Assessing Risk-Taking What To Measure And How To Measure It

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Assessing risk-taking: what to measure and how to
measure it
Alexandre Bran & David C. Vaidis
To cite this article: Alexandre Bran & David C. Vaidis (2019): Assessing risk-taking: what to
measure and how to measure it, Journal of Risk Research, DOI: 10.1080/13669877.2019.1591489
To link to this article: https://doi.org/10.1080/13669877.2019.1591489
Published online: 11 May 2019.
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Assessing risk-taking: what to measure and how to
measure it
Alexandre Bran and David C. Vaidis
Laboratoire de Psychologie Sociale: Menaces et Soci
et
e (EA 4471), Universit
e Paris Descartes, Paris, France
ABSTRACT
Risk-taking has been a major field of interest for scientists and for
applied purposes since decades. However, many researchers have noted
that the current measurement instruments fail to show adequate valid-
ity and predictive power. Given the recent calls to develop new meas-
ures, this paper aims to highlight six key points that should be kept in
mind when constructing or using measures of risk-taking concepts.
Specifically, we encourage risk-taking scholars (a) to pay close attention
to the terminology used in studies, (b) to distinguish measures of gen-
eral and specific risk-taking, (c) to distinguish risk-taking from the appeal
of risky activities, (d) to keep in mind the subjectivity of risk-taking, (e)
to consider the measurement of passive risk-taking, and (f) to favour
more realistic risk-taking tasks. Overall, these recommendations should
help researchers to design and use more relevant risk-taking measures.
ARTICLE HISTORY
Received 20 July 2018
Accepted 19 January 2019
KEYWORDS
Risk-taking; decision
making; risk perception; risk
appraisal; risk attitude;
risk propensity
Over the years, the risk-taking field has developed dozens of instruments to assess its different
concepts. This diversity could be very enriching as it could allow researchers to validate their
hypotheses with converging evidence. However, in the past few years, most of the scales and
tasks used in the literature had seen their validity and their predictive power criticized, with
researchers sometimes even questioning how they really relate to risk-taking. Researchers have
highlighted the lack of convergent validity between the main instruments (Killgore, Kamimori,
and Balkin 2011; Mamerow, Frey, and Mata 2016; L€
onnqvist et al. 2015; Schonberg et al. 2011;
Szrek et al. 2012) along with insufficient predictive power and correlations when compared to
real risk-taking (Anderson and Brown 1984; L€
onnqvist et al. 2015; Szrek et al. 2012; Woelbert and
Riedl 2013). In this context, it is not surprising that several scholars have called for new measure-
ment instruments (Byrnes et al. 1999; Fox and Tannenbaum 2011; Rohrmann 2011; Schonberg
et al. 2011). However, we think that a focus is needed on how these future instruments can per-
form better than the current ones.
Since the beginnings of risk-taking research, scholars have pointed out flaws in the measures
of risk-taking concepts. For instance, after showing a lack of convergent evidence from the
instruments used at that time, Slovic (1962, 1964) highlighted several issues with these instru-
ments such as the lack of emotional arousal induced by the measures or the failure to account
for the context-specific and subjective nature of risk. Despite these early warnings, these flaws
seem to have persisted through the years since they all have been reported again recently
(Byrnes et al. 1999; Fox and Tannenbaum 2011; Schonberg et al. 2011). We think that there are
CONTACT Alexandre Bran alexandre.bran@parisdescartes.fr; alexandre.bran@outlook.com 71 avenue Edouard Vaillant,
92100 Boulogne Billancourt, Paris, France
ß 2019 Informa UK Limited, trading as Taylor Francis Group
JOURNAL OF RISK RESEARCH
https://doi.org/10.1080/13669877.2019.1591489

also several more flaws in risk-taking measures that have yet to be clearly identified, and the cur-
rent literature lacks a thorough overview of the things to consider when attempting to assess
risk-taking and its related concepts. In this article, we review the majors flaws previously identi-
fied in the literature and we highlight other issues in the assessment of risk-taking concepts.
Specifically, we emphasize the need for more vigilance regarding the terms used in studies, for a
continuous distinction between measures of general and specific risk-taking, for a distinction
between risk-taking and the appeal of risky activities, for taking into account the subjectivity of
risk-taking, for more measures of passive risk-taking, and for more realistic risk-taking tasks.
We also offer recommendations to achieve more precise and more adequate means of assessing
risk-taking concepts.
Precisely define what to measure
Risk-taking is a broad construct and the literature on it encompasses diverse concepts such as risk-
taking tendency (e.g. Byrnes et al. 1999), risk-taking propensity (e.g. Brockhaus 1980), risk attitudes
(e.g. Blais and Weber 2006), risk preferences (e.g. Dave et al. 2010), risk aversion (e.g. Holt and
Laury 2002), risk perception (e.g. Slovic, Fischhoff, and Lichtenstein 1980), and risk appraisal (e.g.
Horvath and Zuckerman 1993). This breadth is valuable but may favour ambiguity and confusion,
as any error or imprecision in a paper could mislead readers about what is really measured in a
study. Given the richness of the risk-taking field and the current absence of a consensual typology,
it is no surprise that such imprecisions do happen. For instance, measures of risky behaviours
obtained through a single task are frequently considered as measures of risk-taking tendency (e.g.
Lejuez et al. 2002; Rubio et al. 2010) despite criticisms of the notion that these single measures val-
idly assess such a tendency (Coppola 2014; Dohmen et al. 2011; Szrek et al. 2012). This kind of
imprecision can mislead readers and can also be found in reviews of risk-taking measures that
often mix and compare under the general label risk preference instruments designed to measure
different concepts, such as risk-taking behaviours, risk-taking propensity or risk appraisal (e.g.
Charness, Gneezy, and Imas. 2013; Coppola 2014). Along with risk preference, the labels risk attitude
and risk-taking attitudes have also been associated with very different definitions and measures
depending on the researcher’s conceptualization (e.g. Blais and Weber 2006; Iversen and Rundmo
2004; Schoemaker 1993). Another kind of confusion may arise when authors assess risk-taking
through scale items such as ‘I enjoy taking risks’ (e.g. Epstein and Botvin 2002) although there are
notable differences between enjoying taking risks and actually taking risks.
Overall, we think that these ambiguities may be reduced if the risk-taking field had a consen-
sual typology. A few researchers have already suggested typologies of risk-taking concepts. Fox
and Tannembaum (2011) proposed to distinguish risk-taking behaviour, risk preference and risk
perception, closely matching an earlier model proposed by Sitkin and Pablo (1992) who encom-
passed the distinction between risk behaviour, risk propensity and risk perception. We propose
another typology that builds on these models but goes further by including a fourth core con-
cept and by precisely redefining all their elements. Based on these previous models and on our
own analysis of the literature, we identify four core concepts in the risk-taking literature: risk-tak-
ing behaviour, risk-taking propensity, risk-taking attitude, and risk appraisal.
Risk-taking behaviours refer to actions or inactions involving potential risks. Byrnes et al.
(1999) distinguished three types of risk-taking behaviours measures that are commonly used in
the literature: self-reported behaviour, hypothetical choice, and observed behaviours. With the
intent to make them more precise and more inclusive, we propose to respectively rename them
as reported behaviours, projected behaviours, and actual behaviours. Reported behaviours cover
individual’s report of past or current behaviours. This include self-reports as well as observers
reports, such as parents reporting on their children’s behaviours. Projected behaviours cover
intentions or decisions in hypothetical choices or situations and are usually measured by asking
participants how likely they are to engage in given behaviours in the future (e.g. Weber, Blais,
2 A. BRAN AND D. C. VAIDIS

