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This study examined the relationship between curiosity and emotional intelligence (EI) in a sample of graduate and undergraduate business administration students. Curiosity was assessed using the Melbourne Curiosity Inventory (Naylor, 1981) and the Curiosity and Exploration Inventory (Kashdan, Rose, & Fincham, 2004), and EI was measured using the Trait Meta-Mood Scale (TMMS; Salovey, Mayer, Goldman, Turvey, & Palfai, 1995). Results indicate a significant relationship between trait curiosity and EI. Relationships between the subscales of the 3 measures are also reported. Stepwise regression analysis indicates that trait curiosity and absorption curiosity were the best predictors of total EI; while absorption curiosity was the best predictor of attention to emotions, clarity of emotions, and repair of emotions.
Curiosity and Emotional Intelligence
The Trait of Curiosity as a Predictor of Emotional Intelligence
Nancy H. Leonard
College of Business and Economics
West Virginia University
Michael Harvey*
School of Business Administration
University of Mississippi
*Corresponding Author
Phone: 662.915.5830
Fax: 662.915.5821
School of Business Administration
University of Mississippi
332 Holman Hall
Oxford, MS 38677
Curiosity and Emotional Intelligence
The Trait of Curiosity as a Predictor of Emotional Intelligence
This study examined the relationship between curiosity and emotional intelligence (EI). To
determine the nature of this relationship, curiosity was assessed using the Melbourne Curiosity
Index (MCI; Naylor, 1981) and the Curiosity and Exploration Inventory (CEI; Kashdan, Rose &
Fincham, 2004). Emotional intelligence was measured using the Trait Meta-Mood Scale
(TMMS; Salovey, Mayer, Goldman, Turvey, & Palfai, 1995). The sample was 312 graduate and
undergraduate students enrolled in business administration courses at a large mid-Atlantic state
university in the United States. Results indicate that there is a significant relationship between
trait curiosity and emotional intelligence. Relationships between the subscales of all three
measures are also reported. Stepwise regression analysis indicated that trait curiosity and
absorption curiosity were the best predictors of total emotional intelligence, while absorption
curiosity was the best predictor of attention to emotions, clarity of emotions, and repair of
Curiosity and Emotional Intelligence
The Trail of Curiosity as a Predictor of Emotional Intelligence
“Curiosity [has been] conceptualized as a positive emotional-motivation system associated
with the recognition, pursuit, and self-regulation of novelty and challenge.”
(Kashdan, Rose & Fincham, 2004)
The concept of emotional intelligence has been gaining recognition in the popular press and in
academic literature for the past ten years. While a number of theories associated with emotional
intelligence currently exist, one that has generated a great deal of scientific research is the work
of Salovey and Mayer. Building on the early work of Howard Gardner (1983) and multiple
intelligences, Salovey & Mayer (1990) proposed the concept of emotional intelligence as a set of
social skills and abilities akin to, but distinct from intellectual intelligence. Currently, emotional
intelligence is defined as a type of social intelligence that involves “the ability to monitor one's
own and others' emotions, to discriminate among them, and to use the information to guide one's
thinking and actions" (Mayer & Salovey, 1993, p. 433). It is argued that emotional intelligence
incorporates a set of conceptually related psychological processes involving the processing of
affective information (Mayer & Geher, 1996; Mayer & Salovey, 1997; Salovey & Mayer, 1990,
1994). With regard to these processes, it is suggested that individuals develop skills and abilities
pertaining to the perception of emotions, the regulation of emotions and to the capacity to utilize
or reason with emotions in thought (Mayor, Salovey, & Caruso, 2000).
In light of the work on emotional intelligence, Mayer and Gaschke (1988) devised the Trait
Meta-Mood Scale (TMMS) to measure the ongoing process in which individuals continually
reflect upon psychological states to monitor, discriminate, and regulate their emotions. The
TMMS assesses three underlying components of emotional intelligence: attending to feelings,
Curiosity and Emotional Intelligence
clarifying feelings, and repairing feelings. Attention is one’s perceived ability to attend to moods
and emotions. Clarity is one’s ability to understand and discriminate between different moods
and emotions and repair is one’s ability to maintain positive moods and emotions and repair
negative moods and emotions. The three sub-scales operationalize the theoretically-based
information-processing model proposed by the authors in which inputs (attention to emotions)
lead to mental processes (clarity of emotions) that produced outputs (repair of emotions). The
model is an information processing model of emotional intelligence and it is argued that in order
to repair emotions, one must clarify and understand those emotions and in order to do this, one
must first pay attention to emotions. Salovey, Mayer, Goldman, Turvey, and Palfai (1995) later
demonstrated that the higher-order factor structure of the TMMS was a reliable and valid
measure of core individual differences in the information processing of emotions and further
support for the three-factor structure of the TMMS was found by Palmer, Gignac, Bates, and
Stough (2003).
What has not been addressed in this literature is why some individuals develop much greater
skills and abilities in terms of attending to, clarifying, and repairing emotions. In this study, we
propose that differing levels and types of curiosity motivate learning and when that learning is
directed toward understanding emotions, individuals will develop higher levels of emotional
Curiosity and Emotional Intelligence
One possible predictor of emotional intelligence could be trait curiosity. Curiosity (“that factor
which underlies the willingness of an individual to expose him/herself to information” {Day,
Langevin, Haynes & Spring, 1972: 330}) could be instrumental in searching for information
relative to the emotional dimensions of decision-making processes. In social situations,
individuals interact with family, friends, and even strangers in which these social interactions are
laden with emotions. These interpersonal interactions provide individuals with the opportunity to
develop skills and abilities in dealing with emotions. Kashdan and Roberts (2004, in press)
suggest that curiosity is uniquely associated with the development of interpersonal closeness
with strangers, and Kashdan, Rose, and Fincham (2004) suggest that in a social context, highly
curious people may be more responsive, infuse more novel twists of excitement into interactions,
and might be more likely to seek, capitalize, and build on interaction partner disclosures.
