University of Southern California Dissertations and Theses

Individual differences, science communication, and critical thinking for emergent risks

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Individual differences, science communication, and critical thinking for emergent risks

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Content Copyright 2022 Alexandra Segrè Cohen

Individual Differences, Science Communication, and Critical Thinking
for Emergent Risks
by
Alexandra Segrè Cohen

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
PSYCHOLOGY

May 2022

ii
Acknowledgements
First, I would like to express my sincere gratitude to my advisor and friend, Dr. Joe
Àrvai. Joe taught me more than I thought possible about the research process, how to navigate
academia, and above all, what mentorship should look like. His guidance was unmatched, and I
would not be where I am today without him.
I would also like to thank my committee members, Drs. Wändi Bruine de Bruin, Richard
John, Caitlin Drummond Otten, and Gale Sinatra for their direction and support. Additionally, I
would like to thank my initial committee members, Drs. Robyn Wilson, Shelie Miller, and
Kaitlin Raimi for their insightful comments and suggestions at the early stages of my PhD.
I would like to extend my sincere thanks to Dr. Nancy Love, who introduced me to the
topics in the first two chapters of my dissertation, and in collaboration with Dr. Joe Àrvai,
offered me my first research opportunity in graduate school.
Next, I am deeply grateful for my colleagues, Caitlin Drummond Otten, Doug Bessette,
Angela Bearth, and Melissa Kenney. Your insight and ideas make me feel lucky to call you
collaborators and friends.
To all the JDM lab members, past and present, and especially to Lauren Lutzke. It is
amazing to work with people who deeply inspire me both in my personal and professional life.
This experience would not have been the same without the PhD communities at both USC
Psychology and University of Michigan School for Environment and Sustainability.
Finally, this PhD would not have been possible without my friends and family. I owe
them my deepest thanks and feel honored to be in their lives.

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List of Tables
Appendix A, Table 1. Sample characteristics for Chapter I
Appendix A, Table 2. ANOVA results and Tukey’s post-hoc tests comparing fertilizer types. The
Bonferroni corrected p-value required for significance was set at 0.005.
Appendix A, Table 3. ANOVA results and Tukey’s post-hoc tests comparing the acceptability of
different UDF application.
Appendix A, Table 4A. Hierarchical regression results depicting variables that predict consumer
acceptance of urine-derived fertilizers for human consumption.
Appendix A, Table 4B. Hierarchical regression results depicting variables that predict consumer
acceptance of urine-derived fertilizers for non-human consumption.
Appendix C, Table 1. Sample characteristics for Chapter II
Appendix C, Table 2. ANOVA results and Tukey’s post-hoc tests comparing communication
strategies. The Bonferroni corrected p-value required for significance was set at 0.01.
Appendix C, Table 3. Hierarchical regression results describing predictors of consumer
acceptance of urine-derived fertilizers for human consumption.
Appendix E, Table 1. Descriptive statistics for AOT, risk perception, and compliance.
Appendix E, Table 2. Correlation matrix for AOT, social trust, compliance with CDC guidelines,
and the four risk variables
Appendix E, Table 3. Variables influencing perceived risk, social trust, and compliance with
CDC recommendations

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List of Figures
Appendix B, Figure 1. Methodology framework and research design
Appendix B, Figure 2. Pearson’s correlation matrix for psychological variables used in
regressions
Appendix B, Figure 3. Percentage of participants who indicated a willingness and an
unwillingness to eat fruits and vegetables grown using fertilizers derived from diverted
and recycled human urine (HUDF) as compared to other fertilizer types.
Appendix C, Figure 1. Interaction plots of usefulness of risk communication by (a) age and
strategy and (b) education and strategy
Appendix D, Figure 1. Control treatment for Chapter II.
Appendix D, Figure 2. Short text treatment.
Appendix D, Figure 3. Long text treatment.
Appendix E, Figure 1. Proposed model (H1 – H3) linking AOT, social trust, perceived risk, and
compliance with CDC recommendations. Supplementary analyses shown as H4 and H5.
Appendix E, Figure 2. Standardized coefficients explaining the relationship between AOT, social
trust, perceived risk, and compliance with CDC recommendations

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Table of Contents
Acknowledgements.........................................................................................................................ii
List of Tables……………………………………………………………………………………..iii
List of Figures……………………………………………………………………………...……..iv
Abstract...........................................................................................................................................vi
Introduction.....................................................................................................................................1
Chapter I: Consumers’ acceptance of agricultural fertilizer derived from diverted
and recycled human urine..................................................................................................16
Chapter II: Communicating the Risks and Benefits of Human Urine-Derived
Fertilizer.............................................................................................................................34
Chapter III: I think, therefore I act: The influence of critical thinking ability on
social trust and behavior during the COVID-19 pandemic...............................................53
Conclusion.....................................................................................................................................71
References.....................................................................................................................................76
Appendices....................................................................................................................................95
Appendix A: Chapter I Tables...........................................................................................95
Appendix B: Chapter I Supplementary Materials.............................................................98
Appendix C: Chapter II Tables and Figure..................................................................... 104
Appendix D: Chapter II Supplementary Materials..........................................................106
Appendix E: Chapter III Tables and Figures...................................................................110

vi
Abstract
Emergent crises and challenges, like climate change and pandemics, require creative and
innovative solutions. However, without public support, solutions cannot be implemented
effectively. In this dissertation, I examine how individual psychological differences, risk
communication messaging, and critical thinking ability influence public perceptions of solutions
to crises caused by anthropogenic climate change and the COVID-19 pandemic. In Chapter I, I
explore the impact of individual differences on consumer perceptions of a novel sustainable
technology, namely fertilizer derived from human urine. In Chapter II, I consider the influence
risk communication strategies may have on consumer receptivity this fertilizer, and how individual
differences may moderate the relationship. In Chapter III, I study the relationship between one’s
critical thinking ability and the onboarding on risk information provided by trusted sources and
experts during the COVID-19 pandemic. These findings have several implications for how to
effectively communicate about emergent risks and how individuals may overcome cognitive
processes related to risk in their decision-making.