and Betz 2002) or how they would likely react when confronted with various scenarios (e.g. Ben-
Ari, Florian, and Mikulincer 1999). Finally, actual behaviours cover the direct observation of real
individuals’ behaviours, for instance by using risk-taking tasks (e.g. Lejuez et al. 2002) or by
recording behaviours with cameras (Burns and Wilde 1995; Lajunen, Karola, and Summala 1997).
Risk-taking propensity represents the consistent tendency of an individual to engage in or
avoid risk-taking behaviours when confronted with risky situations. Risk-taking propensity can be
general, alike a general risk-taking trait, or specific to a certain domain, such as the specific pro-
pensity to take risks in financial decisions. There are two main approaches to the measurement
of risk-taking propensity. The first one consists in directly asking individuals about their tendency
to take risks, either in general (e.g. Dohmen et al. 2011) or specifically in certain domains (e.g.
Nicholson et al. 2005). This approach is based on individual reports and seems reliable as its
measures have been repeatedly linked to reported risk-taking behaviours (Coppola 2014;
Dohmen et al. 2011; Szrek et al. 2012). The second approach is to infer risk-taking propensity
based on risk-taking behaviours, assuming that risk-taking behaviours are consistent and repre-
sentative of a general tendency (e.g. Lejuez, et al. 2002; Wong and Carducci 1991). However, the
validity of this second approach is questioned since, to date, there is no risk-taking behavioural
measure that is consistently related to the numerous risk-taking behaviours in the real life
(Coppola 2014; Szrek et al. 2012).
Risk-taking attitudes have several definitions in the literature that differ deeply from one
another. Economists traditionally associate risk-taking attitudes with the degree of risk preference
or the extent to which one will favour or avoid risky choices (Schoemaker 1993). Psychologists,
on the other hand, consider attitudes as a core component when predicting human behaviour
(Ajzen, 1991) and tend to define them as the ‘tendencies to evaluate an entity with some degree
of favour or disfavour, ordinarily expressed in cognitive, affective and behavioural responses’
(Eagly and Chaiken 1993, p.1). The affective component refers to the feelings and emotions
evoked by taking risks (e.g. ‘I find fun to gamble’), the cognitive component refers to the infor-
mation, knowledge, or beliefs one holds about taking risks (e.g. ‘I think that taking risks is a
mark of manliness’), and the behavioural component refers to the willingness to take or to avoid
risks (e.g. ‘I want to take risks tonight’). With this model, the final attitude can be definitely
valenced – that is definitely positive or negative – or more ambiguous. For instance, one can
feel that taking a risk generates a positive thrill and yet be unmotivated to take a risk to pre-
serve personal resources. Scholars have also looked at similar attitudes such as attitudes about
risk-takers (Farthing 2005) or about safety (Ek and Akselsson 2007).
Risk appraisal is the last concept and refers to the subjective assessment of the riskiness of a
specific situation. Risk appraisal is usually called risk perception in the literature but this term may
result in confusion with other concepts. For instance, risk perception has been variously used to
describe the riskiness assessment of situations (Weber et al. 2002), the perception of risk charac-
teristics such as controllability or dreadfulness (Ho et al., 2008), the estimated likelihood of given
undesirable events happening in the future (Lerner et al., 2003), or the relative evaluation of the
importance of various undesirable events (Slimak and Dietz 2006). Moreover, the term risk per-
ception is fundamentally ill-suited as risks are not physical elements that can be perceived by the
human senses but rely on a subjective evaluation (see Wachinger et al., 2013). Hence, while less
often employed, the term risk appraisal appears to us to be more appropriate. Risk appraisal is
usually modelled with two components: severity and vulnerability (Janz and Becker 1984;
Maddux and Rogers 1983). Severity corresponds to the potential negative consequences that
may happen, while vulnerability corresponds to the likelihood that these negative consequences
will occur. Appraisals of greater severity and greater vulnerability are both associated with
appraisal of riskiness (Milne, Sheeran, and Orbell 2000; van der Pligt 1998). Another component
is sometimes proposed and consists of the potential rewards of the risk, that is the potential
benefits consequences that may happen (Maddux and Rogers 1983; Sarin and Weber 1993).
Accordingly, several studies show that the more a given risk is associated with potential rewards,
JOURNAL OF RISK RESEARCH 3

the less it is appraised as risky (Slovic 1987; Weber et al. 2002). Another important factor influ-
encing risk appraisal has been introduced by the self-efficacy theory (Bandura 1982). According
to this theory, self-efficacy (i.e. individuals’ own beliefs about their capabilities) influences how
people perceive and react to situations. Accordingly, studies have shown that self-efficacy influ-
ence risk appraisal (Krueger and Dickson 1994). Another popular conceptualisation of risk
appraisal was proposed by Slovic (1987) and is based on two factors: dread risk, defined by ‘lack
of control, dread, catastrophic potential, fatal consequences and the inequitable distribution of
risks and benefits’ (p. 283), and unknown risk, defined by ‘hazards judged to be unobservable,
unknown, new, and delayed in their manifestation of harm’ (p. 283).
We believe that the distinction between risk-taking behaviours, risk-taking propensity, risk-tak-
ing attitude and risk appraisal (see Figure 1) should closely represent the reality of the field (see
Table 1) and would allow for a common language to refer to risk-taking concepts. It should be
emphasized that these four concepts are not independent from each other. For instance, risk-tak-
ing propensity can be assessed by summing reports of risk-taking behaviours (e.g. Nicholson
et al. 2005). Likewise, risk-taking attitude and risk appraisal are strongly linked with each other:
the attitude one holds towards an activity influences the appraisal of the same situation and,
conversely, the appraisal of a given risk will update the beliefs and the attitude towards this risk
(Slovic et al., 2004). All these dependencies strengthen our main point: that close attention
should be paid to the terms used in order to avoid the ambiguities and confusion that are
sprinkled through the literature.
Differentiate general and context-specific risk-taking
Our second point focuses on the context-specific nature of risk taking (Fox and Tannembaum
2011; Slovic 1964). For a long time, risk-taking propensity has been mainly studied as a stable
Projected
Risk-taking
behaviour
Actual
Risk-taking
propensity
Risk-taking
attitude Risk appraisal
Affect Beliefs Motivation
Reported
'I gambled last
night'
'I am gambling' 'I will probably
gamble tonight'
'It is thrilling
to drive fast'
'I think that driving
fast is a mark of
manliness'
'I want to
drive fast'
General Specific
'I am someone
who often take
risk'
'I never take drugs'
Severity Gains
Vulnerability
'Smoking can
cause serious
health issues'
'I am not likely
to have cancer
by smoking'
'Smoking helps
me relax'
Figure 1. The four core concepts of risk-taking.