Curiosity may play a very important role in the development of emotional intelligence skills and
In academic research, curiosity has been variously described as a drive, as a personality
characteristic, and as motivation to explore. Freud (1915) viewed curiosity as a derivative of the
sex drive and defined it as a “thirst for knowledge” (p. 153) and a “desire to look.” In his work
with laboratory rats, Nissen (1930) began to wonder why rats explored the maze in the absence
of drive states such as thirst or hunger. Through his work, he provided evidence of curiosity as
both an inborn exploratory drive and a secondary or learned drive acquired through classical
conditioning. Curiosity is one of the antecedents that triggers learning and provides the
improvement in the decision-making processes in individuals. Specifically, curiosity might be
the key to the underlying foundation that stimulates learning and, concurrently, increases the
Curiosity and Emotional Intelligence
effectiveness of decision-making and quality of decision-making, particularly when emotions
play an integral part to the decision (Smithson, 1989; Ravitz, 1993; Stocking, 1998, 1999).
One of the earliest behavioral psychologists to explore curiosity as a motivator of exploratory
behavior was Berlyne (1960). Berlyne defined curiosity as a state of increased arousal response,
prompted by a stimulus high in uncertainty and lacking in information, resulting in exploratory
behavior and the search for information. He initially located curiosity along two dimensions
between perceptual and epistemic. Perceptual curiosity or perception-directed curiosity focuses
on an individual’s attention to novel objects in one’s immediate environment. Epistemic curiosity
or knowledge-driven curiosity refers to a desire for information and knowledge. Berlyne (1978)
later added the dimensions of specific and diversive curiosity. He defined specific curiosity as the
desire for a particular piece of information such as the solution to a puzzle and diversive
curiosity as the seeking of stimulation as a result of boredom.
Curiosity is now viewed as a motivational state and past research indicates that curiosity
motivates exploratory behavior that leads to learning or increased knowledge for the individual.
The basic model of the role of curiosity in learning is illustrated in Figure 1. As is developed in
the model, curiosity plays a central role in the type of learning being either exploratory (e.g.,
general learning) or absorption (e.g., specific learning). In addition, the learning may be in-role
or structured learning relative a manager’s position (i.e., either formally or informally) or extra-
role learning which is beyond the normative learning-cycle of the manager (i.e., extra effort to
learn something new or different beyond the normal dimensions of one’s position).
Curiosity and Emotional Intelligence
Both types of learning events can be influenced by a number of exogenous variables contained in
the macro-environment, the organization (setting or context of the learning event) or individual
characteristics of the manager that can stimulate/inhibit learning to take place. It is felt that
higher levels of curiosity improve an individuals quality of decision-making if properly focused
through mindfulness of the individual (i.e., mindfulness is concerned with the adaptive
management of expectations in the context of the unexpected,{Swanson & Ramiller, 2004}).
Without mindfulness the manager may spend too little time on absorbing the information and
therefore its usefulness is diminished to the decision-maker.
Insert Figure 1 about Here
Most of the contemporaneous models of curiosity are stimulus-response models that suggest that
we receive informational cues from the environment that we then compare to our cognitive map
or mental model. If the information does not fit within that existing decision model, one is
stimulated to seek information in order to reduce the perceptual or cognitive tension that was
created by the lack of fit (Berlyne, 1965, 1978). The seeking of information may be either
through active processes or passive processes, but in both cases the information seeking or
exploratory behavior is motivated by curiosity (Day, 1982; Fisher, 2000; Kashdan, Rose &
Fincham, 2004). Diversive, sensation-seeking, and stimulus-seeking curiosity on the other hand
are not explained by the process of response to informational cues from the environment. For
these types of curiosity, the stimulus may be internal rather than external. In the case of this type
of curiosity the individual may just be bored and may actually seek out interesting or stimulating
Curiosity and Emotional Intelligence
situations as relief for boredom or by a need for stimulation. This type of need would fall under
the rubric of intrinsic motivation. Intrinsic motivation has been linked to curiosity for many
years. Meece (1997) argued that intrinsic motivation to engage in an activity arises from internal
sources such as curiosity, and Maslow (1970) argued that curiosity was an important element in
the development of a psychologically healthy person and that satisfying one’s curiosity is one of
the important positive determinants for acquiring knowledge.
Recently, Kashdan and his colleagues (Kashdan, 2004; Kashdan et. al, 2004; Kashdan &
Roberts, 2004) have proposed a two-factor model of curiosity based on the extant literature
discussed above. In the information processing model proposed by these authors, curiosity leads
to an increase in attention allocated to scan and orient oneself toward novel and challenging
stimuli. This, in turn, leads to cognitive and behavioral exploration of rewarding stimuli,
resulting in a flow-like engagement with these stimuli and activities, and ultimately to
integration of novel experiences by assimilation or accommodation (Kashdan et. al, 2004).
