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The challenge–skill balance and antecedents of flow: A meta-analytic investigation


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Flow is an intrinsically motivating state of consciousness characterized by simultaneous perception of high challenge and skill. The position that challenge–skill balance is the primary antecedent for achieving a flow state is unclear, and more research is needed to examine its impact on flow within multiple domains. Therefore, a meta-analysis was conducted on 28 studies examining the challenge–skill balance related to flow and intrinsic motivation in a variety of contexts. The results indicated that the relationship between challenge–skill balance and flow was moderate, and smaller with intrinsic motivation. Moderator analyses revealed weaker correlations when individuals were from an individualistic culture, in work or education contexts, using experience sampling method, and self-reporting state flow vs. trait. Compared to other theorized antecedents, challenge–skill balance was a robust contributor to flow along with clear goals and sense of control.
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The challenge–skill balance and antecedents of flow: A
meta-analytic investigation
Carlton J. Fonga, Diana J. Zaleskib & Jennifer Kay Leachc
a Department of Educational Psychology, The University of Texas at Austin, One University
Station D5800, Austin, TX 78712, USA
b Illinois State Board of Education, Springfield, IL, USA
c The University of Texas at Austin
Published online: 15 Oct 2014.
To cite this article: Carlton J. Fong, Diana J. Zaleski & Jennifer Kay Leach (2014): The challenge–skill balance and
antecedents of flow: A meta-analytic investigation, The Journal of Positive Psychology: Dedicated to furthering research and
promoting good practice, DOI: 10.1080/17439760.2014.967799
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The challengeskill balance and antecedents of ow: A meta-analytic investigation
Carlton J. Fong
*, Diana J. Zaleski
and Jennifer Kay Leach
Department of Educational Psychology, The University of Texas at Austin, One University Station D5800, Austin, TX 78712, USA;
Illinois State Board of Education, Springeld, IL, USA;
The University of Texas at Austin
(Received 17 January 2014; accepted 11 September 2014)
Flow is an intrinsically motivating state of consciousness characterized by simultaneous perception of high challenge
and skill. The position that challengeskill balance is the primary antecedent for achieving a ow state is unclear, and
more research is needed to examine its impact on ow within multiple domains. Therefore, a meta-analysis was
conducted on 28 studies examining the challengeskill balance related to ow and intrinsic motivation in a variety of
contexts. The results indicated that the relationship between challengeskill balance and ow was moderate, and smaller
with intrinsic motivation. Moderator analyses revealed weaker correlations when individuals were from an individualistic
culture, in work or education contexts, using experience sampling method, and self-reporting state ow vs. trait.
Compared to other theorized antecedents, challengeskill balance was a robust contributor to ow along with clear goals
and sense of control.
Keywords: ow; challengeskill balance; antecedents; meta-analysis; intrinsic motivation
Csikszentmihalyis claim that
in ow, the demands of a situation match the individ-
uals ability, and the individual is engaged fully in the
act of doing the activity. In ow, the person loses self-
consciousness and a sense of the passing of time and
enters into a different level of experience. (2003, p. 38)
Most intuitively understand this phenomenon of being in
the zoneor in ow’–a state of total immersion and
merging of action and awareness (Beard & Hoy, 2010).
This highly motivating state raises the question: Why are
some people highly committed to and engaged in activities
without obvious external rewards? Although others have
explained this behavior (e.g. DeCharms, 1968; Deci &
Ryan, 1980; White, 1959), Csikszentmihalyi described
this intrinsicallymotivated behavior as consisting of a
ow state or optimal experience.