personality trait under the expected utility framework and its variants (e.g. Edwards 1954;
Tversky 1967). Within this framework, individuals can be characterized as risk-averse, risk-seeking,
or risk-neutral depending on the shape of their utility function. This approach is very close to a
personality trait view of risk-taking and therefore assumes that the risks taken by an individual in
one context are predictive of the risk taken by this individual in other contexts. This has also
been the underlying assumption of many early scales in psychology which computed a general
risk-taking score based on different scenarios (Jackson 1976; Kogan and Wallach 1964). However,
the expected utility framework fails to explain why individuals have been found to be inconsist-
ent in their risk-taking propensity across different contexts and situations (MacCrimmon and
Wehrung 1990; Schoemaker 1990; Slovic 1962, 1964).
Progressively, researchers converged upon a new conceptualization of risk-taking propensity,
no longer considering it as a single personality trait but as a situation-specific trait, dependent
on the context of the risk. Risk-taking scales started to be divided into subscales examining risk-
taking in several contexts, such as social risk-taking, health risk-taking or crime risk-taking
(Horvath and Zuckerman 1993; MacCrimmon and Wehrung 1986). Today, this contextual view is
widely supported (Hanoch, Johnson, and Wilke 2006; Rohrmann 2011) and contextual scales
have become the primary instruments used to assess risk-taking propensity (e.g. Blais and Weber
2006; Nicholson et al. 2005). Based on a precedent review of the literature, Weber, Blais, and
Betz (2002) identified five major domains of risk-taking propensity: financial, health/safety, recre-
ational, ethics and social. Financial risk-taking is often further decomposed into gambling and
investment risk-taking (Brown and Braver 2007; Weber, Blais, and Betz 2002). While this typology
of risk-taking domains currently appears to be the most popular, it should be noted that differ-
ent typologies have been proposed (e.g. Nicholson et al. 2005; Wilke et al. 2014) with the aim of
representing most activities that involve risk-taking.
In our opinion, results using contextual scales are interesting in two major respects. First, they
definitely demonstrate that risk-taking is dependent on the context with participants simultan-
eously risk-seeking in some domains and risk-neutral in others (Hanoch et al. 2006; Weber et al.
2002). Second, some researchers also aggregate the contextual scores to create a general risk-
taking score. These attempts have repeatedly showed the existence of an underlying general fac-
tor of risk-taking across the different domains (Dohmen et al. 2011; Frey et al. 2017; Highhouse
et al. 2017; Nicholson et al. 2005). Taken together, these results illustrate that individuals have
Table 1. Classification of some of the most used or most typical measures instruments according to their concept
of interest.
Task name or scale name Category Source
Attitude to Risk Taking in Medical
Decision Questionnaire
Risk-taking attitude Grol et al. (1990)
Balloon Analogue Task Actual risk-taking behaviour Lejuez et al. (2002)
Choice Dilemma Questionnaire Projected risk-taking behaviour Kogan and Wallach (1964)
Domain Specific Risk-Taking Scale
– Benefits
Risk appraisal (benefits) Blais and Weber (2006)
- Likelihood
Projected risk-taking behaviour Blais and Weber (2006)
- Perception
Risk appraisal (global) Blais and Weber (2006)
Driving Behavior Questionnaire Reported risk-taking behaviour/Specific
risk-taking propensitya
Reason et al. (1990)
Iowa Gambling Task Actual risk-taking behaviour Bechara et al. (1994)
Military Operational Risk Taking Scale Risk-taking attitude Momen et al. (2010)
One-item general risk-taking General risk-taking propensity Dohmen et al. (2011)
Risk Propensity Questionnaire General risk-taking propensity Rohrmann (2011)
Risk Propensity Scale Reported risk-taking behaviour/Specific
risk-taking propensitya
Nicholson et al. (2005)
The multiple price list method Actual risk-taking behaviour Holt and Laury (2002);
Miller, Meyer, and Lanzetta (1969)
a
Specific risk-taking propensity is often measured by summing reports of risk-taking behaviours.

both a general tendency towards risk-taking and specific preferences depending on the domains
of risk. Consequently, researchers interested in general risk-taking would gain validity in their
measures by using or creating instruments measuring risk-taking across different domains in
order to compute a general score. On the other hand, researchers interested in a specific domain
of risk-taking would gain in precision by using or creating instruments specifically related to their
domain of interest and by avoiding proxy measures. These instruments should comply with
psychometric requirements, especially regarding scale construction (e.g. Furr 2011). In any case,
to avoid overgeneralization, the results of studies focusing only on one specific domain of risk-
taking should not be assumed to generalize to a whole risk-taking concept unless this
generalization is supported by other studies in the literature.
Distinguish risk-taking from the appeal of risk-taking activities
Several instruments associate the appeal of risky situations or the research of sensation to a sign
of risk-taking. For instance, Blais and Weber (2006) compute an index of recreational risk-taking
primarily by asking the probability of taking part in different high-risk sports (e.g. ‘Taking a sky-
diving class’), and many scales measure risk-taking tendency by using items with similar phrasing
(e.g. Nicholson et al. 2005; Rohrmann 2011). But are these ‘high-risk’ activities really risky?
According to American statistics, skydivers are about as likely to die when doing a jump as to
die from being struck by lightning in their everyday life (United States Parachute Associations
2015; National Safety Council 2017). In the meantime, the odds of dying when driving a car are
20 times higher (National Safety Council 2017).
To us, it is not just because people are appealed by an activity that looks risky that they are
risk-takers; rather, it depends on their behaviours during this activity: on whether they take all
necessary precautions and follow safety rules or not. If participants follow all safety procedures,
then most high-risk activities are not riskier than daily activities. Actually, several studies suggest
that part of the appeal of high-risk activities lies in the management and minimization of the
risks involved (Paquette, Lacourse, and Bergeron 2009; Woodman et al. 2013; Ruch and
Zuckerman 2001). For instance, skydivers spend most of their flight time checking their equip-
ment and recalling safety protocols (Celsi, Rose, and Leigh 1993). Supporting this view, the per-
sonality trait of sensation seeking has been associated positively with deliberate risky behaviours
but also with precautionary behaviours (Woodman et al. 2013, Study 2).
In the literature, studies on the links between the appeal of risky activities and injuries or acci-
dents are contradictory, finding positive correlations (e.g. Iversen and Rundmo 2004; Jonah 1997;
Zuckerman 1983), null correlations (e.g. Burns and Wilde 1995; Wishart, Somoray, and Rowland
2017; Whissel and Bigelow 2003), and even negative correlations (e.g. Cherpitel, Meyers, and
Perrine 1998). It is possible that these contradictory results are partly due to the confounding of
risk-minimizers individuals with actual risk-takers. To avoid this confound and to gain in preci-
sion, we suggest assessing risk-taking by measuring both risky and precautionary behaviours
within an activity (e.g. ‘I take time to check for potential hazards’, Woodman et al. 2013; see also
Weinstein 1989) and not just the appeal of the activity.
Take into account the subjectivity of risk-taking
Most definitions of risk insist on the notions of uncertainty, outcome variance, and loss; however,
few authors have commented on how subjective these notions are. Yates and Stone (1992)
made that point when they wrote: ‘What is considered a loss is peculiar to the person con-
cerned, and so is the significance of that loss and its chance of occurring’ (p. 5). Risk-taking is
fundamentally subjective because the same action can be risky for one person but safe for
another, depending on their personal situation or their skills.