Basically, it is argued here that individuals with high levels of trait curiosity will be more likely
to actively pursue and take advantage of varied opportunities to gain information, process
information, and ultimately to learn more about the object (e.g., high levels of emotional
intelligence) of their attention. The object of attention is based on individual differences in
interests, expectations, and prior knowledge. It would seem that when the object of one’s
curiosity is emotions, we could expect that individual to develop higher levels of emotional
intelligence. This belief is reflected in Hypothesis 1.
H1: Higher levels of trait curiosity will be associated with higher levels of
emotional intelligence of an individual.
Curiosity and Emotional Intelligence
Building on Berlyne’s (1960, 1978) two-dimensional model of curiosity, Kashdan et al. (2004)
developed the Curiosity and Exploration Index (CEI) to reflect the two-dimensional nature of
curiosity. They label the tendency to seek out new information and experiences as exploration
and the tendency to become fully engaged in these experiences as absorption (see Figure 1).
These two factors correspond to Berlyne’s dimensions of diversive curiosity as appetitive
motivation (exploration) and specific curiosity leading to flow-like engagement (absorption) in
activities. While they stress that exploration is self-determined, they also note that one’s desire to
explore is reflective of an appetitive motivational orientation (i.e., trait curiosity). It is also argue
that diversive or exploratory curiosity entails scanning, recognizing, pursuing, and allocating
personal resources (e.g., attention) to novel and challenging experiences and that engagement in
these activities brings with it specific curiosity and entails flow-like absorption and investigative
behaviors. We therefore propose that when individuals allocate attention to emotions that they
will be more likely to develop skills in attending to emotions.
H2: Higher levels of exploratory curiosity will be associated with higher levels
of attention to emotions.
During absorption, “people experience clear, immediate goals; maintain deeply focused
concentration; and feel a strong sense of personal control” (Kashdan et. al, 2004, p. 292).
Kashdan, et al., propose that this type of absorption results in personal growth from the
“stretching” of skills and confidence in using those skills. Therefore, we propose that those with
higher absorptive curiosity will be more likely to develop clarity in terms of emotions.
Curiosity and Emotional Intelligence
H3: Higher levels of absorption curiosity will be associated with higher levels
of clarity of emotions.
Finally, if Salovey et al. (1995) are correct in their belief that repair of emotions requires
attention and clarity of emotion, we would expect individuals with high level of repair tendencies
to also have high levels of clarity of emotions and attention to emotions. This is reflected in
Hypothesis 4.
H4a: Higher levels of repair of emotions will be associated with higher levels
of clarity of emotions
H4b: Higher levels of clarity of emotions will be associated with higher levels
of attention to emotions.
The sample was comprised of 312 graduate and undergraduate business administration students
at a large mid-Atlantic state university in the United States. One hundred eighty-five participants
were male and one hundred and twenty-seven participants were female. Two hundred and forty
nine students were juniors, 40 were seniors, and 23 were graduate students. The participants
ranged in age from 19 to 39 with a mean age of 21.04 years (SD = 2.09). Students were given
extra credit points for participation in the study.
Curiosity. Both state and trait curiosity were measured with the Melbourne Curiosity Inventory
(MCI; Naylor, 1981. The MCI contains 40 self-report, 4-point Likert scale questions, 20 for each
scale (1=almost never; 4=almost always).The state curiosity scale asks the respondent to
Curiosity and Emotional Intelligence
designate how they feel at a particular moment, while the trait scale, with the questions worded
somewhat similarly, asks the respondent to answer how they feel in general. Naylor (1981)
describes state curiosity as individual differences in response to a curiosity-arousing situation,
and trait curiosity as individual differences in capacity to experience curiosity in general. Studies
indicate that the MCI demonstrates high internal consistency with Cronbach’s alpha = 0.92
reported in a recent study by Reio (2000). In that study, Reio found that curiosity-induced
behavior such as information seeking play a meaningful role in workplace learning as well as in
job performance
Curiosity was also measured using the Curiosity and Exploration Inventory (CEI; Kashdan et. al,
2004). The CEI contains 7 self-report, 4-point Likert scale questions. Four questions assess
exploratory or diversive components of curiosity and three assess absorption or specific
components of curiosity. According to the authors of the scale, the two-factors, exploration and
absorption are considered separate cut correlated components of curiosity.
Emotional Intelligence. Emotional intelligence was measured using the Trait Meta-Mood Scale
(TMMS; Mayer & Gaschke, 1988). The TMMS is a 30-item self-report measure to which
participants respond on a 5-point Likert scale (1=strongly disagree to 5=strongly agree). The
scale includes three components: attending to feelings (e.g. I often think about my feelings.),
clarifying feelings (e.g. I almost always know exactly how I am feeling.), and repairing feelings
(e.g. When I become upset, I remind myself of all the pleasures of life.). Salovey et al. (1995)
report that the TMMS demonstrates high internal consistency (Cronbach’s alpha = 0.82). Mayer
Curiosity and Emotional Intelligence
and Gaschke (1988) used Chronbachs’ coefficient alpha to evaluate internal consistency in all
three scales and report that attention = .86, clarity = .88 and repair = .82.
Participants were asked to complete the MCI, CEI and TMMS online. The average time to
complete all three surveys was less than 30 minutes.
Data Analysis
Because two of the scales used in this study (CEI and TMMS) were relatively new and had little
previous empirical support, a confirmatory factor analysis was run on each of the scales. In all
cases, the confirmatory factor analysis was performed using the SPSSx program (Nie, Hull,
Jenkins, Steinbrenner, & Bent, 1987). Negative-scored items were reversed and recoded prior to
analysis for uniformity of direction in scoring.