1
Introduction
Research on judgment and decision-making can be subdivided according to three
perspectives: normative, descriptive, and prescriptive. A normative perspective on judgment and
decision-making addresses rules of rationality and utility theory (Von Neumann & Morgenstern,
1944). According to normative decision-making, rational choices are how one ought to act
(Gregory et al., 2012). Descriptive work acknowledges that humans are not always rational
decision-makers and instead explores how decisions are shaped by cognitive shortcuts like
judgment heuristics and biases (Campbell-Arvai et al., 2018). The prescriptive view of judgment
and decision-making focuses on how to aid in facilitating better, higher quality decisions, in which
the stated objectives are in line with the selected alternative (Campbell-Arvai et al., 2018). How
people make decisions can be largely context-dependent wherein decision-making becomes more
complex in unfamiliar situations with increasing levels of uncertainty (Slovic, 1995).
Global sustainability challenges are examples of such context dependence, due to multiple,
competing, and interconnecting objectives and a large diversity of alternatives (Campbell-Arvai et
al., 2018). Challenges range from the depletion of nutrients needed to maintain our global food
systems, mitigating greenhouse gas emissions, the loss of biodiversity, to deforestation. The scale
of these challenges requires solutions that advance technological innovation, wide sweeping policy
rollouts, and changes to human decision-making and consumer expectations. These solutions need
to occur on global, national, regional, and individual levels.
Adding to this complexity, knowledge, attitudes, and perceptions influence how scientific
information is processed at an affective and cognitive level (Sinatra et al., 2014). The familiarity
hypothesis, for example, states that as people become more familiar with an innovation they
become more aware of it and, therefore, more accepting of it also (Kahan et al., 2009). The
familiarity hypothesis complements the knowledge deficit model (Simis et al., 2016), which
2
assumes that people will make rational decisions about policies, programs, and technologies once
they learn more about them (Ahteensuu, 2012; Simis et al., 2016). This assumption is also found
in much of the diffusion of innovation research, which posits that innovations will spread amongst
the public and after time, if a majority of members of the social system will perceive the innovations
positively and adopt them (Rogers, 2003). However, these hypotheses assume that all members of
the public process information in a rational way.
Importantly, researchers have found that the knowledge deficit model, along with the
familiarity hypothesis, doesn’t account for controversial topics like nanotechnology or climate
change (Kahan et al., 2009; Shi et al., 2016). Certain scientific and technical topics, like
vaccinations and anthropogenic climate change are perceived as controversial by general public
even when there is overwhelming consensus among scientists (Sinatra & Seyranian, 2016).
Research from psychology and the decision sciences shows that judgment and decision-making
about novel contexts are influenced by a variety of factors, like values and worldviews (De Groot
& Steg, 2008; Huijts et al., 2012), perceptions of risk and benefit (Slovic & Peters, 2006), social
and institutional trust (Sinatra & Hofer, 2016), and intuitive emotional responses, like affect (Slovic
et al., 2007). This is especially true with emergent and unfamiliar contexts, like novel technologies
(Burton-Jones & Hubona, 2005).
Psychological Variables Predicting Acceptance of Technology
Values. Values serve as a guiding principle for people’s preferences across a wide range of
contexts (L’Orange Seigo et al., 2014). They play a major role in perceptions of technological
acceptance because technologies are not neutral but are, in fact, value-laden; technologies can
either maintain ways of life or disrupt them (Milchram et al., 2018). Researchers have developed a
number of related but distinct theories that seek to explain the relationship between values, norms,
attitudes, and behavior change (or pro-environmental behavior) (see Stern et al. (1999) for a review
3
and comparison). The Value-Belief-Norm theory of environmental behavior links the norm
activation theory, the theory of personal values, and the New Ecological Paradigm hypothesis
together into one construct (Stern et al., 1999). Within this model, there are three general value
orientations, or value systems: egoism (i.e., propensity to value one’s self-interest), altruism (i.e.,
tendency to prioritize the greater good), and biospherism (i.e., unity with the environment and
natural world).
Value orientations have strong predictive power for technology acceptance and sustainable
purchases (Liu & Wu, 2021; Steg et al., 2005). This is especially true for biospheric value
orientations. For example, a systematic literature review found that these pro-environmental values
were a predictor to acceptance of smart grid technologies (Milchram et al., 2018). However, context
and content may impact which value orientations are more powerful predictors. In a cross-cultural
study on solar radiation management, egoism was a more powerful predictor of acceptance of the
technology than biospherism (Visschers et al., 2017). Egoism’s predictive power was strongest in
Canada and the United States, as compared with China, Germany, Switzerland, and the United
Kingdom. The researchers attribute this relationship to different cultural values being more
prominent in specific places (Visschers et al., 2017).
Values, like all social psychological constructs, do not exist in a vacuum; they relate to and
drive other variables which in turn also impact acceptance and perceptions. For example, the
Cultural Theory of Risk (Douglas & Wildavsky, 1982) posits that different value orientations
(individualism, egalitarianism, and hierarchism) influence how individuals and cultures perceive
risk (Siegrist & Árvai, 2020). It is therefore necessary to understand how other psychological
variables impact acceptance.
Perceptions of Risk and Benefit. Two related variables that predict technology acceptance
are perceptions of risk and benefit, which are typically studied in tandem. Risk perception refers to
4
how people evaluate hazards, both in terms of their experiences and beliefs (Rohrmann & Renn,
2000). Perceptions of risk and benefit tend to be inversely correlated with one another; options
under consideration by consumers that are perceived to be of low risk tend to be simultaneously
viewed as highly beneficial, and vice versa (Alhakami & Slovic, 1994). With a given level of
benefit, the public tends to tolerate higher risk levels for voluntary activities than involuntary ones
(Starr, 1969). Additionally, people generally put greater weight on catastrophic potential of a risk
than the risk calculations (Fischhoff, 1995). Especially in the context of technology, catastrophic
potential coupled with uncertainty can lead to increased levels of risk perceptions.
Risk and benefit perceptions have, in the past, been measured via psychometric scaling with
the intent of constructing cognitive maps of various target stimuli such as consumer goods,
emerging technologies, natural hazards, and anthropogenic disturbances. An established finding
from this research is that participants vary in their judged levels of risk and benefit based on
underlying psychological constructs, which include dread, familiarity, controllability, and prior
knowledge or experience; these variables are, in turn, strongly correlated with acceptance. Thus,
more contemporary research on risk and benefit perception have shifted away from psychometric
scaling—especially in studies that must account for several predictive variables—to focus on more
direct measures like individual characteristics and use of heuristics (Siegrist & Árvai, 2020).
Risk perceptions play a key role in technology acceptance, and in acceptance of science-
related policies, across different levels of familiarity with the context (Huijts et al., 2012; L’Orange
Seigo et al., 2014; Siegrist & Árvai, 2020). In general, though, acceptance of a technology may
depend upon how hazardous or controversial the technology is perceived to be (Visschers et al.,
2017). Several studies have detailed the relationship between risk and benefit perception and
consumer acceptance for emerging technologies like genetically modified food and carbon capture
and sequestration (Bearth & Siegrist, 2019; L’Orange Seigo et al., 2014; Visschers et al., 2017).
5
When consumers perceive technologies to be risky, they are less willing to use the technology in
their lives or support related policy measures.
Research has shown that non-expert risk perceptions often do not line up with the expert’s
models and assessments, causing tension (Frewer, 2004; Kahan, 2015; Taylor et al., 2019).
Historically, this has led to the paternalization of non-experts, wherein they are perceived as
irrational and emotional, and lacking in scientific understanding (Frewer, 2004; Kahan, 2015).
While there may be discrepancies between public and expert assessment of risks, disregarding
public opinion not an effective way to build trust, foster understanding, or increase awareness
(Frewer, 2004; Taylor et al., 2019). It can also lead to failures in communicating about what
matters, who to believe, and what the public should prioritize (Taylor et al., 2019). Ensuring the
public’s concerns are accounted for is paramount, as higher levels of trust have been found to
predict lower levels or risk perception, which in turn are associated with acceptance of sustainable
technology and associated policies (Ross et al., 2014).
Trust. Trust may also serve as a heuristic when individuals do not have the time, capacity,
motivation, or capability to evaluate risks (Visschers & Siegrist, 2008). It is unrealistic to expect
every individual to process all information presented to them; people need to rely on experts and
scientists to interpret the information for them (Siegrist et al., 2021; Sinatra & Hofer, 2016). If an
expert, politician, or institution is deemed trustworthy, consumers will likely automatically believe
the information to be valid (Fiske & Dupree, 2014). Making accurate assessments of whom to trust,
however, is not an easy task (Sinatra & Hofer, 2016). Research within this domain has pointed to
a plethora of ways to measure trust, and understand its relationship to risk perceptions, and
judgements about policies and scientific exploration.
General trust is the belief that most people are trustworthy. Those with general trust are
more likely to deem strangers as trustworthy upon meeting for the first time (Earle & Siegrist,
6
2006). General trust does not require prior knowledge of the trusted parties or familiarity; instead,
it can be thought of as a predisposition to believe those around you. It has also been found to be
negatively correlated with risk perceptions; if one generally trusts others, emergent contexts may
be perceived as less risky (Siegrist et al., 2021).
Contrary to general trust, which is a measure of interpersonal beliefs, social trust measures
individuals’ relationship to authorities. The basis of social trust comes from perceived, or salient,
value similarity (Siegrist et al., 2000). It is an individual’s tendency to trust in organizations or
institutions that are perceived to have the same values as them (Siegrist et al., 2000). It is generally
easier to trust people who are seen as “in-group” members because they are perceived to share the
same values and goals (Fiske & Dupree, 2014). Individuals who are more likely to trust the
scientific community are also more likely hold beliefs consistent with the scientific consensus
around issues like anthropogenic climate change and vaccines (Drummond & Fischhoff, 2017b).
Other research has pointed to social trust impacting perceptions of recycled water in
Australia. When community members were provided information about using recycled water,
individuals outsourced their perceptions of the policy and related to technology to the decision-
makers who they felt they shared an identity with, which had a positive impact on acceptance (Ross
et al., 2014). Social trust has been shown to have a causal link to risk and benefit perceptions of
specific technologies like nuclear power, as individuals who perceive experts touting the benefits
of the technology to be trustworthy then perceive the associated risks of the technology to be lower
(Siegrist et al., 2000).
The link between trust and acceptance goes beyond sustainable technology and related
policies. A recent study of Swiss responses to COVID-19 found that general trust and social trust
had opposite effects on acceptance of mitigation policies to slow the spread of the virus (Siegrist
et al., 2021). Specifically, the researchers found that individuals with higher general trust had lower
7
risk perceptions of COVID-19 and were less likely to perceive others as posing a threat to their
health. Increased levels of social trust, on the other hand, was positively associated with risk
perceptions and adherence to protocols to slow the spread because these individuals’ believed
experts and government officials to be objective regarding the recommendations.
Affect. Another individual difference that drives technology and policy perceptions is affect.
Affect, or the reliance on intuitive emotions to guide judgements and decisions, is pivotal in dual-
process theories of thinking (Campbell-Arvai et al., 2018; Slovic & Peters, 2006). Dual process
theory posits that individuals conceive of reality in two ways; one intuitively and automatically
(known as System 1), and the other operates deliberatively and analytically, also called System 2
(Epstein, 1994).
The key to affect is instinctual emotional response. Responses based on affect occur rapidly
and automatically (Wilson & Arvai, 2006). The use of these feelings guiding judgment and
decision-making is known as the affect heuristic (Finucane et al., 2000a; Slovic, 2000; Slovic et
al., 2007). Relying on affect to evaluate risks and alternatives produces decisions that are more
efficient; there is a need for analytics with many decisions (i.e., purchasing a home), but it is not as
necessary for others (i.e., whether to wear a red or blue shirt). Therefore, like all heuristics, affect
is necessary to ensure people do not spend all their time evaluating every alternative to every
decision they face. Affect is different from emotions, as it does not depend on specific feelings, as
well as mood, which is not directly caused by a stimulus (Visschers & Siegrist, 2008). Affect’s role
in people’s decision-making can be beneficial or troublesome, depending on the decision context.
Affect has been to shown to impact individuals’ policy preferences, perceptions of
technologies, and decisions related to novel contexts. For example, researchers found that negative
affect moderated the relationship between risk and benefit perceptions of hydrogen engine buses
and nuclear power (Montijn ‐Dorgelo & Midden, 2008; Peters & Slovic, 1996), which have a direct
8
relationship on acceptance. Affect also impacted perceptions of carbon storage, which, when the
survey was conducted, was an emergent technology (Midden & Huijts, 2009). It is clear from the
literature that these psychological variables play important and related roles in predicting
technology acceptance. However, acceptance and uptake of technologies are influences by other
factors beyond individual differences.
Risk Communication and Acceptance
Historically, the goal of risk communication has been to inform individuals (or groups)
about a given risk (Covello et al., 1987) and to better align peoples’ perceptions of the risk with the
actual calculated risk (Zwickle & Wilson, 2014). Related, much risk communication research
examines how individuals deal with risk information to aid in their decision-making processes (see
Yang et al., 2014 for a meta-analysis). When done well, risk communication messages can address
cognitive gaps by reinforcing accurate beliefs and correcting misunderstandings (Fischhoff, 1995).
Recent work has called for risk communications to shift from solely informing audiences about
hazards to also aid individuals in making higher quality decisions related to the hazards (Árvai,
2014).
Regardless of the intention to inform or support in decision-making, to be effective,
communication messages must have certain characteristics. They should present the most important
risks, benefits, and associated uncertainties clearly (Fischhoff, 1995). The information presented
must be enough to increase understanding, be salient, and address public concerns on the topic
(Frewer, 2004). However, information-heavy risk communication messages do not necessarily lead
to more informed decision-making (Árvai, 2014); communicators must address the varying needs
of the consumers, and adjust their messaging to account for the heuristics consumers use when
encountering messages, especially when these messages are about unfamiliar or complex topics.
9
Persuasive Communications. Petty and Cacioppo (1986) developed a theory to explain how
messages were processed by audiences and what types of communications are most persuasive in
specific contexts. According to the Elaboration Likelihood Model of Persuasion (ELM), there are
two general pathways to how new information is consumed, and how the information can(not)
change an individual’s beliefs (Dole & Sinatra, 2019; Petty & Cacioppo, 1986). The first pathway
is called the central route; individuals process information with this route when they are
concentrated, focusing on information they care about, and when the material is easily understood
(Petty & Cacioppo, 1986; Rucker & Petty, 2006). Processing from the peripheral route, on the other
hand, occurs based on external cues and when information is repeated regularly. When one is in a
high elaboration state, information is processed through the central route is generally held with
confidence and certainty. Processing in a low elaboration state happens through the peripheral
route; attitudes formed under this level of elaboration are generally weaker and can change more
easily.
Processing through the peripheral route occurs using cognitive heuristics, or mental
shortcuts. These include affect, where people process messages based as an intuitive and emotional
level, or the familiarity heuristic, wherein people may process information differently if it is about
a product they have heard of previously. An example of this is the tendency for individuals to prefer
name brand medications over generic drugs. Halo effects also affect how information is processed,
and how judgments are formed and decisions are made; if audiences perceive presenters are
attractive, they are more likely to also perceive them as smart, credible, or trustworthy (Dole &
Sinatra, 2019; Fischhoff, 1995). Audiences are more likely to process messages peripherally when
they fall prey to framing effects, or the tendency to perceive the same information differently
depending on how it is presented. For these reasons, peripheral processing is not likely to lead to
long term behavior change (Dole & Sinatra, 2019). Risk communications can either take advantage
10
of cognitive biases and heuristics, or help them overcome the biases (Arvai & Campbell-Arvai,
2013).
In contrast to peripheral processing, central route processing is more likely to produce
behavior change. However, this can only happen when audiences are reflecting critically and
weighing the evidence (Dole & Sinatra, 2019). To increase the likelihood of central route
processing, information should be made explicit, and the wording should be simplified, especially
when communicating about complex or controversial topics. This will allow for more thorough
processing, as difficult messages require more cognitive effort (Bruine de Bruin et al., 2021b; Dole
& Sinatra, 2019; Krishnamurti & de Bruin, 2018).
Format of Risk Communications. When it comes to novel or emergent contexts, more
communication is generally better than no communication at all (van der Wal et al., 2021).
However, information is regularly abstract, difficult to understand, or not carefully framed in a
manner that allows for effective delivery to the target population (Visschers et al., 2012). Therefore,
the manner or format in which the information is conveyed will likely impact how the information
is processed and perceived. Different delivery methods, or formats will impact audiences
differently (Krishnamurti & de Bruin, 2018).
For example, written communication has been shown to improve message retention and
accurate transmission, as compared to spoken communications (Edworthy et al., 2015). At the same
time, video can make risk information more salient, and also may be more user friendly for younger
audiences (Downs, 2013). In medical decision-making, patients who watch videos about treatment
options have been shown to be more aware of both the risks and benefits of procedures (Krouse,
2001). Other research has found that videos, compared to the written scripts also increased
understanding of climate change as well as the associated risks (Goldberg et al., 2019). However,
uninteresting videos, or videos that are perceived as disingenuous, may backfire, prompting
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audiences to not trust or believe the information (Downs, 2013). The same is true for written
information; scripts need to be tailored for the audience (Downs, 2013). It is imperative, therefore,
that scientists have training in communication skills (Fischhoff, 1995), and know how to frame the
information and present it in a format that is beneficial to the target audience.
Overall, effective communication is paramount for the public to navigate and manage risks.
However, the science presented in communications does not matter if it is not accurately
understood and then used to make relevant decisions (Fischhoff, 1995). Examining if and how
people onboard the communication presented to them is a crucial next step in ensuring that they
are making value-aligned decisions about emergent risks.
Onboarding of Communications: The Need for Critical Thinking
One method of assessing the quality of information onboarding is to look how those with
more domain-specific knowledge (compared to those with less) respond to risk communications.
In both cross-national and national studies, domain specific knowledge about the causes and
consequences of climate change predicted concern about climate change when controlling for
political ideology and value orientations (Shi et al., 2015; Shi et al., 2016). However, scientific (or
domain-specific) knowledge may be used to defend nonscientific motivations, rather than
onboarding information accurately. For example, when using data from the General Social Survey,
researchers found that those with more scientific knowledge were more likely to hold politically
charged preferences on controversial topics like human evolution, the Big Bang theory, stem cell
research, and anthropogenic climate change (Drummond & Fischhoff, 2017b). Therefore, there
may be other factors at play beyond domain-specific knowledge, that account for how individuals
interpret and then use information. Indeed, how people understand and engage with science is not
dependent on solely scientific or domain specific knowledge, but on their ability to evaluate
information (Sinatra & Hofer, 2016).
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One of the most crucial components of onboarding of information through the central route
is deep and critical reasoning (Dole & Sinatra, 2019). Critical reasoning can be thought of as how
one understands new information, how one thinks about the information, and then how one employs
this information in their decision making. One method of critical thinking which may be
particularly useful in novel contexts is actively openminded thinking (AOT) (Stanovich & West,
1998). AOT is the learned tendency to seek out and weigh new evidence against favored beliefs,
spend sufficient time on problems, and carefully consider others’ opinions when forming one’s
own. Active seeking is primarily motivated by one’s psychological need for information
sufficiency (Yang et al., 2014). Weighing evidence, considering different perspectives can be
viewed as an advanced understanding of science (Sinatra et al., 2014). Compared with individual
differences, attributes like AOT may be more potent drivers of both decision-making and
onboarding of information because AOT can be applied across manifold contexts (Fischhoff &
Broomell, 2020). Within the context of acceptance of policy and technologies, actively open-
minded thinkers are more likely to make accurate judgments about risks, and better decisions under
uncertainty in a wide range of situations (Baron, 2019; Haran et al., 2013; Stanovich & West, 1998).
AOT can be thought of as both a skill that can be harnessed, or a norm that can be adhered
to. Regarding the former, thinking in an actively open-minded way necessitates a reflective
approach to consuming information. This requires individuals to follow a process. They must of
consider contrasting beliefs, take sufficient time to problem-solve, and update their opinions based
on what they learn (Haran et al., 2013). Thus, the skills that underlie AOT can be thought of as a
necessary component to competent decision-making, as the process of coming to a decision matters
as much (if not more) than the outcome (Hammond et al., 1999). Others have argued that seeking
out new information, adjusting one’s belief, and succumbing to motivated reasoning are behaviors
one ought to exhibit; therefore, AOT can also be thought of as a norm (Baron et al., 1986).
13
Whether a skill or a norm, critical thinking can be taught both formally and informally
(Baron, 1993; Baron et al., 1986). Interventions exist that teach controversial issues in a way that
fosters higher engagement both in and outside the classroom (Alongi et al., 2016). Especially within
the field of environmental education, there are ways to incorporate lessons about decision-making
and critical thinking into curricula (Arvai et al., 2004). Helping people develop these skills through
formal and informal education are a necessary complement to risk communication efforts that focus
on providing risk information or recommended risk management actions.
Thus far, minimal work has been conducted on how critical reasoning ability is associated
with risk perceptions; more research is needed to understand the relationships between critical
reasoning ability (measured through AOT), risk perceptions, trust, and acceptance of measures
aimed at mitigation emergent risks.
Research Questions
The combination of the many internal factors influencing decision-making and the
multifaceted nature of global concerns calls for effective and well-informed decisions that are both
realistic and in line with decision-makers’ values (Bessette et al., 2019). With this as a backdrop,
the following chapters explore questions related to what leads people to like or dislike technologies
that could advance sustainability; how can communicators facilitate understanding and acceptance
through different risk communication techniques; and how can critical thinking aid people in
making judgements and decisions in emergent contexts? Specifically, this dissertation examines
three main questions, with a chapter devoted to each.
Research Question 1: To what degree do individual differences and values influence
acceptance of emergent scientific technologies? This chapter examines consumer receptivity to
fertilizers derived from human urine diversion and recycling that is used to grow foods for human
consumption. Individual differences and values reported in this work include judgments of risks
14
and benefits, food disgust sensitivity, value orientations, age, education, gender, race, and
perceived naturalness. Secondary aims of this research are to assess U.S. food consumers’
acceptance of human urine-derived fertilizer (HUDF) when it is used to grow plants that are for
nonedible uses and the relative acceptability of HUDF compared to other fertilizer types, namely,
organic, synthetic, and biosolid-based.
Research Question 2: How do alternative risk communication approaches influence
perceptions of emerging technologies, and peoples’ ratings of the usefulness of the information
contained in them? Specifically, this study focused on how watching a risk communication video
vs. reading a text-based presentation containing the same information may influence consumers’
acceptance of HUDF. We were also interested in the effect of the length of these videos and texts
and how it would influence consumers’ acceptance of HUDF.
Research Question 3: How does critical thinking ability correspond to individuals’
interactions with science and scientists in emergent contexts? The research reported in this chapter
focuses on the relationship between actively open-minded thinking (AOT), risk perceptions
surrounding COVID-19, trust in experts, and compliance with CDC guidelines to manage one’s
personal risk.
It should be noted that studies 1 and 2 explore these questions within the context of fertilizer
derived from diverted and recycled human urine, and study 3 instead shifts focus to the COVID-
19 pandemic. These contexts both fall within the domain of public health and long-term well-being,
and each examine emergent and unfamiliar risks to the public (innovative technologies and novel
viruses). They also both require solutions that are multiscale (individual choices and global
consensus), multisector (government intervention and economic innovation, and that have
tradeoffs (profit, lives lost, etc). Therefore, a case study of how critical thinking is associated with
social trust and adherence to mitigation policies may be applicable to sustainability and
15
environmental scenarios in the future. The following three chapters are as follows: (1) Consumers’
acceptance of agricultural fertilizer derived from diverted and recycled human urine; (2)
Communicating the Risks and Benefits of Human Urine-Derived Fertilizer; and (3) I think,
therefore I act: The influence of critical thinking ability on social trust and behavior during the
COVID-19 pandemic.
16
Chapter I
Consumers’ acceptance of agricultural fertilizer derived from diverted and recycled
human urine
Introduction. The cycling of nitrogen and phosphorus is vital to supporting life on earth
(Frissel, 1978; Magdoff et al., 1997). Both nutrients are particularly important in agriculture
because they are essential ingredients in fertilizer (Stewart et al., 2005) and are, therefore, crucial
for feeding a growing world population (Ashley et al., 2011). While abundant and affordable
supplies of these nutrients was once commonplace, constraints on nitrogen and phosphorus have
emerged. Conventional nitrogen fertilizer production is energy-intensive, has a large water and
carbon footprint, emits ozone-depleting nitrous oxide, and—by way of runoff and wastewater from
agriculture—leads to the eutrophication of both terrestrial and aquatic systems and to global
acidification (Gruber & Galloway, 2008; Magdoff et al., 1997). At the same time, accessible,
affordable, and high-quality phosphate rock reserves are declining (Cooper et al., 2011). Thus,
phosphate prices are vulnerable to global economic fluctuations as evidenced by the twofold
increase in super phosphate farm prices between 2005 and 2009. Phosphorus prices are expected
to become increasingly volatile in the future, having greater impacts on future global food prices
(Mew, 2016). Complicating matters further, the present use of nitrogen and phosphorus is
inefficient. For example, throughout 15 European countries, only 10% of applied phosphorus
reaches consumers (Ott & Rechberger, 2012). The same is true of nitrogen; unrecovered nitrogen
was estimated to be approximately 65% with significant nutrient loss through both wastewater
effluent and biosolid disposal (Forkes, 2007). A related study found that only 29% of applied
phosphorus made its way to productive use while 64% of nitrogen was unrecovered (Liang et al.,
2019).
17
Considering these challenges, the recovery and recycling of nitrogen and phosphorus from
wastewater is drawing the attention of the scientific community, NGOs, and policymakers. While
there are several ways to recover and recycle these nutrients (e.g., like using domestic biosolids or
sewage sludge as fertilizer, or recycling ammonia from livestock wastewater), human urine
diversion and recycling is emerging as an energy-efficient method of producing high-quality
fertilizer for use in agriculture since the vast majority of nitrogen consumed by people is excreted
as urine (Larsen et al., 2009; Simha & Ganesapillai, 2017). In addition, human urine diversion and
recycling recovers phosphorus, nitrogen, and potassium together, producing a more beneficial
fertilizer product that is equivalent to mineral fertilizers (Mihelcic et al., 2011). Recovering
nutrients from human urine and redirecting them for agricultural fertilizer would also increase the
energy efficiency and sustainability of food production (Kalmykova et al., 2012; Mihelcic et al.,
2011; Wilsenach & Van Loosdrecht, 2003; Wu et al., 2016).
Human urine can be processed into different forms of urine-derived fertilizers (HUDF) that
are compatible with soil types and agronomy practices across large sectors of the world, including
about half of North America (Trimmer et al., 2019). And, producing HUDF has the added benefit
of protecting water resources because nutrients and micropollutants (e.g., pharmaceuticals and
personal care products) can be removed more efficiently from urine than after it is diluted into
wastewater (Larsen et al., 2009). Technologies like struvite precipitation, nitrification-distillation,
and alkaline urine dehydration are all in advanced stages of development, and some are being used
on small-scales for diverting, recycling, and converting human urine for HUDF production
(Christiaens et al., 2019; Simha et al., 2018b; Tarpeh et al., 2017; Udert & Wächter, 2012). The
deployment of infrastructure for urine diversion and HUDF production at scale requires retrofitting
or replacing public and building-scale wastewater systems. This task, though formidable, is not
18
impossible. However, building the momentum to turn proof-of-concept into widespread
distribution depends on public acceptance.
Psychological Variables, consumer acceptance, and HUDF. Research on public
perceptions of human urine diversion, recycling, and HUDF is beginning to emerge. However,
results from these studies are either highly localized or non-generalizable. For example, in a study
of 2,700 users of urine separating toilets, Lienert and Larsen (2010) observed high levels of support
for HUDF; however, these results are expected from a self-selected sample of people who already
adopted them in their homes or workplaces. Looking at fertilization methods and nutrient
management broadly, researchers observed low levels of awareness and knowledge amongst
consumers (Pahl-Wostl, 2005). Yet, prior research on consumers’ acceptance of fertilization
methods shows that organic fertilizers (like bone meal and compost) are viewed quite positively
(Bergström et al., 2009); synthetic fertilizers (like ammonium nitrate and potassium sulfate), in
contrast, are not (Williams & Hammitt, 2001).
The disconnect between low levels of awareness and strong opinions about fertilizers
demonstrate that consumer acceptance is not necessarily only related to objective knowledge about
emerging (or established) technologies. Several psychological variables may predict consumers’
acceptance of emerging technologies related to the production of food (which we expect to also be
the case with HUDF). They include value orientations (De Groot & Steg, 2008), instinctive
emotional responses such as disgust (Rozin & Fallon, 1987), generalized perceptions of risk and
benefit (Slovic, 2000), and perceived naturalness (Verhoog et al., 2007). However, to date, these
variables have received little attention in research on consumers’ perceptions of HUDF.
Value orientations. People’s values serve as guidance for preferences and are used to predict
attitudes and behavioral intentions (De Groot & Steg, 2008). One approach to account for values
is via value-belief-norm theory, which posits that different value orientations (e.g., altruism,
19
biospherism, and egoism) lead to varying levels of acceptance of pro-environmental behavior and
technologies (Stern et al., 1999). Generally, those with a stronger egoistic value orientation, or a
deep regard for one’s self-interests and personal well-being, tend to be less amenable to pro-
environmental options. On the other hand, those with altruistic (emblematic of high levels of
concern for the welfare of others) and biospheric (emblematic of high levels of concern for the
environment because of its intrinsic value) tend to prefer pro-environmental outcomes like
technologies with clear environmental benefits (L’Orange Seigo et al., 2014).
Value orientations have been used to predict levels of acceptance of controversial or
emerging environmental technologies like solar radiation management (Visschers et al., 2017),
nuclear power (Whitfield et al., 2009), nanotechnology (Siegrist et al., 2007), and genetically
modified organisms (GMOs) (Finucane, 2002). Urine separation and reuse leading to HUDF
production shares similarities with solar radiation management and nuclear power in that they all
require infrastructure development or retrofits. However, given that the scale of change is smaller
than the other technologies, and because HUDF adoption would call for changes in personal
consumption, consumers’ acceptance of HUDF is likely to be judged on a more personal (vs.
societal) and environmental level like GMOs and nanotechnology. Thus, we might expect egoistic
and biospheric value orientations to be especially relevant. Regarding the latter, for example,
people who ascribe strongly to a biospheric value orientation tend to be more supportive of products
and technologies that are in greater harmony with nature; e.g., manufacturing processes that rely
on recycling ingredients and raw materials that would otherwise be discarded as waste (De Groot
& Steg, 2008).
Affect and disgust. Two parallel systems operate in the mind when people form judgments
and preferences (Epstein, 1994). System 1 is intuitive, automatic, and experiential. System 2, by
contrast, is analytic, deliberative, and reliant on rational calculation. The defining characteristic of
20
System 1 is termed affect; the rapid and intuitive emotional state that people experience in response
to a stimulus (Finucane et al., 2000a; Wilson & Arvai, 2006). Affect consists of positive or negative
feelings of arousal (e.g., happiness or sadness) and intuitive assignments of positive or negative
valence (e.g., beauty or ugliness) to stimuli. A reliance on System 1 to guide judgment and decision-
making was termed the affect heuristic (Finucane et al., 2000a).
Recent research in the food domain extended the influence of the affect heuristic to another
important intuitive response: disgust. Disgust is associated with negative valence and arousal;
however, studies of food disgust also emphasize its essential, evolutionary roots. Specifically,
intuitive feelings of disgust are thought to function as protective mechanisms to prevent animals
from ingesting potentially harmful materials (Curtis et al., 2011).
Research on consumers’ acceptance of certain foods supports the connection between affect
and disgust. In research on cultured meat and insect protein, for example, high levels of disgust
were linked to negative arousal and valence, and to low levels of acceptance (Ruby & Rozin, 2019;
2018). While research has not focused on the connection between disgust and HUDF, research on
treated wastewater (for human consumption through potable reuse) shows that those who find it
disgusting are three times less likely to consume it (Massoud et al., 2018). Similarly, research on
public perceptions of nutrient recycling in treated sewage sludge indicates that disgust is a key
factor influencing U.S. consumers’ lack of receptivity to biosolid-based fertilizers (Beecher et al.,
2005). It stands to reason, therefore, that feelings of disgust may play an important role in
judgments about HUDF acceptance.
Judgments about risk and benefit. Two related variables that predict technology acceptance
are perceptions of risk and benefit, which are typically studied in tandem. Risk and benefit
perceptions tend to be inversely correlated; marketplace options judged as minimally risky tend to
be simultaneously viewed as highly beneficial, and vice versa (Alhakami & Slovic, 1994). Several
21
studies detail the relationship between judgments of risk and benefit, and consumer acceptance for
emerging technologies like GMOs and carbon capture and sequestration (Bearth & Siegrist, 2019;
L’Orange Seigo et al., 2014; Visschers et al., 2017). However, quantitative studies of judgments
about risk and benefit in urine separation have been scant. In a recent study of Indian university
students, attitudes towards human urine separation and HUDF were more positive when they
learned that the human urine could be treated so as not to pose a health risk (Simha et al., 2018a).
Another study of Dutch consumers found that risk and benefit perceptions were major predictors
of acceptance of urine separating technologies (Poortvliet et al., 2018).
Within the context of fertilizers more broadly, research in the U.S. points to judgments of
high risk for biosolid-based fertilizers (Beecher et al., 2005; Robinson et al., 2012). The heightened
attention on potential risks of biosolid application negatively impacted levels of acceptance
generally, but judged risks decreased when biosolid application was geographically distant from
participants’ homes (Robinson et al., 2012). Therefore, acceptance of fertilizer application may
also vary based on the type of use (e.g., crops for human consumption vs. ornamental plants).
Perceived naturalness. People tend to have an innate attachment to nature (Wilson, 2017)
and a desire for natural vs. processed or synthetic options influences judgments and decision-
making in a variety of contexts from the protection of ecosystem services to food choice (Campbell-
Arvai, 2019; Siegrist & Sütterlin, 2017). A reliance on the symbolism of naturalness can, however,
lead to biased decisions, wherein identical alternatives—save for their biological or anthropogenic
roots—are judged differently (Campbell-Arvai, 2019). There is much work detailing the positive
associations people have with “natural” foods. For example, organic food is often perceived as
more natural and a healthier alternative to non-organic certified foods. Similarly, labels misleading
consumers by highlighting food ingredients as “natural” are both commonplace and are often
22
conflated with perceptions of healthiness and may lead to suboptimal purchasing and consumption
decisions (Sütterlin & Siegrist, 2015).
When it comes to emerging technologies in food and agriculture, perceptions of naturalness
may play a pivotal role in acceptance (Román et al., 2017). Products perceived as unnatural have a
marked reduction in acceptance levels, even if the product in question is more environmentally
friendly than its “natural” alternative (Siegrist & Sütterlin, 2017). For example, meat produced
through the growing of in vitro animal cells requires dramatically lower levels of energy, land use,
and water than farm-raised meat (Tuomisto & de Mattos, 2011). However, these products are
perceived as being less natural, leading to significantly lower levels of acceptability compared to
conventional alternatives (Siegrist & Sütterlin, 2017).
Research on the perceived naturalness of novel nutrient recycling and fertilization methods
is limited. In the context of conventional fertilizers, perceived naturalness may contribute to
positive associations with organic fertilizer use and negative associations with agrobiotechnology
and biosolid use (Beecher et al., 2005; Verhoog et al., 2007). The conventional option, synthetic
fertilizer, is less natural than HUDF. Thus, even though nutrient recycling and the creation of
HUDF will require extensive processing, the natural and more sustainable source of the nutrients—
namely human urine—may be a dominant factor in perceptions of naturalness and, ultimately,
consumers’ judgments about acceptability.
Aims of present study. The primary aim of this research was to examine the degree to which
judgments of risk and benefit, food disgust sensitivity, value orientations, and perceived naturalness
influence U.S. food consumers’ acceptance of fertilizer derived from human urine diversion and
nutrient recycling, and that is used to grow foods for human consumption. Secondary aims of this
research were to study U.S. food consumers’ acceptance of HUDF when used to grow plants that
are for non-edible uses, and the relative acceptability of HUDF compared to other fertilizer types,
23
namely organic, synthetic, and biosolid-based. We chose these fertilizers for comparison because
all three are widely used options. In addition, biosolids, like HUDF, are a waste-based fertilizer,
though they differ in content as biosolids are derived from household, industrial, and commercial
wastewater, as opposed to solely human urine.
Materials and Methods
Participants. Participants were United States residents recruited from a representative
internet panel sourced by Qualtrics. We applied random quota sampling for assignment to one of
four fertilizer treatments; likewise, we sought equal distributions across both gender and age (ages
18-29; 30-39; 40-49, 50-59; and 60+). The initial sample consisted of 5,326 participants. After data
cleaning, the total sample size dropped to 2,007 participants distributed roughly equally across the
treatments (Appendix A, Table 1); 3,319 participants were removed from the dataset because they
did not give their informed consent (n=357), did not complete (n=429) or spent less than half the
median time (2m56s) on the instrument (n=1,742), because of the absence of variation (i.e.,
“straight-lining”) in responses to questions on Likert scales (n=158), because they failed a series
of attention checks (n=609), or because they began the survey after we reached the quota for
participants (n=24). The final sample sizes exceeded the sample size required for adequate
statistical power (Faul et al., 2009).
Across the four fertilizers studied, 49.2% of participants were women; the mean age of
participants was 44.9 (SD=15.9); and the median education level corresponded with self-reports of
“some college”, which was the midpoint of the included question about education level (Appendix
A, Table 1). These demographic characteristics mirror what constitutes the “average” American
(US Census Bureau, 2017).
Design. Participants responded to a survey (Appendix B, Figure 1) built around one of four
different fertilizers: (1) HUDF; (2) organic fertilizer; (3) synthetic fertilizer; or (4) biosolid-based
24
fertilizer. There may be overlap across some of these fertilizers depending upon how they are
manufactured. However, given that consumer knowledge of fertilizers is low, because HUDF has
not yet been the subject of regulatory certification, and because our study focuses on the source—
via urine diversion—of nutrients used in fertilizer manufacturing, we clearly differentiated these
fertilizers in the information provided. The centerpiece of each survey was a short (1-page) primer
about each participant’s assigned fertilizer developed specifically for this research (Appendix B,
Figures 4 – 7). The primers provided participants with general information about why fertilizers
are used, the basic content of each fertilizer, how nitrogen and phosphorus are sourced for each
fertilizer, how each fertilizer is manufactured, and information about the corresponding benefits,
risks, and uncertainties. This information was presented as text with an accompanying schematic
that were reviewed for content and accuracy by water system designers, biophysical scientists, and
agronomists from our broader team. The primers were immediately followed by an attention check
question to determine whether participants could correctly identify the fertilizer they just learned
about.
Identical survey instruments were shown to every participant, regardless of treatment group.
The first question posed to participants asked, when you think about [this fertilizer], to what extent
do you think of it as natural? Responses were collected on 7-point Likert scales from “not at all
natural” to “extremely natural” (the midpoint was unlabeled). In the actual survey instrument, [this
fertilizer] was replaced with the name of each participant’s assigned fertilizer for all questions.
Next, judgments about the risks and benefits of each fertilizer were measured using four questions
across two contexts: people and environment. For judgments about risk, we asked for participants’
level of agreement with two statements: [this fertilizer] will be harmful to my health and the health
of my family and [this fertilizer] will be harmful to the environment (Cronbach’s α=0.89). For
judgments about benefits, we created a scale (Cronbach’s α = 0.88) based on participants’ level of
25
agreement with two statements: using [this fertilizer] will result in foods that are healthier for
people and using [this fertilizer] will be healthy for the environment overall. Responses for all four
questions were collected on 7-point Likert scales from “strongly disagree” to “strongly agree”
(midpoint = “neither disagree nor agree”).
We measured participants’ level of acceptance regarding each fertilizer. Rather than overall
acceptance, we subdivided and scaled (Cronbach’s α = 0.89) questions according to four distinct
end-uses. We asked for participants’ level of agreement with four statements: It is acceptable to
use [this fertilizer] to grow non-edible plants like flowers; It is acceptable to use [this fertilizer] to
grow fruits and vegetables that will be eaten by only animals; It is acceptable to use [this fertilizer]
on the plants, trees, and grasses that grow around my home; and It is acceptable to use [this
fertilizer] to grow fruits and vegetables for people to eat. Responses for all four questions were
collected on 7-point Likert scales from “strongly disagree” to “strongly agree” (midpoint = “neither
disagree nor agree”).
Finally, we collected data on participants’ level of food disgust sensitivity using an 8-item,
pre-validated scale (Cronbach’s α = 0.92) (Ammann et al., 2018), and on their value orientations
using a 12-item, pre-validated scale (De Groot & Steg, 2008). Regarding the former, respondents
were asked to indicate how disgusting they perceived a series of items (e.g., hard cheese with the
mold cut off, brown avocado pulp) or situations (e.g., eating with dirty silverware in a restaurant,
discovering a snail in a salad) to be on 6-point Likert scales ranging from 1 = “Not disgusting at
all” to 6 = “Extremely disgusting”. Regarding the latter, we measured egoistic (four questions;
Cronbach’s α = 0.75), altruistic (four questions; Cronbach’s α = 0.82), and biospheric value
orientations (four questions; Cronbach’s α = 0.90).
Analysis. We conducted analyses of variance with Tukey’s post-hoc comparisons to detect
differences between HUDF and other fertilizers across the main study variables outlined above:
26
perceptions of naturalness, judgments of risk and benefit, and ratings of acceptability. For these
analyses of variance, we treated risk and benefit, and acceptability variables separately. To lower
the rate of Type II errors due to multiple comparisons, we applied a Bonferroni correction; thus,
the p-value required for significance in the ANOVA’s and post-hoc tests was set at 0.005. We also
conducted analyses of variance with Tukey’s post hoc comparisons to understand distinctions
between the various types of acceptance within the HUDF treatment group.
We also conducted exploratory linear hierarchical regressions to study the extent to which
demographic characteristics (i.e., age, gender, and education), food disgust sensitivity, and
perceptions of risk, benefit, and naturalness explained participants’ acceptance of HUDF. We
compared applications of HUDF where the product (food) was suitable for human consumption vs.
situations where HUDF would be applied to plants or crops intended for non-edible use. For
simplicity, we combined all non-consumptive uses of HUDF (i.e., applications on non-edible crops
and plants, on crops consumed only by animals, and on non-edible plants around the home) into a
single variable (Cronbach’s α=0.85).
Results and Discussion
Comparisons of fertilizer types. An ANOVA detected a significant main effect
(F(3,2003)=294.85, p<0.001) between perceptions of naturalness across the four fertilizer types
(Appendix A, Table 2). Post-hoc testing revealed significant differences between organic fertilizers
and all other fertilizer types, wherein organic fertilizer was rated as the most natural. The opposite
was the case for synthetic fertilizers; HUDF and biosolid-based fertilizer were perceived as
significantly more natural than synthetic.
An ANOVA revealed a significant main effect between the four fertilizer types for
judgments about risk (Appendix A, Table 2) to human (F(3,2003) = 124.03, p<0.0001) and
environmental (F(3,2003)=195.91, p < 0.0001) health. Post-hoc testing for risk perceptions revealed
27
a similar pattern; organic fertilizers were judged to be significantly less risky than the other three
fertilizer types, and synthetic fertilizers were perceived as most risky. Participants rated HUDF and
biosolids as similarly risky; there was no significant difference between these two fertilizers
(p>.005).
Regarding perceptions of the benefits of each fertilizer type (Appendix A, Table 2), an
ANOVA detected a significant main effect for benefits to human (F (3,2003) = 141.17, p<0.0001) and
environmental (F(3,2003) = 208.12, p<0.000) health. Concerning between-fertilizer differences,
synthetic was judged to be the least beneficial, with significant post-hoc differences between it and
the other fertilizer types. Organic fertilizers were rated, on average, to be the most beneficial when
compared with all other fertilizer types. As was the case for judgments about risk, participants rated
biosolids and HUDF similarly beneficial.
Considering the acceptance variables (Appendix A, Table 2), an ANOVA detected
significant main effects for all four fertilizer applications: for use on non-edible crops:
(F(3,2003)=90.21, p<0.0001); for use on crops grown for animal consumption (F(3,2003)=93.31,
p<0.0001); for use on plants around one’s home (F(3,2003)=109.74, p<0.0001); and for use on crops
intended for human consumption (F(3,2003)=143.03, p<0.0001). Post-hoc testing showed that, in all
cases, participants were most accepting of organic fertilizer. Biosolids and HUDF were not
significantly different from one another across any of the applications. Across all above ANOVAs,
we generally observed medium to large effect sizes (Appendix A, Table 2).
Analyses of variance on the risk, benefit, naturalness, and acceptance variables across the
four fertilizers revealed three main findings. First, organic fertilizers outperformed biosolid-based
fertilizers, HUDF, and synthetic fertilizers on all variables. Participants perceived organic fertilizer,
on average, to be the most beneficial and natural, the least risky, and were most accepting of it
relative to the other fertilizer types. Second, our results also suggest that while synthetic fertilizers
28
are the most widely used nationally, they were outperformed by all other fertilizer types across
every variable (Appendix A, Table 2). Third, HUDF and biosolid-based fertilizers exhibit identical
patterns of preference with respect to perceived naturalness, and judgments about risk and benefit
as it relates to both human and environment health (Appendix A, Table 2). The acceptability
profiles for both HUDF and biosolid-based fertilizers were also identical when considering their
application across four distinct end uses.
In light of these results, it is worth repeating that HUDF and biosolids-based fertilizers come
from different forms of waste and differ in composition. Biosolids are the solid residual from
centralized wastewater treatment facilities and are derived from combined household wastewater
(including both feces and urine), industrial and commercial wastewater (which often includes fats,
oils, heavy metals, and particulate chemicals), and in some cases, particulate runoff with
stormwater (including agricultural or urban landscape chemicals). Diverted human urine, by
contrast, is captured in specialized toilets or urinals where it is separated from feces; likewise, if
managed properly, this wastewater should be free from other wastes. While it is true that both
HUDF and biosolids are treated to kill pathogens and remove residual pharmaceuticals to avoid
accumulation in plants(Sabourin et al., 2012; Wigginton et al., 2017), the production chain for
HUDF is less energy intensive(Wigginton et al., 2017). Despite these significant differences
between HUDF and biosolids-based fertilizers, participants were not more accepting of one over
the other. It may be the case, therefore, that the provision of comparative risk information to
consumers could tip the acceptability balance in favor of HUDF.
Comparison of HUDF applications. In terms of within-HUDF differences in acceptability
ratings (Appendix A, Table 3), an ANOVA revealed a significant main effect (F(3,2048)=43.60,
p<0.0001). Post-hoc testing detected significant differences between all acceptance comparisons;
HUDF use for non-edible plants was most preferred, and HUDF use on crops for human
29
consumption was least preferred. Mid-range levels of mean acceptability were observed for use of
HUDF on vegetation around the home, and on crops intended for only animal consumption. These
differences in acceptability ratings of HUDF applications on fruits and vegetables for human
consumption vs. cases where HUDF is used on plants and crops are not intended for consumption
by humans are in line with other research. For example, a study of Indian farmers found that they
were generally accepting of HUDF but preferred it when people socially distal to them deployed it
(vs. people, such as family members, situated within their close social circles) (Simha et al., 2017).
Similarly, a study of nanotechnology applications in food packaging vs. in the foods
themselves(2007) demonstrated consumers were more accepting of and exhibited a greater
willingness to purchase the former over the latter.
Predictors of HUDF acceptance. The hierarchical exploratory regression examining
consumers’ acceptance of HUDF for use on crops intended for human consumption (Appendix A,
Table 4A) had three steps. The first included demographic variables (age, gender, and education
level). Gender was statistically significant with a small effect size (η
2
=0.03) in that men were, on
average, more accepting of HUDF use than women. The second step of the regression included the
above-mentioned predictors as well as intuitive variables, specifically value orientations and food
disgust sensitivity. Gender remained statistically significant with a small effect size (η
2
=0.03); men
continued to be, on average, more accepting than women. Biospherism and food disgust sensitivity
were also both significant predictors of acceptance of HUDF use. Specifically, biospherism had a
positive relationship to HUDF acceptance and food disgust sensitivity was inversely related to
acceptance.
The third step of the hierarchical regression included cognitive (i.e., System 2) variables,
particularly judgments about HUDF’s risks, benefits, and naturalness. We refer to these variables
as “cognitive” because the 1-page primers about each fertilizer type included specific information
30
for participants about its risks, benefits, and naturalness. Risks and benefits were highly correlated,
inversely related, and highly significant (Appendix A, Table 4 and Appendix B, Figure 2). The
effect size for risk was medium (η
2
=0.06) while the effect size for benefit was large (η
2
=0.31).
Food disgust sensitivity, perceived naturalness, value orientations, and the battery of demographic
factors no longer predicted acceptance of HUDF use on crops intended for human consumption.
The questions on consumers’ acceptance of HUDF for use on crops not intended for human
consumption were combined into a single, internally consistent (Cronbach’s α=0.85) variable for a
more straightforward analysis. The order of steps in this hierarchical regression (Table 5) were
identical to those outlined above (Appendix A, Table 4).
Once again, in the third step, judgments about risk and benefit were both significant and
inversely related predictors of acceptance and had small (η
2
risk=0.04) and medium (η
2
benefit=0.16)
effect sizes. Perceived naturalness was a significant and positive predictor of acceptance with a
low-medium effect size (η
2
=0.06). Like risk perceptions, identifying with an egoistic value
orientation was also a significant, inverse predictor of acceptance, with a small effect size
(η
2
=0.01). The same pattern was observed for food disgust sensitivity, which had a significant and
negative predictive relationship with acceptance (with a small effect size; η
2
=0.01). In the final step
of the regression, the demographic factors did not predict acceptance of HUDF.
With respect to the specific variables that predict consumers’ acceptance of HUDF in either
context, our research suggests that the cognitive risk and benefit variables are central factors. Like
other work related to urine separation and its associated technologies, benefit perceptions had a
larger impact on acceptance than risk perceptions(Poortvliet et al., 2018). However, in designing
our study, our goal was to go beyond just studying risk and benefit; hence the reason we included
measures of value orientations, perceived naturalness, and food disgust sensitivity in our survey
and exploratory regressions.
31
We speculated based on other research (De Groot & Steg, 2008) that identifying strongly
with a biospheric value orientation would lead to greater support for HUDF. We did so because the
way this fertilizer is manufactured—namely urine diversion and nutrient recycling, the process
behind which was clearly articulated to participants in this research (Appendix B, Figures 4 – 7)—
does not waste important and limited natural resources. We speculated about a similar mechanism
with respect to perceived naturalness; products derived from natural ingredients (e.g., nutrients
from a biological process in the case of HUDF) and processes (e.g., nutrient recycling in the case
of HUDF) tend to be viewed more favorably than the same products developed through artificial
or synthetic means.
As a counterweight to biospherism, we speculated that consumers who exhibit high levels
of food disgust sensitivity would be less supportive of HUDF; the label “human urine-derived
fertilizer” is more explicit about human waste as the source of nutrients (vs. biosolids, which is a
more ambiguous label even though the source is also human waste) and we know from prior
research that most people view urine and feces as disgust-inducing (Rozin & Fallon, 1987). As
with the process of nutrient recycling outlined in the previous example, the source of nutrients was
clearly articulated to participants (Appendix B, Figures 4 – 7).
Regarding value orientations, perceived naturalness, and food disgust sensitivity, only
perceived naturalness exhibited a significant predictive relationship to acceptance of HUDF, and
only when participants considered crops and vegetation for non-edible use. This was a surprising
result and warrants further research. It may be that, because naturalness was correlated with risk
and benefit (Appendix B, Figure 2), when all were included in the regression models, this variable
was rendered either marginally significant or insignificant. We expected the intuitive variables of
food disgust sensitivity and value orientations to play a pivotal role in understanding of acceptance
of HUDF. The variables, however, along with the included demographic variables, were either not
32
robust or significant predictors when participants considered crops both intended for human
consumption or for non-edible use.
While it was the case that value orientations, disgust sensitivity, and demographic factors
were significant predictors in initial model blocks, they were either rendered insignificant when
perceived risks and benefits were added to the hierarchical regression, or they remained significant
but with trivial effect sizes. Thus, it is safe to conclude that, despite differences in the significance
of certain variables between the two modeled contexts (produce intended for edible vs. non-edible
use), the overarching conclusions about risk and benefit perceptions are fundamentally the same in
that these are the two primary predictors of HUDF acceptance across both scenarios.
Practical implications, limitations, and future research. These results are positive from an
applied perspective regarding the potential for the more widespread deployment of human urine
diversion, nutrient recycling, and—ultimately—HUDF production and use. Synthetic fertilizers
and biosolids are currently widely used in agriculture. In the current study, consumers’ acceptance
of HUDF is on par with biosolid-based fertilizers and outperforms synthetic fertilizers. This
observation, coupled with the fact that a large proportion of consumers seem open to consuming
foods produced with HUDF (Appendix B, Figure 3), leads us to believe there is a clear opportunity
to begin to introduce HUDF to the marketplace.
However, we do not advocate taking this step without informing consumers. Some
American consumers may have a general understanding of organic food and agriculture (Yiridoe
et al., 2005), but in-depth knowledge about HUDF (and fertilizers in general) is quite low amongst
the general population (Lamichhane & Babcock Jr, 2013; Palm et al., 2004). Therefore, informing
consumers about how their food is grown, and the impact of fertilizer production and use on
environmental and human health will be essential (Lamichhane & Babcock Jr, 2013). In doing so,
we advocate a research-based approach focusing on testing alternative messages and formats prior
33
to their widespread release. We recommend this approach because raising awareness about a
technology that science deems safe or beneficial can easily backfire (Frewer, 2004). In short, it
should not be assumed that simply providing more information about HUDF will be adequate to
positively influence consumer sentiment.
This study was not without limitations. For example, based on the lack of regulation
regarding the certification of HUDF, we examined it as distinct from organics, synthetics, and
biosolids. Going forward, it may be challenging to make this distinction. Changes to how nutrients
from urine diversion are certified may lead them to be classified as a subset (or ingredient) of one
or more of these other fertilizer categories; alternatively, HUDF could be classified as its own
category of fertilizer. The unknown future pathway towards certification and classification should
be accounted for in future research about consumers’ perceptions and communication about HUDF.
Specifically, future research should test for interactions between HUDF and other classes of
fertilizers. Future work may also focus on the role of prior knowledge about HUDF as a predictor
of HUDF acceptance. And exploring a broader array of environmental and health risk perception
contexts may provide more richness in understanding how risk contexts impact food consumer
acceptance.
In the end, the results reported here should be accounted for by a wide range of researchers
and practitioners working on research and development for HUDF deployment. Specifically,
people working in agricultural supply chains, resource managers working to improve nutrient
recycling efforts and wastewater systems, and sanitation practitioners considering new
infrastructure could use this work to deepen their understanding of consumers’ perceptions of
HUDF. And because consumers’ acceptance of HUDF seems strongly tied to intuitive perceptions
of risk and benefit, an important next step is new research on risk communication efforts aimed at
informing decisions about using and consuming agricultural products grown with HUDF.
34
Chapter II
Communicating the Risks and Benefits of Human Urine-Derived Fertilizer
Introduction. One of the most pressing natural resource challenges facing the world today
is ensuring long-term access to nitrogen and phosphorus (Frissel, 1978). These nutrients are vital
ingredients in fertilizers that have become essential in global agricultural systems(Ashley et al.,
2011; Stewart et al., 2005). Access to nitrogen and phosphorus is a growing concern, in part due to
phosphate rock being a non-renewable resource (Smil, 2000). Adding to the challenge, humans’
use of nitrogen and phosphorus is inefficient. For example, in a study conducted in the Detroit,
Michigan, only 29% of applied phosphorus reached the consumer, and 64% of nitrogen was lost
through fly ash, wastewater effluent, and solid waste incineration (Liang et al., 2019). Due to the
severity and timeliness of these challenges, engineered solutions for recovering and recycling
nitrogen and phosphorus from wastewater are emerging.
One of these solutions, human urine diversion and recycling, is emerging as a viable and
energy-efficient means of recovering nitrogen and phosphorus (and potassium) from wastewater;
the added benefit of this approach is that it yields nutrients for manufacturing agricultural fertilizers
that mirror conventional options (Kalmykova et al., 2012). Diverting and recycling human urine to
use as an agricultural fertilizer increases the sustainability of food production (Mihelcic et al., 2011;
Wilsenach & Van Loosdrecht, 2003; Wu et al., 2016) while also protecting waterways from
nutrients and micropollutants which can be more easily removed from the concentrated urine than
after it is diluted into wastewater. These human urine-derived fertilizers (HUDF) can be tailored to
match the soil types and agronomy practices of sizable swatches of the world, including about half
of North America (Trimmer et al., 2019).
The technology required for diverting, recycling, and transforming human urine into
fertilizer has been developed and is currently being implemented in a series of small-scale pilot
35
initiatives (Christiaens et al., 2019; Larsen et al., 2001; Lind et al., 2001; Simha et al., 2018b;
Tarpeh et al., 2017; Udert & Wächter, 2012). However, the deployment of infrastructure for urine
diversion and HUDF production at scale will require retrofitting or replacing both public and
building-size wastewater systems. While technically feasible, retrofitting and rebuilding these
systems is unlikely without broad public acceptance of HUDF use.
A relatively small number of studies on public acceptance of HUDF point to relatively high
levels of receptivity to HUDF among users the fertilizers themselves (e.g., farmers and other
growers) and among users of the infrastructure required to collect human urine (e.g., people willing
to use modified toilets). Most of these studies have relied on qualitative methods, have been based
on small samples of consumers and users, and have focused on HUDF deployment in Europe
(Lamichhane & Babcock Jr, 2013; Lienert & Larsen, 2010; Lienert et al., 2006; Simha et al., 2018a;
Simha et al., 2017).
However, a recent quantitative study conducted in the United States with a large,
representative sample of American consumers (Segrè Cohen et al., 2020) supports the assertion
that public receptivity to HUDF may indeed be high. This research highlighted broad consumer
openness to HUDF, especially when it was compared with other common fertilizers. But, in this
study, consumer support for HUDF did vary by end use; specifically, receptivity to HUDF was
highest for crops that are not intended for human consumption (Segrè Cohen et al., 2020). This
research also showed that public and consumer knowledge about urine diversion and recycling
leading to the production and use of HUDF is low (Lamichhane & Babcock Jr, 2013; Palm et al.,
2004). Taken together, these findings suggest obstacles to the more widespread deployment of
nutrient recapture and recycling via human urine diversion. Specifically, as consumers hear more—
but don’t necessarily learn more—about the technology, they may sour on the acceptability of
HUDF for use on crops for human (and perhaps even non-human) consumption.
36
This phenomenon is not new in the domain of emerging technologies. Rapid and precipitous
declines in public acceptability ratings have been observed for a wide range of emerging
technologies. These declines are usually accompanied by growing public awareness of these
technologies alongside social risk amplification (Kasperson & Kasperson, 1996; Kasperson et al.,
1987). Examples include nanotechnology (Siegrist et al., 2007), carbon capture and storage
(L’Orange Seigo et al., 2014), atmospheric geoengineering (Visschers et al., 2017), wastewater
recycling (Fielding et al., 2019), nuclear power (Gamson & Modigliani, 1989), and nuclear waste
disposal (Flynn et al., 1992), among others. For each of these technologies, researchers and
practitioners have argued that better risk communication could have helped to avoid the
controversies about them that ensued.
The overreaching goal of risk communication for emerging technologies is to facilitate an
understanding of their underlying processes, benefits, and risks (including how they are assessed
and may be managed) so that people can make more informed and defensible judgments and
decisions about them (Árvai, 2014; Frewer, 2004). When done well, risk communication addresses
cognitive gaps by correcting misunderstandings (Fischhoff, 1995). Successful risk
communication—in that it is informative and supports judgment and decision-making—depends,
however, on whether it addresses consumers’ information needs by being both content-specific and
accessible.
In terms of content, risk communication should present relevant risks, benefits, and
uncertainties clearly (Fischhoff, 1995). The information must also be accurate and thorough such
that it increases understanding. Most importantly, for risk communication to be effective, it must
be responsive to the needs of the user (Frewer, 2004). This is not to say effective risk
communication messages are overloaded with technical details; indeed, information-heavy risk
communication does not necessarily lead to more informed participants or better decision-making
37
(Árvai, 2014). Rather, designers and practitioners of risk communication must be attentive to
essential gaps in understanding about risks and must design content that both fills these gaps and—
importantly— reinforces existing knowledge (Árvai, 2014; Arvai & Campbell-Arvai, 2013;
Morgan et al., 2002). At the same time, risk communication that accounts for the established
communication norms of its participants, alongside creative messaging, is more likely to capture
people’s attention thereby increasing the likelihood that the information conveyed is both
understood and put to use (Árvai, 2014). In this decade for example, risk communication efforts
that take advantage of social (e.g., Facebook, Twitter, YouTube, etc.) and digital (e.g., videos,
podcasts, etc.) media are seen by many as more accessible than printed materials (Panagiotopoulos
et al., 2016; Rutsaert et al., 2013).
When designing risk communications, it is equally important to note that people from
different generations and life stages, as well as those with different educational levels, typically do
not process information in the same way. Thus, risk communication efforts around a single topic
may require different risk messaging approaches such that they are carefully tailored to the needs
of different groups. For example, young children and elderly adults are uniquely sensitive to the
quantity of information provided, and may benefit from more straightforward and succinct
materials (John & Cole, 1986).
In addition, how information is disseminated may affect the degree to which information in
risk commination is internalized. For example, in medicine, patients who watch videos (vs. reading
text) about treatment options have been shown to be more aware of both the risks and benefits
associated with them (Krouse, 2001). Similarly, videos—in contrast to printed materials—have
increased individual’s understanding of the risks from climate change (Goldberg et al., 2019). Other
work also shows that risk messages that include either videos or photos influence risk perceptions
more strongly than risk messages that rely on text alone (Visschers et al., 2008). Thus, different
38
risk communication strategies (e.g., presenting information via video, text, or in-person) as well as
individual’s demographic factors may have moderating effects on the usefulness (e.g., by
improving recipients’ understanding of the risks, or leading to acceptance) of risk communication
about HUDF.
In addition to the risk communication strategy employed, and the end use of HUDF,
receptivity to it by consumers and other stakeholders may also depend upon a host of psychological
considerations. They include cognitive variables such as risk and benefit perceptions (Slovic, 2000)
and perceptions of naturalness (Verhoog et al., 2007), as well as visceral factors like disgust (Rozin
& Fallon, 1987) and ideological aspects like value orientations (De Groot & Steg, 2008). Risk and
benefit perceptions are regularly studied together to predict technology acceptance (Bearth &
Siegrist, 2019; L’Orange Seigo et al., 2014; Visschers et al., 2017); low levels of perceived risk
tend to lead to high levels of acceptance, and vice versa (Alhakami & Slovic, 1994). Within the
context of HUDF, prior research suggests that risk and benefit perceptions are strong predictors of
acceptance for different categories of end-use (e.g., crops intended for human consumption) (Segrè
Cohen et al., 2020).
In addition, people tend to prefer natural options (e.g., when considering consumer
products, or when evaluating such things as manufacturing processes and disturbances) over
artificial or anthropogenic ones (Campbell-Arvai, 2019; Siegrist, 2008). This preference can be
traced to humans’ inherent attachment to nature. This partiality can be so strong, in fact, that it
creates a bias wherein people may opt for what is framed as “natural” over otherwise identical
artificial engineered options (Campbell-Arvai, 2019). When assessing perceptions of naturalness
and fertilizer preferences, participants in one study had an overwhelming preference for organic
fertilizer and rated synthetic fertilizer as both the least natural and the least acceptable option (Segrè
Cohen et al., 2020). HUDF was rated in between these two options, as somewhat natural and
39
somewhat acceptable. How various information about HUDF is portrayed, and whether there is
emphasis put on HUDF’s source material, namely human urine, may impact consumer perceptions
of naturalness, and in turn, acceptance.
We also know that intuitive, fast acting emotions play a role in our perceptions, judgements,
and ultimately our decisions (Finucane et al., 2000a; Wilson & Arvai, 2006). One of these
emotions, disgust, is especially relevant to research on food and food-related products. Disgust has
an instinctive and rapid response linked to evolutionary origins. It historically served as a
mechanism to keep animals ingesting harmful items (Curtis et al., 2011). We know from both
research (Curtis et al., 2011; Rozin & Fallon, 1987) and personal experience that human waste is
typically disgust inducing. However, it remains to be seen if nutrients extracted, and fertilizers
manufactured from human urine would be viewed as disgust inducing and if this, in turn, would
negatively affect both risk communication efforts and consumers’ receptivity to HUDF.
Finally, values serve as guides for judgments and decisions about emerging technologies
such as nuclear power (Whitfield et al., 2009), nanotechnology (Siegrist et al., 2007), genetically
modified organisms (Finucane, 2002), and solar radiation management (Visschers et al., 2017).
Specifically, different value orientations (e.g., altruism, biospherism, and egoism) have been shown
to lead to different degrees of receptivity to pro-environmental behavior and technologies (Stern et
al., 1999). For example, those who ascribe strongly with altruistic (i.e. a high level of concern for
others) and biospheric (i.e. a high level of concern for the natural environment) values compared
to those with egoistic (i.e. a high level of concern for one’s self and personal well-being) value
orientations tend to exhibit more pro-environmental behaviors and tend to be more accepting of
technologies and policies with high environmental benefits (L’Orange Seigo et al., 2014).
With this as backdrop, the objective of this research was to study the influence of alternative
risk communication approaches on peoples’ ratings of the usefulness of the information contained
40
in them, and on their acceptance of HUDF for use in food production. Specifically, this study
focused on how watching a risk communication video vs. reading a text-based presentation
containing the same information may influence consumers’ acceptance of HUDF. We were also
interested in the effect of the length of these videos and texts would influence consumers’
acceptance of HUDF. In addition, we studied the potential moderating effects of age, education,
and individual characteristics like food disgust sensitivity and value orientations impact the
usefulness of the risk communication messages.
Materials and Methods
Participants. Participants were residents of the United States over the age of 18 recruited
from a representative internet panel sourced by Qualtrics. Data collection took place in 2019. Quota
sampling was applied to balance gender, age, and to randomly assign participants into one of five
conditions. Initially 6,814 participants respondent to the instrument. A total of 3,464 participants
were removed by Qualtrics because they did not give their informed consent (n = 2,711); because
they did not complete the study (n = 743); or because they began the survey after quotas were
achieved (n = 10). An additional 873 participants were removed because they failed a series of
instructed choice attention checks (n = 400); because they spent less than half the median time
(00:04:01) on the instrument (n = 264); or because they selected the same response on all items of
a 13-item scale and/or an 8-item scale (n = 209). The final sample size was 2,477, which provided
adequate statistical power (Faul et al., 2009).
Across the five risk communication strategies, 50% of respondents were women; the mean
age of participants was 40-48 years and the median education level corresponded with self-reports
of “associate degree” (Appendix C, Table 1). These demographic characteristics are emblematic of
an “average” American (US Census Bureau, 2017).
41
Design. To address our research objectives, participants first provided informed consent
1