The first reason for this subjectivity is that the nature of a loss and its significance vary
between individuals. Imagine a group of skaters doing perilous jumps, risking falling and being
injured. This injury would probably not be as significant for a young person as it would be for
an elderly one. In risk-taking tasks, money is the most commonly used incentive yet the value of
money depends on the context and the individual (Bernoulli 1954; Buechel and Morewedge
2014; Kahneman and Tversky 1979). Accordingly, Holt and Laury (2002) showed that risk-taking
behaviours decreased sharply when they increased the sum of money in lotteries to reach hun-
dreds of dollars, a sum most participants would find significant (see also Xu et al. 2018). The
point is, if the potential losses are more significant for some participants than for others, then
they are not exposed to the same degree of risk. Likewise, if the losses are not significant for
some participants, then there is very low risk involved for them. The absence of significant losses
could explain the insufficient predictive power of tasks that use low incentives or that rest solely
upon measures of projected behaviours (Xu et al. 2016, 2018).
A second reason for the subjectivity of risk-taking is that there are many risky situations in
which the skills or knowledge of a person can influence the actual risk, either by modulating the
magnitude of the outcomes or their chances of occurring (Miller and Byrnes 1997). In our skater
example, professional skaters would be less likely to fall than novices and, even if an accident
occurs, professionals might have reduced their risk of injury by learning how to fall safely. Many
risk-taking measures do not take into account these differences. For instance, Blais and Weber
(2006) assess projected risk-taking by asking the probability of ‘Piloting a small plane.’ Piloting a
plane is certainly risky for a novice, but for an experienced pilot it is much safer than driving a
car (National Safety Council 2017). This is troublesome as those who are likely to take that ‘risk’
can be those for whom the situation is not risky. Several studies in high-risk sports do show that
skilled individuals are more likely to engage in risky behaviours while also being less prone to
accidents and injuries (Made and Elmqvist 2004; Ruedl et al. 2016). In this regard, individual’s
beliefs are also important. The self-efficacy theory (Bandura 1982) posits that individual will
adopt different behaviours according to their own beliefs about their capabilities, and studies
have shown that self-efficacy influence risk-taking behaviours (Krueger and Dickson 1994) as well
as preventive behaviours (Bandura 1990).
There are at least two possibilities to cope with the general bias of risk subjectivity when
assessing risk-taking concepts. First, researchers interested in general risk-taking can focus on sit-
uations that involve both a low degree of possible control and losses that should be significant
for most, such as eating toxic mushrooms or having unprotected sex with unfamilar partners.
These situations should be risky for most participants and would thus reduce subjectivity.
Second, researchers can enhance the objectivity of their measures by relativizing the situation to
the participant’s point of view. For instance, scales with subjective situations can be objectified
by using items that relate to the participant’s skill (e.g. ‘Going down a ski run that is beyond your
ability’, Blais and Weber 2006). Monetary tasks with real incentives could also reduce the subject-
ivity bias by increasing their incentives or by presenting them differently to make them appear
more significant (Brandt and Martin 2015; Romanowich and Lamb 2013). These measures can be
costly but they should ensure that risk is involved for every participant, thus increasing the valid-
ity of the measures and reducing the noise in the results.
Consider measuring passive risk-taking
Active risk-taking represents behaviours putting people into risky situations, such as deliberately
parking a car in a restricted zone. Passive risk-taking represents inaction putting people into risky
situations, such as not moving a car once one realizes it is parked in a restricted zone (Keinan
and Bereby-Meyer 2017). Overall, the distinction between active and passive risk-taking is rarely
made in the literature, and only a few definitions emphasize that risky behaviours can involve

either action or inaction (Campbell and Viceira 2005; Furby and Beyth-Marom 1992). More
recently, Keinan and Bereby-Meyer (2012) have been the first to formally study these two types
of risk-taking with the development of the Passive Risk-Taking scale. The few studies which com-
pared active and passive risk-taking found differences in how they are influenced and perceived.
Contrary to active risk-taking, passive risk-taking is linked to procrastination and avoidance but
not to sensation seeking (Keinan and Bereby-Meyer 2012). In risky decision making, passive
choices generate less regret than active ones (Luce 1998) and participants perceive less risk and
less personal responsibility in passive risk-taking situations (Keinan and Bereby-Meyer 2017).
In the literature, results are often generalised to an entire risk-taking concept despite the fact
that instruments mostly measure active risk-taking. Most risk-taking tasks require an action from
the participant, such as taking a bet or pushing a button to pump a balloon. To our knowledge,
there is no task that measures risk-taking by looking at the participants’ inaction. Likewise, in
risk-taking scales, most items are framed in an active form. For instance, the most used scale
today, the DOSPERT (Blais and Weber 2006), frames only one of its 30 items in a passive form,
and this predominance can also be seen in others scales (e.g. Nicholson et al. 2005). Active and
passive risk-taking are two complementary forms of risk-taking. Consequently, if a given factor is
found to increase one while decreasing the other, it would be logically spurious to state that
this factor raises risk-taking as a whole. However, the current instruments do not allow discrimin-
ation between influences on active and passive risk-taking.
This focus on active risk-taking can be problematic as many results in the literature can be
understood not in terms of risk-taking but in terms of activeness. If a given factor increases the
propensity to take risky actions, is it because this factor favours risk-taking or because it raises
the motivation to act, whatever the action? For instance, emotions have been linked to risk-tak-
ing but also to changes in the state of action readiness (Frijda, Kuipers, and ter Schure 1989).
Anger has been linked to an increase in active risk-taking (Lerner and Keltner 2001; Rydell et al.
2008) but also to a general action motivation (Glowinsky et al., 2011, Harmon-Jones et al. 2012)
that could decreases passive risk-taking. Conversely, sadness has been linked to a decrease in
risk-taking (Yuen and Lee 2003) but also to an increase in passivity (Glowinski et al., 2011;
Harmon-Jones et al. 2012) that could increases passive risk-taking (Eisenberg, Baron, and
Seligman 1996). Apart from emotions, the personality trait of impulsiveness could also be a fac-
tor that would theoretically increases active risk-taking but also decreases passive risk-taking.
While passive risk-taking is still largely understudied in the literature, it appears to us as a
very relevant way to distinguish risk-taking from activeness. It would be interesting to develop
instruments that measure these two forms of risk-taking, especially for actual risk-taking for
which we did not find any existing passive measures. On their part, future scales assessing risk-
taking concepts should include both active and passive items, thus allowing to pursue their com-
parison and to study their characteristics, for instance by using factor analysis (e.g. J€
oreskog
1969). The prevalence of active items in current scales does not allow making such ana-
lysis today.
Increase the realistic aspect of risk-taking tasks
The literature abounds with tasks designed to assess actual risk-taking behaviours and this is
valuable because measuring actual behaviours is essential to the experimental study of risk-tak-
ing. However, when measured, the links between these tasks and real-life behaviours are often
low and unsatisfactory (Gahagen, 2014; Mamerow et al. 2016; Szrek et al. 2012; Woelbert Riedl
2013). Some explanations of this lack of correlation have already been suggested, emphasizing
for instance the untypicality of these tasks, their non-intuitive nature or their necessity to under-
stand probabilities (Charness and Viceisza 2016; Eisenberg, Baron, and Seligman 1996). Here, we
highlight two explanations that may have been overlooked in the literature: the low level of

arousal induced by these tasks and the level of action identification they induce through their
game aspect.
First, many risk-taking tasks may not be as emotionally involving as real life risky situations
(Anderson and Brown 1984; Schonberg et al. 2011). Although many studies show that antici-
pated emotions and sensation seeking are core motivations in risk-taking (Kerr, 1991;
Loewenstein et al., 2001), few studies have measures of the level of arousal that tasks induce.
Yet, to us, many of the current risk-taking tasks do not seem able to generate strong sensations
to their participants, especially when compared to real-life risk-taking. For instance, Anderson
and Brown (1984) found that gambling in a casino induced more thrill and excitement than in a
typical laboratory task and that, accordingly, gamblers behaved differently in the two contexts.
When laboratory tasks are used to predict real-life gambling, risky driving behaviours or risky sex-
ual behaviours, the low predictive power obtained may come from this gap in arousal and sensa-
tions. Overall, high sensations seekers should be more motivated to take risks outside of the
laboratory, where situations involve more arousal (Anderson and Brown 1984). Conversely, we
may expect sensation avoiders to be more inclined to take risks inside the laboratory, where
arousal is kept lower than outside.
Second, nowadays many tasks are computerized and look like small video games. This aspect
is even reinforced in some studies by presenting the task as a ‘game’ to the participant (e.g.
Braams et al. 2014) or by representing gains as scores or as casino chips (e.g. Osmont et al.
2017). Yet, this game context may favour risk-taking due to factors unrelated to real-life situa-
tions, such as the activation of a performance goal or a stronger feeling of safety. For instance,
video games often encourage players to take risks and to look for the best possible outcome,
alike the best score that is often presented as an objective (Bailey 2012). In lotteries and gam-
bles, this best possible outcome is the maximum money one could possibly earn and is often
only attainable by taking the riskier choices. Modulation of risk-taking in these tasks could there-
fore be linked to the activation of a performance goal, a desire to rea Azjen, ch the best out-
come, which may be irrelevant in other measures or in a real-life context. Moreover, individuals
are used to playing games in a safe context where they do not risk significant losses: losing in
video games usually only means losing time and restarting a sequence. This context of relative
safety can also impair comparisons with real-life risk-taking and lessen correlations between risk-
taking measures. Finally, according to the Action Identification Theory (Vallacher and Wegner,
2000), people can evaluate their own behaviour using different levels. At a lower level of action
identity, people mainly consider the procedural aspect of a task while, at a higher level, they
also take into account the ultimate goal of the task. When participating in a risk-taking task, one
may wonder if the participants identify their behaviours as participation in a game or in a more
serious task of decision making. Depending on this identification, the links with other behaviours
could be fairly different.
Currently, there are many differences between the risk-taking tasks used in studies and real-
life risk-taking behaviours. The more similar these tasks are to real risk-taking situations, the bet-
ter they should predict risk-taking in these situations. From a pragmatic point of view, risk-taking
tasks should be emotionally involving, include potential real losses, and happen in the real
world, not only in a virtual environment. Moreover, the context of the study should lead partici-
pants to identify their actions as relevant in the field of risk, favouring a level of action identifica-
tion that suits the research objective.
Conclusion
Risk-taking is a very important field of both fundamental and applied research. It links many
scholars from different disciplines, each with different views, methods and interests. This diversity
makes risk-taking a very rich field, but also a complex and confusing one. Given the calls to