Curiosity and Exploration Index. Because the CEI is a newly developed scale and empirical
support other than that provided by the authors was not available, it was particularly important to
run the confirmatory factor analysis on this scale. The data were submitted to three factor
extraction techniques, alpha factoring, image factoring, and principle components analysis.
Alpha factoring (Kaiser, 1963) and image factoring (Kaiser & Caffry, 1965) examine the
factorial structure of a set of variables by extracting the greatest possible number of cohesive
factors. The third exploratory technique, principal component analysis (Harman, 1976), searches
for a small number of factors within a group of variables. After the data were submitted to these
extraction techniques to determine a plausible and workable number of interpretable factors
Curiosity and Emotional Intelligence
based on the distribution and magnitude of their loadings, the factors were submitted to iterated
principal axis factor analysis (Harman, 1976). Unlike principal component analysis, which
determines factor clusters by common, specific, and error variance, principal axis factoring uses
solely the common variance (commonality) of the variables (i.e., only the percentage of the
variance which is common to the variable and factor on which that variable loads) to determine
factor loadings and the magnitudes of these loadings.
An oblique (oblimin) rotation of the axes, which assumes some correlation between items and
factors, was used as the preferred interpretive analysis procedure. Orthogonal rotation, which
assumes no correlation between the extracted factors, does not meet the assumptions of this
analysis and was not used.
The three factor analysis extraction methods: alpha, image and principal component, each
extracted two distinct factors, all very similar and all with approximately the same item and
magnitude loadings. The iterated principal axis factor analysis yielded a two-factor solution
which accounted for 55.58% of the total variance. Results closely reflect the findings of Kashdan
et al. (2004), which suggested that the CEI appears to measure two distinct dimensions of
curiosity. The two factors, scale items and structure matrix loadings of each item on each factor
are presented in Table 1.
Insert Table I about Here
Curiosity and Emotional Intelligence
According to Kashdan, et al. (2004), Items 1, 3, 4, and 7 measure Exploration Curiosity and
items 2, 5, and 6 measure Absorption Curiosity. The difference in this analysis was that in all
cases, Factor 2, “When I am participating in an activity, I tend to get so involved that I lose track
of time” cross loaded on both factors and Factor 4, the only negatively worded item in the scale,
“I am not the type of person who probes deeply into new situations or things.” failed to load on
either factor. For all other items, the highest factor loading was on the theorized factor. Although
this difference requires further exploration, because this is a very new scale and has strong
theoretical grounding, the scale was used as described by the scale’s authors. In this analysis, the
first factor, accounting for 43.18% of the variance was labeled exploratory curiosity and the
second factor, accounting for 15.40% was labeled absorption curiosity, as suggested by Kashdan
and his coauthors.
Trait Meta-Mood Scale. The same three factor analysis techniques followed by principle axis
factoring as discussed above, were applied to the TMMS. All three factor extraction techniques,
alpha factoring, image factoring, and principle components analysis produced similar results.
Each extraction technique produced six factors with eigenvalues over 1.0 that accounted for
57.7% of the variance. The principle axis factoring method using Oblimin rotation indicated
three strong factors similar to those theorized by Salovey, et al. (1995) but 13 items crossloaded
at the .30 or higher level. Those 13 items accounted for only 3.89% of the variance in the model
and were eliminated in further analysis. The remaining 17 items loaded strongly on three factors.
All three factors had eigenvalues greater than 1.0 and jointly accounted for 53.81% of the
variance in the model. The scree plot indicated a bend at the third factor. Factor 1 accounted for
27.04% of the total variance and included 8 items, all theorized by Salovey, et al. (1995) to
Curiosity and Emotional Intelligence
assess attention to emotions. Factor 2 accounted for an additional 13.98% of the variance and
included four items theorized to assess emotional repair and Factor 3 accounted for 12.79% of
the variance and included five items theorized to assess clarity of emotions. Only the 17 factors
described above were used in further analysis. The three-factor solution is shown in Table 2.
Insert Table 2 about Here
To examine the relationship between curiosity and emotional intelligence, the total scores and
underlying scale scores of the CEI (Exploratory and Absorption), MCI, and TMMS (Attention,
Clarity, Repair) were correlated. The mean scores, standard deviation, and reliabilities
(coefficient alpha) for each scale and subscale are shown in Table 3 and the Pearson correlations
between the measures are shown in Table 4.
Insert Table 3 about Here
Insert Table 4 about Here
As indicated in Table 4, trait curiosity as measured by the MCI and Exploration and Absorption
as measured by the CEI as well as the total CEI scores were highly correlated with emotional
intelligence abilities as measured by the TMMS (p0.01 in all cases), thus providing support for
H1. For the CEI, Exploration and Absorption were highly correlated as predicted by Kashdan et
al. (2004). According to these authors, the two underlying dimensions of curiosity are expected
Curiosity and Emotional Intelligence
to be interrelated. Of greater interest, are the correlations between the theorized underlying
dimensions of curiosity and the theorized underlying dimensions of the TMMS. The results also
indicate that individuals high on exploratory behavior will be high on the attention dimension of
emotional intelligence, providing support for H2. Higher levels of absorption are related to higher
levels of emotional clarity (H3). Finally, higher levels of repair are associated with higher levels
of clarity (H4a) and attention and higher levels of clarity are associated with higher levels of
attention (H4b). These findings support the theorized information processing model of emotional
intelligence underlying the TMMS and the belief that in order to repair emotions, one must first
understand emotions (clarity) and in order to understand emotions, one must first attend to them
Additional Exploratory Analyses. To explore the predictive value of trait curiosity in relation to
emotional intelligence, a stepwise multiple regression analysis was performed with emotional
intelligence entered as the dependent variable. The MCI, as well as the exploratory and
absorption factors of the CEI were included as independent variables. The stepwise regression
model accounted for 27.4% of the variance in total EI score (R=.523, R2=.269, F (2, 309)= 58.17,
P0.001). The significant predictors of this model were the MCI (R2=.242, b=0.48, P0.001),
followed by absorption curiosity (R2=.274, b=0.18, P0.001). Exploratory curiosity was not a
significant predictor of total EI.