and then were randomly assigned to one of five different risk communication treatment groups: (1)
a control comprised of a brief, two-sentence description of agricultural fertilizers and HUDF; (2)
a 6.5-minute animated video; (3) a short version (3.7 minutes) of the video used in treatment 2; (4)
information in text form based on the complete script for the long video used in treatment 2; and
(5) information in text form based on the complete script for the short video used in treatment 3.
Each of the four risk communication strategies (apart from the control) provided detailed
information about why fertilizers are used in agriculture, technological advancements leading to
human urine diversion and nutrient recycling, HUDF production, HUDF as an alternative to
conventional fertilizers, and information about the benefits, risks, and uncertainties associated with
HUDF use. The information in the videos used in strategies 2 and 3 was conveyed by an animated
narrator (“Uri”) who was portrayed as anthropomorphized drop of human urine
2
. The videos and
related texts were developed by Linda Macpherson and reviewed for content and accuracy by a
group of geoscientists, ecologists, and agronomists from a multidisciplinary study team associated
with other elements of this HUDF research initiative.
At the start of the instrument, the risk communication treatments were immediately
followed by an attention check to determine whether respondents could correctly identify the
fertilizer type they just learned about. The same survey instrument was then shown to each
participant, regardless of their treatment group.
To study the effects of the risk communication treatments on participants’ acceptance of
HUDF, we posed questions for four distinct end-uses. Specifically, we asked for participant’s level