develop new measurement instruments (Byrnes et al. 1999; Fox and Tannembaum 2011;
Rohrmann 2011; Schonberg et al. 2011), our objective was to highlight the key points that
should be kept in mind when constructing or using measures of risk-taking concepts. We identi-
fied six key points (see Table 2): the need for more vigilance regarding the terms used in studies,
the need for a consistent distinction between measures of general and specific risk-taking, the
need for a distinction between risk-taking and the appeal of risky activities, the need to take
into account the subjectivity of risk-taking, the need for more measures of passive risk-taking,
and the importance of more realistic risk-taking tasks. We believe that these recommendations
should help in developing or using more precise and more adequate means of assessing risk-tak-
ing concepts.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was jointly supported by the Association Nationale de la Recherche Scientifique and Pacifica under the
CIFRE grant 2017/0245.
ORCID
Alexandre Bran http://orcid.org/0000-0003-2838-3886
References
Ajzen I. 1991. “The theory of planned behavior”. Organizational Behavior and Human Decision Processes 50 (2):
179–211. doi:10.1016/0749-5978(91)90020-T.
Anderson, G., and R. Brown. 1984. “Real and Laboratory Gambling, Sensation-Seeking and Arousal.” British Journal
of Psychology 75 (3): 401–410. doi:10.1111/j.2044-8295.1984.tb01910.x.
Brandt, A. E., and J. Martin. 2015. “Simulating Personal Wealth in the Laboratory.” Journal of General Psychology 142
(3): 167–181. doi:10.1080/00221309.2015.1060937.
Bailey, K. 2012. “What would my avatar do? Video games and risky decision making.” Doctoral disseration.
Retrieved from ProQuest Dissertations Theses (3539334)
Bandura, A. 1982. “Self-Efficacy Mechanism in Human Agency.” American Psychologist 37 (2): 122–147. doi:10.1037/
0003-066X.37.2.122.
Bandura, A. 1990. “Perceived Self-Efficacy in the Exercice of Control over AIDS Infection.” Evaluation and Program
Planning 13 (1): 9–17. doi:10.1016/0149-7189(90)90004-G.
Table 2. Our main suggestions concerning the measure of risk-taking concepts.
Part Main suggestions
Precisely define what to measure Distinguish measurements of risk-taking behavior, risk-taking
propensity, risk-taking attitude and risk appraisal
Differentiate general and context-specific risk-taking Measure general risk-taking with several specific indexes Be
wary of overgeneralize measures of specific risk-taking
Distinguish risk-taking from the appeal of risk-taking
activities
Measures precise risky and precautionary behaviours
Take into account the subjectivity of risk-taking Use potential losses that should be significant for most
Relativize the risky situation to the participants’ skills
Consider measuring passive risk-taking Be wary of the overgeneralization of active risk-taking
Measure both active and passive risk-taking
Increase the realistic aspect of risk-taking tasks Make tasks that more emotionally engaging Avoid
game contexts