Given the exploratory nature of this study, stepwise regression was also run using trait curiosity
subscales as independent variables and each of the underlying dimensions of emotional
intelligence as the dependent variables. For all three dimensions, attention, clarity, and repair,
Curiosity and Emotional Intelligence
absorption was the only significant predictor (R2=.023, b= 0.16, P0.005), (R2= .016, b= 0.14,
P0.01), (R2=.013, b= 0.13, P0.02), respectively. For attention, the resulting model explained
2.3% of the variance in attention (R=.160, R2=.026, F (1, 310) = 8.17, P0.005). For clarity,
1.9% of the variance (R=.138, R2=.019, F (1, 310) = 6.05, P0.01). Finally, for repair, the model
explained 1.6% of the variance (R=.127, R2=.016, F (1, 310) =5.05, P0.02).
Curiosity has been an often forgotten element in the learning context of the business literature.
But, the role of curiosity in learning is almost undeniable after a cursory examination of the
fundamentals of the construct. The level of curiosity an individual has can have a direct impact
on ones willingness to take on difficult tasks or the motivation to undertake difficult assignments
(e.g., global) and therefore is pivotal in complex business environments of the 21st Century.
The findings of the present study do a number of things. First, the hypothesis testing provides
initial support for the notion that trait curiosity accounts for some of the individual differences in
emotional intelligence. Second, the confirmatory factor analyses performed on both the CEI and
the TMMS provide support for the underlying structure of both scales. Like Kashdan et al.
(2004), the results indicate that curiosity is a multi-dimensional construct that can be measured
with two distinct, though related factors. Findings from the confirmatory factor analysis of the
TMMS suggest that a shorter version of the scale may be suitable for future research. More
importantly, the findings provide support for the three-factor information processing model of
emotional intelligence.
Curiosity and Emotional Intelligence
There would appear to be a number of steps that management could take to assess the level and
level of utilization of curiosity among the managers in an organization. The following
managerial steps could be taken: 1.) make an assessment of the level of innate curiosity of
managers; 2.) assess the level of ‘newness’ of managers’ present positions and what the most
likely next assignment might be for managers, thereby ascertaining the need for new knowledge
and conversely the need for curiosity; 3.) assessment of the context/environment that the
manager is/will be working in that, the contextual complexity of the environment may
necessitate additional learning making curiosity a key ingredient to success; 4.) assessment of the
nature or tasks that are being assigned to the manager will indicate to a degree the level of
complexity and need for new knowledge in order to accomplish the major task of the
assignment; 5.) assessment of the inhibitors to learning and the level of potential conflict
resulting from learning in each situational context; and 6.) monitoring the level of curiosity of
managers as they undertake new positions/tasks. Through this systematic assessment of
managers and their varying levels of curiosity, management can determine the need to stimulate
the overall level of curiosity amongst the managers.
Some limitations of the present study bear mentioning. Since the present data was collected
from university students, future researchers will need to include other types of groups to see how
well the present findings generalize to other segments of society. In addition, the present
research utilized a self-report method for assessing emotional intelligence. Nonetheless, the
present research takes an important step in examining the relationship of trait curiosity to
emotional intelligence and provides support for the information processing models of curiosity
and emotional intelligence.
Curiosity and Emotional Intelligence
Future research on the role of curiosity in making better decisions could examine how the level
of curiosity varies by individual. Can curiosity be used in the initial selection of managers? Can
curiosity be ‘taught’ to managers? Can curiosity become a negative attribute of managers
reducing their ability to effectively manage (i.e., too much searching and analysis to be an
effective)? What is the on-going relationship between curiosity and emotional intelligence? Each
of these topics would appear to warrant closer inspection by academic researchers, the only thing
that would not seem worthwhile would be to continue to ignore the role of curiosity in learning.
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Curiosity and Emotional Intelligence
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Curiosity and Emotional Intelligence
* The authors would like to thank Professor Milorad Novicevic for his input and assistance with
the development of this model.
Figure 1*
A Model of Curiosity
Managerial Curiosity
Organizational, &
Individual Factors
Curiosity and Emotional Intelligence
Table 1
Factor Loadings for Principle Axis Factoring with Oblimin Rotation of the Two Factors of
the Curiosity and Exploration Index
3. I frequently find myself looking for new opportunities
to grow as a person (e.g., information, people,
.83 .33
1. I would describe myself as someone who actively seeks
as much information as I can in a new situation.
.71 .45
7. Everywhere I go, I am out looking for new things to
.66 .50
2. When I am participating in an activity, I tend to get so
involved that I lose track of time.
.58 .44
4. I am not the type of person who probes deeply into new
situations or things.
.29 .11
6. My friends would describe me as someone who is
“extremely intense” when in the middle of doing
.39 .75
5. When I am actively interested in something, it takes a
great deal to interrupt me.
.31 .61
Percentage of Variance Explained 43.18 15.40
Reverse coded in analysis.