1 This study was approved by the Health Sciences and Behavioral Sciences Institutional Review Board (protocol number
HUM00156157) at the University of Michigan.
2 The choice to use an animated video featuring Uri as the narrator was made before this research was designed and conducted.
Though the video was designed with best practices in risk communication in mind, it was produced to inform external
stakeholders and partners about the broader NSF-funded initiative (under the Innovations at the Nexus of Food, Energy and Water
Systems program) to study nutrient recycling and HUDF. It was later, when the first author joined the project, that we decided to
use the video as the basis for a broader study on risk communication and consumer acceptance of HUDF.
42
of agreement with the statement It is acceptable to use urine-derived fertilizer to grow [non-edible
plants like flowers]; the remaining three questions asked about fruits and vegetables that will be
eaten by only animals; plants, trees, and grasses that grow around my home; and fruits and
vegetables for people to eat. Responses for all four questions were collected for each fertilizer type
on 7-point Likert scales from “strongly disagree” to “strongly agree” (midpoint = “neither disagree
nor agree”). The three statements about HUDF use on agricultural products that are not intended
for human consumption were combined to create an index (Cronbach’s α = 0.85).
Next, we posed a series of questions focused on how useful participants found the different
risk communication formats to be. Specifically, participants were asked to respond to the following
statements on 7-point Likert scales ranging from “Not at all” to “Extremely” (the midpoint was not
labeled): (1) I found the information about urine-derived fertilizer to be interesting; (2) I found the
information about urine-derived fertilizer to be informative; (3) I found the information about
urine-derived fertilizer to be frustrating; and (4) I found the information about urine-derived
fertilizer to be worrisome. Responses to questions 3 and 4 were reverse coded; all responses were
combined to create an index of “usefulness” (Cronbach’s α = 0.77).
The effect of the risk communication treatments on perceived risks and benefits of HUDF
were measured using four questions; two questions concerned risks and benefits to environmental
health, and two questions concerned human health. Responses for all four questions were collected
on 7-point Likert scales from “strongly disagree” to “strongly agree” (midpoint = “neither disagree
nor agree”). For perceived risk, we asked for participant’s level of agreement with two statements,
which were combined to create a scale (Cronbach’s α = 0.87): (1) Urine-derived fertilizer will be
harmful to my health and the heath of my family and (2) Urine-derived fertilizer will be harmful to
the environment. Similarly, for perceived benefits, we also asked for participant’s level of
agreement with two statements, which were combined to create a scale (Cronbach’s α = 0.88): (1)
43
Using urine-derived fertilizer will result in foods that are healthier for people and (2) Using urine-
derived fertilizer will be healthy for the environment overall.
With respect to individual characteristics that might moderate the effectiveness of different
risk communication strategies, we focused on two main co-variates. First, we assessed participants’
level of food disgust sensitivity using the 8-item Food Disgust Scale (Cronbach’s α = 0.76)
developed and validated by Ammann and colleagues (2018). We did so because the idea of
fertilizers derived from human urine may prompt food-related disgust, and we wanted to test for
the effect of these feelings on different risk communication strategies. Responses to these 8-items
were collected on 6-point Likert scales ranging from 1 = “Not disgusting at all” to 6 = “Extremely
disgusting”; the midpoint was not labeled. The final scale measured participants’ value orientations,
specifically as they related to the environment and sustainability. Here, participants responded to
questions used for the biospherism subscale (four items that focused on one’s consideration of
environmental well-being; Cronbach’s α = 0.91) of the value orientations battery developed by de
Groot and Steg (2008). Lastly, we asked for demographic information from participants.
Analysis. We conducted analyses of variance with Tukey’s post-hoc comparisons to detect
differences between the control group and other communication strategies across the main study
variables: usefulness of the communication strategy, perceptions of risk and benefit, and ratings of
acceptability. To account for the prospect of Type II errors due to multiple comparisons, we applied
a Bonferroni correction; thus, the p-value required for significance in the ANOVAs and post-hoc
assessments was set at 0.01. In addition, we conducted moderation analyses through hierarchical
linear regression to assess how age and education impacted perceptions of usefulness differently if
participants read text or watched videos.
We also performed a hierarchical linear regression to assess how the different risk
communication strategies alongside demographic factors (age, gender, and education), value
44
orientations, food disgust sensitivity, and perceptions of HUDF’s risks, benefits, and naturalness
explained ratings of acceptance of HUDF. This regression only explored acceptance of HUDF use
on products intended for human consumption. The first step included the risk communication
treatments and demographic characteristics, the second included value orientations and food
disgust sensitivity, and the third added the risk, benefit. and naturalness perceptions.
Results
Comparisons of communication strategies. When assessing the usefulness of the different
risk communications about HUDF, an ANOVA detected a main effect of risk communication
strategy (F (5,2989) = 30.06, p<0.0001); see Appendix C, Table 2. All communication strategies
were perceived to be at least positively useful (all means being above the midpoint, zero). However,
post hoc testing exposed significant differences between the control and all other communication
strategies; the control had the least useful information. There were also statistical differences
between all other strategies except between the short video and long video and the short text and
long text. The short video was, on average, perceived to provide more useful information compared
to both the short and long texts.
An ANOVA (Appendix C, Table 2) also revealed a significant main effect of risk
communication strategy on risk perceptions (F (5,2988) = 22.46, p < 0.0001). Post-hoc testing
discovered significant differences between the control and the short video, long video, and long
text, wherein HUDF was perceived riskier to participants who read the control. There were no
statistical differences between the control and short text, nor among the other strategies. An
ANOVA (Appendix C, Table 2) also found a significant main effect of risk communication strategy
on benefit perceptions (F(5,2988) = 37.15, p < 0.0001). Post hoc tested revealed statistical
differences between the control and all other communication strategies; those in the control group
45
perceived HUDF, on average, to have lower environmental and human health benefits. Both video
groups also received statistically higher benefit ratings than the short text group.
Considering the acceptance of HUDF, an ANOVA detected statistically significant main
effects of both non-edible (F (5,2988) = 16.90, p<0.0001) and edible uses (F(5,2989) = 25.83, p <
0.0001); see Appendix C, Table 2. The use of HUDF on non-edible crops was preferred more
highly to the use of HUDF on crops intended for human consumption across all communication
strategies. Post hoc testing showed that for both end-uses of HUDF, the control prompted the lowest
ratings of acceptance when compared to the alternative risk communication strategies. Reading the
short text also yielded significantly lower levels of acceptance than watching either of the videos.
Moderating variables: Strategy, age, and education level. When assessing the usefulness
of the risk communication strategies, the text strategies were not distinct from each other, nor were
the video strategies (Appendix C, Table 2). For this reason, both the short and long texts were
combined into a single “text” variable, and the short and long videos were combined into a single
“video” variable to understand differences between the two modes of communication strategies.
Age was divided into five categories: 18-29, 30-39, 40-48, 49-59, and 60+ (per Appendix C, Table
1). A hierarchical regression was conducted to determine how age and risk communication strategy
affected the usefulness of the information presented. The first step of the regression tested the
hypothesis that usefulness is a function of both age and strategy. These variables were both
statically significant (p<0.05). Next, the interaction of strategy and age was included, which was
significant (p =0.042). With the interaction term included, there was a full moderation effect;
neither communication strategy, nor age alone, was statistically significant. The interaction plot
(Appendix C, Figure 1, pane A) shows an increased effect of video on usefulness regardless of age
group, but especially so for older individuals (49 and above).
46
A second hierarchical linear regression was conducted to test the hypothesis that education
and risk communication strategy would affect usefulness. Education was categorized as low (no
high school, some high school, or graduated high school/GED), medium (some college or associate
degree), or high (bachelor’s degree or graduate/professional degree) per Appendix C, Table 1. The
first step of the regression tested whether strategy and education were predictors of usefulness
(pstrategy < 0.000; ploweducation = 0.000; pmediumeducation = 0.001; phigheducation = 0.002). The second step
involved an interaction between strategy and education level. There was no moderation effect; the
relationships between strategy and medium education (p = 0.848) or high education (p = 0.873)
were not statistically distinct from the reference group (low education, p<0.000). Once the
interaction term was included, education levels remained significant (pmedium = 0.010 and phigh =
0.042). The interaction plot (Figure 1, pane b) indicates that video was generally preferred across
all education levels, and those with low education found both communication strategies to be the
least useful.
Predictors of HUDF acceptance. The hierarchical linear regression assessing consumer
acceptance of HUDF for use on products intended for human consumption (Appendix C, Table 3)
was divided into three steps. The first step consisted of only demographic factors (age, gender, and
education), as well as dummy variables for the other strategies (with the control group being the
baseline). Gender was statistically significant with a small effect size (η
2
= 0.02) in that men were,
on average, more accepting of HUDF use than women
3
. Education was also positively correlated
and statistically significant with a small effect size (η
2
=0.003). In this step, all four strategies were
statistically distinct from the control group and had higher levels of acceptance. The second step
included intuitive and ideological factors involved in people’s understanding acceptance, namely
peoples’ values and their sensitivity to disgusting food items. Gender continued to be significant

