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Follow Your Gut? Emotional Intelligence Moderates the Association Between Physiologically Measured Somatic Markers and Risk-Taking

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Abstract

Emotional Intelligence (EI) is a set of adaptive skills that involve emotions and emotional information. Prior research suggests that lower EI individuals behave maladaptively in social situations compared to higher EI individuals. However, there is a paucity of research on whether EI promotes adaptive decision-making. Leveraging the somatic marker hypothesis, we explore whether EI moderates the relationship between skin conductance responses (SCRs) and risky decision-making. In two separate sessions in the behavioral lab, participants (N = 52) completed tests of emotional intelligence and made a total of 5,145 decisions involving risk. At Time 1, participants completed an ability test of EI and cognitive intelligence. At Time 2, participants completed 100 decision trials of the Iowa Gambling Task (IGT). Consistent with prior research using the IGT, participants played a computerized card game with real monetary rewards in which two "safe" decks led to higher average monetary rewards and two "risky" decks led to higher average losses. We found that EI moderates the relationship between physiological arousal, as measured by SCRs, and risk-taking. Specifically, lower EI individuals exhibited a maladaptive, positive association between SCRs and risk-taking, whereas higher EI individuals did not exhibit a relationship between SCRs and risk-taking. Our findings suggest one important way in which low EI may lead to maladaptive decision-making is through appraising physiological arousal incorrectly. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Emotion
Follow Your Gut? Emotional Intelligence Moderates the
Association Between Physiologically Measured Somatic
Markers and Risk-Taking
Jeremy A. Yip, Daniel H. Stein, Stéphane Côté, and Dana R. Carney
Online First Publication, March 4, 2019. http://dx.doi.org/10.1037/emo0000561
CITATION
Yip, J. A., Stein, D. H., Côté, S., & Carney, D. R. (2019, March 4). Follow Your Gut? Emotional
Intelligence Moderates the Association Between Physiologically Measured Somatic Markers and
Risk-Taking. Emotion. Advance online publication. http://dx.doi.org/10.1037/emo0000561
Follow Your Gut? Emotional Intelligence Moderates the Association
Between Physiologically Measured Somatic Markers and Risk-Taking
Jeremy A. Yip
Georgetown University Daniel H. Stein
University of California, Berkeley
Stéphane Côté
University of Toronto Dana R. Carney
University of California, Berkeley
Emotional Intelligence (EI) is a set of adaptive skills that involve emotions and emotional information.
Prior research suggests that lower EI individuals behave maladaptively in social situations compared to
higher EI individuals. However, there is a paucity of research on whether EI promotes adaptive
decision-making. Leveraging the somatic marker hypothesis, we explore whether EI moderates the
relationship between skin conductance responses (SCRs) and risky decision-making. In two separate
sessions in the behavioral lab, participants (N52) completed tests of emotional intelligence and made
a total of 5,145 decisions involving risk. At Time 1, participants completed an ability test of EI and
cognitive intelligence. At Time 2, participants completed 100 decision trials of the Iowa Gambling Task
(IGT). Consistent with prior research using the IGT, participants played a computerized card game with
real monetary rewards in which two “safe” decks led to higher average monetary rewards and two “risky”
decks led to higher average losses. We found that EI moderates the relationship between physiological
arousal, as measured by SCRs, and risk-taking. Specifically, lower EI individuals exhibited a maladap-
tive, positive association between SCRs and risk-taking, whereas higher EI individuals did not exhibit a
relationship between SCRs and risk-taking. Our findings suggest one important way in which low EI may
lead to maladaptive decision-making is through appraising physiological arousal incorrectly.
Keywords: decision-making, emotional intelligence, Iowa Gambling Task, physiology, skin conductance
Supplemental materials: http://dx.doi.org/10.1037/emo0000561.supp
Emotional Intelligence (EI) is a mental ability to solve problems
about emotions and is considered a separate construct from cog-
nitive ability and personality (Mayer, Roberts, & Barsade, 2008;
Mayer & Salovey, 1997). At a broad level, EI includes the abilities
to (a) perceive emotions accurately, (b) use emotions to facilitate
thought, (c) understand the causes of emotions, and (d) regulate
emotions (Mayer, Caruso, & Salovey, 2016; Mayer & Salovey,
1997; Salovey & Mayer, 1990).
There is some evidence that individuals with lower EI experi-
ence worse life outcomes compared with individuals with higher
EI (Côté, 2014; Fernández-Berrocal & Extremera, 2016; Lopes,
2016; Mayer et al., 2008; Mayer, Salovey, & Caruso, 2004; Rob-
erts et al., 2006; Rossen & Kranzler, 2009). For example, relative
to individuals with higher EI, individuals with lower EI are more
likely to develop weak social relationships (Brackett, Rivers,
Shiffman, Lerner, & Salovey, 2006; Lopes, Salovey, Coté, Beers,
& Petty, 2005), achieve worse job performance (Côté & Miners,
2006; Libbrecht, Lievens, Carette, & Côté, 2014), and perform
worse in negotiations (Elfenbein, Der Foo, White, Tan, & Aik,
2007; Sharma, Bottom, & Elfenbein, 2013).
So why do individuals with lower EI experience relatively
worse overall life outcomes? This question, although intuitively
sensible, remains a puzzle because there is a paucity of research on
the microprocesses through which EI might relate to adaptive and
maladaptive life outcomes. In the current research, we advance our
understanding of the manifestations of EI by investigating whether
EI moderates the effect of physiological arousal on risk-taking.
We draw theoretically and methodologically from research on
the somatic marker hypothesis (SMH). The SMH suggests that
somatic markers, which are automatic emotion-related signals that
Jeremy A. Yip, Management Department, McDonough School of Business,
Georgetown University; Daniel H. Stein, Management of Organizations De-
partment, Haas School of Business, University of California, Berkeley;
Stéphane Côté, OB/HRM Department, Rotman School of Management, Uni-
versity of Toronto; Dana R. Carney, Management of Organizations Depart-
ment, Haas School of Business, University of California, Berkeley.
We thank our Associate Editor Hillary Elfenbein for her constructive
feedback. We are grateful for suggestions provided by Bernd Figner,
Elizabeth Page-Gould, Amie Gordon, Markus Groth, Dilip Soman, Glen
Whyte, Andy Yap, and Chen-Bo Zhong. We wish to thank Vivian Chan,
Yunjie Shi, Man-On Tong, Joyce Wong, Jacky Tam, and James McGee for
their data collection assistance.
