediating role, wave arrival time or risk

proximity revealed to have an influence on both perceived probability and perceived severity. Wave

arrival time showed a negative significant association with perceived severity, meaning that

individuals living in households further away from the coastline (i.e. threats of physical risks are

lower) reported lower perceived severity, aligning with arguments of previous research findings

(Apatu et al. 2013, 2016; Lindell et al. 2015; Charnkol and Tanaboriboon, 2006; Wei et al. 2017; Arias

et al. 2017). Pearson’s correlation between wave arrival time and perceived severity was also negative

and significant (r = -0.374, p < 0.001). Interestingly, association between wave arrival time and

perceived likelihood (i.e. perception of the probability of CSZ earthquake and tsunami occurrence in

the next 50 years) was positive and significant in the mediation analysis, meaning that people living

in households further away from the coastline reported having higher perceived likelihood of CSZ

earthquake and tsunami happening in the next 50 years. This association is likely to be driven by a

different factor than the threat of risk. Perception of likelihood of a disaster event occurrence may

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depend more on people’s access to information and trust in information sources about the disaster

event. In general, it is interesting to observe that out of four socio-environmental variables only

wave arrival time showed a significant association with perceived severity and perceived probability.

Certainly, there are other factors that could influence the level of risk perception, but this analysis

revealed that the level of objective risk had the greatest association with the subjective level of risk,

perhaps suggesting that respondents approach the assessment of threats based on objective

evaluations of available information.

Cognitive theories of behavior argue that as a psychological construct self-efficacy can

mediate the effects of outside observable variables on behavioral intent and behavior. In addition,

studies of natural disasters showed that self-efficacy has an influence on the adoption of protective

behaviors (Johnston et al. 2005). Mediation analysis did not show support for the theoretical claim of

the mediating role of self-efficacy between socio-environmental factors and either leadership nor

delay behavioral intentions. However, analysis of indirect effects according to Hayes’ (2013) method

revealed a significant indirect influence of wave arrival and experience on the leadership behavioral

intent. Even though there was no initial impact of wave arrival and experience on behavioral intent

to mediate, self-efficacy still served as a path through which these two factors were associated with

the leadership behavior. Greater wave arrival time was associated with higher level of self-efficacy,

meaning individuals living in households that are located further away from the coastline had a

higher level of confidence in their abilities to protect themselves from risks. In turn, higher self-

efficacy was associated with higher likelihood of leadership behavioral intent, supporting previous

research findings and assumptions of behavioral theories. Also, a greater number of disaster events

experienced was associated with a higher level of self-efficacy, which, in turn, positively influenced

the likelihood of leadership behavioral intent.

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Similarly, preparation and experience had an indirect significant influence on the delay

behavioral intent through self-efficacy, which, in turn, was positively associated with the likelihood

of delay behavioral intent. This is an interesting outcome that implies preparation and experience are

important factors that positively influence one’s attitudes toward their abilities to protect themselves

in the event of CSZ earthquakes and tsunamis. At the same time, an increase in the level of self-

efficacy could create an effect of overconfidence. This attitude may result from a perception of

being sufficiently prepared (Lindell and Whitney 2000). Also, past experiences with disasters that did

not result in severe damages or a lack of personal experience with disasters (especially low-

probability, high impact) may lead to overconfidence (Eiser et al. 2012). That can potentially lead to

engaging in delay behaviors, because of one’s belief that they can still survive the tsunami even if

they take time to help family and friends before evacuating. In addition, in distant tsunami events,

previously experienced tsunami warnings that never manifested themselves into an actual tsunami

(i.e. false tsunami alarms), may create hesitation and mistrust in the population to react to warnings

in the future (Simmons and Sutter 2009). In the latter situation, as a part of public education, it is

important to emphasize that there is a difference between local and distant tsunami events. In the

event of a distant tsunami there is rarely a felt earthquake, therefore centrally issued warnings is the

indication for the public of an upcoming tsunami. In the event of a local tsunami, an earthquake is

inevitable, and it is the only necessary sign of an upcoming tsunami.

Response efficacy is the only cognitive variable that demonstrated a mediating effect

between tsunami-relevant knowledge confidence and preparation variables and leadership behavioral

intent according to Baron and Kenny’s (1986) criteria for mediation. It suggests that knowledge

confidence and preparation had a direct influence on the leadership behavioral intent with some of

the influence minimized or mediated by the response efficacy. It is encouraging to see that

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respondents who claimed confidence in tsunami-relevant knowledge also reported a higher level of

response efficacy, suggesting a link between knowledge and a belief in the importance of protective

actions. Similarly, a higher number of preparedness measures was associated with a higher level of

response efficacy, implying that adoption of preparatory measures facilitates people’s favorable

evaluation of preparatory and tsunami protective actions. In turn, response efficacy displayed a

positive significant association with the leadership behavioral intent when controlling for all four

socio-environmental variables. Response efficacy did not play a mediating role between socio-

environmental and delay behavioral intent, nor did it show a direct influence on the delay behavioral

intent, suggesting that the delay behavioral intent construct is not a conceptual opposite to the

leadership behavioral construct.

Previous findings suggest that greater experience is positively related with an adoption of

recommended protective actions (Lindell et al. 2016); an increase in likelihood of evacuation to

higher ground (Charnkol and Tanaboriboon 2016); and with a recognition of the need to evacuate

quickly (Suppasri et al. 2013). This study did not find a strong support for these claims. Experience

did not show a significant association with leadership behavioral intent. This outcome likely reflects

how the experience variable was operationalized. Because of the rarity of local tsunamis on the west

coast of North America, to increase variation in responses the survey asked individuals to indicate

their experience with any natural disasters in the past. However, past experiences with any disaster

may not necessarily prepare individuals for a specific event such as a local tsunami. General disaster

experience can help people to be more psychologically prepared, but it does not necessarily lead to

the acquisition of specialized information. This assumption is supported in research by Goltz et al.