3A third category of gender was included in the survey results (non-binary, third gender). This category was not statistically
distinct and had a small enough sample size that it was excluded from the analysis (n = 39).
47
with a small effect size (η
2
=0.001). Egoism and biospherism both had significant positive
relationships with acceptance with small effect sizes (η
2
=0.004 and η
2
=0.02, accordingly). Food
disgust sensitivity was also a significant predictor of acceptance (η
2
=0.03) with an inverse
relationship. Specifically, higher food disgust sensitivity led to lower levels of acceptance for
HUDF use on crops intended for human consumption.
The third step went beyond solely instinctive (i.e., affect-laden) factors to incorporate
cognitive components of acceptance, specifically perceptions of the risks, benefits, and naturalness
of HUDF. We considered these variables as being more cognitive in nature because each
communication strategy (except the control), had information regarding HUDF’s risks, benefits,
and naturalness. These three variables were all significant predictors of acceptance with varying
effect sizes (η
2
risk= 0.05; η
2
benefit=0.29; and η
2
naturalness=0.01). As expected, risk and benefit
perceptions were inversely related to each other, wherein benefit perceptions had a positive
relationship with HUDF acceptance and risk perceptions were negatively correlated. Gender and
egoism remained significant in the third step both with a small effect size (η
2
= 0.01 and η
2
= 0.002,
respectively). Additionally, age became statistically significant and positively correlated with
acceptance, albeit with a small effect size (η2 = 0.004). In this last step of the regression, only the
long video and the short video strategies were significantly distinct from the control.
Discussion
The usefulness of HUDF risk communication. We observed statistically significant
differences between the control group and all other risk communication strategies: short and long
text, and short and long videos. Except for the mean risk perception score following exposure to
the short text, all strategies outperformed the control across all the variables we studied (Appendix
C, Table 2). There were also some notable distinctions between the various risk communication
strategies, namely that the videos—and, specifically, the short video—outperformed both the short
48
and long texts in participants’ ratings of usefulness, perceived benefit of HUDF, and acceptance of
HUDF use for use on plants and crops not intended for human consumption.
These findings build upon prior research; for example, other studies have shown that risk
communication videos can outperform text containing the same information when it comes to
improving people’s understanding of novel topics and technologies, mitigating their perceptions of
risk, and helping people to make better informed decisions (Goldberg et al., 2019; Krouse, 2001;
Ludwig et al., 2018). In addition, videos have been shown in other research to both increase the
level of engagement between people and the material being presented to them, as well as knowledge
retention, when compared to people responding to the same information in written form (Yadav et
al., 2011).
These results also align with other research which found that videos, compared to other
communication strategies, may be more effective to address consumer interests around how food
is produced (Musto et al., 2015). This may be even more pertinent to a novel food or food-related
product, like HUDF. Moreover, narrated videos, compared to texts, may improve consumer
understanding, and help consumers make informed choices around food (Musto et al., 2015). Not
only are videos more effective at increasing knowledge, but participants also rated videos
(compared to written materials) as more useful (Idriss et al., 2009). Videos may have led to a higher
acceptability of HUDF than texts because of the multiple sensory experience videos have. Hearing
and seeing material in tandem may be more convincing than just reading identical information.
Other work suggests that videos may indeed be more helpful at building consumer trust toward
food production (Musto et al., 2015).
In addition to studying how risk communication strategies effect perceptions of usefulness,
we also sought to better understand how certain individual characteristics might also shape these
perceptions. We found that the interaction between age risk communication strategy (text vs. video)
49
fully moderated ratings of usefulness. In general, younger participants found both text and video
strategies less useful than older participants. However, ratings of the usefulness of the risk
communication videos were significantly higher than for text among older participants (Figure 1).
These results confirmed our hypothesis that older individuals would find videos to be more useful
than their younger counterparts, and they align with prior studies that suggest age moderates the
effects of usefulness of when considering different risk communication strategies (Tarhini et al.,
2014). Older adults also seem to prefer succinct materials, and that they also especially find videos
more helpful than texts at explaining new information (John & Cole, 1986; Thomas et al., 1999).
This may because episodic memory retention is easier for older adults than other memory retention
options, and it is easier to activate episodic memory through videos over text (Garg et al., 2012).
We did not find ratings of usefulness for the different risk communication strategies to be
moderated by self-reported education level. We initially hypothesized that those with lower levels
of education may perceive the videos to be more useful than texts. Particularly, because videos
have been shown to be easier to understand and increase information retention and knowledge,
especially for populations with lower levels of education or literacy (Calderon et al., 2006; Murphy
et al., 2000). However, much of the literature on this topic is related to informing medical patients
in clinical practices. In that domain, general education level may in fact be a main driver of
usefulness of communication strategy. Within the domain of HUDF, education may not be as strong
as a driver because the topic is so novel, whereas the notion of medical procedures is not. It may
also be the case that asking about domain-specific knowledge, like familiarity with fertilization
practices and farming, may have been a better indicator than general education levels.
Risk communication and HUDF acceptance. Our hierarchical regression results indicated
that perceptions of both HUDF risk, and especially benefit, were the strongest predictors of HUDF
acceptance; this finding is in line with prior research (Poortvliet et al., 2018; Segrè Cohen et al.,
50
2020). However, there were other variables that also played an explanatory role in HUDF
acceptance. We hypothesized based on other research that biospherism and egoism would both be
related to acceptance (De Groot & Steg, 2008; Poortvliet et al., 2018), as the former is emblematic
of concern for the environment and the latter is emblematic of a deep a care for personal well-being
to which consumption is closely tied. In the third step of the regression, egoism was the only
statistically significant value orientation. In hindsight, this relationship makes intuitive sense
because consumers could continue to act their usual ways, but the ingredients of their purchases
would change. This technological change, that would not require individuals to act differently, may
be appealing to those with higher levels of egoism, as it aligns with values of self-interest (Visschers
et al., 2017).
We also hypothesized that people exhibiting higher levels of food disgust sensitivity would
exhibit lower levels of HUDF acceptance, and that people who associated higher levels of perceived
naturalness with HUDF would be more accepting. Both hypotheses were supported, albeit with
small effect sizes (η2 = 0.01). Perceived naturalness was positively related to acceptance of HUDF,
which supports a trend found in a recent systematic review of this variable and food technology
acceptance (Román et al., 2017). Perceived naturalness can be understood through an product’s
origin, production, and final outcome (Román et al., 2017). It makes sense, then, that participants
perceived HUDF as natural, since the ingredients, final product, and in some cases, the production
process as well, are all accomplished through “natural” means.
As a counterweight to perceived naturalness, we also included food disgust sensitivity as a
covariate in our model. Our results on disgust sensitivity were in line with other research, where
this variable regularly has an inverse relationship with food technology acceptance, and especially
for products that that can easily be linked to waste (e.g. human urine derived fertilizer, in the case
of this research) (Ammann et al., 2018; Curtis et al., 2011; Segrè Cohen et al., 2020). As we note
51
in the introduction, while human urine may be natural, it may also trigger feelings of disgust as it
relates to waste. It seems that HUDF was indeed disgust inducing, and this disgust negatively
affective risk communication efforts and consumers’ receptivity to it.
Age was a positive and statistically significant predictor of acceptance in in our research.
Some studies have shown that older individuals are more open-minded and accepting of new
technologies (Arning & Ziefle, 2009), though findings linking age to acceptance have been mixed
(Hauk et al., 2018). These findings, combined with the results of our mediation analyses, indicate
that more work may be needed to address how different age groups may respond to risk
communication strategies about novel technologies, and specifically HUDF.
Conclusions
Human urine-derived fertilizer is not yet widely available for consumers or farmers to
purchase. Because of this, the present study focused on the usefulness of the communication
strategy and general acceptance, rather than consumer behavior. However, if HUDF does come to
the market, future studies should explore how different communication strategies that include
perceived efficacy message may change behavior over just perceived acceptance. Efficacy, in this
context, refers to people’s sense of being able to do something to either produce a positive outcome
or prevent a negative one (Bandura, 1977; Basil & Witte, 2012). As one becomes more familiar
with a product, it is likely that one’s self-efficacy around it increases (Rogers, 2003). Messages,
therefore, that frame a product as something that people are more familiar with, that they have
encountered before, and provide ways to use it effectively, may have a higher likelihood of
influencing both hypothetical acceptability and actual behavior change.
In the end, the results from this study may be of use to not only risk communication
researchers, but also practitioners interested in developing educational materials for the public on
waste related technology, and specifically HUDF. These results are promising, as they indicate
52
general open mindedness to HUDF, especially when the information is communicated in a succinct
and video-based format. Overall, our results illuminated the fact that the short and long videos
were the most useful risk communication strategies, and that HUDF was most accepted by
individuals who watched either video over those individuals who read either text. In addition, this
research may serve as a jumping off point for future studies focused on how risk communication
strategies may affect consumer acceptance of other novel food technologies, like cultured meat,
insect protein, and nanotechnology.