Curiosity and Emotional Intelligence
Table 2
Factor Loadings for Principle Axis Factoring with Oblimin Rotation of the Three Factors
of the Shortened Trait Meta-Mood Scale
Paraphrased Items
It is usually a waste of time to think about your emotions. .79 -- --
I don’t pay much attention to my feelings. .74 -- --
Not worth paying attention to emotions and moods. .74 -- --
Feelings are a weakness humans have. .67 -- --
One should never be guided by emotions. .64 -- --
I never give into my emotions. .57 -- --
Feelings give direction to life. .57 -- --
People would be better off if they felt less and thought more. .49 -- --
No matter how badly I feel, I try to think about pleasant things. -- .81 --
When upset, I remind myself of all the pleasures in life. -- .72 --
I try to think good thoughts no matter how badly I feel. .66
Although I am sometimes happy, I have a mostly pessimistic
outlook. .54
Sometimes I can’t tell what my feelings are. -- -- .69
I am usually confused about how I feel. -- -- .69
I can’t make sense out of my feelings. -- -- .69
I am rarely confused about how I feel. -- -- .58
I almost always know exactly how I am feeling. -- -- .53
Percentage of Variance Explained 27.04 13.98 12.79
Note: Correlations below .30 not reported.
Reverse coded in analysis.
Curiosity and Emotional Intelligence
Table 3
Means, Standard Deviations and Reliabilities (coefficient alpha) for the Measures (n = 312).
Measure M S.D. Coefficient Alpha
Repair 9.33 3.13 .75
Attention 19.05 5.79 .78
Clarity 12.63 3.78 .73
TMMS 74.71 12.28 .81
Melbourne Trait 57.21 10.10 .93
Exploration 19.74 3.88 .69
Absorption 13.97 3.11 .63
CEI 33.71 6.08 .71
TMMS, Trait Meta-Mood Scale; CEI, Curiosity and Exploration Index.
Table 4
Correlations Among the Measures (n=312)
Exploration Absorption CEI Attention Clarity Repair TMMS
Melbourne Trait 1.00 -.003 .050 .023 .081 .069 -.017 .492**
Exploration -.003 1.00 .507** .897** .145* .099 .071 .135*
Absorption .050 .507** 1.00 .835** .160** .138* .127* .201**
CEI .023 .897** .835** 1.00 .175** .134* .110 .189**
Attention .081 .145* .160** .175** 1.00 .241** .163** .775**
Clarity .069 .099 .138* .134* .241** 1.00 .136* .259**
Repair -.017 .0.71 .127* .110** .163** .136* 1.00 .484**
TMMS .492** .135* .201** .189** .775** .259** .484** 1.00
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
TMMS, Trait Meta-Mood Scale; CEI, Curiosity and Exploration Index
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... Curiosity has also been observed to facilitate a good employee-workplace fit (Reio & Callahan, 2004;Savickas, 1997). Therefore, employees who are more curious are more likely to experience positive emotional reactions and attitudes than those indicating emotional exhaustion (Antwi et al., 2019;Kashdan & Steger, 2007;Leonard & Harvey, 2007;Wang & Li, 2015). Job-related stress can act to enhance creativity/innovation because it raises the demand for innovative solutions and stimulates the cognitive and motivational arousal needed for creative thinking (Andrews & Farris, 1972;Antwi et al., 2019;Pelz, 1988). ...
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Drawing on Cognitive Appraisal and Job Demands-Resources (JD-R) theories, this study examines how job stress and work-related curiosity may affect employee’s innovative behavior. The study utilized two-wave longitudinal data collected from 311 frontline employees from five-star hotels in the UAE. The study found that employees who demonstrated curiosity about different aspects of their work were more likely to engage in innovative behavior. On the other hand, stress was observed to have a different effect. The negative effect of job stress on innovative behavior tended to become positive and significant when curiosity was part of the response to stress.
... Young livestock animals will show motivation to explore and play with their physical environment (85)(86)(87)(88). In humans, the degree of curiosity can also be highly correlated with the degree of emotional intelligence (89,90), thus highlighting the potential interdependence between enrichment provision to stimulate neural development and future engagement with enrichments. Livestock will also actively engage with cognitive enrichments [e.g., pigs: (91); laying hens: (92); dwarf goats: (93)], and show motivation to learn (94) as well as physiological evidence of learning processes being rewarding (95)(96)(97). ...
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Livestock animals are sentient beings with cognitive and emotional capacities and their brain development, similar to humans and other animal species, is affected by their surrounding environmental conditions. Current intensive production systems, through the restrictions of safely managing large numbers of animals, may not facilitate optimal neurological development which can contribute to negative affective states, abnormal behaviors, and reduce experiences of positive welfare states. Enrichment provision is likely necessary to enable animals to reach toward their neurological potential, optimizing their cognitive capacity and emotional intelligence, improving their ability to cope with stressors as well as experience positive affect. However, greater understanding of the neurological impacts of specific types of enrichment strategies is needed to ensure enrichment programs are effectively improving the individual's welfare. Enrichment programs during animal development that target key neurological pathways that may be most utilized by the individual within specific types of housing or management situations is proposed to result in the greatest positive impacts on animal welfare. Research within livestock animals is needed in this regard to ensure future deployment of enrichment for livestock animals is widespread and effective in enhancing their neurological capacities.