(1992) who analyzed evacuation behaviors in the U.S. 1987 Los Angeles County earthquake; by

Lindell et al. (2016) in the study of 2011 earthquake in Christchurch, New Zealand and Hitachi,

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Japan; and by Shapira et al. (2018) in the evaluation of behavioral intentions in earthquake threats in

Israel. In all three cases, due to the lack of previous experience with large scale earthquakes, studies

did not find strong associations between previous experience and adoption of preparedness and

protective actions. Yet, in this study, experience showed an indirect influence on the leadership and

the delay behavioral intent though self-efficacy as discussed earlier. In addition, experience was

negatively significantly associated with the delay behavioral intent, when controlled for self-efficacy.

This outcome supports previous research findings, suggesting that the greater number of disasters

experienced in the past is associated with a decrease in the intent to adopt delay behaviors.

Confidence in tsunami-relevant knowledge demonstrated a significant positive association

with leadership behavioral intent independently and when controlling for each of the four cognitive

constructs. This finding aligns with the previously reported importance of knowledge in supporting

effective reactions in emergency situations. The knowledge confidence variable is not the most

precise assessment of people’s knowledge, because it captures one’s confidence rather than actual

knowledge. However, the connection between knowledge confidence and evacuation intention

suggests that people have access to information that motivates them to have the leadership

behavioral intent.

Similar to the knowledge construct, preparedness shows a significant positive association

with the leadership behavioral intent independently and when controlling for each of the four

cognitive constructs, suggesting the utility of engaging in preparedness actions. Higher number of

preparatory measures also showed association with higher level of self-efficacy and response

efficacy, instilling in people confidence to engage in protective actions. Preparation also revealed an

indirect positive significant association with the delay behavioral intent through self-efficacy,

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suggesting that preparation could possibly lead to overconfidence in one’s skills, knowledge, and

ability to respond to disasters (Lindell and Whitney 2000).

Tsunami arrival time did not show a significant association with the leadership behavioral

intent. When controlled for response efficacy and perceived probability, wave arrival showed a

negative significant influence on the leadership behavioral intent, suggesting that individuals living in

households located further away from the coastline are less likely to exhibit leadership behavioral

intent. It is possible that individuals perceived themselves to be in no rush to evacuate to a high

ground immediately because of the closer location to safety. However, when controlled for self-

efficacy, wave arrival time was positively significantly associated with leadership behavioral intent,

implying that respondents perceived themselves to have the ability to do so due to the shorter

distance that they need to cover to get themselves to safety.

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6 Limitations and suggestions for future research

This research examined individual perceptions and behavioral intentions of the Oregon

coastal population under the threat of a M9 CSZ earthquake and tsunami. The goal of this study was

to understand how individuals at risk along the Oregon coast would respond in the event of the

tsunami and what factors may explain variations in their behavioral intentions. A structured, self-

administered household survey instrument was used to obtain information from a randomly selected

sample of households in a purposively targeted community of Seaside, Oregon. There are some

limitations associated with the implemented research design and methodology that could potentially

limit the quality of findings and the ability to address study goals, even though it is difficult to assess

the true extent of the limitations.

Generalizability of results to a broader Oregon coastal population can be questionable due to

the nature of the sampling design. First, non-probability sampling was used by purposively selecting

Seaside, Oregon as a population of interest. In the methods section I discussed how Seaside is

representative of other coastal communities vulnerable to CSZ earthquakes and tsunamis in terms of

number of individuals and businesses exposed to tsunami risks. However, one must be cautious

when considering application of findings outside of Seaside. Generalizability of results could also be

improved by sampling a greater number of individuals not exposed to a high level of tsunami risk.

There are communities in Oregon that are located high above sea level and are not exposed to

tsunami dangers. In addition, in this study, in order to increase the response rate, we targeted

households that are primary residency for their inhabitants. However, it is possible that seasonal

residents have different levels of risk perceptions and knowledge about tsunamis and tsunami

protective behaviors compared to primary residents. There is an opportunity for future studies,

given the appropriate timing of data collection, to assess opinions of coastal seasonal residents.

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I can say with more confidence that results of this study are generalizable to the population

of Seaside, from which a probability sample was drawn. Yet, there could be issues of external and

internal validity related to the response rate of 22.2%. Some may argue that 22.2% is a low response

rate. However, mail-based household survey response rates are sensitive to locations and, in general,

are declining (Allen et al. 2015). Some researchers are switching to different modes of response

solicitation. Email-based surveys are becoming more popular. In a comparative analysis of multiple

modes of survey distribution, a sample that received a mail questionnaire displayed a 21.1% response

rate, while a sample that received a questionnaire via internet yielded a 42.9% response rate

(Ansolabehere and Schaffner 2014). In the study of hurricane Lili evacuation response rate equaled

to 24.6% (Kang et al. 2007).