Data are available at https://osf.io/nz4k9/.
Correspondence concerning this article should be addressed to Jeremy
A. Yip, Management Department, McDonough School of Business,
Georgetown University, 3700 O Street NW, Washington, DC 20057.
E-mail: jeremy.yip@georgetown.edu
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Emotion
© 2019 American Psychological Association 2019, Vol. 1, No. 999, 000
1528-3542/19/$12.00 http://dx.doi.org/10.1037/emo0000561
1
are manifested by physiological arousal such as skin conductance
responses (SCRs), “warn” individuals in advance of risky decision
options and “guide” individuals to safe decision options (Bechara
& Damasio, 2005; Bechara, Damasio, Tranel, & Damasio, 1997,
2005; Bechara, Tranel, Damasio, & Damasio, 1996; Damasio,
1994). Prior research suggests that somatic markers promote ap-
proach or avoidance behavior, but it is unclear whether some
individuals are more effective at translating their physiological
arousal into information that guides approach or avoidance behav-
ior than others. In this work, we examine whether emotional
intelligence may moderate the relationship between SCRs and
risk-taking. That is, we explore whether individuals with higher EI
utilize physiological arousal to adaptively avoid risky decision
options, whereas individuals with lower EI do not utilize physio-
logical arousal to guide behavior or even utilize physiological
arousal to maladaptively approach risky decision options.
Arousal and Decision-Making
Emotions influence decision-making by providing critical sig-
nals to avoid danger and capitalize on profitable social and eco-
nomic opportunities (Johnson & Tversky, 1983; Loewenstein,
Weber, Hsee, & Welch, 2001; Schwarz & Clore, 2007; Seo &
Barrett, 2007; Sinaceur, Heath, & Cole, 2005; Slovic, Finucane,
Peters, & MacGregor, 2002; Yip & Schweitzer, 2016). Previous
research conceptualizes emotion as being characterized by physi-
ological arousal, valence, and cognitive appraisals (Barrett, 2012;
Ekman, 1999; Russell, 2003; Russell & Barrett, 2014; Yip &
Schweitzer, 2019). When individuals experience physiological
arousal (which often manifests in SCRs) in reaction to decision
options, they are experiencing an emotion, but they are unable to
determine the specific emotion without assessing valence and
cognitive appraisals. For example, when individuals encounter
risky decision options, individuals may experience anxiety, which
is characterized by negative valence and appraisal of uncertainty,
or excitement, which is characterized by positive valence and
appraisal of uncertainty (Akinola, 2010; Bechara et al., 1997;
Figner & Murphy, 2011; Lerner, Li, Valdesolo, & Kassam, 2015).
Recent research suggests that anxiety and excitement are similar
because both emotions are characterized by the anticipation of an
outcome and by uncertainty, but they differ by valence and the
behavioral consequences are distinct (Brooks, 2014). Individuals
appraising their arousal as anxiety become more likely to choose
safer decision options because they seek to protect themselves and
their resources (Akinola & Mendes, 2012; Gino, Brooks, &
Schweitzer, 2012; Larrick, 1993; Wood Brooks & Schweitzer,
2011). However, individuals who appraise their arousal as excite-
ment become more likely to choose riskier decision options be-
cause they believe that they can achieve more positive outcomes
(Ashby, Isen, & Turken, 1999; Brooks, 2014).
It is important to note that arousal can be consciously experi-
enced, but can also be unconsciously experienced, and influence
judgment and behavior (Bechara et al., 1997; Damasio, 1994). For
example, when participants observe liars who appeal to the press
for the return of a missing person even though they actually
murdered the missing person, they often experience physiological
arousal, which provide a signal of threat as measured with pulse
plethysmography, relative to when participants observe truth-
telling individuals (ten Brinke, Lee, & Carney, in press).
Prior research suggests that people can make mistakes in attrib-
uting physiological arousal (Schachter & Singer, 1962). For ex-
ample, consistent with the feeling-as-information model, Dutton
and Aron (1974) found that males were more likely to contact a
female experimenter when they had crossed an arousal-inducing
suspension bridge instead of a stable bridge, because they misat-
tributed the physiological arousal from the suspension bridge to
the sexual attraction associated with the female experimenter
(Savitsky, Medvec, Charlton, & Gilovich, 1998; Schwarz & Clore,
2007; White, Fishbein, & Rutsein, 1981). Relatedly, according to the
affect infusion model, affect infuses decision-making when a deci-
sion is complex, requires constructive processing, and is unfamil-
iar (Forgas, 1992, 1995). Taken together, physiological arousal is
a main component of emotion, and physiological arousal influ-
ences judgment.
Somatic Marker Hypothesis
The somatic marker hypothesis (SMH) proposes that the uncer-
tainty of decision options generates somatic markers in a complex
decision context. Empirical support for the SMH is based on
studies employing the Iowa Gambling Task (IGT; Bechara, Dama-
sio, Damasio, & Anderson, 1994; Bechara et al., 1996, 1997). In
the IGT, participants select cards from decks that differ in the
uncertainty of the payoffs. Two of the decks are disadvantageous
because they contain cards with high rewards but even higher
relative losses, resulting in an overall loss in the long-run. Two
other decks are advantageous because they contain cards with
small rewards but relatively smaller losses, resulting in an overall
gain in the long-run.
As individuals ponder decisions in the IGT, somatic marker
signals are generated which activates autonomic nervous system
activity in the skin’s sweat glands. Because autonomic nervous
system activity can be unconscious and difficult to report, electro-
dermal activity, which is assessed by skin conductance responses
(SCRs), is simultaneously recorded with decisions and used to
index the magnitude of somatic markers (Damasio, 1994; Dunn,
Dalgleish, & Lawrence, 2006). The SMH posits that these somatic
markers—although involuntary—serve as affective signals that
guide adaptive decision making.