... Being attuned to affective signals, rather, could magnify the intensity of both positive and negative emotions and there is support for this perspective (Gohm & Clore, 2002a;Thompson et al., 2011). In addition, other research has linked emotional attention to factors such as curiosity (Leonard & Harvey, 2007), openness to experience (Coffey, Berenbaum, & Kerns, 2003), and both private and public self-consciousness (Salovey et al., 1995). These correlates suggest a greater degree of emotional openness among individuals with higher levels of attention to emotion (Gohm & Clore, 2002b). ...
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Individuals are thought to differ in the extent to which they attend to and value their feelings, as captured by the construct of attention to emotion. The well-being correlates of attention to emotion have been extensively studied, but the decision-making correlates have not been. A three study program of research (total N = 328) sought to examine relationships between stimulus-specific feelings and decisions concerning those stimuli in the context of high levels of within-subject power. Evidence for the pleasure principle was robust, in that individuals placed a virtual self closer to stimuli that they found more pleasant (Study 1) and they wished to re-view such stimuli more frequently (Studies 2 & 3). These relationships, however, were more pronounced at higher levels of attention to emotion. The findings affirm the importance of feelings in decision-making while highlighting ways in which individual differences in attention to emotion operate.
... Finally, curiosity can be described as a person's desire for information that interferes with the decisionmaking process [22]. The medical teacher can increase students' questioning by managing his emotions and paying attention to the promotion of others. ...
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Background Effective teaching in clinical environment is one of the challenges faced by clinical faculty members due to the complexities of the variables involved in the training. Using appropriate teaching methods by professors in this environment can promise efficient graduates in the field. The present study, as a follow up of Omid research, which designed a clinical teaching model based on emotional intelligence, was carried out to evaluate the results of implementing this model in a clinical setting. Methods A qualitative study was conducted using inductive content analysis in one of the educational hospitals affiliated with Esfahan University of Medical Sciences. A total of 20 volunteer medical students from different levels of education were selected using purposeful sampling and were asked to express their experiences of attending the round with its clinical teaching based on emotional intelligence. Participants included 4 stagers, 11 interns, and 5 residents. Data were collected using semi-structured individual interviews; each interview lasted for 40–60 minutes and began with the question: what do you think are the important features of this professor's clinical education? The following questions were asked based on the issues raised in the interview. Data collected were analyzed immediately after the interviews Results Two main categories emerged from data analysis: Health outcome and Learning outcome. Conclusion The use of teaching based on emotional intelligence in the clinical environment can be effective in promoting their learning while ensuring the health of the learners.
... Curiosity as a personal trait is a significant predictor of so-called emotional intelligence, or a grasp of and respect for other minds. 12 Cynthia Enloe, writing The Curious Feminist: Searching for Women in a New Age of Empire, characterizes taking women and their lives seriously as "having curiosity" about women. This kind of respectful curiosity engages with other realities and other existences as valuable on their own terms, not as instruments for us. ...
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... The studies carried out in the organisational environment empirically show the influence of curiosity on the increase of psychological well-being and on the decrease of professional burnout (Wang & Li, 2015). It is considered that employees with great curiosity are more likely to experience positive emotions or subjective feelings (Leonard & Harvey, 2007). However, other studies consider that only stretching predicts the positive affect, while embracin & Gavrilov-Jerkovic, 2014). ...
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This study aims to investigate the influence of curiosity on subjective well-being (SWB). More specifically, we examine the mediating role that the Big Five personality traits play in the relationships between these two variables. To this purpose, we used questionnaires in order to measure curiosity (Curiosity and Exploration Inventory-II), SWB (Satisfaction with Life Scale and Scale of Positive and Negative Experiences) and the Big Five personality factors (Big Five Inventory-10) in a case of a sample of 330 undergraduates (Mean age = 18.93). The analysis carried out is based on correlations, regressions and structural equation modelling. The model obtained using structural equation modelling revealed a significant relationship between curiosity and SWB via personality characteristics (χ²/df =1.74; comparative fit index = 0.95; root mean square error of approximation = 0.051; standardised root mean square residual = 0.032). Therefore, curiosity correlates significantly with SWB, but individuals characterised by a high degree of curiosity tend to have well-developed well-being since they tend to be extroverted, perseverant and emotionally stable. Future studies should also focus on other types of personality traits. Keywords: Arterial Five personality traits, curiosity, mediation, subjective well-being.
... However, if C seems to be a protective factor against disease, it is helpful to differentiate the healthy C that promotes SA from the extreme inflexibility or compulsive behaviour that may represent personality disorder as a risk factor of mortality. Furthermore, O is associated with mechanisms of emotional reflection and metacognition and, therefore, is likely to contribute to maintaining a high quality of life (Leonard and Harvey 2007). To further explore the indirect effects of personality on SA, longitudinal studies are needed to provide answers on the relationship between O, cognitive functions, and health outcomes. ...
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In this paper, we highlighted links between personality traits and successful ageing through a systematic review of recent empirical studies. Particularly, we addressed the question of whether personality traits are related to successful ageing and, if so, why and how? Answers to this question provided, for example, arguments that supported personality’s role in planning an individual’s future based on self-knowledge, thereby contributing to a sense of identity throughout their life. Then, considering longitudinal studies, we examined whether personality is stable over one’s life course, or does it change and, if so, why and under what conditions? Answers to this question gave substance to the idea that a stable personality allows for continuous and consistent development. In addition, certain personality changes are likely to allow an individual to develop the resilience to better adapt to life’s challenges. Therefore, the arguments brought by these two questions can help clarify the modulating role of personality for successful ageing via health and well-being outcomes. These insights may contribute to the development of new prevention approaches, more focused on inter- and intraindividual differences, to promote successful ageing.