However, low response rate does not automatically invalidate the results (Newman 2009;

Curtin et al. 2000; Keeter et al. 2000). Unless all participants in the sample respond to the survey

(100% response rate), there is a possibility of having a nonresponse bias in the sample. Yet, it is

almost impossible to conduct unbiased research. The importance is to be cognizant of possible

biases and try to minimize them. Low response rate could lead to survey level non-response bias,

meaning that the answers obtained from the respondents may not be representative of the views of

the individuals in the original sample, because those selected into the sample purposefully did not

return surveys. In practice, it is impossible to determine whether the data is missing completely at

random, because that would require comparing observed values of Y to missing values of Y that do

not exist (Lance and Vandenberg 2009). There is a possibility of self-selection of respondents in this

study because of the questionnaire subject. Only those concerned about the risks of CSZ

earthquakes and tsunamis answered the survey. It is possible to minimize the risk of a non-response

bias by comparing the survey sample to U.S. Census. However, one cannot compare sample and

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population on the main variables of interest such as risk perceptions and behavioral intentions. As

such, I demonstrated the representativeness of the sample to the population of Seaside on main

demographic characteristics. The survey sample under-represented younger individuals, so the data

was weighted to give more value to observations from younger participants.

Low response rate could also result in a nonresponse bias as a function of a sample size.

That problem can lead to type II error: a failure to reject the null, affecting internal validity of the

results. Even when there may be a non-zero effect in the population, the sample may be too small to

yield statistically significant results. While the response rate affects the ability to generalize the results

to the population, sample size affects the certainty of the conclusions – that is, the ability to estimate

the true effect. The working sample of 206 in this study is considered an acceptable sample size

within survey research design.

Internal validity of the results could also be affected by the validity of survey construct

measurements. In this study, there are several variables that could be improved to ensure more valid

outcomes. When thinking about construct validity, one must consider whether a variable is

measuring what it is supposed to measure according to theory and whether the questionnaire

wording and formatting elicits appropriate responses. This study followed Dillman et al. (2014)

questionnaire design, relying on previous research for question design to make them as clear as

possible. Yet, there is always a possibility that questions could be misinterpreted, although pre-

testing of the instrument was conducted. There is especially a higher risk of questions’

misinterpretation when trying to assess people’s perceptions because of vagueness of the concepts.

As an alternative way of question construction, behavioral intentions could be assessed by asking

respondents to rank the likelihood of adopting each behavior, to avoid the possibility of respondents

indicating the same likelihood of engaging in conceptually different behaviors.

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In terms of concepts’ accuracy in representing theory, response efficacy measurements could

be improved to better assess association with evacuation behavioral intention. Instead of asking

about one’s perception of efficacy of preparedness, it is better to assess one’s perceptions of efficacy

of protecting themselves in an earthquake and tsunami. It is important to tie perception

measurements more precisely to the behavior that the cognitive concepts are trying predict. In

addition, experience with any natural hazards maybe not be the best proxy for understanding how

experience influences intentions of a specific behavior such as evacuation in tsunamis. Yet, in this

study, the experience construct was difficult to modify to measure experience in terms of experience

with tsunami disaster events because of the rarity of tsunamis on the Oregon coast.

In terms of timing of the survey distribution, it is important to consider a possibility of

unexpected events that could ‘artificially’ alter people’s perceptions from a normal level. Risk

perceptions are easily influenced by emergence of new information that specifically targets certain

images related to risk. For example, Dunn et al. (2016) in a survey of residents in Washington,

Oregon, and California about perceptions of earthquake hazards found that the New Yorker’s “The

Really Big One” article about threats of CSZ earthquakes and tsunamis, and the film “San Andreas”

about earthquake threats in California had increased people’s risk perceptions of earthquakes,

suggesting that mass media have an influence on people’s perceptions. Increase in awareness of risks

because of media campaigns or hazard events around the globe is beneficial from the disaster

preparedness perspective, but from the research design perspective these types of events may create

an above normal level of risk perceptions for a short period of time, compromising internal validity

because of possible inconsistency of results.

Also, it could be useful to consider the role of group dynamics and social context more

explicitly is shaping people’s perceptions and behavior. Solberg et al. (2010) argue that the limitation

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of social psychological studies is that they have a narrow focus on the primacy of individual

cognitive processes in explaining how people interpret information and act to protect themselves

from risks, without proper regard for influence of wider social contexts, community and

neighborhoods. They argue that because of the lack of such considerations, ‘one size fits all’

preparedness programs generally do not motivate people to prepare. While this study did not

thoroughly consider cultural approaches to understand people’s perceptions and behavioral

intentions, important considerations were made to capture certain aspects of place uniqueness. For

instance, experience variables had absorbed some of the context related variation. In addition,

demographic variables and analysis of variance were employed specifically to identify groups that are

more susceptible to risks and difficulties of performing protective actions. Applying cultural

paradigms in research on natural disaster is highly important and can yield significant results,

because people’s attitudes and perceptions are shaped by fundamental beliefs and norms, according

to theories of behavior. However, in light of limited time and considerations of the survey length,

cultural questions were not the main focus in this study.

As an opportunity for future research, soliciting knowledge and risk perceptions of CSZ

earthquakes and tsunami from tourist populations could give an important insight into the level of

preparedness of coastal communities for the disaster. Tourists are generally the most vulnerable

population group in a disaster context, because they tend to be less familiar with areas they visit,

including evacuation routes and safety procedures compare to the resident population (Arce et al.

2017). According to Raskin and Wang (2016) the population of Seaside can double and triple during

summer months because of the influx of tourists. Targeting tourists in Seaside could be a way to

compare different sub-groups allowing to examine how cultural differences between populations

influence outcomes, behaviors, and perceptions. Yet, tourists are difficult to target as study

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participants. Different research designs rather than self-administered mail survey would yield better

results for understanding tourists’ behavior.