Bechara, A., A. R. Dam
asio, H. Dam
asio, and S. W. Anderson. 1994. “Insensitivity to Future Consequences following
Damage to Human Prefrontal Cortex.” Cognition 50 (1-3): 7–15. doi:10.1016/0010-0277(94)90018-3.
Ben-Ari, O. T., V. Florian, and M. Mikulincer. 1999. “The Impact of Mortality Salience on Reckless Driving: A Test of
Terror Management Mechanisms.” Journal of Personality and Social Psychology 76 (1): 35–45. doi:10.1037/0022-
3514.76.1.35.
Bernoulli, D. 1954. “Exposition of a New Theory on the Measurement of Risk.” Econometrica 22 (1): 23–36.
Retrieved from: http://links.jstor.org/sici?sici=0012-9682%28195401%2922%3A1%3C23%3AEOANTO%3E2.0.
CO%3B2-X
Blais, A., and E. U. Weber. 2006. “A Domain-Specific Risk-Taking (DOSPERT) Scale for Adult Populations.” Judgment
and Decision Making 1 (1): 33–47.
Braams, B. R., S. Peters, J. S. Peper, B. G€
uro
glu, and E. A. Crone. 2014. “Gambling for Self, Friends, and Antagonists:
Differential Contributions of Affective and Social Brain Regions on Adolescent Reward Processing.” Neuroimage
100 (15): 281–289. doi:10.1016/j.neuroimage.2014.06.020.
Brockhaus, R. 1980. “Risk Taking Propensity of Entrepreneurs.” The Academy of Management Journal 23 (3): 509–520.
doi:10.2307/255515.
Brown, J. W., and T. S. Braver. 2007. “Risk Prediction and Aversion by Anterior Cingulate Cortex.” Cognitive, Affective
Behavioral Neuroscience 7 (4): 266–277. doi:10.3758/CABN.7.4.266.
Buechel, E. C., and C. K. Morewedge. 2014. “The (Relative and Absolute) Subjective Value of Money.” In The
Psychological Science of Money, edited by Bijleveld E. and H. Aarts, 93–120. New York, USA: Springer.
Burns, P. C., Wilde. G. J., and S. 1995. “Risk Taking in Male Taxi Drivers: Relationships among Personality,
Observational Data and Driver Records.” Personality and Individual Differences 18 (2): 267–278. doi:10.1016/0191-
8869(94)00150-Q.
Byrnes, J. P., D. C. Miller, and W. D. Schafer. 1999. “Gender Differences in Risk Taking: A Meta-Analysis.”
Psychological Bulletin 125 (3): 367–383. doi:10.1037/0033-2909.125.3.367.
Campbell, J. Y., and L. M. Viceira. 2005. “The Term Structure of the Risk–Return Trade-Off.” Financial Analysts Journal
61 (1): 34–44. doi:10.2469/faj.v61.n1.2682.
Celsi, R. L., R. L. Rose, and T. W. Leigh. 1993. “An Exploration of High-Risk Leisure Consumption through Skydiving.”
Journal of Consumer Research 20 (1): 1–23. doi:209330/209330.
Charness, G., U. Gneezy, and A. Imas. 2013. “Experimental methods: Eliciting risk preferences.” Journal of Economic
Behavior Organization 87: 43-51. doi: 10.1016/j.jebo.2012.12.023
Charness, G., and A. Viceisza. 2016. “Three Risk-Elicitation Methods in the Field: Evidence from Rural Senegal.”
Review of Behavioral Economics 3 (2): 145–171. doi:10.1561/105.00000046.
Cherpitel, A. J., A. R. Meyers, and M. W. Perrine. 1998. “Alcohol Consumption, Sensation Seeking and Ski Injury: A
Case-Control Study.” Journal of Studies on Alcohol 59 (2): 216–221. doi:10.15288/jsa.1998.59.216.
Coppola, M. 2014. “Eliciting Risk-Preferences in Socio-Economic Surveys: How Do Different Measures Perform?.”
Journal of Socio-Economics 48: 1–10. doi:10.1016/j.socec.2013.08.010.
Dave, C., C. C. Eckel, C. A. Johnson, and C. Rojas. 2010. “Eliciting Risk Preferences: When Is Simple Better?” Journal
of Risk and Uncertainty 41 (3): 219–243. doi:10.1007/s11166-010-9103-z.
Dohmen, T., A. Falk, D. Huffman, U. Sunde, J. Schupp, and G. G. Wagner. 2011. “Individual Risk Attitudes:
Measurement, Determinants, and Behavioral Consequences.” Journal of the European Economic Association 9 (3):
522–550. doi:10.1111/j.1542-4774.2011.01015.x.
Eagly, A. H., and S. Chaiken. 1993. The Psychology of Attitudes. Orlando, FL, US: Harcourt Brace Jovanovich College
Publishers.
Edwards, W. 1954. “The Theory of Decision Making.” Psychological Bulletin 51 (4): 380–417. doi:10.1037/h0053870.
Eisenberg, A. E., J. Baron, and M. E. P. Seligman. 1996. Individual Differences in Risk Aversion and Anxiety. Retrieved
from: http://www.sas.upenn.edu/baron/papers.htm/amyold.html
Ek, Å., and R. Akselsson. 2007. “Aviation on the Ground: Safety Culture in a Ground Handling Company.”
International Journal of Aviation Psychology 17 (1): 59–76. doi:10.1207/s15327108ijap1701_4.
Epstein, J. A., and G. J. Botvin. 2002. “The Moderating Role of Risk-Taking Tendency and Refusal Assertiveness on
Social Influences in Alcohol Use among Inner-City Adolescents.” Journal of Studies on Alcohol 63 (4): 456–459.
doi:10.15288/jsa.2002.63.456.
Farthing, G. W. 2005. “Attitudes toward Heroic and Nonheroic Physical Risk Takers as Mates and as Friends.”
Evolution and Human Behavior 26 (2): 171–185. doi:10.1016/j.evolhumbehav.2004.08.004.
Fox, C. R., and D. Tannenbaum. 2011. “The Elusive Search for Stable Risk Preferences.” Frontiers in Psychology 2: 298.
doi:10.3389/fpsyg.2011.00298.
Frey, R., A. Pedroni, R. Mata, J. Rieskamp, and R. Hertwig. 2017. “Risk Preference Shares the Psychometric Structure
of Major Psychological Traits.” Science Advances 3 (10): E1701381. doi:10.1126/sciadv.1701381.
Frijda, N. H., P. Kuipers, and E. ter Schure. 1989. “Relations among Emotion, Appraisal, and Emotional Action
Readiness.” Journal of Personality and Social Psychology 57 (2): 212–228. doi:10.1037//0022-3514.57.2.212.
Furby, L., and R. Beyth-Marom. 1992. “Risk-Taking in Adolescence: A Decision-Making Perspective.” Developmental
Review 12 (1): 1–44. doi:10.1016/0273-2297(92)90002-J.

Furr, M. 2011. Scale Construction and Psychometrics for Social and Personality Psychology. USA: Wake Forest
University.
Gahagen, D. H. 2014. “Meta-Analysis of the Validity of the Balloon Analogue Risk Task.” Electronic Doctoral
Dissertation. Retrieved from https://etd.ohiolink.edu/
Glowinsky, D., N. Dael, A. Camurri, G. Volpe, M. Mortillaro, and K. Scherer. 2011. “Toward a Minimal Representation
of Affective Gestures.” IEEE Transactions on Affective Computing 2 (2): 106–118. doi:10.1109/T-AFFC.2011.7.
Grol, R., M. Whitfield, J. De Maeseneer, and H. Mokkink. 1990. “Attitudes to Risk Taking in Medical Decision Making
among British, Dutch and Belgian General Practitioners.” The British Journal of General Practice 40 (333): 134–136.
Hanoch, Y., J. G. Johnson, and A. Wilke. 2006. “Domain Specificity in Experimental Measures and Participant
Recruitment: An Application to Risk-Taking Behavior.” Psychological Science 17 (4): 300–304. doi:10.1111/j.1467-
9280.2006.01702.x.
Harmon-Jones, E., E. Gable, and T. F. Price. 2012. “The Influence of Affective States Varying in Motivational Intensity
on Cognitive Scope.” Frontiers in Integrative Neuroscience 6 (2012). doi:10.3389/fnint.2012.00073/full.
Highhouse, S., C. D. Nye, D. C. Zhang, and T. B. Rada. 2017. “Structure of the Dospert: Is There Evidence for a
General Risk Factor?” Journal of Behavioral Decision Making 30 (2): 400–406. doi:10.1002/bdm.1953.
Ho, M., D. Shaw, S. Lin, and Y. Chiu. 2008. “How Do Disaster Characteristics Influence Risk Perception?” Risk Analysis
28 (3): 635–643. doi:10.1111/j.1539-6924.2008.01040.x.
Holt, C. A., and S. K. Laury. 2002. “Risk Aversion and Incentive Effects.” American Economic Review 92 (5):1
644–1655. doi:10.1257/000282802762024700.
Horvath, P., and M. Zuckerman. 1993. “Sensation Seeking, Risk Appraisal, and Risky Behavior.” Personality and
Individual Differences 14 (1): 41–52. doi:10.1016/0191-8869(93)90173-Z.
Iversen, H., and T. Rundmo. 2004. “Attitudes towards Traffic Safety, Driving Behaviour and Accident Involvement
among the Norwegian Public.” Ergonomics 47 (5): 555–572. doi:10.1080/00140130410001658709.
Jackson, D. N. 1976. Jackson Personality Inventory Manual. Port Huron, USA: Research Psychologists Press.
Janz, N. K., and M. H. Becker. 1984. “The Health Belief Model: A Decade Later.” Health Education Behavior 11 (1):
1–47. doi:10.1177/109019818401100101.
Jonah, B. A. 1997. “Sensation Seeking and Risky Driving: A Review and Synthesis of the Literature.” Accident Analysis
Prevention 29 (5): 651–665. doi:10.1016/S0001-4575(97)00017-1.
J€
oreskog, K. G. 1969. “A General Approach to Confirmatory Maximum Likelihood Factor Analysis.” Psychometrika 34
(2): 183–202. doi:10.1007/BF02289343.
Kahneman, D., and A. Tversky. 1979. “An Analysis of Decision under Risk.” Econometrica 47 (2): 263–291. doi:
10.2307/1914185.
Keinan, R., and Y. Bereby-Meyer. 2012. “Leaving It to Chance’—Passive Risk Taking in Everyday Life.” Judgment and
Decision Making 7 (6): 705–715.
Keinan, R., and Y. Bereby-Meyer. 2017. “Perceptions of Active versus Passive Risks, and the Effect of Personal
Responsibility.” Personality and Social Psychology Bulletin 43 (7): 999–1007. doi:10.1177/0146167217703079.
Kerr J.H. 1991. Arousal-seeking in risk sport participants. Personality and Individual Differences 12 (6): 613–616. doi:
10.1016/0191-8869(91)90258-D
Killgore, W. S., G. H. Kamimori, and T. J. Balkin. 2011. “Caffeine Protects against Increased Risk-Taking Propensity
during Severe Sleep Deprivation.” Journal of Sleep Research 20 (3): 395–403. doi:10.1111/j.1365-2869.2010.
00893.x.
Kogan, N., and M. A. Wallach. 1964. Risk Taking: A Study in Cognition and Personality. Oxford, England: Holt.,
Rinehart Winston.
Krueger, N., Dickson. F. P., and R. 1994. “How Believing in Ourselves Increases Risk Taking: Perceived Self-Efficacy
and Opportunity Recognition.” Decision Sciences 25 (3): 385–400. doi:10.1111/j.1540-5915.1994.tb00810.x.
Lajunen, T., J. Karola, and H. Summala. 1997. “Speed and Acceleration as Measures of Driving Style in Young Male
Drivers.” Perceptual and Motor Skills 85 (1): 3–16. doi:10.2466/pms.1997.85.1.3.
Lejuez, C. W., J. P. Read, C. W. Kahler, J. B. Richards, S. E. Ramsey, G. L. Stuart, … R. A. Brown. 2002. “Evaluation
of a Behavioral Measure of Risk Taking: The Balloon Analogue Risk Task (BART).” Journal of Experimental
Psychology: Applied 8 (2): 75–84. doi:10.1037/1076-898X.8.2.75.
Lerner, J. S., R. M. Gonzalez, D. A. Small, and B. Fischhoff. 2003. “Effects of Fear and Anger on Perceived Risks of
Terrorism: A National Field Experiment.” Psychological Science 14 (2): 144–150. doi:10.1111/1467-9280.01433.
Lerner, J. S., and D. Keltner. 2001. “Fear, Anger, and Risk.” Journal of Personality and Social Psychology 81 (1):
146–159. doi:10.1037/0022-3514.81.1.146.
Loewenstein, G. F., E. U. Weber, C. K. Hsee, and N. Welch. 2001. “Risk as Feelings.” Psychological Bulletin 127 (2):
267–286. doi:10.1037/0033-2909.127.2.267.
L€
onnqvist, J.-E., M. Verkasalo, G. Walkowitz, and P. Wichardt. 2015. “Measuring Individual Risk Attitudes in the Lab:
Task or Ask? An Empirical Comparison.” Journal of Economic Behavior Organization 119: 254–266. doi:10.1016/
j.jebo.2015.08.003.
Luce, M. F. 1998. “Choosing to Avoid: Coping with Negatively Emotionladen Consumer Decisions.” Journal of
Consumer Research 24 (4): 409–433. doi:10.1086/209518.