53
Chapter III
I think, therefore I act: The influence of critical thinking ability on social trust and behavior
during the COVID-19 pandemic
Introduction
Many countries continue to struggle with the COVID-19 pandemic. Between when the
pandemic was declared by the World Health Organization in March 2020 and the time of this
writing (in June 2021), nearly 175 million people have been infected worldwide, and nearly 4
million people have perished. Adding to the tragedy, the case count and numbers of fatalities remain
high in many countries
4
. And, though several different vaccines are either in development or have
been granted emergency approval for use, their rollout in many countries has thus far been slow or
clumsy.
Even though widespread access to one of several vaccines is beginning to emerge,
managing the health risks associated with the SARS-CoV-2 virus that causes COVID-19 also
depends upon behavioral measures that can be taken by individuals. Recommendations aimed at
reducing the risks to individuals from COVID-19 have been agreed upon widely. They include
frequent handwashing; avoiding touching one’s eyes, nose, and mouth; regularly disinfecting
frequently touched surfaces; covering coughs and sneezes; avoiding spaces that are closed or
confined, crowded, or involve close contact with others; avoiding contact with people who are ill
or symptomatic; and covering one’s mouth and nose with a fabric mask or face cover when in
public spaces.
Despite this, there has been a broad and persistent misunderstanding of the transmission of
the SARS-CoV-2 virus and the symptoms of COVID-19 (e.g., Chesser et al., 2020; Krause et al.,
2020), and engagement in personal protective actions has been limited in many areas. As a result,

4
For up-to-date data on COVID-19 infections and mortality, see: https://covid19.who.int
54
many localities have faced extraordinarily high infection and mortality rates since the pandemic
was first declared; the United States is among the most high-profile examples. And while
conditions in the United States are finally improving, nearly 33 million American have been
infected with COVID-19 and over 600,000 have died. A variety of experts and on-the-ground
responders have argued that many of these infections and deaths in the United States could have
been avoided through a better coordinated and science-based response across all levels of
government, and—importantly—through higher levels of public compliance with the behavioral
recommendations outlined above
5
.
For these reasons, researchers and medical practitioners have emphasized since the
beginning of the pandemic that communicating only about the science and epidemiology behind
COVID-19 is insufficient; what is also required are clear consistent messages, delivered by trusted
messengers, which emphasize the preventative behaviors that should be undertaken individuals
(Abrams & Greenhawt, 2020; Kim & Kreps, 2020; WHO 2020).
Indeed, trust in the sources of risk information is a critical component of efforts aimed at
keeping people safe during a pandemic. This is the case because, in the absence of more in-depth
epidemiological knowledge, trust serves as a sort of heuristic that facilitates action based on the
degree to which a trusted individual or organization recommends it. For example, during and after
prior epidemics such as the H1N1 (swine flu) outbreak in 2009, trust in medical professionals and
government risk managers predicted risk perceptions and an increase in preventative behavioral
measures by members of the affected public (Siegrist et al., 2021).
Recent research on COVID-19 specifically points to the likelihood that trust in science,
along with other variables such as value orientations, may also positively influence risk perceptions

5

5
See the final report of the Independent Panel for Pandemic Preparedness and Response:
https://theindependentpanel.org/mainreport/

55
(Dryhurst et al., 2020). Related, higher levels of perceived COVID-19 risk may positively influence
the adoption of risk mitigation behaviors (Bavel et al., 2020; Motta Zanin et al., 2020). However,
Siegrist and colleagues (2021) remind us that trust in science per se is likely only a weak proxy for
trust in risk managers, just as generalized scientific knowledge is a weak proxy for domain-specific
scientific knowledge (Siegrist & Árvai, 2020). Instead, social trust may be a more important factor
because it accounts for the opinions of people regarding public health authorities and institutions.
Indeed, Siegrist and colleagues (2021; 2021) observed a significant, positive relationship between
social trust and COVID-19 risk perception in a pair of studies conducted in Switzerland. Based on
the theory that trust may serve as a heuristic, one may surmise that a high degree of social trust
might lead to elevated perception and—in turn—higher levels of compliance with
recommendations intended to protect people
6
.
It is important to note, however, that social trust does not materialize in a vacuum. Prior
research suggests that the underlying driver of social trust is salient value similarity (Siegrist et al.,
2000). In this model, people will judge an organization to be trustworthy if they perceive that the
organization shares certain, situationally relevant (i.e., salient) values with them. This same path
applies to judgments about the trustworthiness of individual actors. It is also noteworthy that
judgments about the similarity of salient values is more reflexive than it is reflective (Wilson &
Arvai, 2006). That is to say, these judgments are intuitive in nature, and are not the product of an
internally consistent (Bessette et al., 2019) exercise in first accounting for and then making
comparisons across the values of individuals and organizations.

6
This contrasts with general trust, which is exemplified by the belief in the benevolence of others. A high degree of general trust
may have a negative influence on people's COVID-19 risk perceptions, a negative effect on the social acceptability of pandemic-
related risk management efforts, and no effect on compliance with public health recommendations. In other words, if one
generally trusts others around them to do the right thing, then concerns about risk—and the need to adopt personal behaviors to
manage these risks—may be dampened.
56
But, at the same time, our prior research on judgment and decision-making suggests that
cognitive ability—which can be defined by how well people understand (Drummond & Fischhoff,
2017a; Drummond & Fischhoff, 2020) and use (Bessette et al., 2016; Bessette et al., 2019) techno-
scientific data when making judgments and decisions—may also be important when people make
judgments about whom to trust. It stands to reason, then, that thinking critically about data and
evidence will also lead people to think critically about where the data came from (e.g., reputable
scientific bodies vs. sources of pseudo-science). Thus, beyond salient value similarity (which is
reflexive), we hypothesize that higher levels of critical thinking ability (which is reflective) will
also positively affect social trust; this, in turn, will positively affect (A) risk perceptions and (B)
compliance with government recommendations aimed at protecting people from the COVID-19.
We draw these hypothesized connections—A and B—because public health agencies in the
United States (e.g., the CDC) have been consistent in their messaging that the risks posed by
COVID-19 are real and high (relative to other viral pathogens such as influenza B), and that people
must take precautions (mask wearing, social distancing, etc.) to protect themselves. Moreover, prior
research points to a connection between critical thinking ability, risk perceptions, and related
decisions. Specifically, research on decision support suggests critical thinking skills may help both
children and adults more deeply consider risks, and make higher quality—i.e., more substantively
rational—judgments and decisions about them (Gregory, 1991; Lutzke et al., 2019; Slovic et al.,
1987). Critical thinking ability may also help to dampen the influence of cognitive biases during
decision-making (Baron, 2000). This may be the case because critical reasoning leads people to
hold information to a higher standard (Lutzke et al., 2019) as well as to seek out other information
(Stenhouse et al., 2018) when thinking through a problem before they respond to it.
One approach to studying critical reasoning ability is to assess the degree to which people
identify with or exhibit the tenets of actively open-minded thinking (Baron, 2019; Haran et al.,
57
2013; Stanovich & West, 2007). Actively open-minded thinking (AOT) reflects the tendency to
evaluate information in a manner that is resistant to biases driven by prior beliefs or motivations
(Mellers et al., 2015). Actively open-minded thinkers have been observed to make more accurate
judgments about risks, and more evidence-based decisions under uncertainty in a wide range of
situations, such as climate change and politics (Baron, 2019; Haran et al., 2013; Stanovich & West,
1998). Individuals who score highly on scales used to measure AOT may also be less reliant on
motivated reasoning when forming opinions, regardless of their political ideology (Baron, 2019;
Stanovich & Toplak, 2019; Stenhouse et al., 2018).
AOT draws on decision theory, specifically regarding setting standards for the conduct of
objectives-directed thinking. Much like value-focused thinking (Arvai et al., 2001), it involves the
setting of parameters such as the attributes that will be used to establish a judgment or evaluate
alternatives (Árvai & Gregory, 2021), the relative priority of attributes (Bessette et al., 2019), level
of confidence that is ascribed to judgments as they are being formulated (Baron, 2019), or amount
of effort that ought be devoted to a judgment or decision before it is considered to be complete
(Johnson & Payne, 1985).
AOT is thought to consist of two processes. One of these is the search for counter-attitudinal
information. The other is the more effortful, active processing of this information. AOT is active
because people who engage in it take the initiative to identify evidence and, by extension, credible
sources of it. And AOT is open-minded because those who engage in it are open to changing their
opinions and judgments even if the information that first led to them seemed strong (Stenhouse et
al., 2018). Overall, individuals who score highly on the scales used to measure AOT tend to invest
more time and effort when searching for information upon which to base a judgment or decision.
And people who receive high AOT scores also tend to place a higher premium on considering the
opinions and insights of others’ while forming their own (Haran et al., 2013).
58
It is this last point that guides our thinking in the research reported here. Beyond governing
the process by which people formulate their own judgments, people who endorse or follow the
tenets of AOT may also “outsource” the job of actively open-minded thinking to others (e.g., to
technical experts). In other words, AOT may also be viewed as a standard for evaluating the
trustworthiness of a source of information. Mechanistically, individuals who ascribe to the tenets
of AOT may be more sensitive to—or may actively seek out—cues that are shared by people who
are in a position to provide information that they too are actively open-minded thinkers (Baron,
2019)
7
. These cues may take the form of explanations or behaviors which suggest that attributes
are being considered and prioritized, that sufficient time is being taken to draw conclusions, or that
alternative conclusions have been considered.
With this as backdrop, the research reported here focused on the relationship between AOT,
risk perceptions surrounding COVID-19, trust in experts, and compliance with CDC guidelines to
manage one’s personal risk (Appendix E, Figure 1). In undertaking this research, we drew on
evidence from prior pandemics which suggests that those who perceive health risks more acutely
are more likely to take countermeasures to avoid infection (Rudisill, 2013). Specifically, we
hypothesized that AOT will help people identify more credible sources of information about
COVID-19 (i.e., from “experts”), and subsequently place greater trust in them (H1). Because these
experts have been consistently messaging that COVID-19 is a real and serious threat to public
health, we also hypothesized that trust in experts would be positively associated with an increase
of risk perceptions (H2). Experts have also noted how the public should adopt CDC-recommended
guidelines to protect themselves from COVID-19, which should have a positive influence on (self-
reported) compliance with CDC recommendations (H3). Because AOT is a self-directed thinking

7
Thus AOT has three functions: (1)A AOT serves as a norm accounting for how one should think; (2) AOT is process that one
follows in accordance with the norm; and (3) AOT sets standards for evaluating the thinking of others, particularly the
trustworthiness of sources that claim authority Baron, J. (2019). Actively open-minded thinking in politics. Cognition, 188, 8-18.
https://doi.org/https://doi.org/10.1016/j.cognition.2018.10.004 .
59
style in which people are thought to take their time to weigh multiple sources of evidence, we also
expected it to directly influence risk perceptions and, by extension, compliance (H4). And we
explore whether high levels of social trust also influence compliance with recommended behaviors
aimed at protecting people and slowing the spread of COVID-19 (H5).
Methods
Participants. Data collection occurred during the initial wave of the COVID-19 pandemic
in the United States (May 7-13, 2020); data was collected using the YouGov survey platform and
panel. The survey instrument was sent to a nationally representative sample of United States
residents aged 18 and older. Initially, 1,000 participants responded, of which 143 participants were
removed because they failed an instructed-choice attention check. Within the final sample of 857
participants, the mean age was 49 years (SD = 18 years); the sample was 49% male, 72% white,
and 33% possessed a bachelor’s degree. In terms of political ideology, 32% of the sample was
liberal, 28% was moderate, and 30% was conservative. This sample was emblematic of the United
States population (United States Census Bureau, 2017).
Design. The University of Michigan Institutional Review Board determined this study to
be exempt from review. Prior to answering any questions related to our research, participants
received background information about the intent of our study and were asked to provide informed
consent. Participants were also asked if they had experienced any of the symptoms of COVID-19
(including fever; cough; shortness of breath or difficulty breathing; chills; repeated shaking with
chills; muscle pain; headache; sore throat; and/or new loss of taste or smell) since February 2020;
participants waiting for results from a COVID-19 test, and those who tested positive for COVID-
19 were excluded from the sample.
Prior to assessing participants’ level of compliance with CDC-recommended behaviors, we
first offered them the opportunity to review these recommendations online. Participants were told:
60
“The United States Centers for Disease Control and Prevention (CDC) has published a list of
behaviors that people can take to slow the spread of COVID-19. We then provided respondents
with an internet link
8
to information about these behaviors.
Then, we listed each of the CDC’s recommended behaviors and then prompted respondents
with the following instruction: For each of the CDC’s recommended behaviors listed below, please
tell us the extent to which you personally take this behavior.” The list of behaviors included: (1)
Washing your hands often, with soap and water and for at least 20 seconds; (2) Avoiding touching
your eyes, nose and mouth with unwashed hands; (3) Staying home as much as possible, and only
leaving to do essential errands; (4) Covering your mouth and nose with a cloth mask or face cover
when you are around others; (5) Covering coughs and sneezes; (6) Cleaning and disinfecting
frequently touched surfaces daily; (7) Avoiding close contact with people who are sick; and (8)
Keeping distance between myself and other people. Responses were collected on a slider ranging
from 1 = I never do this to 100 = I always do this; the midpoint was not labeled. The scale was
constructed by standardizing the z-scores of each variable ( = 79.20; SD 16.43; Cronbach’s α =
0.80).
Social trust was then measured with the question, “How much do you trust United States
public health experts to understand how to slow the spread of COVID-19?”. Responses were
collected on a 7-point Likert scale ranging from 1= “Not at all” to 7= “Completely” (the midpoint
was not labeled). The mean social trust score was 4.67 (SD = 1.72).
Next, we asked participants to report their level of perceived risk regarding COVID-19.
Four risk perception variables were measured. We measured generalized concern with the question:
“How concerned are you personally about contracting COVID-19?” Answers were provided on a
7-point Likert scale where 1 = not at all concerned and 7 = very concerned (the midpoint was not

8
https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.

x
61
labeled). The chance of contracting COVID-19 was measured with the question: In your opinion,
what is the percent chance that you will contract COVID-19 before June 31? Responses were
collected on a slider ranging from 0% to 100%. Another risk perception variable measured
judgment about the severity of a COVID-19 infection. The question posed to participants
was: “Which of the following statements most accurately reflects your beliefs about what you think
will happen to you if you become exposed to COVID-19?”. Answers ranged from 1 = I won’t be
infected to 7 = I will be infected, hospitalized, and I will end up dying from the virus within 7-14
days. The mid-point, 4, was labeled I will be infected and I will feel like I have the seasonal flu
(e.g. a mild fever; aches/pains; coughing) for 7-14 days. Probability of death was measured as
follows: In your opinion, what is the percent chance that you will die from COVID-19 if you become
infected? Answers ranged from 0%-100%. These variables’ z-scores were standardized, and they
were combined to create a single risk variable (Cronbach’s α = 0.78).
After reporting their risk perceptions, participants responded to the 7-item Actively Open-
Minded Thinking scale (Haran et al., 2013). Participants responded to the following statements: (1)
“Allowing oneself to be convinced by an opposing argument is a sign of good character”; (2)
“People should take into consideration evidence that goes against their beliefs”; (3) “People
should revise their beliefs in response to new information or evidence”; (4) “Changing your mind
is a sign of weakness”; (5) “Intuition is the best guide when making decisions”; (6) “It is important
to persevere in your beliefs even when evidence is brought to bear against them”; and (7) “One
should disregard evidence that conflicts with one’s established beliefs.” Responses were collected
using 7-point Likert scales where 1 = completely disagree, 4 = neutral, and 7 = completely agree).
These variables’ z-scores were standardized and were made into the AOT scale ( = 5.03; SD =
0.99; Cronbach’s α = 0.72).

x
62
Finally, we collected demographic information, including age, sex, race, education, income,
and political ideology. Participants indicated their age and binary sex (male or female). Options for
race included those found on the US Census; namely, White, Black, Hispanic, Asian, Native
American, Mixed, Other, or Middle Eastern. Education was measured on a six-point scale (1 = No
high school, 2 = High school graduate, 3 = Some college, 4 = 2-year degree, 5 = 4-year degree, and
6 = post-graduate degree). Income was measured on a 16-point scale: 1 = Less than $10,000, 2 =
$10,000 - $19,999, 3 = $20,000 - $29,999, 4 = $30,000 – $39,999, 5 = $40,000 – $49,999, 6 =
$50,000 – $59,999, 7 = $60,000 – $69,999, 8 = $70,000 – $79,999, 9 = $80,000 – $99,999, 10 =
$100,000 – $119,999, 11 = $120,000 – $149,999, 12 = $150,000 – $199,999, 13 = $200,000 –
$249,999, 14 = $250,000 – $349,999, 15 = $350,000 – 499,999, and 16 = $500,000 or more; a
“prefer not to say” option was also. Political ideology was measured on a five-point scale (1 = Very
liberal, 2 = Liberal, 3 = Moderate, 4 = Conservative, and 5 = Very conservative); “not sure” was
also available as an option.
Analysis. We constructed a structural equation model (in STATA) to study the relationships
between AOT, COVID-19 risk perceptions, social trust in experts, and compliance with
recommendations from the CDC (Appendix E, Figure 1). The model controlled for demographic
characteristics in each step.
Results
Descriptive statistics. Descriptive statistics for AOT scores, the risk perception variable,
social trust in experts, and CDC compliance are shown in Appendix E, Table 1. The correlation
matrix for these variables can be found in Appendix E, Table 2.
Social trust (H1). Participants who self-reported greater levels of actively open-minded
thinking also had, on average, higher levels of trust in experts, though the effect size was small (β
= 0.20, p < 0.01, ባ
2
= 0.01).
63
Perceived risk (H2 and H4). Both social trust (H2: β = 0.11, p < 0.001, ባ
2
= 0.05) and AOT
(H2: β = 0.06, p < 0.05, ባ
2
= 0.01) were positively associated with perceived COVID-19 risk (Figure
2 and Table 3); the effect sizes were medium and small, respectively. In addition, AOT was both
directly and indirectly (through social trust) related to risk perceptions.
Compliance with CDC recommendations (H3 and H5). Both perceived risk (H3: β = 4.92,
p < 0.001, ባ
2
= 0.06) and social trust (H5: β = 2.16, p < 0.001, ባ
2
= 0.06) were positively associated
with self-reported measures of compliance with CDC recommendations (both with medium effect
sizes).
Other significant predictors. In addition to relationships outlined above, we found that
political ideology was also associated with social trust. Specifically, conservatives exhibited lower
levels of social trust than their more liberal counterparts with a medium effect size (β =-0.32, p <
0.001, ባ
2
= 0.05). The same was true of sex; females reported higher levels of social trust than
males (β = -0.33, p < 0.01, ባ
2
= 0.01).
Age, sex, and political orientation were also significant predictors of perceived COVID-19
risk (Appendix E, Table 3). Specifically, older participants (β = 0.06, p < 0.001, ባ
2
= 0.02)
perceived greater risks from COVID-19, though the effect size was small; the same was true of
liberals (β = -0.12, p < 0.05, ባ
2
= 0.03) and females (β = -0.16, p < 0.01, ባ
2
= 0.01). Finally, age
and sex were significant predictors of self-reported compliance with CDC recommendations. Older
adults reported higher levels of compliance with recommendations (β = 0.07, p < 0.05, ባ
2
= 0.01)
as did females (β = -7.44, p < 0.001, ባ
2
= 0.07).