In terms of data gathering techniques, other approaches could be implemented to reach

residents of coastal communities as well, besides household self-administered surveys. As this study

revealed, people were not motivated to respond to the questionnaire. Therefore, perhaps in-person

interviews or onsite surveys could solicit more responses. With in-person data gathering techniques

it is also possible to reach more targeted audiences such as low-income neighborhoods or younger

populations that are generally under-represented in household survey samples, as was indicated in

this study.

Future research efforts may consider using Structural Equation Modeling (SEM) in testing

for mediation effects. SEM uses a path diagram and system of linked regression-style equations to

capture relationships among observed and unobserved variables. Unlike a hierarchical regression

approach to testing mediation that requires making an assumption about dependent and

independent variables, SEM does not make such assumptions and can tests multiple directional

relationships between different variables at the same time, which may exist in cross-sectional designs

(Gunzler et al. 2013). More importantly, however, a hierarchical regression approach applied in this

study assumes no measurement errors in variables to achieve unbiased estimates of mediation

effects. Mediators often are latent constructs of perceptions and attitudes and measurement errors

are almost unavoidable (Baron and Kenny 1986). It is likely that there are measurements errors

within cognitive constructs in this study. SEM is claimed to provide a better tool for estimating

latent variables with several indicators and control for measurement errors in the process of

examination of relationships among variables (Preacher and Hayes 2004; Cheung and Lau 2008).

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7 Key findings and policy recommendations

The state of Oregon in general and Oregon coastal communities in particular face high risks

of inevitable future Cascadia Subduction Zone earthquakes and tsunamis, as advocated in the 2013

Oregon Resilience Plan. According to Secretary of State Audit Report of January 2018, Oregon’s

preparedness level for the disaster event is poor. According to the Oregon Resilience Plan, calculated

impacts of CSZ earthquakes and tsunami include fatalities ranging from 1,250 to more than 10,000,

tens of thousands damaged buildings and displaced households, and more than $30 billion in direct

and indirect economic losses. Estimated recovery time to 90% of state’s pre-earthquake operational

levels is 1-3 years. To reduce post-disaster recovery time and improve state resilience, investments

are necessary in community-, infrastructural-, institutional-, and individual-level preparedness.

This study has been designed to understand individual-level preparedness, perceptions of

risks, and behavioral intentions of residents in Oregon coastal communities in the face of tsunami

risks. Because of the variety of possible behaviors that people can adopt in emergency situations,

measuring individual perceptions and behaviors is an integral part of preparedness and risk

reduction efforts to ensure efficient disaster response (Dunn et al. 2016). Knowledge about people’s

intentions, factors that influence intentions, and how intentions vary across different populations

can aid emergency personnel in targeting areas that need more attention. This information can be

used in developing public education and awareness programs to change people’s behavioral

intentions to align with recommended tsunami preparedness and response.

The key findings of this research include information about people’s behavioral intentions,

about how behavioral intentions are connected with people’s perceptions of risks and their abilities

to reduce those risks, about people’s current state of preparedness, and how people’s intentions vary

depending on their demographic characteristics.

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Behavioral intentions

The top three behaviors that the respondents said they would very unlikely adopt in the

event of M9 CSZ earthquakes and tsunamis included continuing normal routine behavior (63.1%),

waiting for family at home (47.1%), and following friends and neighbors (43.7%). The top three

behaviors that respondents indicated they would very likely assume included evacuating to higher

ground immediately following the earthquake (51%), contacting loved ones (49.5%), and checking

social media, TV (40.3%). From a policy and disaster preparedness perspectives, it is encouraging to

see that more than half of the respondents indicated that they would evacuate to the high ground

immediately following the earthquake. Yet, there are those who have different behavioral intentions,

driven by a variety factors. The presence of variation in behavioral intentions creates a necessity to

design policies and programs to encourage people to avoid having a delay to the start of their

evacuation in a case of a local tsunami.

Preparation and information dissemination

The research revealed several factors that had an influence on behavioral intentions.

Research shows that hazard knowledge and availability of information about risks can motivate

people to prepare for disasters and lead to effective evacuation response (Walshe and Nunn 2012;

Dudley et al. 2011; Liu and Jiao, 2018; Gregg et al. 2006). As results of this study indicate, there are

individuals who are not fully confident in their knowledge of natural warning signs of tsunamis,

tsunami evacuation routes, tsunami safe areas in their community, and other tsunami related

information. For example, survey participants show the least confidence in knowing government

recommendations about how to prepare for tsunamis (26.2% are not confident and 21.4% are

somewhat confident) and tsunami safe places closest to their homes (17% are not confident and

11.2% are somewhat confident). A policy recommendation is to design programs that convey

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information about tsunami evacuation areas, shortest routes to reach them, and the importance of

starting evacuation on their own immediately after an earthquake without waiting for other tsunami

warning signals should be a priority in preparing the public for successful tsunami response

(Takabatake et al. 2018; Hall et al. 2017).

Higher levels of preparation and tsunami-relevant knowledge also show a direct positive

association with the leadership behavioral intent, suggesting the importance of education and

preparation as a method to influence people’s behavioral intentions toward immediate evacuation

during tsunamis. While knowledge is important, it may be difficult to disseminate information to the

public in ways they will use. People are not active in attending public educational meetings. Survey

results indicate that 47.5% of respondents have not attended a meeting or have received information

about earthquakes and tsunamis in the past two years. A policy recommendation is to, use a diverse

set of channels to deliver information. One way to disseminate information about preparedness for

tsunamis is through education in public schools and encouraging children to discuss the information

with their parents. Mimura et al. (2011) showed a successful outcome of schools’ tsunami awareness

programs when during the 2011 great east Japan tsunami, about 3,000 school children were able to

successfully evacuate relying on ground shaking as a warning sign and prior to instructions by

authorities and the alert system.