MacCrimmon, K. R., and D. A. Wehrung. 1986. Assessing Risk Propensity. In Recent Developments in the Foundations
of Utility and Risk Theory, edited by L. Daboni, A. Montesano, and M. Lines, 291–309. Dordrecht, Netherlands:
Springer.
MacCrimmon, K. R., and D. A. Wehrung. 1990. “Characteristics of Risk Taking Executives.” Management Science 36
(4): 422–435. doi:10.1287/mnsc.36.4.422.
Maddux, J. E., and R. W. Rogers. 1983. “Protection Motivation and Self-Efficacy: A Revised Theory of Fear Appeals
and Attitude Change.” Journal of Experimental Social Psychology 19 (5): 469–479. doi:10.1016/0022-
1031(83)90023-9.
Made, C., and L. G. Elmqvist. 2004. “A 10-Year Study of Snowboard Injuries in Lapland Sweden.” Scandinavian
Journal of Medicine and Science in Sports 14 (2): 128–133. doi:10.1111/j.1600-0838.2003.00342.x.
Mamerow, L., R. Frey, and R. Mata. 2016. “Risk Taking across the Life Span: A Comparison of Self-Report and
Behavioral Measures of Risk Taking.” Psychology and Aging 31 (7): 711–723. doi:10.1037/pag0000124.
Miller, D. C., and J. P. Byrnes. 1997. “The Role of Contextual and Personal Factors in Children’s Risk Taking.”
Developmental Psychology 33 (5): 814–823. doi:10.1037/0012-1649.33.5.814.
Miller, L., D. E. Meyer, and J. T. Lanzetta. 1969. “Choice among Equal Expected Value Alternatives: Sequential Effects
of Winning Probability Level on Risk Preferences.” Journal of Experimental Psychology 79 (3, Pt.1): 419–423. doi:
10.1037/h0026968.
Milne, S., P. Sheeran, and S. Orbell. 2000. “Prediction and Intervention in Health-Related Behavior: A Meta-Analytic
Review of Protection Motivation Theory.” Journal of Applied Social Psychology 30 (1): 106–143. doi:10.1111/j.1559-
1816.2000.tb02308.x.
Momen, N., M. K. Taylor, R. Pietrobon, M. Gandhi, A. E. Markham, G. A. Padilla, P. W. Miller, K. E. Evans, and T. C.
Sander. 2010. “Initial Validation of the Military Operational Risk Taking Scale (MORTS).” Military Psychology 22 (2):
128–142. doi:10.1080/08995601003638942.
National Safety Council. 2017. Injury FactsV
R
2017 Edition. Itasca, USA; NCS.
Nicholson, N., E. Soane, M. Fenton-O’Creevy, and P. Willman. 2005. “Personality and Domain Specific Risk Taking.”
Journal of Risk Research 8 (2): 157–176. doi:10.1080/1366987032000123856.
Osmont, A., S. Moutier, G. Simon, L. Bouhours, O. Houd
e, and M. Cassotti. 2017. “How Does Explicit versus Implicit
Risk Information Influence Adolescent Risk-Taking Engagement?” Journal of Behavioral Decision Making 30 (5):
1093–1103. doi:10.1002/bdm.2026.
Paquette, L.,
E. Lacourse, and J. Bergeron. 2009. “Construction d’une
echelle de prise de risques et validation aupr
es
d’adolescents pratiquant un sport alpin de glisse [Construction of a scale of taking risk and validation near teen-
agers practitioner an alpine ski sports]. Revue canadienne des sciences du comportement 41 (3): 133–142. doi:
10.1037/a0015256.
Reason, J. T., A. Manstead, S. G. Stradling, J. Baxter, and K. Campbell. 1990. “Errors and Violations on the Road – a
Real Distinction.” Ergonomics 33 (10-11): 1315–1332. doi:10.1080/00140139008925335.
Rohrmann, B. 2011. Risk attitude scales: Concepts, questionnaires, utilizations. Information about crafted instruments.
Melbourne, Australia: Roman Research Road. Retrieved from http://www.rohrmannresearch.net/pdfs/ras-report.pdf
Romanowich, P., and R. Lamb. 2013. “The Effect of Framing Incentives as Either Losses or Gains with Contingency
Management for Smoking Cessation.” Addictive Behaviors 38 (4): 2084–2088. doi:10.1016/j.addbeh.2013.01.007.
Rubio, V. J., J. M. Hern
andez, F. Zald
ıvar, O. M
arquez, and J. Santacreu. 2010. “Can We Predict Risk-Taking Behavior?
Two Behavioral Tests for Predicting Guessing Tendencies in a Multiple-Choice Test.” European Journal of
Psychological Assessment 26 (2): 87–94. doi:10.1027/1015-5759/a000013.
Ruch, W., and M. Zuckerman. 2001. “Sensation Seeking and Adolescence.” In Risikoverhaltensweisen Jugendlicher,
edited by J. Raithel, pp. 97–110. Wiesbaden: VS Verlag f€
ur Sozialwissenschaften. doi:10.1007/978-3-663-11310-2_5
Ruedl, G., M. Schnitzer, W. Kirschner, R. Spiegel, H. Platzgummer, M. Kopp, M. Burtscher, and E. Pocecco. 2016.
“Sports Injuries and Illnesses during the 2015 Winter European Youth Olympic Festival.” British Journal of Sports
Medicine 50 (10): 631–636. doi:10.1136/bjsports-2015-095665.
Rydell, R. J., D. M. Mackie, A. T. Maitner, H. M. Claypool, M. J. Ryan, and E. R. Smith. 2008. “Arousal, Processing, and
Risk Taking: Consequences of Intergroup Anger.” Personality and Social Psychology Bulletin 34 (8): 1141–1152.
doi:10.1177/0146167208319694.
Sarin, R. K., and M. Weber. 1993. “Risk-Value Models.” European Journal of Operational Research 70 (2): 135–149. doi:
10.1016/0377-2217(93)90033-J.
Schoemaker, P. 1990. “Are Risk-Attitudes Related across Domains and Response Modes?” Management Science 36
(12): 1451–1463. doi:10.1287/mnsc.36.12.1451.
Schoemaker, P. J. H. 1993. “Determinants of risk-taking: behavioral and economic views.” Journal of Risk and
Uncertainty, 6(1): 49–73. https://www.jstor.org/stable/41760673
Schonberg, T., C. R. Fox, and R. A. Poldrack. 2011. “Mind the Gap: Bridging Economic and Naturalistic Risk-Taking
with Cognitive Neuroscience.” Trends in Cognitive Sciences 15 (1): 11–19. doi:10.1016/j.tics.2010.10.002.
Sitkin, S. B., and A. L. Pablo. 1992. “Reconceptualizing the Determinants of Risk Behavior.” The Academy of
Management Review 17 (1): 9–38. doi:10.2307/258646.