Flow is considered to be an optimal state associated
with positive emotional, motivational, and cognitive expe-
riences (Csikszentmihalyi, Abuhamdeh, & Nakamura,
2005; Hektner, Schmidt, & Csikszentmihalyi, 2007;
Waterman et al., 2003). Csikszentmihalyi (1975)dened
optimal experience or ow as a positive and intrinsically
motivating state of consciousness associated with
perception of high challenge and personal skills adequate
to meet those challenges (see also Bakker, 2005;
Csikszentmihalyi, Rathunde, & Whalen, 1993; Hodge,
Lonsdale, & Jackson, 2009). A large number of studies
have identied ow experiences in the lives of people
from diverse cultural and economic backgrounds (see
Csikszentmihalyi & Csikszentmihalyi, 1988; Massimini &
Delle Fave, 2000). Also, the importance of ow has
spread to elds such as education (e.g. Bassi & Della
Fave, 2012) or work (e.g. Moneta, 2012) given that ow
can lead to greater concentration, determination, persis-
tence, and motivation, which in turn contributes to
increased performance (see Aube, Brunelle, & Rousseau,
Theoretically, ow should be related to enhanced per-
formance for numerous reasons. First, ow is a highly
functional state, which should in itself foster higher per-
formance. Second, individuals experiencing ow are
intrinsically motivated to re-engage in future activities
(Engeser & Rheinberg, 2008). In addition, in order to
experience ow again, there is greater desire to take on
more challenging tasks (Nakamura & Csikszentmihalyi,
2005). Thus, ow could be understood as an internally
motivating force for achievement and enhanced perfor-
mance. Not only is the idea of an optimal experiential
state an intriguing topic, but also deeper understanding of
ow has the potential to raise productivity, to better
human life, and to foster life satisfaction and happiness
across the lifespan (Csikszentmihalyi, 1997). The concept
of ow has had a prominent status in the eld of positive
*Corresponding author: Email:
Department of Educational Administration, The University of Texas at Austin, One University Station D5400, Austin, TX 78712,
Oregon State University, Academic Success Center, Corvallis, OR, USA.
© 2014 Taylor & Francis
The Journal of Positive Psychology, 2014
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psychology, and research encourages maximization of
ow experiences (Keller, Bless, Blomann, & Kleinbohl,
2011); however, much debate exists regarding the
existence and strength of ows antecedents.
The concept of ow: the challengeskill balance and
other antecedents
The concept of ow has been difcult to dene and op-
erationalize (Lovoll & Vitterso, 2012). Csikzentmihalyi
(1990) himself cautioned dening ow too precisely lest
it break the spirit of this dynamic construct. Yet one of
the most common and accepted conceptualizations of
ow is the balance between perceived challenges and
perceived skills(Csikzentmihalyi, 2009, p. 398).
Csikzentmihalyi argued that the challengeskill balance
leads to the optimal experience and maintaining such
balance in itself is intrinsically rewarding.
Flows dynamic structure of the perceived match
between high challenge and adequate personal skill has
been described by four channels of daily experience:
ow (high challenge and high skill), boredom or relaxa-
tion (low challenge and high skill), apathy (low chal-
lenge and low skill), and anxiety (high challenge and
low skill) (Csikszentmihalyi, 1975; Csikszentmihalyi,
et al., 1993; Deichter, 2011). Therefore, if an activity is
either very easy or very difcult in comparison to ones
skill level, the experience of ow will be weak. In the
state of ow, one feels optimally challenged and con-
dent. This has a strong functional aspect and explains
why people in ow are committed to tasks despite the
lack of foreseeable results. Csikzentmihalyi and
Nakamura (2010) further discussed how the ratio of
challenges to skills should be around 50/50 for optimal
experience, and even a slight imbalance can induce
anxiety and displeasure.
Previous research has indicated the centrality of the
challengeskill balance to the induction of ow. In an
experimental study, Keller and Bless (2008) supported
the challengeskill balance by testing three conditions: a
balanced condition vs. two controls of high challenge or
low challenge. Participants reported more positive sub-
jective experiences and had higher performance in the
balanced condition compared to the control conditions.
Moneta and Csikszentmihalyi (1996) measured the bal-
ance between challenge and skill with an adolescent
sample, using the experience sampling method (ESM).