Education of children (in concert with their parents) should include information about

appropriate behavior in the event of a local tsunami, evacuating to high ground immediately after

the earthquakes without waiting for parents, no matter where they find themselves when an

earthquake strikes. At the same time, it is important to deliver a message to parents that searching

for their children after the earthquake minimizes chances of survival in a tsunami for everyone,

including other people in a community because a chaotic movement of people creates traffic jams

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and delays movement of people to the higher ground. In addition, emphasizing earthquake and

tsunami awareness in schools and appealing to younger generation to be the leaders in creating and

promoting a “culture of preparedness”, similar to places like Japan or Chile that experience

earthquakes and tsunamis on a regular basis, can help shift the whole community toward greater

awareness. With assistance of social media, broader audiences can be captured with tsunami

educational and preparedness messages and photos, shifting people’s thinking about CSZ

earthquakes and tsunamis from inevitable uncontrollable disaster to a state of preparedness and

awareness.

Informational messages and factors that influence intentions

Content of messages is as important as means of delivering messages. Certain messages are

better able to change peoples’ intentions than others (Dunn et al. 2016). This study revealed that

there are two kinds of messages that needs to be emphasized that could have an influence on

people’s behavior. One should be focused on perceptions of risk, because the analysis showed that

higher level of risk perception is associated with higher likelihood of having leadership behavioral

intention. And second message should emphasize importance of preparation to increase people’s

confidence in their abilities to protect themselves and confidence in protective actions. Peacock et

al. (2003) argues that public participation in awareness programs and increasing people’s cognitive

reactions to risk exposure are effective strategies to motivate people to adopt preparedness activities.

They also suggest that risk communication programs should emphasize repeated messages about

possible personal consequences and impacts of the disaster to increase the frequency of thought and

discussion about the hazard. Results of this study support the fact that higher level of risk

perception is associated with an intent for leadership behavior, suggesting that people who perceive

greater risk are more aware about the hazard. However, in addition, results of this research also

112

show the importance of promoting a message of how to increase one’s ability in protecting

themselves in disasters and emphasizing that preparation and specific actions during a tsunami can

make a difference in a successful outcome. Increasing the frequency and the degree of risk messages,

i.e. influencing public perceptions of risk, may be a catalyst for motivating a greater number of

people to put pressure on their elected officials to implement policies that contribute to

communities’ resilience in the face of risk from CSZ earthquakes and tsunamis.

Risk perception, self-efficacy, and response efficacy

The study showed that higher risk perceptions motivate people to adopt leadership

behaviors, while lower risk perceptions motivate people to engage in delay behaviors. Wave arrival

time, or the proxy for physical risk proximity was the only variable associated with perceived

severity. Wave arrival, together with experience, also displayed an indirect positive influence on the

leadership behavior via self-efficacy, which, in return, had a positive association with the leadership

behavior. This outcome suggests that confidence in one’s abilities to protect themself in disaster

situations, is an important factor that influences people’s intentions to adopt leadership behavior.

However, higher self-efficacy, especially in association with higher levels of preparation and

experience, is shown to have a positive influence on delay behavioral intentions, implying a certain

level of overconfidence among those that either had previous experience and believe that they are

able to protect themselves in tsunamis or among individuals who believe that they are well-prepared

to be able to afford delaying their evacuation and perhaps assisting others. While it is important to

increase individuals’ self-efficacy because it motivates people to adopt leadership behavior,

preparedness efforts must also convey the message of immediate evacuation as detrimental to one’s

safety in tsunamis, no matter how much one feels prepared for it.

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Response efficacy played a mediating role between socio-environmental variables of

tsunami-relevant knowledge confidence and preparation and the leadership behavioral intent,

suggesting that an increase in one’s confidence about tsunami knowledge also improves one’s view

of the efficacy of preparing for earthquakes and tsunamis, which in turn promotes intentions to

engage in leadership behaviors. This result suggests that increasing people’s confidence in their

knowledge about tsunamis can lead to positive outcomes. It also suggests that promoting the

message that preparation and protective actions can make a difference in disaster survival is as

important as conveying the message of potential negative consequences of a tsunami. That is,

conveying positive information about the importance of preparation and options for survival may be

as effective as trying to scare people into making preparatory decisions.

Vulnerable populations

Another key finding includes the fact that there are vulnerable groups that may need greater

assistance during the disaster event due to various situational barriers and may require greater

outreach efforts for information dissemination because of other barriers that prevent them from

easily acquiring knowledge. As the results of the survey show, older residents require greater

attention, especially considering their large representation in the population along the coast. Older

residents tend to have lower self-efficacy, lower confidence in tsunami-relevant knowledge, more

likely to have intentions to continue normal routine behavior, and less likely to engage in the

leadership behavior in the tsunami situation. These attitudes can arise from the fact that older

residents also tend to have mobility issues, suggesting that they may not expect to be able to

evacuate from the risk zone prior to the onset of the tsunami. Oregon coastal communities are

home to many retirees and assisted living facilities, making tsunami preparedness particularly

challenging, because of the central role that physical mobility plays in successful outcomes.

114

Evacuation models developed by Wang et al. (2015) suggest that tsunami mortality rate is sensitive to

the variations in walking speed of the evacuee population. A policy recommendation is to pay more

attention to vulnerable groups, specifically older populations and people with mobility problems,

when creating educational programs, improving evacuation routes and roads, designing future plans

for cities, and other tsunami preparedness actions.