... Organizations need to adopt this view of curiosity if they are to capitalize on curiosity to ignite positive workplace outcomes. Namely, if organizations understand how their employees are curious, important insights can be gained about how that curiosity can be cultivated to manifest imperative workplace soft skills, which have been empirically associated with curiosity such as emotional intelligence (Leonard and Harvey, 2007), empathy (Halpern, 2007), situational and cultural adaptability (Harrison et al., 2010;Van der Zee and Van Oudenhoven, 2000) and proactive coping (Seaton and Beaumont, 2008). The way curiosity acts as a bridge (mediates) the development of soft skills is further discussed in the next section. ...
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Purpose This paper examines the role of curiosity in volatile, uncertain, complex and ambiguous (VUCA) work contexts. Design/methodology/approach This conceptual article relied upon an examination of literature about curiosity, VUCA and soft skills. Findings Curiosity, when encouraged and supported within the workforce, may aid organizations in closing soft skill gaps and better navigating ambiguity, perpetually changing business landscapes, and rapidly advancing technology. Research limitations/implications Empirical research is needed to validate, confirm and further explicate the specific mechanisms and value of curiosity within VUCA environments. Practical implications Organizations need to move beyond espousing a value of curiosity to deliberately and effectively cultivating and supporting it within their employees. Originality/value Although ample research and literature has examined curiosity, soft skills and VUCA environments independently, the body of literature on the specific role of curiosity in such environments is limited.
To better define the boundaries of conceptually overlapping constructs of intrapersonal emotion knowledge (EK), we examined meta-analytic correlations among five intrapersonal EK-related constructs (affect labelling, alexithymia, emotional awareness, emotional clarity, emotion differentiation) and attention to emotion. Affect labelling, alexithymia, and emotional clarity were strongly associated, and they were moderately associated with attention to emotion. Alexithymia and emotional awareness were weakly associated, and emotion differentiation was unrelated with emotional clarity. Sample characteristics and measures moderated some of the associations. Publication bias was not found, except for the alexithymia-emotional awareness association. This study helped to clarify the extent to which similarly defined constructs overlap or are distinct, which can inform our decision to adequately label important constructs and employ corresponding measures.
This paper focuses on the emergent importance of curiosity at work for individuals and organizations by reviewing management research on curiosity at work. We start by leveraging prior reviews on early and contemporary foundations of the curiosity construct in the larger psychological literature, with a focus on definitional clarity, dimen-sionality, and differences with other constructs in its nomological network. Next, we review different streams of management research on curiosity at work (i.e., broad generative and nongenerative effects, curiosity as a catalyst for personal action, curiosity as a catalyst for interpersonal action, curiosity as a catalyst for leadership, curiosity as an organizational or professional norm, and curiosity as a catalyst for organizing). Inter-weaving these diverse literatures and research streams gives us the wherewithal to provide conceptual clarity to curiosity research and highlight how curiosity not only has generative effects at the individual level but also acts as a more dynamic, interpersonal, and organizational property. In addition, our review brings attention to the potential dark side of curiosity. We end by outlining how the more nuanced insights of the role of curiosity at work generated by our review provide an impetus for future research.
through virtues of thought. Through our actions and attitudes toward scholarly work and through the academic expectations we place upon our students, we represent and convey certain beliefs about the ethics of teaching and learning One of the most valuable intellectual capacities we can foster in our students is the art of being curious. As a state of inquisitive attention to the world, curiosity embodies both intellectual and moral virtues. By drawing on the medieval monastic unity of intellectual and moral life, traditional religious practices such as hospitality, obedience, and charity can be applied to academic work. By cultivating students' intellectual curiosity, we encourage in them a more balanced set of scholarly skills and attitudes, and we help them to grow in wisdom, kindness, and generosity.
This article presents a framework for emotional intelligence, a set of skills hypothesized to contribute to the accurate appraisal and expression of emotion in oneself and in others, the effective regulation of emotion in self and others, and the use of feelings to motivate, plan, and achieve in one's life. We start by reviewing the debate about the adaptive versus maladaptive qualities of emotion. We then explore the literature on intelligence, and especially social intelligence, to examine the place of emotion in traditional intelligence conceptions. A framework for integrating the research on emotion-related skills is then described. Next, we review the components of emotional intelligence. To conclude the review, the role of emotional intelligence in mental health is discussed and avenues for further investigation are suggested.
Emotional intelligence is a type of social intelligence that involves the ability to monitor one's own and others' emotions, to discriminate among them, and to use the information to guide one's thinking and actions (Salovey & Mayer, 1990). We discuss (a) whether intelligence is an appropriate metaphor for the construct, and (b) the abilities and mechanisms that may underlie emotional intelligence. © 1993.
We examined the roles of curiosity, social anxiety, and positive affect (PA) and neg- ative affect (NA) in the development of interpersonal closeness. A reciprocal self-disclosure task was used wherein participants and trained confederates asked and answered questions escalating in personal and emotional depth (mimicking closeness-development). Relationships between curiosity and relationship out- comes were examined using regression analyses. Controlling for trait measures of social anxiety, PA, and NA, trait curiosity predicted greater partner ratings of attrac- tion and closeness. Social anxiety moderated the relationship between trait curios- ity and self-ratings of attraction such that curiosity was associated with greater attraction among those low in social anxiety compared to those high in social anxi- ety. In contrast, trait PA was related to greater self-ratings of attraction but had no relationship with partners' ratings. Trait curiosity predicted positive relationship outcomes as a function of state curiosity generated during the interaction, even after controlling for state PA.