Discussion
How people perceive and then respond (e.g., via measures of acceptability or behavior) to
all manner of risks—e.g., climate change (Shi et al., 2016), food (Segrè Cohen et al., 2020),
64
geoengineering (Visschers et al., 2017), nuclear power (de Groot et al., 2020), novel technologies
(Lutzke & Árvai, 2021), etc.—has been studied extensively. However, it has been exceedingly rare
that researchers are presented with an opportunity to study risk perceptions and behaviors in a
context that is not only significant and salient, but one that affects virtually every living human at
the same point in time. COVID-19, for all its tragedy and loss, reflects one such opportunity.
Yet, despite COVID-19 being so well-known and widespread, misunderstandings about the
causes and consequences of COVID-19, as well as countermeasures for addressing it, have been
equally commonplace (Bruine de Bruin et al., 2021a; Chesser et al., 2020). These observations are
understandable given how quickly SARS-CoV-2 is evolving (Chu et al., 2020; Madewell et al.,
2020; McDonald et al., 2021; Tabatabaeizadeh, 2021). Compounding the challenges posed by this
uncertainty is the plethora of incorrect information and deliberate misinformation about what
COVID-19 is, how it spreads, what it does, and how it can be treated and managed. The end result
has been a multi-layered risk where epidemiological misunderstandings and social factors have
interacted to compound the dangers of the pandemic for the general public (Krause et al., 2020).
Under these circumstances, it is unreasonable to expect the public to both understand the
range of evolving epidemiological details surrounding COVID-19, and then to separate pandemic
facts from fictions. Thus, timely and deliberative risk communications which emphasize the
exchange of risk information and provision of decision-support such that people may make up their
own minds (Árvai, 2014) is—in our view—unrealistic and unlikely to be effective. Instead, what
is needed during a public health crisis are consistent and more directive risk messages (Abrams &
Greenhawt, 2020; WHO 2020); i.e., messages that strongly recommend specific behaviors. In
addition, it is crucial that these risk messages be delivered by people (or by recognizable
institutions) who—because of their domain-specific technical expertise and experience—warrant
a high degree of public trust (NASEM 2020).
65
In industrialized countries with modern public health infrastructure, these messengers tend
to be individuals (e.g., doctors, researchers, senior administrators, spokespersons, etc.) who
represent public or government health agencies like the CDC, regional hospitals and healthcare
networks, research-intensive universities, and the like; alternatively, the messengers may be the
entirety of institutions themselves (e.g., when recommendations come directly from the CDC).
Social trust (Siegrist, 2021), which accounts for the degree to which people trust public-facing
officials and institutions, is therefore a crucial element of risk communication—and, by extension,
behavior change—initiatives. As we note in the introduction, social trust serves as a heuristic that
helps to facilitate behaviors based on the degree to which a trusted individual or organization
recommends them (Siegrist et al., 2000).
It is for this reason that our research focuses on the role of AOT in promoting social trust.
Specifically, we hypothesized that critical thinking ability (AOT) would be positively associated
with social trust; we believed this to be the case because people who score highly on AOT would
seek out the most credible sources of information about COVID-19 (i.e., public health agencies or
experts who work in them) which, in turn, would be manifest in higher levels of social trust (H1).
And because AOT is a self-directed thinking style in which people are thought to take their time to
weigh multiple sources of evidence, we also expected it to directly influence risk perceptions (H4).
Our results support both hypotheses (Figure 2).
Researchers who have studied AOT suggest that its positive effects are the result of the
ground-rules it sets for the formulation of judgments and decisions (Baron, 2019; Pennycook et al.,
2020). As a result, people who score highly on the scales that measure AOT have a better
understanding of the attributes that define a problem or solution and, therefore, can engage more
readily (and more deliberatively) in priority setting and evidence-based evaluation (Baron, 2019).
And, importantly, people who score more highly for AOT are more likely to consider the opinions
66
and insights of others with relevant knowledge or experience during judgment and decision-making
(Haran et al., 2013).
It is for these reasons that we believe AOT positively affects social trust. Mechanistically,
we speculate that because critical thinkers seek a better understanding of the component attributes
of a risk (e.g., the different exposure pathways for SARS-CoV-2), they will look to experts and
organizations who they believe can provide the most accurate information about it (Appendix E,
Figure 2). Roozenbeek et al. (2020) propose a similar mechanism for how critical thinking ability
may help people to be less susceptible to misinformation about COVID-19. In a cross-national
study, they found that higher levels of numeracy (which they used as a proxy for critical thinking
ability) were strongly associated with trust in scientists (i.e., social trust), both of which were
associated with lower susceptibility to misinformation about COVID-19. The findings of
Roozenbeek et al. (2020) also suggests that critical thinking ability leads people to be more
discerning about whom they look to for information about COVID-19; this, in turn, leads people
to gravitate toward trusted sources of information—scientists—who, in turn, offer insights that
contradict misinformation.
We also hypothesized that social trust would be positively associated with heightened risk
perceptions (H2). We also hypothesized that these heightened risk perceptions would be positively
associated with higher levels of self-reported compliance with guidelines (from the CDC) aimed at
preventing people from contracting COVID-19 (H3). The data we collected supports both
hypotheses (Figure 2).
Our findings in support of H2 (Figure 2) are in line with other research carried out during
the pandemic. For example, in a study of people in Switzerland, Siegrist et al. (2021; 2021)
similarly found that high levels of social trust predict heightened risk perceptions. Dryhurst et al.
(2020) corroborate these findings in studies conducted in ten countries across Europe, North
67
America, and Asia. The mechanism underlying this connection is elementary: To our knowledge,
every legitimate public health agency around the world has emphasized that COVID-19 is
dangerous and, at the extreme, deadly. If one has a high degree of social trust, it stands to reason
that they too would adopt this perspective, which would manifest in terms of perceived risk.
To this end, we also speculated that a high degree of social trust would lead to higher levels
of self-reported compliance with guidelines (from the CDC) aimed at preventing people from
contracting COVID-19 (H5). Here too, we find a significant, positive relationship. However, our
research also supports our hypothesis that this relationship is mediated by perceived risk (H3;
Figure 2). Once again, the mechanism underlying this connection is relatively straightforward:
across a wide range of hazards, higher levels of perceived risk have been shown to heighten
personal concern and promote changes in preferences (e.g., about risk management policies) and
behaviors that lead to lower levels of exposure (Siegrist & Árvai, 2020).
As above, our findings with respect to COVID-19 risk perceptions and compliance with
recommended behaviors are in line with other research conducted during the pandemic (e.g., Bruine
de Bruin et al., 2021a). A noteworthy exception, however, is research conducted by Siegrist et al.
(2021) who did not observe a direct link between social trust and recommended behaviors aimed
at reducing the risk of a COVID-19 infection. (They did, however, observe an indirect link; they
found significant and positive relationship between social trust and perceived risk, and perceived
risk and behavior.) We speculate that the differences between our findings and those of Siegrist et
al. (2021) are methodological in nature.
Specifically, Siegrist et al. (2021) created two indices of behavior; one for "physical
distancing” and another for “hygienic behaviors”. Physical distancing was measured using six
highly specific items (e.g., that asked about whether people hosted friends or neighbors, relatives,
a housekeeper, or personal service providers at their home). Hygienic behaviors were measured
68
using seven different and specific items such as whether people regularly disinfected items such as
food packaging and their mail. Our behavioral measures were fewer in number and represented
more common behavior derived directly from the CDC’s recommendations (see Design section
above); moreover, all our behavioral items were combined to create a single scale. Thus, the
significant differences between our behavioral measures and those used by Siegrist et al. (2021)
are likely behind our divergent conclusions.
We also hypothesized that AOT would be positively associated with risk perceptions (H4);
our findings support this hypothesis (Appendix E, Figure 2). Prior research has shown that people
who score high for AOT tend to make more accurate judgments and predictions (Baron, 2019;
Haran et al., 2013; Stanovich & West, 1998). The “accuracy” of a risk perception is a tricky
question because these judgments tend to vary on an individual level. However, much of the
information from credible scientists suggests that the risks from COVID-19 are much greater than
those for other coronaviruses such as Influenza A and B (Pormohammad et al., 2021). In addition,
people who score high on AOT tend to be more resistant to prejudicial biases or motivations when
formulating their judgments (Stenhouse et al., 2018). Thus, it is logical to conclude that people who
score high on AOT will also judge the risks associated with COVID-19 as high.
Beyond our hypothesized relationships (Appendix E, Figure 1), we found that—in addition
to AOT—a small set of other variables also influenced social trust, perceived risk, and compliance
in predictable ways (Appendix E, Table 3). For example, a conservative political ideology was
associated with low social trust and low perceived risk; this matches concurrent research from Pew
which shows Republicans more than Democrats do not agree with government agencies’—such as
the CDC’s—conclusion that COVID-19 poses a significant public health threat
9
. We also found
that older adults perceived higher COVID-19 risk and demonstrated higher levels of compliance

9
See: https://www.pewresearch.org/2021/03/05/a-year-of-u-s-public-opinion-on-the-coronavirus-pandemic/
69
with CDC recommendations; these findings are in line with research and epidemiological data
which shows that older adults are more concerned because they are much more vulnerable if they
become infected with COVID-19 (Bruine de Bruin, 2021). Finally, our research suggests that males
more than females report lower levels of social trust, lower COVID-19 risk perceptions, and lower
compliance with CDC recommendations aimed at addressing the pandemic. These findings are also
in line with other studies of COVID-19 (Bruine de Bruin & Bennett, 2020; Siegrist et al., 2021) as
well as a long line of research on sex
10
differences in risk perceptions (Finucane et al., 2000b;
Rivers et al., 2010; Siegrist & Árvai, 2020).
In terms of limitations, our research relied (as does other research on AOT) on self-reported
measures of actively open-minded thinking. It is possible, if not likely, that many people will over-
report that they are in-fact critical thinkers. Future studies of this type may benefit from methods
that more objectively assess critical thinking styles. For example, future research might find it
fruitful to measure decision-making competence (Parker et al., 2007) or the degree to which
intuitive judgments are calibrated with the same judgments based on a more formal multi-criteria
assessment (Bessette et al., 2019).
In the end, much has been written about risk communication that emphasizes the importance
of providing people with not just a framework within which to share information about risks, but
also tools that help them to both formulate evidence-based judgments about risks and more
internally consistent decisions about how to manage them. In much of our past work (Árvai &
Gregory, 2021; Bessette et al., 2016; Bessette et al., 2019) we have advocated for more elaborate
decision-support tools that help people to explore their values, determine how these values align
with the different attributes of a problem or alternative, and then make more internally consistent

10
We acknowledge that there are more than two genders. However, at this time, YouGov provides demographic data based on
sex.
70
choices. However, in the case of AOT, different avenues for facilitating higher quality risk
judgments and management decisions become apparent.
On the one hand, prior research suggests that critical thinking can be primed in the moments
before judgments or decisions need to be made; simply priming people to consider the credibility
of evidence and their sources may be a powerful tool for promoting more science-based judgments
and decisions (Drummond & Fischhoff, 2019; Lutzke et al., 2019). On the other, critical thinking
styles may be taught in formal or informal education settings (Baron, 2019; Gregory, 1991). For
example, Baron and colleagues (Baron, 1993; Baron et al., 1986) have developed courses aimed at
teaching critical thinking skills that can be deployed by people during the evaluation of information
and arguments, and to motivate the search for information before making judgments and decisions.
Helping people develop these skills—either as a series of steps to follow or norms to abide by—
during formal and informal education may serve as an important complement to risk
communication efforts that focus solely on providing risk information or recommended risk
management actions.

71
Conclusion
Major Findings.
Literature from decision science and social psychology notes how difficult it is for people
to grapple with multifaceted and emergent risks (Campbell-Arvai et al., 2018; Slovic, 1995). Trying
to understand a new phenomenon, form relevant beliefs, and then act on those beliefs, is no easy
task. This dissertation explored the complexity around individual perceptions of emergent risks,
communication about those risks, and the cognitive mechanisms that may be needed for people to
onboard information about them. The three studies centered around nutrient scarcity and COVID-
19.
Research Question 1: To what degree do individual differences and values influence
acceptance of emergent scientific technologies? Risk and benefit perceptions were major drivers
of acceptance of HUDF in Chapters I and II. These results are in line with other work, which
discovered risk and benefit perceptions in general to be inverse and powerful predictors of
technology acceptance (Alhakami & Slovic, 1994). Past research on HUDF specifically has also
found this relationship (Poortvliet et al., 2018). Value orientations, particularly egoism, played a
weak but significant role in predicting acceptance of HUDF in Chapter II. This result is similar to
research on solar radiation management; a technology that benefits the environment but does not
require individual behavior change may be especially appealing to those with higher levels of
egoistic values (Visschers et al., 2017). Other variables, such as disgust sensitivity, perceptions of
naturalness, and demographic characteristics predicted acceptance in earlier steps of the
hierarchical regression but were no longer predictive once risk and benefit perceptions were
included in the models.
Research Question 2: How do alternative risk communication approaches influence
perceptions of emerging technologies, and peoples’ ratings of the usefulness of the information
72
contained in them? Results from study two indicate that within the contexts of fertilizers derived
from human urine, more information (i.e., being placed in any of the treatments compared to the
control condition) led to higher levels of acceptance. Learning about both risks and benefits can
help people gain a holistic understanding of novel technologies and therefore may lead to higher
levels of acceptance (Fischhoff, 1995). Another major finding from Chapter II was that video
communications were perceived as more useful than text communications regardless of participant
age and education level. This result is in line with prior studies that find risk communications in
video form outperform text in terms of participant understanding, decreased risk perceptions, and
overall acceptance of novel technologies (Goldberg et al., 2019; Krouse, 2001; Ludwig et al.,
2018). What is more, age mediated the relationship between treatment group and how useful
participants found the information about HUDF. While individuals, on average, preferred videos
to text, this relationship was more apparent for older participants. This aligns with other work that
found a similar relationship among older adults preferring videos over text (John & Cole, 1986;
Thomas et al., 1999). We hypothesize that this relationship may be due to videos, rather than text,
harnessing episodic memory, which individuals tend to rely more on as they age (Garg et al., 2012).
Research Question 3: How does critical thinking ability correspond to individuals’
interactions with science and scientists in emergent contexts? As hypothesized in Chapter III, AOT
was positively associated with social trust and risk perceptions of COVID-19, and both variables
were predictors of adherence to CDC guidelines to mitigate the spread of the virus. Because those
who score highly on AOT tend to have a better understanding of how to define problems and
solutions, are more deliberate about making decisions, and are more accurate in their predictions,
AOT seemed like an important cognitive mechanism for better understanding risks and who to trust
during an emergent hazard (Baron, 2019; Haran et al., 2013; Stanovich & West, 1998). Trust has
often be shown to act as a heuristic for which messages are valid and what guidance to follow(Fiske
73
& Dupree, 2014) However, results from Chapter III show that trust may not be based solely in
automatic processing but also in how individuals intentionally and deeply think about the
information presented to them, and importantly, what information they seek out before forming an
opinion. Relatedly, people who score highly on AOT scales are less likely to make choices
motivated by political or motivated reasoning, which is especially relevant given the polarized
rhetoric around COVID-19 and preventative behaviors (Bruine de Bruin et al., 2020; Stenhouse et
al., 2018).
Limitations.
Reliance on self-reported data in all three studies resulted in the findings being based on
subjective rather than objective data. Research has shown that individuals may over- or under-
estimate their responses on self-report questionnaires due to issues such as social desirability bias
(Fisher, 1993). In addition, the designs of studies one and three were correlational and quasi-
experiments rather than experiments. This limits the results insofar as it is unknown if there is a
causal relationship between the psychological variables measured and acceptance (or adherence).
Lastly, examining acceptance rather than behavior, was prioritized for studies 1 and 2
because individuals do not have access to products grown from HUDF. Many behavior-change
frameworks claim that beliefs and attitudes (e.g., acceptance), are linearly related to behavior, and
therefore, measuring attitudinal factors predict what will impact behavior. For example, the Theory
of Reasoned Action (Fishbein, 1967), and later, the Theory of Planned Behavior (Ajzen, 1991)
detail how beliefs, attitudes, norms, and perceived behavioral control (in the Theory of Planned
Behavior) predict intention to act, which predicts behavior. The linear link between intention and
action is, however, quite weak, and perhaps not linear at all (Fischhoff, 2005; Stern, 2000). If we
are to assume that these variables are not the same, and may not even be linearly related, then risk
74
communication messages may impact them differently. It would be interesting to see how
communications may impact behavior change, should HUDF products become available.
Next Steps.
This dissertation leaves open exciting pathways for future research. First, how do models
that include AOT apply across other emergent risks? To date, no study has assessed how AOT
impacts perceptions of emergent technology acceptance. Reproducing study three’s framework for
sustainability contexts, specifically a novel and controversial topic, is especially appealing. To that
end, the context in which I plan to conduct my next study is the use of genome editing on
agricultural crops. Editing the genes of plants can both protect crop yields while also minimizing
the use of pesticides (Saleh et al., 2021). However, genetically modified crops are widely
controversial, as consumers generally perceive them as unnatural; a perception which is well-
documented as being associated with perceptions of decreased healthfulness, humans tampering
with nature, and low public acceptance (Frewer et al., 2013; Lucht, 2015; Raimi et al., 2019). In a
recent nationally representative study conducted in the United States, researchers found that those
with the most extreme negative opinions about genetically modified food in fact had the least
objective knowledge, but perceived themselves as being the most informed on the topic (Fernbach
et al., 2019). Applying the framework used in Chapter III about social trust, risk perception, and
critical thinking (measured via AOT), then, on a topic that is both polarizing and in which public
perceptions do not seem to be based on scientific consensus, may be an important avenue for future
research.
I am also curious about other methods of initiating deliberative and intentional thinking
when it comes to making decisions about the environment. To address the complex and unfamiliar
nature of environmental decisions, a tool called structured decision making (SDM) has the potential
to help people make decisions that overcome heuristics and better align with their values.
75
Researchers have utilized SDM in renewable energy decisions (Bessette et al., 2016),
environmental management (Gregory et al., 2012), and farmer weed practice decisions (Bessette et
al., 2019). SDM employs strategies that assist individuals in identifying their values that are
relevant to a specific decision, as well as evaluating their options (i.e., alternatives) and the
characteristics of each option (i.e., attributes), in accordance with their values (Gregory et al.,
2012). Success of SDM is measured in terms of internal consistency, or the degree to which
alternatives meet people’s fundamental objectives. I plan to explore if people make internally
consistent choices, if eliciting values can help people rely on them when making a choice, and if
being told that their choices are inconsistent with their values, who is willing to switch to a choice
that is consistent.
In sum, while it may feel at times that there are an overwhelming number of crises, there
are in fact, as many, if not more, solutions. The challenge lies in implementing a solution whose
likely success is supported by research and evidence. It is my hope that research examining how
people grapple with and make decisions in novel contexts can aid in operationalizing solutions.