115

8 Conclusion

Main goals of this research were to provide information on public awareness of the Cascadia

Subduction Zone earthquakes and tsunamis, people’s perceptions of risk and behavioral intentions,

and factors that influence attitudes and intentions, to expand previous research on human behavior

in a rapid onset natural hazard context and to suggests ways in which people’s intentions can be

influenced to motivate preparedness for the anticipated CSZ earthquakes and tsunamis in Oregon.

To explore these goals this research applied a survey questionnaire to a random sample of

households in the coastal town of Seaside, Oregon. The survey questionnaire was designed based on

theories of cognition and previous research findings in the natural disaster literature. The research

framework developed in this study examined associations between socio-environmental factors,

variables of cognition, and behavioral intentions. Results of mediation analysis and analysis of

variance supported previous research findings, showing that risk perception had a positive

association with evacuation behavioral intentions. Self-efficacy and response efficacy also displayed

an influence on behavioral intentions directly and served as a moderator between socio-

environmental factors and behavioral intentions. In terms of public disaster preparedness and

recommendations for emergency planning, respondents showed relatively high awareness of risks of

CSZ earthquakes and tsunamis, with 72.8% of respondents having perceptions that M9 Cascadia

earthquake and tsunami would likely lead to injuries and 85.4% of respondents believing that M9

CSZ earthquake and tsunami would create a life-threatening situation. However, the study also

found that respondents reported low confidence in their knowledge of government

recommendations about how to prepare for tsunamis and about tsunami safe locations in their

community. In addition, almost half of the respondents did not attended tsunami informational

meetings and did not make a communication plan with family or friends. Educational outreach,

116

especially appealing to school students and their parents as a possible effective channel of

information dissemination, was proposed as one of the most salient recommendations to improve

emergency preparedness and shift Oregon coastal communities’ culture toward the “culture of

preparedness”.

117

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APPENDICES

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Appendix A Survey Cover Letter

Dear <<GivenName>> <<Surname>> (otherwise insert <<resident>>), As you may be aware, the Oregon coast is located in the zone of a potential earthquake. Its source is the Cascadia Subduction Zone (CSZ). An earthquake along this zone is believed to occur on average once every ~240 to ~530 years, varying by the location and the degree. The last Cascadia earthquake happened in 1700. During a Cascadia earthquake the Oregon coast can experience a magnitude of 8.0 to 9.0 earthquake followed immediately by tsunamis.

We are asking for your help in improving our understanding of perceptions of risk and potential behavior in the event of Cascadia earthquakes and tsunamis among coastal residents. The ultimate goal of our study is to enhance community preparedness for such an event. Your household is one of small number that has been randomly selected to help in this study. This project is being conducted by Oregon State University with funding from the National Science Foundation. We would appreciate if you take about 15-25 minutes to respond to the enclosed questionnaire and return it in the prepaid postage envelope provided. We ask that a respondent 18 years of age and older who has had the most recent birthday complete the survey (if unavailable, an adult with the next most recent birthday). Your participation in this study is voluntary and you may refuse to answer any question(s) for any reason. Your household location will be combined with others to understand the difference in perceptions based on distance from the ocean and location in the community. Your responses will be reported in a summary format that does not identify individuals or households. The ID number on your questionnaire is used only to ensure that we can check your household off the mailing list when the survey is completed. You have an opportunity to take the online version of the survey by following this link <<QualtricsURL>>. We recommend taking the online survey on a computer instead of a phone. Your name will not be included in the reports and your responses will be kept confidential to the extent permitted by the technology and the law. If you do not want to participate in the study, please return the uncompleted survey in the enclosed envelope. If you would like a copy of the results, include a note with an address and ‘survey results requested’. Do not put this information on the survey. If you have any questions about the survey, please contact us at (541)737-5382 or by email at lcramer@oregonstate.edu. If you have any questions about your rights as a participant in this research project, please contact the Oregon State University Institutional Review Board (IRB) office at (541)737-8008 or by email at IRB@oregonstate.edu

Thank you for your help. We hope you enjoy completing the survey. Respectfully,

signature signature

Lori A. Cramer, Ph.D. Alexandra Buylova Associate Professor of Sociology Ph.D. Candidate School of Public Policy Public Policy Graduate School Oregon State University Oregon State University lcramer@oregonstate.edu buylovaa@oregonstate.edu

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Appendix B Survey Questionnaire

Cascadia Earthquakes and Tsunamis in Oregon: Perceptions and Behavioral Intentions of Local Residents

Fall 2017

Please return survey to:

Cascadia Perception Survey

School of Public Policy

Bexell Hall Oregon State University

Corvallis, Oregon 97331

541-737-5382

ID # ______________________

[for mailing purposes only]

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Q1. How likely or unlikely is a M9 Cascadia earthquake and tsunami to happen in the next 50 years? (Circle one number)

1. Very unlikely 2. Unlikely 3. Neither 4. Likely 5. Very likely

Q2. How much do you trust experts, if at all, to accurately estimate the likelihood of a M9 Cascadia earthquake and tsunami happening in the next 50 years? (Circle one number)

1. Don’t trust at all 2. Trust a little 3. Somewhat trust 4. Trust 5. Trust a lot

Q3. Have you ever experienced a natural disaster such as flood, earthquake, wildfire, or other types of natural disasters? (Circle one number)

1. No (Pass to Q4.) 2. Not sure (Pass to Q4.) 3. Yes (Answer Q3.1. and Q3.2.)

Q3.1. Which of these natural disasters have you experienced? (Check all that apply)