In the canonical demonstration of the SMH (Bechara et al.,
1997; Bechara & Damasio, 2002; Reimann & Bechara, 2010),
participants who had ventromedial prefrontal cortex lesions and
participants who were normal made a series of decisions in the
IGT. Though individuals with lesions to the ventromedial prefron-
tal cortex have normal intelligence and problem-solving skills,
they encounter problems experiencing affect and generating so-
matic markers. Results demonstrated that normal participants gen-
erated more pronounced somatic markers (i.e., anticipatory SCRs)
prior to the disadvantageous decks and eventually choose more
cards from the advantageous decks. In contrast, individuals with
damage to the ventromedial prefrontal cortex did not generate
SCRs in response to disadvantageous decks, and as a result, did not
modify their decision-making. This finding offers an empirical
demonstration that anticipatory SCRs can serve as affective signals
that nudge participants away from making risky choices.
Emerging work has begun to explore the role of individual
differences in the SMH involving normal, fully functioning
participants (e.g., Fernández-Serrano, Pérez-García, & Verdejo-
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2YIP, STEIN, CÔTÉ, AND CARNEY
García, 2011). For example, cognitive intelligence predicts
performance on the IGT (Dunn et al., 2006; Guillaume et al.,
2009; Maia & McClelland, 2004; Toplak, Sorge, Benoit, West,
& Stanovich, 2010).
Recent work suggests that cognitive intelligence, rather than
emotional intelligence, is more strongly associated with perfor-
mance on the IGT (Demaree, Burns, & DeDonno, 2010; Li et al.,
2017; Webb, DelDonno, & Killgore, 2014). These studies found
no association between EI and performance on the IGT (Demaree
et al., 2010) or that EI failed to contribute above and beyond
cognitive ability in the prediction of IGT performance (Li et al.,
2017; Webb et al., 2014). However, none of these studies mea-
sured somatic markers (i.e., SCRs) simultaneously with each de-
cision trial. In fact, no research has directly investigated whether
EI influences the utilization of somatic markers to avoid or ap-
proach risky decisions. In our investigation, we explore whether EI
moderates the association between SCRs and risk-taking.
Emotional Intelligence as a Moderator of the
Association Between Somatic Markers and
Risk-Taking
The ability model of emotional intelligence (EI) conceptualizes
EI as four distinct abilities: (1) perceiving emotions, (2) using
emotions to facilitate performance, (3) understanding emotions,
and (4) managing emotions (Mayer & Salovey, 1997; Mayer,
Salovey, Caruso, & Sitarenios, 2003; Salovey & Mayer, 1990; Yip
& Martin, 2006). However, the ability model of emotional intel-
ligence is evolving. For example, in more recent models, EI
encompasses six distinct skills (Elfenbein & MacCann, 2017;
Elfenbein, Jang, Sharma, & Sanchez-Burks, 2017).
We propose that EI moderates the relationship between somatic
markers and risk-taking. Specifically, we expect that individuals
with high EI exhibit a negative association between SCRs and
risk-taking, whereas individuals with low EI exhibit no association
or a positive association with risk-taking.
There is some indirect evidence suggesting that individuals with
low EI may struggle to use emotions as a source of information
appropriately to make adaptive decisions. First, existing research
revealed that incidental anxiety reduces risk-taking among indi-
viduals with lower rather higher levels of emotion-understanding
ability (Yip & Côté, 2013). Incidental anxiety refers to anxiety that
is triggered by an unrelated situation and irrelevant to the decision
at hand (Loewenstein & Lerner, 2003). Relative to individuals
with high EI, individuals with low EI exhibit a negative effect of
incidental anxiety on risk-taking because they incorrectly attribute
their anxiety and mistakenly believe that their anxiety is relevant to
their decision-making (Yip & Côté, 2013). We build on this line of
research by focusing our investigation on integral arousal, which is
physiological arousal that is related and relevant to the judgment at
hand (Lerner & Keltner, 2001; Schwarz & Clore, 2007). That is,
anticipatory SCRs generated on the IGT reflect integral arousal
because the physiological arousal is associated with the riskiness
of the decision options. We explore whether low EI individuals
may experience difficulty in interpreting their integral arousal
correctly when making decisions.
Second, previous research suggests that individuals who can cor-
rectly interpret their physiological signals exhibit better decision-
making. For instance, individuals with lower levels of interoceptive
awareness—the ability to detect changes in bodily systems that is
measured with a heartbeat detection task—exhibited a positive asso-
ciation between bodily signals (a composite of heart rate and electro-
dermal activity) and risky decisions (Dunn et al., 2010; Farb et al.,
2015). In contrast, individuals with higher levels of interoceptive
awareness exhibited a negative association between bodily signals
and risky decisions (Dunn et al., 2010). Furthermore, Wölk, Sütterlin,
Koch, Vögele, & Schulz (2014) found that interoception reduces
risk-taking on the Iowa Gambling Task.
In our work, we extend our understanding about the SMH by
directly linking EI, physiological arousal, and risk-taking. Specif-
ically, we expect that EI moderates the association between SCRs
and risky decisions. In particular, among individuals with higher
EI, we expect a negative association between SCRs and risk-
taking. Among individuals with lower EI, we expect no association
or a positive association between SCRs and risk-taking.
Overview of the Current Research
To test whether individual variation in EI predicts the link
between SCRs and risk-taking, we first administered a standard-
ized test of EI and cognitive ability, and then exposed participants
to 100 decision trials involving a trade-off between risk and
rewards in the IGT (Bechara, Damasio, Tranel, & Damasio, 2005).
The IGT is a card game with real monetary rewards in which two
(low risk) decks of cards lead to high average monetary rewards
and the other two (high risk) decks lead to high average losses. We
measured SCRs and recorded whether participants chose from a
risky deck in each trial. We measured trait-level arousal through
baseline skin-conductance level. We include both baseline skin-
conductance level and momentary SCRs because state-level arousal
may be related to trait-level arousal (Fleeson, 2001).
Method
We report how we determined our sample size, all data exclu-
sions, and all measures in this study (Simmons, Nelson, & Simon-
sohn, 2011). We set sample size before any data were collected.
We determined our sample size based on available resources; with
these resources, we were able to double the sample size used in
past SMH research (e.g., Bechara et al., 1997). No data were
analyzed until after data collection ended.