Slimak, M. W., and T. Dietz. 2006. “Personal Values, Beliefs, and Ecological Risk Perception.” Risk Analysis 26 (6):
1689–1705. doi:10.1111/j.1539-6924.2006.00832.x.
Slovic, P. 1962. “Convergent Validation of Risk Taking Measures.” The Journal of Abnormal and Social Psychology 65
(1): 68–71. doi:10.1037/h0048048.
Slovic, P. 1964. “Assessment of Risk Taking Behavior.” Psychological Bulletin 61 (3): 220–233. doi:10.1037/h0043608.
Slovic, P. 1987. “Perception of Risk.” Science 236 (4799): 280–285. doi:10.1126/science.3563507.
Slovic, P., M. L. Finucane, E. Peters, and D. G. MacGregor. 2004. “Risk as Analysis and Risk as Feelings: Some
Thoughts about Affect, Reason, Risk, and Rationality.” Risk Analysis: An International Journal 24 (2): 311–322. doi:
10.1111/j.0272-4332.2004.00433.x.
Slovic, P., B. Fischhoff, and S. Lichtenstein. 1980. Facts and Fears: Understanding Perceived Risk. In Societal Risk
Assessment, edited by Schwing R.C., and Albers W.A. General Motors Research Laboratories. Boston, USA:
Springer.
Szrek, H., L. Chao, S. Ramlagan, and K. Peltzer. 2012. “Predicting (un)Healthy Behavior: A Comparison of Risk-Taking
Propensity Measures.” Judgment and Decision Making 7 (6): 716–727.
Tversky, A. 1967. “Additivity, Utility, and Subjective Probability.” Journal of Mathematical Psychology 4 (2): 175–201.
doi:10.1016/0022-2496(67)90049-1.
United States Parachute Association. 2015. Skydiving Safety. Retrieved from: https://uspa.org/Find/FAQs/Safety
Vallacher, R. R., and D. M. Wegner. 2000. What Do People Think They Are Doing? Action Identification and Human
Behavior. In E. T. Higgins, A. W. Kruglanski, E. T. Higgins, A. W. Kruglanski (Eds.), Motivational science: Social
and personality perspectives (pp. 215–228). New York, NY, US: Psychology Press.
Pligt, J. 1998. “Perceived Risk and Vulnerability as Predictors of Precautionary Behaviour.” British Journal of Health
Psychology 3 (1): 1–14. doi:10.1111/j.2044-8287.1998.tb00551.x.
Wachinger, G., O. Renn, C. Begg, and C. Kuhlicke. 2013. “The Risk Perception Paradox–Implications for Governance
and Communication of Natural Hazards.” Risk Analysis 33 (6): 1049–1065. doi:10.1111/j.1539-6924.2012.01942.x.
Weber, E. U., A. Blais, and N. E. Betz. 2002. “A Domain-Specific Risk-Attitude Scale: Measuring Risk Perceptions and
Risk Behaviors.” Journal of Behavioral Decision Making 15 (4): 263–290. doi:10.1002/bdm.414.
Weinstein, N. D. 1989. “Optimistic Biases about Personal Risks.” Science (New York, N.Y.) 246 (4935): 1232–1233.
Whissell, R. W., and B. J. Bigelow. 2003. “The Speeding Attitude Scale and the Role of Sensation Seeking in Profiling
Young Drivers at Risk.” Risk Analysis 23 (4): 811–820. doi:10.1111/1539-6924.00358.
Wilke, A., A. Sherman, B. Curdt, S. Mondal, C. Fitzgerald, and D. J. Kruger. 2014. “An Evolutionary Domain-Specific
Risk Scale.” Evolutionary Behavioral Sciences 8 (3): 123–141. doi:10.1037/ebs0000011.
Wishart, D., K. Somoray, and B. Rowland. 2017. “Role of Thrill and Adventure Seeking in Risky Work-Related Driving
Behaviours.” Personality and Individual Differences 104: 362–367. doi:10.1016/j.paid.2016.08.033.
Woelbert, E., and A. Riedl. 2013. “Measuring Time and Risk Preferences: Reliability.” Stability, Domain Specificity.
Netspar Discussion Paper No. 07/2013:044. doi:10.2139/ssrn.2354907.
Wong, A., and B. J. Carducci. 1991. “Sensation Seeking and Financial Risk Taking in Everyday Money Matters.”
Journal of Business and Psychology 5 (4): 525–530. doi:10.1007/BF01014500.
Woodman, T., M. Barlow, C. Bandura, M. Hill, D. Kupciw, and A. MacGregor. 2013. “Not All Risks Are Equal: The Risk
Taking Inventory for High-Risk Sports.” Journal of Sport Exercise Psychology 35 (5): 479–492. doi:10.1123/
jsep.35.5.479.
Xu, S., Y. Pan, Y. Wang, A. M. Spaeth, Z. Qu, and H. Rao. 2016. “Real and Hypothetical Monetary Rewards Modulate
Risk Taking in the Brain.” Scientific Reports 6 (29520). doi:10.1038/srep29520.
Xu, S., Y. Pan, Z. Qu, Z. Fang, Z. Yang, F. Yang, F. Wang, et al. 2018. “Differential Effects of Real versus Hypothetical
Monetary Reward Magnitude on Risk-Taking Behavior and Brain Activity.” Scientific Reports 8 (1): 3712. doi:
10.1038/s41598-018-21820-0.
Yates, J. F., and E. R. Stone. 1992. “The Risk Construct.” In Wiley Series in Human Performance and Cognition. Risk-
Taking Behavior, edited by J. F. Yates, 1–25. Oxford, England: John Wiley.
Yuen, K. L., and T. C. Lee. 2003. “Could Mood State Affect Risk-Taking Decisions?” Journal of Affective Disorders
75 (1): 11–18. doi:10.1016/S0165-0327(02)00022-8.
Zuckerman, M. 1983. Biological Bases of Sensation Seeking, Impulsivity, and Anxiety. Hillsdale, N.J, USA: Lawrence.
Erlbaum Associates Inc.

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