Hurricane

Wildfire

Landslide

Flood

Tornado

Earthquake

Tsunami

Dam Failure

Other (specify)

Q3.2. Was your life or lives of your family members in danger during any of those events? (Circle one number)

1. No 2. Yes

SECTION 1

This section will ask you about your knowledge, perceptions, and preparation for Cascadia

earthquakes and tsunamis. As you are answering the questions, we ask you to consider the

case of 9.0 magnitude (M9) Cascadia earthquake that triggers a tsunami wave affecting

the entire Oregon coast. Please remember that ‘you’ in this questionnaire refers to you

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Q4. Have you ever experienced a natural disaster where you needed to evacuate? (Circle one number)

1. No. 2. Yes. What kind of disaster was it and where? (Briefly describe)

Q5. How confident are you about each of the following statements? (Circle one number for each statement)

Statement Not at all confident

A little confident

Somewhat confident

Confident Very

confident

1. I know the difference between local and distant tsunami events

1 2 3 4 5

2. I understand natural warning signs of tsunamis

1 2 3 4 5

3. I understand emergency warning messages of tsunamis

1 2 3 4 5

4. I know where to get the information about preparation for tsunamis

1 2 3 4 5

5. I know government recommendations on how to prepare for tsunamis

1 2 3 4 5

6. I know what to do when there is a warning about a tsunami

1 2 3 4 5

7. I know tsunami evacuation routes from my home

1 2 3 4 5

8. I know a tsunami safe place close to my home

1 2 3 4 5

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Q6. If a M9 Cascadia earthquake and tsunami were to happen tomorrow, how likely or unlikely are they to result in the following? (Circle one number for each statement)

Statement Very

unlikely Unlikely Neither Likely

Very likely

1. Lead to injuries for you or your family

1 2 3 4 5

2. Create a life-threatening situation for you or your family

1 2 3 4 5

3. Severely damage or destroy your home

1 2 3 4 5

4. Create a severe financial burden for you or your family

1 2 3 4 5

5. Destroy or severely damage roads, homes, etc. in your town

1 2 3 4 5

6. Create a life-threatening situation for people in your town

1 2 3 4 5

Q7. Given your personal skills and resources, how confident are you that you could effectively protect yourself and reduce the chance of severe injuries in the event of a M9 Cascadia earthquake and tsunami? (Circle one number)

1. Not at all confident 2. A little confident 3. Somewhat confident 4. Confident 5. Very confident

Q8. Do you agree or disagree that people could increase their chances of surviving a M9 Cascadia earthquake and tsunami by making various emergency plans or preparatory actions? (Circle one number)

1. Strongly disagree 2. Disagree 3. Neither 4. Agree 5. Strongly agree

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Q9. Check all the measures that you have taken in the past 2 years, if any, that can assist you in the event of Cascadia earthquakes and tsunamis. (Check all that apply)

Attended a meeting or received written information on how to be better prepared for Cascadia earthquakes and tsunamis.

Prepared an ‘emergency supply kit’ (stored extra food, water, batteries, etc.).

Discussed the topic of preparedness for Cascadia earthquakes and tsunamis with others in the community.

Developed an ‘emergency plan’ with family or friends in order to decide what everyone would do in the event of Cascadia earthquakes and tsunamis.

Developed a communication plan (e.g. a plan that establishes how to get in touch with each other if meeting at home is not possible and phones do not work).

Attended First Aid or Cardio-Pulmonary Resuscitation (CPR) training.

Identified an emergency contact person outside of the Northwest.

Other (Specify) Q10. If you were at home and you feel the ground shaking, would you try to evacuate to a safe or high ground to escape a tsunami? (Circle one number)

1. No, (Please explain) 2. Not sure, (Please explain) 3. Yes

Q11. Is your home located in Cascadia tsunamis inundation area? (Circle one number)

1. No 2. Not sure 3. Yes

SECTION 2

This section will ask you about your potential behavior and factors related to how you

would respond in the event of a M9 or greater Cascadia earthquake and tsunami.

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Q12. How likely or unlikely are you to adopt each of the following behaviors if you were at your home in the event of a M9 Cascadia earthquake and tsunami? (Circle one number for each statement)

Behavior Very

unlikely Unlikely Neither Likely

Very likely

1. Continue normal routine behavior 1 2 3 4 5

2. Follow earthquake emergency guidelines (e.g. shelter under a table if inside)

1 2 3 4 5

3. Evacuate to higher ground immediately after ground shaking

1 2 3 4 5

4. Wait to hear from an authority figure/sirens/warning for tsunami confirmation

1 2 3 4 5

5. Check social media, radio, TV for additional information about a tsunami

1 2 3 4 5

6. Contact loved ones (phone or text family or friends to see what they are doing and if they are safe)

1 2 3 4 5

7. Collect important documents and other items

1 2 3 4 5

8. Travel to gather family or friends (e.g. kids at school, parents or grandparents at their house)

1 2 3 4 5

9. Go towards the ocean to see if a tsunami is coming

1 2 3 4 5

10. Talk with neighbors 1 2 3 4 5

11. Take the lead to evacuate and encourage people to leave with me

1 2 3 4 5

12. Wait for my family at home before evacuating

1 2 3 4 5

13. Wait to see if my friends or neighbors are evacuating, then I would follow them

1 2 3 4 5

14. Try to help and rescue people 1 2 3 4 5

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15. Panic 1 2 3 4 5

Q13. If you were at your home, how confident are you that you could escape to a tsunami safe zone in the event of a M9 Cascadia earthquake and tsunami? (Circle one number)