Participants
We aimed to recruit 60 participants from a North American
university on the East Coast and ended up recruiting a sample of
62. As is often the case in highly software-dependent physiological
research, some participants’ data failed to be saved or were over-
written by the computer. Skin-conductance responses were not
recorded for six participants, IGT data were not saved for one
participant, and the event markers for synchronizing the skin-
conductance responses with the IGT were not properly recorded
for three participants. No data were excluded. The data were not
merged or analyzed until after data collection ended. The final
sample size was 52 participants (M
age
24 years, SD
age
4
years; 63% female). For some secondary analyses, the sample size
was smaller (see footnotes for explanation). Overall, each of the
participants completed 100 trials of the Iowa Gambling Task and
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3
EMOTIONAL INTELLIGENCE AND AROUSAL
physiology was measured prior to each decision trial. In total, we
collected data for 5,145 observations.
1
Procedure
We collected data in two sessions that occurred within 10 days
of each other to separate the measurement of EI from the mea-
surement of the physiological index of somatic markers and risky
decision-making in the Iowa Gambling Task. In an initial 60-min
testing session, participants completed measures of EI and cogni-
tive ability and were paid $10 for their time. Approximately 10
days later, participants arrived at the laboratory for a 30-min
individual experimental session and completed the Iowa Gambling
Task (IGT) while SCRs were continuously measured (Bechara et
al., 1997). Baseline physiology was measured during this session
as is typical (e.g., Miu, Heilman, & Houser, 2008) so that tonic
skin conductance at rest could serve as a control variable for the
reactivity SCRs to the stimuli. Participants were compensated with
1/200 of any winnings accrued from the Iowa Gambling Task
(range: $2.50 to $28.22, M$14.05; SD $5.92).
Measures
Emotional intelligence (EI). We assessed EI using the most
widely used and validated measure of EI, the Mayer-Salovey-
Caruso Emotional Intelligence Test (MSCEIT; Mayer & Salovey,
1997). The MSCEIT is a 141-item ability measure of EI
that consists of four branches: emotion-recognition, emotion-
facilitation, emotion-understanding, and emotion-management.
The MSCEIT produces scores for four branches and a score for
total EI (Mayer et al., 2004). The total EI score is an average of
a participant’s performance across all four branches (M
96.30, SD 17.64).
Emotion-recognition ability. The emotion-recognition abil-
ity subscale of the MSCEIT consists of 48 problems about iden-
tifying the emotion being expressed in a photograph of a face or
identifying the emotion being evoked by a photograph of a land-
scape (M98.70, SD 14.88; ␣⫽.89).
Emotion-facilitation ability. The emotion-facilitation ability
subscale of the MSCEIT is composed of 30 problems about
identifying the usefulness of a specific emotion to perform an
activity or identifying the sensations affiliated with an emotion
(M97.20, SD 14.39; ␣⫽.41).
Emotion-understanding ability. The emotion-understanding
ability subscale of the MSCEIT includes 32 problems about iden-
tifying the cause of emotional reactions and labeling complex
emotions that result from blending basic emotions (M103.01,
SD 18.00; ␣⫽.81).
We also administered a newer measure of emotion-understanding
ability called the Situational Test of Emotion Understanding (STEU;
MacCann & Roberts, 2008). The STEU presents a series of 42
scenarios and asks respondents to choose the emotion that is most
likely to be generated by the situation depicted in each scenario,
among five options that are presented (M27.22, SD 5.94; ␣⫽
.78). As expected, the STEU was positively correlated with the
analogous emotion-understanding ability branch of the MSCEIT,
r(44) .61, p.001.
2
Emotion-management ability. The emotion-management abil-
ity subscale of the MSCEIT includes 29 problems about identifying
the most effective course of action to influence emotions in another
person and manage personal relationships (M89.77, SD 14.99;
␣⫽.72).
Cognitive ability. Past research has demonstrated that cognitive
ability is positively correlated with EI (Joseph & Newman, 2010), and
cognitive ability is positively associated with decision-making in the
IGT (Toplak et al., 2010). To verify that EI was not confounded by
cognitive ability, we administered the Wonderlic Personnel Test, a
commonly used test that includes 50 verbal, mathematical, and ana-
lytical problems (M116.76, SD 10.89; ␣⫽.90; (Wonderlic,
1992).
3
Baseline skin conductance level. We controlled for individ-
ual differences in tonic electrodermal activity to capture whether
some individuals are more sensitive in their physiological reac-
tions compared to others (Wilder, 1962). We calculated baseline
skin conductance levels for each participant over the last three
minutes of a 5-min resting period, after the participant had become
habituated to the equipment (Cacioppo, Tassinary, & Berntson,
2007; SCL was M5.81; SD 4.08). We report all analyses both
with and without controlling for individual differences in baseline
tonic skin conductance level. The results are identical (see online
supplemental material).
Skin-conductance responses (SCRs). Data were acquired us-
ing the GSR100C amplifier connected to the BIOPAC MP150
system at a rate of 10 samples per second in a noise-free environ-
ment. SCRs were recorded by placing a pair of silver-silver chlo-
ride electrodes with 0.05 M sodium chloride gel on the distal
phalanges of the first and second digits of the nondominant hand.
SCR data were analyzed using the SCR analysis module of Acq-
Knowledge. As in past research (e.g., Miu, Heilman, & Houser,
2008; Oya et al., 2005), we measured the amplitude of the SCRs
during the 5-s window that preceded each decision. The average
SCR amplitude was 1.95 (SD 4.53).
Risk-taking in the Iowa Gambling Task (IGT). Using an
exact version of the IGT (Bechara et al., 2005), we recorded
whether participants chose from one of the four decks of cards
which varied in risk (and long-term loss) versus reward (and
long-term gain). To begin the IGT, participants were endowed
with a $2,000 loan and told they would be able to keep 1/200 of
whatever they made in the game as a bonus in addition to their
payment for participation in the two-part study. The goal of the
IGT was to earn as much money as possible by choosing among
four computerized card decks for 100 trials. Participants were not
provided with explicit probabilities of the payoffs associated with
the decks. Two decks were considered “risky” because these decks
were associated with high rewards ($100), but also severe losses
($1,250), resulting in an overall negative expected value
of $250 over 10 trials (Bechara et al., 2005). Two decks were
considered “safe” and were associated with low rewards ($50),
but periodically less severe losses ($250), resulting in an overall
positive expected value of $250 over 10 trials (Bechara et al.,
1
The total number of observations is 5,145 because some participants
did not complete all 100 decision trials because of technical issues with
equipment or time constraints.