76

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95
Appendix A: Chapter I Tables

Table 1. Sample characteristics. Individual education levels were combined and categorized as low (no high school
+ some high school + graduated high school/GED), medium (medium education = some college + associate ’s
degree), or high (bachelor ’s degree + graduate/professional degree); however, education was treated as a continuous
variable in the regression analysis.

n Women
Age (SD)
Low
Education
Medium
Education
High
Education
UDF 513 51% 44.7 (16.3) 22% 36% 42%
Organic 471 48% 44.5 (16.0) 25% 38% 37%
Synthetic 516 50% 45.3 (16.0) 24% 36% 42%
Biosolids 508 48% 45.0 (15.7) 23% 39% 37%
TOTAL 2,007 49.2% 44.9 (15.9) 24% 37% 39%

x
Table 2. ANOVA results and Tukey ’s post-hoc tests comparing fertilizer types. The Bonferroni corrected p-value
required for significance was set at 0.005.

UDF Organic Synthetic Biosolids
F p

SE SE SE SE
Perceived Naturalness 4.85
ab
0.07 5.5
de
0.07 2.74
f
0.07 4.56 0.07 294.85 < 0.0001
Perceived Risk
Human Health 3.99
ab
0.08 2.72
de
0.07 4.73
f
0.07 3.99 0.07 124.03 < 0.0001
Environmental Health 3.74
ab
0.08 2.9
de
0.07 5.14
f
0.07 3.93 0.08 195.91 < 0.0001
Perceived Benefit

Human Health 4.05
ab
0.07 5.38
de
0.06 3.50
f
0.07 4.21 0.07 141.17 < 0.0001
Environmental Health 4.38
ab
0.07 5.47
de
0.06 3.07
f
0.07 4.37 0.07 208.12 < 0.0001
Acceptability
Non-Edible Plants 5.19
ab
0.07 5.67
de
0.06 4.17
f
0.07 5.19 0.07 90.21 < 0.0001
Animal Consumption 4.37
ab
0.08 5.34
de
0.07 3.56
f
0.08 4.48 0.07 93.31 < 0.0001
Home Use 4.67
ab
0.08 5.68
de
0.06 3.78
f
0.08 4.63 0.08 109.74 < 0.0001
Human Consumption 3.95
ab
0.08 5.55
de
0.06 3.44
f
0.08 4.06 0.08 142.03 < 0.0001
Key to post-hoc comparisons:
a
UDF ≠ Organic (p ≤ 0.001)
c
UDF ≠ Biosolid (p ≤ 0.005)
e
Organic ≠ Biosolid (p ≤ 0.001)
b
UDF ≠ Synthetic (p ≤ 0.001)
d
Organic ≠ Synthetic (p ≤ 0.001)
f
Synthetic ≠ Biosolid (p ≤ 0.001)

x

x

x

x
96

Table 3. ANOVA results and Tukey ’s post-hoc tests comparing the acceptability of different UDF application.

Acceptability

SE
UDF Application
Non-Edible Plants (NE) 5.19
abc
0.07
Animal Consumption (AC) 4.37
de
0.08
Home Use (HU) 4.67
f
0.08
Human Consumption (HC) 3.95 0.08
F 43.60
p < 0.0001
Key to post-hoc comparisons:

a
NE ≠ AC (p ≤ 0.001)
b
NE ≠ HU (p ≤ 0.001)

c
NE ≠ HC (p ≤ 0.001)
d
AC ≠ HU (p ≤ 0.05)

e
AC ≠ HC (p ≤ 0.001)
f
HU ≠ HC (p ≤ 0.001)

x
Table 4A. Hierarchical regression results depicting variables that predict consumer acceptance of urine-derived
fertilizers for human consumption.

Demographics Demographics + Worldview
Demographics + Worldview
+ UDF Perceptions
β 95% CI η
2
β 95% CI η
2
β 95% CI η
2

Constant 3.52*** 2.81;4.23 3.89*** 2.63;5.14 1.02 -0.04;2.08
Age 0.01 -0.01;0.02 0.00 0.01 -0.01;0.01 0.00 0.01 -0.00;0.01 0.01
Gender (0 = man) -0.59*** -0.91;0.28 0.03 -0.57*** -0.89; 0.26 0.03 -0.16 -0.37;0.06 0.01
Education 0.10 -0.01;0.21 0.01 0.07 -0.04;0.18 0.00 0.06 -0.02;0.13 0.01
Altruism -0.08 -0.33;0.18 0.00 -0.05 -0.21;0.12 0.00
Egoism 0.04 -0.13;0.21 0.00 -0.02 -0.14;0.09 0.00
Biospherism 0.31** 0.07;0.55 0.01 0.14 -0.02;0.29 0.01
Food Disgust Sensitivity -0.31*** -0.48; 0.14 0.03 -0.00 -0.12;0.11 0.00
Perceived Risk -0.26*** -0.35; -0.17 0.06
Perceived Benefit 0.73*** 0.63;0.82 0.31
Perceived Naturalness 0.03 -0.06;0.11 0.00
R
2
0.04 0.08 0.60
F 4.94 5.20 69.08
(df1, df 2) (4, 507) (8, 503) (11, 500)
* p < .05, ** p < .01, *** p < .001.
97

Table 4B. Hierarchical regression results depicting variables that predict consumer acceptance of urine-derived
fertilizers for non-human consumption.

Demographics Demographics + Worldview
Demographics + Worldview
+ UDF Perceptions
β 95% CI η
2
β 95% CI η
2
β 95% CI η
2

Constant 4.52*** 3.94;5.10 5.32*** 4.31;6.34 2.88*** 1.95;3.80
Age -0.00 -0.01;0.01 0.00 -0.00 -0.01;0.01 0.00 0.00 -0.00;0.01 0.00
Gender (0 = male) -0.44*** -0.70; -0.18 0.02 -0.43*** -0.69; -0.18 0.02 -0.09 -0.27;0.10 0.00
Education
0.10* 0.08;0.19 0.01
0.05 -0.03;0.14 0.00 0.03 -0.03;0.09 0.00
Altruism 0.12 -0.09;0.32 0.00 0.10 -0.05;0.25 0.00
Egoism -0.07 -0.21;0.07 0.00 -0.11* -0.21; 0.01 0.01
Biospherism 0.16 -0.03;0.35 0.01 0.03 -0.11; 0.10 0.00
Food Disgust Sensitivity -0.36*** -0.50; -0.22 0.05 -0.12* -0.22; -0.02 0.01
Perceived Risk -0.18*** -0.25; -0.10 0.04
Perceived Benefit 0.40*** 0.32;0.49 0.16
Perceived Naturalness

0.21*** 0.14;0.28 0.06
R
2

0.04
0.10 0.55
F 4.94 7.10 55.04
(df1, df 2) (4, 507) (8, 503) (11, 500)
* p < .05, ** p < .01, *** p < .001.

98
Appendix B: Chapter I Supplementary Materials

Figure 1. Methodology framework and research design

Figure 2. Pearson’s correlation matrix for psychological variables used in regressions.
An additional question asked if respondents would eat fruits and vegetables grown using
their assigned fertilizer type; response options were “yes”, “maybe”, and “no”. A chi-squared test
of goodness of fit was performed to assess whether the three levels of willingness to consume
products grown with a given fertilizer was equally distributed (Figure B1). Willingness to
consume was not equally distributed in the population, ( 𝒳 2
(N = 2,008) = 68.25, p < 0.0001).
Participants indicated a strong willingness to consume fruits and vegetables grown with organic
fertilizers when compared to the other three fertilizer types. A relatively smaller proportion of
participants (12.7%) were willing to consume foods grown with UDF, though the proportion of
participants that indicated “maybe” was large for this (and the other) fertilizer types. The findings
Part 1: Informed
Consent
Part 2: Background
Information
Part 3: Independent
and Dependent
Variable Questions
Part 4: Co-Variate
Questions
Part 5: Survey Close

Informed Consent
Fertilizer primers
Q ’s: Food Disgust,
Value Orientations,
Demographics
END
Q’s: Acceptance,
Risk, Benefit,
Naturalness
1. 2. 3. 4. 5. 6. 7.
1. Altruism 1.00
2. Egoism 0.13 1.00
3. Biospherism 0.70 0.12 1.00
4.Food Disgust Sensitivity 0.08 0.16 0.08 1.00
5. Risk Perceptions -0.01 0.04 0.01 0.17 1.00
6. Benefit Perceptions 0.04 0.09 0.04 -0.10 -0.71 1.00
7. Perceived Naturalness 0.04 0.07 0.04 -0.10 -0.62 0.67 1.00

99
pertaining to the high level of acceptability of organic fertilizers were supported by consumers’
willingness to consume foods grown with different fertilizers. Specifically, consumers were much
more willing to eat fruits and vegetables grown using organic fertilizers in comparison to all other
fertilizer types (Figure B1).

Figure 3. Percentage of participants who indicated a willingness and an unwillingness to eat
fruits and vegetables grown using fertilizers derived from diverted and recycled human urine
(UDF) as compared to other fertilizer types.

100

Figure 4. HUDF primer
101
Figure 5. Organic fertilizer primer
102

Figure 6. Biosolids as fertilizer primer
103

Figure 7. Synthetic fertilizer primer

104
Appendix C: Chapter II Tables and Figure

Table 1. Sample characteristics. Individual education levels were combined and categorized as low (no high school +
some high school + graduated high school/GED), medium (some college + associate degree), or high (bachelor’s degree
+ graduate/professional degree).
n Women
Age
(18-29)
Age
(30-39)
Age
(40-48)
Age
(49-59)
Age
(60+)
Low
Education
Medium
Education
High
Education
Control 510 52% 18% 18% 20% 22% 22% 18% 30% 52%
Short
video
503 50% 18% 19% 20% 20% 23% 18% 28% 54%
Short
text
515 49% 18% 19% 20% 21% 21% 20% 33% 47%
Long
video
506 50% 17% 20% 21% 21% 22% 17% 33% 50%
Long
text
443 49% 20% 19% 19% 20% 21% 20% 32% 50%
TOTAL 2477 50% 18% 19% 20% 21% 21% 19% 31% 51%

Table 2. ANOVA ’s and Tuke y’s post-hoc tests comparing communication strategies: Short Text (ST), Long Text (LT),
Short Video (SV), Long Video (LV), and the control (C). The Bonferroni corrected p-value required for significance
was set at 0.01.
C ST LT SV LV
F p

SE

SE

SE

SE

SE
Usefulness 4.94
1234
0.06 5.35
67
0.05 5.36
89
0.06 5.67 0.05 5.61 0.05 30.06 < 0.0001
Perceived Risk 3.48
234
0.07 3.13 0.07 3.03
8
0.07 2.84 0.07 2.90 0.07 22.46 < 0.0001
Perceived
Benefit
4.24
1234
0.06 4.73
67
0.06 4.87 0.07 5.11 0.06 5.05 0.06 37.15 < 0.0001
Acceptability

Non-Edible Use 4.85
1234
0.06 5.19 0.06 5.22 0.06 5.46 0.06 5.41 0.05 16.90 < 0.0001
Human
Consumption
3.97
1234
0.08 4.39
67
0.08 4.62

0.08 4.96

0.07 4.91 0.07 25.83 < 0.0001
1
C ≠ ST
2
C ≠ LT
3
C ≠ SV
4
C ≠ LV
5
ST ≠LT
6
ST ≠ SV
7
ST ≠ LV
8
LT ≠ SV

9
LT ≠
LV

10
SV ≠ LV

x

x

x

x

x
105

(a) (b)
Figure 1. Interaction plots of usefulness of risk communication by (a) age and strategy, and (b) education level and
strategy.

5
5.2
5.4
5.6
5.8
6
T e x t V i d e o
USEFULNESS RATING
18-29 30-39 40-48
49-59 60+
5
5.2
5.4
5.6
5.8
6
T e x t V i d e o
USEFULNESS RATING
low medium high
Table 3. Hierarchical linear regression results describing predictors of consumer acceptance of urine-derived fertilizers
for human consumption.
Step 1 Step 2 Step 3
β 95% CI η
2
β 95% CI η
2
β 95% CI η
2

Constant (Control) 3.88*** 3.56; 4.20 3.31*** 2.76; 3.86 1.34*** 0.89; 1.78
Age 0.00 -0.00; 0.01 0.00 -0.00 -0.01; 0.00 0.00 0.01** 0.00; 0.01 0.00
Gender (0 = man)
-
0.49***
-0.63; -0.36 0.02
-
0.54***
-0.67; -0.40 0.03
-
0.24***
-0.34; -0.15 0.01
Education 0.07** -0.02; 0.11 0.00 0.04 -0.01; 0.08 0.00 0.00 -0.03; 0.03 0.00
Altruism 0.09 -0.02; 0.20 0.00 -0.03 -0.10; 0.05 0.00
Egoism 0.13*** 0.05; 0.21 0.00 0.06* 0.01; 0.12 0.00
Biospherism 0.37*** 0.27; 0.46 0.02 0.05 -0.01; 0.12 0.00
Food Disgust
Sensitivity

-
0.33***
-0.41; 0.26 0.03
-
0.10***
-0.16; -0.05 0.01
Perceived Risk
-
0.21***
-0.24; -0.17 0.05
Perceived Benefit 0.72*** 0.68; 0.77 0.29
Perceived
Naturalness
0.08*** 0.05; 0.12 0.01
Long Video 0.93*** 0.72; 1.14 0.03 0.90*** 0.70; 1.10 0.03 0.18* 0.04; 0.32 0.00
Short Video 0.98*** 0.77; 1.19 0.03 0.95*** 0.75; 1.16 0.04 0.17* 0.03; 0.31 0.00
Long Text 0.22*** 0.14; 0.29 0.01 0.21*** 0.14; 0.28 0.02 -0.03 -0.02; 0.08 0.00
Short Text 0.43*** 0.22; 0.64 0.01 0.43*** 0.23; 0.63 0.01 -0.02 -0.16; 0.12 0.00
R2 0.07 0.14 0.60
F 22.41 34.81 243.11
(df1, df 2) (8, 2459) (12, 2466) (15, 2448)
* p < .05, ** p < .01, *** p < .001.

106

Appendix D: Chapter II Supplementary Materials

Figure 1: Control Treatment

107

Figure 2: Short Text Treatment
108

Figure 3: Long Text Treatment

109
Link to Short Video:
https://www.youtube.com/watch?v=5KpI44VDkRw&t=1s&ab_channel=LoveResearchGroup

Link to Long Video:
https://www.youtube.com/watch?v=iX1F4dYLF84&ab_channel=LoveResearchGroup

110
Appendix E: Chapter III Tables and Figures

Table 1. Descriptive statistics for AOT, risk perception, and compliance.
*Items were reverse-coded.

Min Max SD
AOT 1: Allowing oneself to be convinced by an opposing argument … 1 7 4.23 1.64
AOT 2: People should take into consideration evidence that goes against … 1 7 5.59 1.48
AOT 3: People should revise their beliefs in response to new information … 1 7 5.21 1.56
AOT 4: Changing your mind is a sign of weakness …* 1 7 6.11 1.38
AOT 5: Intuition is the best guide when making decisions …* 1 7 4.23 1.55
AOT 6: It is important to persevere in your beliefs …* 1 7 4.87 1.70
AOT 7: One should disregard evidence that conflicts …* 1 7 5.69 1.52
Personal concern 1 7 4.20 1.94
Percent chance of contracting COVID-19 1 100 32.54 24.07
Severity: What will happen if you are exposed to COVID-19? 1 7 3.55 1.83
Percent chance of dying 1 100 28.54 29.12
Compliance 1: Wash hands 1 100 83.94 20.89
Compliance 2: Avoid touching face 1 100 65.36 27.08
Compliance 3: Stay home 1 100 78.40 26.70
Compliance 4: Wear a mask 1 100 75.20 32.13
Compliance 5: Cover coughs 1 100 91.36 16.44
Compliance 6: Clean surfaces 1 100 66.70 29.84
Compliance 7: Avoid close contact with sick people 1 100 90.58 17.02
Compliance 8: Social distancing 1 100 83.00 21.17

x
111

Table 2. Correlation matrix for AOT, social trust, compliance with CDC guidelines, and the four
risk variables (
*
p ≤ 0.05).

AOT Compliance
Social
Trust Severity
%
Chance:
Death
% Chance:
Contraction
Personal
concern
AOT 1
Compliance 0.067* 1
Social
Trust
0.228* 0.371* 1
Severity 0.342* 0.214* 0.203* 1
% Chance:
Death
0.019 0.233* 0.184* 0.521* 1
% Chance:
Contraction
0.077* 0.210* 0.244* 0.303* 0.475* 1
Personal
concern
0.124* 0.439* 0.383* 0.426* 0.506* 0.590* 1

112

Table 3. Variables influencing perceived risk, social trust, and compliance with CDC recommendations.

113

Figure. 1. Proposed model (H1 – H3) linking AOT, social trust, perceived risk, and compliance
with CDC recommendations. Supplementary analyses shown as H4 and H5.

Figure. 2. Standardized coefficients explaining the relationship between AOT, social trust,
perceived risk, and compliance with CDC recommendations (*p < 0.05, **p < 0.001, ***p <
0.0001).

Abstract (if available)

Abstract Emergent crises and challenges, like climate change and pandemics, require creative and innovative solutions. However, without public support, solutions cannot be implemented effectively. In this dissertation, I examine how individual psychological differences, risk communication messaging, and critical thinking ability influence public perceptions of solutions to crises caused by anthropogenic climate change and the COVID-19 pandemic. In Chapter I, I explore the impact of individual differences on consumer perceptions of a novel sustainable technology, namely fertilizer derived from human urine. In Chapter II, I consider the influence risk communication strategies may have on consumer receptivity this fertilizer, and how individual differences may moderate the relationship. In Chapter III, I study the relationship between one’s critical thinking ability and the onboarding on risk information provided by trusted sources and experts during the COVID-19 pandemic. These findings have several implications for how to effectively communicate about emergent risks and how individuals may overcome cognitive processes related to risk in their decision-making.

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Asset Metadata

Creator Segrè Cohen, Alexandra (author)

Core Title Individual differences, science communication, and critical thinking for emergent risks

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Degree Conferral Date 2022-05

Publication Date 04/07/2022

Defense Date 03/04/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag critical thinking,novel technology,OAI-PMH Harvest,risk communication,risk perception,science communication,Social Psychology

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Arvai, Joseph (committee chair), Bruin de Bruine, Wändi (committee member), Drummond Otten, Caitlin (committee member), John, Richard (committee member), Sinatra, Gale (committee member)

Creator Email acohen06@usc.edu,asegreco@uoregon.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC110883178

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