1. Not at all confident 2. A little confident 3. Somewhat confident 4. Confident 5. Very confident 6. Not relevant (my home is not in an inundation zone)

Q14. If you were at home, what is the most likely mode you would try to use to reach the safe zone? (Circle one number)

1. Drive 2. By bicycle 3. By foot 4. Other (specify) 5. Not relevant (my home is not in an inundation zone)

Q15. If you were to use the evacuation mode of your choice, how long would it take you to

reach a tsunami safe zone from your home? (Circle one number)

1. Less than 15 minutes 2. Between 15 and 30 minutes 3. Between 30 and 45 minutes 4. Between 45 minutes and 1 hour 5. Between 1 and 1.5 hours 6. Between 1.5 and 2 hours 7. Not relevant (my home is not in an inundation zone)

Q16. The next two questions ask you about your personal networks. Exploring personal contacts will help us better understand people’s potential movements on the ground in a case of emergency and better plan for a real event.

Q16.1. Imagine that you are at home and you feel an earthquake and/or you learn of a public warning of a tsunami. How many people in your community would you try to immediately contact to check on their safety, if any at all? (Write a number)

Q16.2. How many of these contacts would you try to reach in person to assist them in their mobility or evacuation? (Write a number)

Q17. Approximately, how many times in the past 5 years have you either participated in tsunami evacuation drills in your community or walked your neighborhood evacuation route? (Circle one number)

1. Never 2. 1-2 times

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3. 3-5 times 4. More than 5 times

Q18. How effective are tsunami evacuation drills (e.g. “walking the evacuation route”) in preparing for evacuation in a real event? (Circle one number)

1. Not at all effective 2. A little effective 3. Somewhat effective 4. Effective 5. Very effective

Q19. What is your assessment of the general level of community preparedness for Cascadia earthquakes and tsunamis in your local area? (Circle one answer)

1. Lacking 2. Poor 3. Adequate 4. Good 5. Excellent 6. I don’t know

Q20. How effective have each of the following individuals or groups been in earthquakes and tsunamis preparedness in your community? (Circle one number for each statement; check “Not familiar” if you haven’t heard of a group or an individual participating in preparedness activities)

Organization Not

familiar Not at all effective

A little effective

Somewhat effective

Effective Very

effective

1. American Red Cross

1 2 3 4 5

2. University or research institutions

1 2 3 4 5

3. Local government 1 2 3 4 5

4. County government

1 2 3 4 5

5. Tribal authorities or governments

1 2 3 4 5

6. State of Oregon Department of Emergency Management

1 2 3 4 5

7. Oregon Parks and Recreation Department

1 2 3 4 5

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8. Local police department

1 2 3 4 5

9. Local fire department

1 2 3 4 5

10. Governor of Oregon

1 2 3 4 5

11. US Coast Guard 1 2 3 4 5

12. National Oceanic and Atmospheric Administration

1 2 3 4 5

13. Federal Emergency Management Agency (FEMA)

1 2 3 4 5

Q21. What is your sex? (Circle one number)

1. Female 2. Male 3. Other

Q22. What is your current age in years? (Write the response) Q23. Because of a physical, mental, or emotional condition, do you have serious difficulty: (Check one answer for each statement)

1. Concentrating, remembering, or making decisions Yes No 2. Walking or climbing stairs Yes No 3. Dressing or bathing Yes No

Q24. How many people, besides you, are living or staying at this address right now? (Write the response) Q25. What are their ages? (Provide your best estimates) Q26. What is your ownership status of this residence? (Circle one number)

1. My family or I are the primary owners 2. I am a renter/occupier (with or without payment of rent)

SECTION 3

To best evaluate our survey results we need some information about your

background. Remember that all responses will be confidential.

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3. Other (Specify) Q27. Is the address where you received this survey your: (Circle one number)

1. Primary residence 2. Part-time residence/vacation home 3. Other (Specify)

Q28. Approximately how many months or years have you lived in this community (not necessarily at this address)? (Circle one number)

1. Less than a year 2. A year and more. How many years? (Write the response) years

Q29. Approximately how many months or years have you lived at this address? (Circle one number)

1. Less than a year 2. A year and more. How many years? (Write the response) years

Q30. In the past 12 months have you been involved in any emergency response organizations or organizations related to earthquakes and tsunamis preparedness (e.g. Red Cross, CERTs)? (Circle one number)

1. No 2. Yes. If yes, what organization(s) have you been involved with? (Write the response)

Q31. What is the highest year of formal education that you have completed? (Circle one number)

1. Less than high school diploma 2. High school diploma or GED equivalent 3. Vocational, trade, or business school 4. Some college

5. Associate's degree 6. Bachelor’s degree 7. Graduate degree or other professional degree

Q32. Which category best describes your household income before taxes in 2016? (Circle one number) 1. Less than $10,000 2. $10,000-$14,999 3. $15,000-$19,999 4. $20,000-$29,999 5. $30,000-$39,999

6. $40,000-$49,000 7. $50,000-$59,999 8. $60,000-$74,999 9. $75,000 or higher

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If you want to add explanations for your responses and/or if you have any comments, questions, suggestions regarding this questionnaire, please write those below.

We appreciate you taking the time to fill out this survey. Please return this completed survey as soon as possible in the enclosed postal paid envelope. If you have any additional questions, please feel free to contact Dr. Lori Cramer at 541-737-5382 or lcramer@oregonstate.edu. For information regarding your rights as a participant, contact the Institutional Review Board at irb@oregonstate.edu or 541-737-3467.