2
Six participants did not complete the STEU because they ran out of
time in the 60-minute session.
3
Two participants did not complete the cognitive ability test because
they ran out of time in the 60-minute session.
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4YIP, STEIN, CÔTÉ, AND CARNEY
2005). Taking a card from the risky deck was coded as “1” and
taking a card from a safe deck was coded as “0.” Overall, our
participants selected cards from the safe deck 36% of the time
(SD .48), which is consistent with past research on the IGT
(Bechara et al., 1997).
Results
To analyze the data with 100 trials nested within each partici-
pant, we conducted linear mixed effects binary logistic analyses,
with responses nested within participants (Tabachnick & Fidell,
2007). Specifically, we analyzed associations with risk-taking with
generalized linear mixed-effects model, using the lme4 package in
the statistical program R (Bates, Maechler, Bolker, & Walker,
2015). Participants were specified as a random factor and we
added a random slope to allow SCR (a within-participant predic-
tor) to vary within individuals which is a random intercept and
random slope model (Aguinis, Gottfredson, & Culpepper, 2013;
Brauer & Curtin, 2018; Pinheiro & Bates, 2000). To disentangle
within- and between-individual effects, we centered all intra-
individual (level-1) predictors around the mean for each individ-
ual, and all between-individual (level-2) predictors around the
mean across individuals (Aguinis et al., 2013; Aiken, West, &
Reno, 1991).
In each analysis, risk-taking in each trial (binary) was regressed
on the measure of EI (continuous), SCR corresponding to each trial
(continuous), and their interaction term (continuous), controlling
for baseline skin conductance (continuous). This analysis tested
whether EI moderate the relationship between SCR and risk-
taking. Follow-up analyses using simple slopes (Aiken, West, &
Reno, 1991) clarified the significance value of each slope in the
interaction. Specifically, the association between SCR and risk-
taking were tested for low (1SD below the mean) and high (1
SD above the mean) levels of EI.
Does Emotional Intelligence Moderate the Association
Between Skin Conductance Responses and
Risk-Taking?
Consistent with our prediction, we found a significant two-
way interaction between SCR and total EI, b⫽⫺0.0023,
SE(b)0.0008, Wald test ⫽⫺2.590, p.009, OR 0.998,
95% CI [0.996, 0.999]. We present the results in Table 1.
Follow-up analyses using simple slopes further investigated
the interaction to determine whether one slope or both were
responsible for the statistically significant interaction. Among
individuals lower on EI, SCRs were positively associated with
risk-taking, b0.0302, SE(b)0.0141, Wald test 2.140,
p.032, OR 1.031, 95% CI [1.003, 1.060]. The odds ratio
indicates that for a one-unit increase in SCR, the odds of
risk-taking are 3.1% larger, holding all other variables constant.
This finding supports our prediction that among individuals
with lower EI, SCRs were positively associated with risk-
taking. In contrast, among individuals with higher EI, we found
a negative—albeit not statistically significant—association be-
tween SCRs and risk-taking, b⫽⫺0.0265, SE(b)0.0180, Wald
test ⫽⫺1.473, p.140, OR 0.974, 95% CI [0.940, 1.009]. The
odds ratio indicates that for a one-unit increase in SCR, the odds of
risk-taking are 2.6% smaller, holding all other variables constant. This
finding does not support our prediction that SCRs were associated
with less risky decisions among individuals higher on EI.
Historically, EI and cognitive ability have been difficult to
disassociate (Carroll, 1993; Jensen, 1998; Mackintosh, 2011). To
disentangle EI from cognitive ability, researchers often test
whether EI improves predictions of outcomes above and beyond
cognitive ability (e.g., Côté, Lopes, Salovey, & Miners, 2010;
Libbrecht et al., 2014). Therefore, we conducted additional anal-
yses controlling for cognitive ability.
As presented in Table 1, when controlling for cognitive ability,
the two-way interaction between SCR and EI was significant,
b⫽⫺0.0022, SE(b)0.0009, Wald test ⫽⫺2.281, p.022,
OR 0.998, 95% CI [0.996, 1.000]. However, when controlling
for the interaction between cognitive ability and SCR, the two-way
interaction between SCR and EI was not significant, b⫽⫺0.0019,
SE(b)0.0012, Wald test ⫽⫺1.560, p.118, OR 0.998, 95%
CI [0.996, 1.000]. To further explore this result, we tested whether
cognitive ability and SCR interacted to predict risky decision-
making. Controlling for the two-way interaction between SCR and
EI cognitive ability did not interact with SCR to predict risky
decision-making, b⫽⫺0.0007, SE(b)0.0014, Wald
test ⫽⫺0.536, p.592, OR 0.999, 95% CI [0.996, 1.00]. This
suggests that cognitive ability did not act as a confound in the
interaction between EI and SCR predicting risky decision-making.
Taken together, these results provide some evidence that EI, rather
than cognitive ability, moderates the relationship between SCRs
and risk-taking.
We conducted additional analyses with each of the four separate
branches of EI. The pattern observed for the overall EI composite
variable was also observed for three of the four branches of EI
when examined individually. We present the results for each of the
EI branches in Figure 1 and Table 2.
EI Branch #1: Emotion-Recognition Ability
As expected, we found a significant two-way interaction be-
tween SCR and emotion-recognition ability, b⫽⫺0.0023,
Table 1
Analyses for Total Emotional Intelligence
Dependent variable: Risk
Measure (1) (2)
Baseline SCL .001 (.027) .015 (.027)
SCR .002 (.012) .003 (.012)
EI .013 (.009) .001 (.012)
IQ .025
ⴱⴱ
(.013)
SCR EI .002
ⴱⴱⴱ
(.001) .002
ⴱⴱ
(.001)
Constant .683
ⴱⴱⴱ
(.110) .692
ⴱⴱⴱ
(.109)
Observations 5,145 4,945
Log likelihood 3,146.115 3,010.495
Akaike inf. crit. 6,308.230 6,038.990
Bayesian inf. crit. 6,360.596 6,097.546
Note. N 52 for Model 1 and N50 for Model 2. For all analyses, there
is a random intercept of participant and random slope of SCR. SCL
skin-conductance level. SCR skin-conductance responses. EI total
emotional intelligence. IQ cognitive ability. Values are unstandardized
regression coefficients.
p.10.
ⴱⴱ
p.05.
ⴱⴱⴱ
p.01.
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5
EMOTIONAL INTELLIGENCE AND AROUSAL
SE(b)0.0008, Wald test ⫽⫺2.785, p.005, OR 0.998, 95%
CI [0.996, 0.999]. This interaction supports our hypothesis that EI
moderates the link between SCR and risk-taking. We probed the
interaction using a test of simple slopes. For individuals lower on
emotion-recognition ability, SCR was positively associated with risk-
taking, b0.0343, SE(b)0.0147, Wald test 2.321, p.020,
OR 1.035, 95% CI [1.005, 1.065]. The odds ratio indicates that for
a one-unit increase in SCR, the odds of risk-taking are 3.5% larger,
holding all other variables constant. As expected, among individuals
with lower emotion-recognition ability, SCRs were positively associ-
ated with risk-taking. In contrast, among individuals with higher
emotion-recognition ability, there was a negative, marginally signif-
icant association between SCR and risk-taking, b⫽⫺0.0350,
SE(b)0.0199, Wald test ⫽⫺1.760, p.078, OR 0.966, 95%
CI [0.929, 1.004]. The odds ratio indicates that for a one-unit increase
in SCR, the odds of risk-taking are 3.4% smaller, holding all other
variables constant.
EI Branch #2: Emotion-Facilitation Ability
We did not find a significant two-way interaction between SCR
and emotion-recognition ability, b⫽⫺0.0010, SE(b)0.0008,
Wald test ⫽⫺1.229, p.219, OR 0.999, 95% CI [0.997,
1.001]. Therefore, the data did not support our prediction.
EI Branch #3: Emotion-Understanding Ability
Our focal measure of emotion-understanding ability was the
third branch of the MSCEIT. As predicted, we found a significant
two-way interaction between SCR and emotion-understanding
ability, b⫽⫺0.0016, SE(b)0.0006, Wald test ⫽⫺2.531, p
.011, OR 0.998, 95% CI [0.997, 1.000]. Follow-up analyses
using simple slopes probed the interaction. As expected, for indi-
viduals lower on emotion-understanding ability, SCR was posi-
tively associated with risk-taking, b0.0324, SE(b)0.0146,
Wald test 2.219, p.026, OR 1.033, 95% CI [1.004, 1.063].
The odds ratio indicates that for a one-unit increase in SCR, the
odds of risk-taking are 3.3% larger, holding all other variables
constant. In contrast, among individuals higher on emotion-
understanding ability, we found no association between SCR and
risk-taking, b⫽⫺0.0252, SE(b)0.0175, Wald test ⫽⫺1.442,
p.149, OR 0.975, 95% CI [0.942, 1.009]. The odds ratio
indicates that for a one-unit increase in SCR, the odds of risk-
taking are 2.5% smaller, holding all other variables constant.
We also measured emotion-understanding ability using the
Situational Test of Emotion Understanding (STEU). We did not
find significant two-way interaction between SCR and emotion-
understanding ability, b⫽⫺0.0024, SE(b)0.0024, Wald
test ⫽⫺0.990, p.322, OR 0.998, 95% CI [0.993, 1.002].
EI Branch #4: Emotion-Management Ability
Consistent with the other branches of EI, we found a significant
two-way interaction between SCR and emotion-management abil-
ity, b⫽⫺0.0015, SE(b)0.0007, Wald test ⫽⫺2.179, p.029,
OR 0.998, 95% CI [0.997, 1.000]. We conducted a test of simple
slopes to assess the two-way interaction. We found that, for individ-
uals lower on emotion-management ability, SCR was positively as-
sociated with risk-taking, b0.0292, SE(b)0.0141, Wald test
2.067, p.038, OR 1.030, 95% CI [1.002, 1.059]. The odds ratio
indicates that for a one-unit increase in SCR, the odds of risk-taking
are 3.0% larger, holding all other variables constant. Whereas, among
individuals higher on emotion-management ability, there was no
association between SCR and risk-taking, b⫽⫺0.0182, SE(b)
0.0171, Wald test ⫽⫺1.059, p.289, OR 0.982, 95% CI [0.949,
1.016]. The odds ratio indicates that for a one-unit increase in SCR,
the odds of risk-taking are 1.8% smaller, holding all other variables
constant.
Discussion
Our findings highlight the role of EI in the relationship between
physiological arousal and decision-making about risk. Individuals
Figure 1. Two-way interaction of skin-conductance response (SCR) and
emotional intelligence (EI) on risk-taking (EI measure graphed at 1SD).
EI total emotional intelligence. ERA emotion-recognition ability.
EUA emotion-understanding ability. EMA emotion-management
ability. See the online article for the color version of this figure.
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6YIP, STEIN, CÔTÉ, AND CARNEY
with lower EI are more likely to appraise SCRs as “approach”
signals, which promote greater risk-taking. By contrast, individu-
als with higher EI are less likely to exhibit an association between
SCRs and risk-taking. We found this pattern of results for total EI
and for three of the four EI branches (i.e. emotion-recognition
ability, emotion-understanding ability, and emotion-management
ability). Furthermore, we found that EI moderates the association
between SCRs and risk-taking independently of cognitive ability.
Our results tentatively suggest that individuals with low EI
appraise their arousal differently than individuals with high EI.
That is, relative to individuals with high EI, individuals with low
EI might be more likely to interpret their physiological arousal as
excitement, which promotes risk-seeking behavior. One potential
explanation for our findings pertains to the attribution of arousal.
By misattributing arousal to other factors than the risky decision
options, individuals with low EI may be more likely to interpret
their arousal as a signal of gambling, which promotes risk-taking.
By comparison, individuals with high EI are more likely to inter-
pret their arousal as a signal of danger, but do not reliably reduce
their risk-taking. Based on relative outcomes, individuals with low
EI are more likely to behave maladaptively than individuals with
high EI.
Theoretical Implications
Our research advances the theoretical understanding about individ-
uals’ propensity to make risky decisions. Prior research has high-
lighted the importance of understanding the mechanisms underlying
risk-taking by identifying “who takes risks when and why” (Figner &
Weber, 2011). Recent research demonstrates that the decision process
about risk is influenced by individual’s emotions depending on their
age (Figner, Mackinlay, Wilkening, & Weber, 2009) and their level of
brain functioning (Bechara et al., 1997). However, previous studies
have overlooked the role of specific psychological abilities that con-
nect immediate feelings to decision-making behaviors such as EI.
In addition, our findings provide some clarity to the literature
about the influence of cognitive ability and emotional abilities on
IGT performance. The original formulation of the SMH suggested
somatic markers serve as emotional signals that guide decision-
making on the IGT (Bechara et al., 1994, 1997). However, recent
work suggests that cognitive processes may explain IGT perfor-
mance more accurately and comprehensively (e.g., Dunn et al.,
2006; Maia & McClelland, 2004). Some studies recently suggested
that cognitive intelligence is more positively associated with IGT
performance than EI (Demaree et al., 2010; Li et al., 2017; Webb
et al., 2014). However, an important limitation is that these studies
did not simultaneously measure SCRs with IGT performance when
contrasting these individual differences. In our work, we measured
EI, cognitive intelligence, SCRs, and risk-taking. We found EI
moderates the association between SCRs and risky-taking in the
IGT. Cognitive intelligence, by contrast, did not moderate the
association between SCRs and risk-taking.
This work also contributes to a broader understanding about
how integral emotions can drive decision making (Lerner et al.,
2015). Previous research has demonstrated incidental anxiety,
which is triggered by unrelated situations, reduced risk-taking
more strongly among individuals with lower rather than higher
levels of emotion-understanding ability, because they misattrib-
ute the source of their anxiety (Yip & Côté, 2013). Here, we
present evidence that integral emotions show different associ-
ations with decision-making depending on levels of EI, such
that physiological arousal arising from the judgment at hand
leads individuals lower on EI—but not their higher EI coun-
terparts—to approach risk.
Table 2
Emotional Intelligence (EI) Branches
Dependent variable: Risk
Measure (1) (2) (3) (4) (5)
Baseline SCL .007 (.029) .006 (.027) .005 (.026) .006 (.035) .008 (.027)
SCR .0003 (.012) .007 (.012) .004 (.011) .008 (.015) .006 (.011)
Emotion-Recognition Ability .009 (.008)
SCR Emotion-Recognition Ability .002
ⴱⴱⴱ
(.001)
Emotion-Facilitation Ability .010 (.008)
SCR Emotion-Facilitation Ability .001 (.001)
Emotion-Understanding Ability (MSCEIT) .006 (.006)
SCR Emotion-Understanding Ability (MSCEIT) .002
ⴱⴱ
(.001)
Emotion-Understanding Ability (STEU) .008 (.020)
SCR Emotion-Understanding Ability (STEU) .002 (.002)
Emotion-Management Ability .008 (.007)
SCR Emotion-Management Ability .002
ⴱⴱ
(.001)
Constant .686
ⴱⴱⴱ
(.111) .683
ⴱⴱⴱ
(.110) .683
ⴱⴱⴱ
(.112) .697
ⴱⴱⴱ
(.119) .683
ⴱⴱⴱ
(.111)
Observations 5,145 5,145 5,145 4,545 5,145
Log likelihood 3,145.613 3,148.361 3,146.883 2,781.868 3,147.187
Akaike inf. crit. 6,307.227 6,312.723 6,309.766 5,579.736 6,310.374
Bayesian inf. crit. 6,359.593 6,365.089 6,362.132 5,631.111 6,362.740
Note. N 52, except for analyses that measured EUA using the STEU (where N46). For all analyses, there is a random intercept of participant and
random slope of SCR. SCL skin-conductance level. SCR skin-conductance responses. Values are unstandardized regression coefficients.
p.10.
ⴱⴱ
p.05.
ⴱⴱⴱ
p.01.
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7
EMOTIONAL INTELLIGENCE AND AROUSAL
Finally, our research is the first to find evidence linking EI,
physiological responses, and decision-making. Prior work has
shown that EI is associated with the subjective experience of
self-reported and expressed emotions (see Mayer et al., 2008, for
a review). However, SCRs represent a different channel by which
individuals experience emotion. We find that individuals with low
EI misinterpret their physiology as a source of information when
making decisions, compared to individuals with high EI.
Limitations and Future Directions
Several limitations point to directions for future research. First,
we found that EI moderates the SMH for total EI and three
separate branches of EI (i.e. emotion-recognition ability, emotion-
understanding ability, and emotion-management ability) but not
for emotion-facilitation ability. This is consistent with growing
evidence that has questioned the construct validity of emotion-
facilitation ability. Emotion facilitation ability is the second branch
of the Mayer and Salovey (1997) ability model of EI and involves
generating and using emotions to guide thinking. Joseph and
Newman (2010) conducted a meta-analysis of EI and chose to
exclude emotion-facilitation ability from their cascading model of
EI (see also Legree, Psotka, Robbins, Roberts, Putka, & Mullins,
2014; MacCann, Joseph, Newman, & Roberts, 2014). There is
conceptual redundancy of the emotion-facilitation ability with the
other emotional abilities. Furthermore, factor analyses revealed
poor fit for models that included emotion-facilitation ability, but
superior fit for models that excluded emotion-facilitation ability
(Gignac, 2005; Palmer, Gignac, Manocha, & Stough, 2005; Ros-
sen, Kranzler, & Algina, 2008). Future research should investigate
the theoretical meaning and predictive validity of emotion-
facilitation ability.
Second, we used the IGT to elicit SCRs and to measure subse-
quent risk-taking. Support for the association between physiolog-
ical responses and risk avoidance has largely been drawn from data
using the IGT as an established research paradigm (Bechara et al.,
1997). The decision-making task operates on the assumption that
riskier choices are correlated with overall losses (Bechara et al.,
1994; Dunn et al., 2006). If the contingencies in IGT were reversed
so that higher risk is associated with greater gains, SCRs would
reflect excitement rather than anxiety. In this instance, our theory
would predict that individuals with low EI could exhibit a negative
association between SCR and risk-taking because they are more
likely to misinterpret SCRs as anxiety. Future work needs to
consider whether low EI decision-makers may reduce risk-taking
when riskier choices correspond to overall gains.
Third, we used SCRs to assess physiological arousal. Although
SCRs are a common measure of general arousal (e.g., Akinola,
2010; Mauss, Levenson, McCarter, Wilhelm, & Gross, 2005;
Pennebaker, Hughes, & O’Heeron, 1987; Russell, 2003), SCRs do
not differentiate the anticipated valence of outcomes. Instead,
SCRs index the magnitude of arousal (Bechara, 2016). Thus,
although we theorize that individuals with lower EI are more likely
to misinterpret somatic markers as signals of excitement, and that
this misinterpretation promotes risk approach, we acknowledge
that we did not obtain a direct measure of self-reported emotional
experiences. To address this limitation, future research should
consider measuring self-reported emotional states in addition to
physiological arousal.
Fourth, we focused on our investigation involving decisions
about financial risk. Future research is needed to understand
whether EI aids decision-making in social contexts. For example,
EI may play an important role in mitigating conflict when coun-
terparts express anger (Yip & Schweinsberg, 2017) or when com-
petitors engage in trash-talking (Yip, Schweitzer, & Nurmohamed,
2018). Another fruitful avenue of research may be exploring
whether lie detection accuracy is a function of EI. Although there
are obvious advantages of detecting lies accurately, evidence
abounds that humans are poor lie detectors (e.g., Bond & DePaulo,
2006; Minson, VanEpps, Yip & Schweitzer, 2018; Yip &
Schweitzer, 2015). Recent work demonstrates that individuals
experience more physiological arousal while observing liars versus
truth-tellers (ten Brinke et al., in press; ten Brinke, Vohs, &
Carney, 2016). Individuals with low EI may be poor at detecting
deception because they incorrectly identify this arousal as excite-
ment, encouraging them to approach liars.
Fifth, our investigation focused on risky decisions with imme-
diate outcomes. Future research could examine whether our find-
ings can be extended to risky decisions that are related to future
financial outcomes because prior research has shown that individ-
uals make different decisions when the decision outcomes are
immediate instead of delayed (Lee & Zhao, 2014; Okdie, Buelow,
& Bevelhymer-Rangel, 2016; Trope & Liberman, 2003).
Conclusion
Prior research has assumed that, when individuals generate
physiological arousal that is integral to the decision options, they
rely on their physiological arousal to make adaptive decisions
(Bechara et al., 1997). Importantly, our research qualifies this
assumption. Our evidence suggests that lower levels of EI guide
individuals to misinterpret their physiological arousal to maladap-
tively approach a situation, stimulus, or person that is risky.
In conclusion, individuals with high EI develop better social
relationships (Lopes, Brackett, Nezlek, Schütz, Sellin, & Salovey,
2004), achieve better job performance (Côté, & Miners, 2006;
Elfenbein et al., 2007), and enjoy greater psychological well-being
(Matthews et al., 2006), whereas individuals with low EI report
higher drug and alcohol abuse (Brackett, Mayer, & Warner, 2004),
engage in more deviant behavior (Brackett & Mayer, 2003), and
are rated as more aggressive (Mayer, Perkins, Caruso, & Salovey,
2001). We demonstrate that individuals with low EI maladaptively
utilize somatic markers to approach risky situations, compared to
individuals with high EI. Our work offers preliminary evidence
that somatic markers reflect a microprocess through which EI
relates to broad life outcomes.
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Received July 3, 2015
Revision received October 23, 2018
Accepted November 5, 2018
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EMOTIONAL INTELLIGENCE AND AROUSAL
... Further research discusses the need to study product quality, digital advertisement, and price in the apparel industry to lose their customers. The research has also been to show the value of advertisement, product quality, and price as a potential mediator between customer preference and retaining customers for a longer period whereas the importance of studying the factor of consumer preference is being underscored respectively (Yip, Stein, Côté, & Carney, 2020). In Pakistan, branded and fashion clothing has been the primary objective for the last couple of years among the concerned population. ...
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Ever since the modern era has begun, the evolving technological progressions amplified the marketing process of conveying the products and services to the customers where the foremost priority would be based upon their preference and intention to purchase. The key drive of executing this study is to seek the effect of apparel quality, price and digital advertising on the preference of the customers. Moreover, this study involves around four basic dimensions which were used in order to find the influence of independent variables includes price, quality and digital advertisement of apparel brands whereas customer brand preference taken as dependent variable. The following research is quantitative in manner and cast a deductive approach. Further, this study has few questions related to our objective however the solutions of those question is being analyzed through correlation analysis which also express the relationship among the designated variables/dimensions. For the purpose of finding the effect of independent variables on the dependent variable, Regression test was carried out for the prior understanding. With the help of data analysis, it can be conclude that all the dimensions have a significant relationship among them however the significant impact of independent variables on the dependent variable has been found. Eventually, there were 239 respondents taken as sample size of population though the respondents are majorly categorized as those who used the mentioned apparel brands and are the residents of metropolitan cities of Pakistan.
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... Researchers have also shown that decision-makers who are able to understand emotions (Branch 3) take more efficient decisions as they are less influenced by irrelevant feelingssuch as incidental anxiety -which are unrelated to the decision at hand (Yip & Côté, 2013). More recently, these researchers have observed that decision makers with low EI tend to adopt maladaptive decision-making, due to their incorrect appraisal of intensity of physiological arousal (Yip et al., 2020). ...
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Managers’ interest in the concept of emotional intelligence (EI) has grown steadily due to an accumulation of published articles and books touting EI’s benefits. For over thirty years, many researchers have used or designed tools for measuring EI, most of which raise important psychometric, cultural and contextual issues. The aim of this article is to address some of the main limitations observed in previous studies of EI. By developing and validating QEPro we propose a new performance-based measure of EI based on a modified version of Mayer and Salovey’s (1997) four-branch model. QEPro is an ability EI measure specifically dedicated to managers and business executives in a French cultural environment (N = 1035 managers and executives). In order to increase both the ecological and the face validity of the test for the target population we used the Situational Judgment Tests framework and a theory-based item development and scoring approach. For all items, correct and incorrect response options were developed using established theories from the emotion and management fields. Our study showed that QEPro has good psychometric qualities such as high measurement precision and internal consistency, an appropriate level of difficulty and a clear factorial structure. The tool also correlates in meaningful and theoretically congruent ways with general intelligence, Trait EI measures, the Big Five factors of personality, and the Affect measures used in this study. For all these reasons, QEPro is a promising tool for studying the role of EI competencies in managerial outcomes.
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