Across multiple contexts and domains, they found that
the challengeskill balance had a positive effect on ado-
lescentsperceptions of concentration, wishing to do the
activity, involvement, and happiness. However, these
ndings were not found within all contexts and domains,
and across all dimensions of experience. For example,
the challengeskill balance may have a positive effect on
one dimension of experience within one context and no
effect on others. A different context may yield different
results. Similarly, in a later study, Moneta and
Csikszentmihalyi (1999) showed that quite a signicant
amount (47%) of the variance in self-reported concentra-
tion was explained by the balance of skills and
On the other hand, there has also been a great deal
of research that suggests that the challengeskill balance
is not a salient predictor of ow experiences. Some stud-
ies have shown that the challengeskill balance explains
as little as 24% of the variance of emotional experience
(Lovoll & Vitterso, 2012; Voelkl, 1990). Experimental
research also supports the greater importance of an
imbalance in challenge and skill compared to a balance
(see Clarke & Haworth, 1994). For example, a study on
chess players revealed that levels of enjoyment were
highest when playing better opponents compare to equal-
ranked opponents (Abuhamdeh & Csikszentmihalyi,
2009). Essentially, when perceived challenges were
higher than skills, the games were more enjoyable than
when the challenge matched ones skills.
Other arguments have contested the original opera-
tional denition of ow as a balance between skill and
challenge (Engeser & Rheinberg, 2008). One of the rst
problems is that people vary in the extent to which ones
skills and the perception of challenge are related.
Furthermore, the construct of perceived challenge com-
pounds both perceived difculty and skill; for example,
an easy task could be highly challenging because of a
lack of skill. Theoretically, this is a problematic issue;
however, empirically, comparing the balance of chal-
lenge-skill and difculty-skill yielded no substantial
differences (Pster, 2002).
Another problematic issue to the challengeskill
balance and ow relationship is that some people more
frequently experience ow when they are engaged in
challenging activities (Engeser & Rheinberg, 2008);
therefore, an imbalance in skill and challenge is posited
to have a greater association with ow. Empirically,
Moneta and Csikszentmihalyi (1996) found that the
challengeskill balance was not compatible with certain
ow indicators or dimensions of experience such as
wishing to do the activity and happiness. On the other
hand, other research has supported that relatively chal-
lenging tasks were no more enjoyable than easy tasks
(Haworth & Evans, 1995; Shernoff, Csikszentmihalyi,
Schneider, & Shernoff, 2003).
Since ows original conception, Csikszentmihalyi
(1990) has also identied eight other dimensions of the
ow experience beyond the challengeskill balance, with
nine antecedents all together: (a) challengeskill balance
or engaging in challenges that meet ones current skill
level; (b) action-awareness merging; (c) clear goals; (d)
unambiguous feedback; (e) concentration on the task at
hand; (f ) sense of control; (g) loss of self-consciousness
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or self-awareness; (h) transformation of time or the
distorted sense of time; and (i) the autotelic experience
(Kawabata & Mallet, 2011; Payne, Jackson, Noh, &
Stine-Morrow, 2011). Kawabata and Mallet (2011)
described the components as follows. The challengeskill
balance refers to the perception that an activitys chal-
lenge is matched or balanced with ones ability. Action-
awareness merging is involvement in the ow activity to
a point of spontaneity or automaticity. Clear goals refer
to ones perception of the goals of the activity before or
during the activity. Unambiguous feedback refers to the
monitoring of ones behavior that provides immediate
and clear feedback concerning the activity. Concentration
is the complete and intense sense of focus on the activity
at hand. Sense of control refers to the perception that
one is able to respond to any challenge while engaged in
the activity. Loss of self-consciousness refers to the lack
of concern about the perception of others. The transfor-
mation of time involves a sense that time has passed
either faster or slower than normal. The autotelic experi-
ence refers to the experience of the activity being intrin-
sically rewarding and enjoyable, or that the task has a
purpose in and of itself.
These nine dimensions do not necessarily occur
simultaneously. Hypothesized by the Quinn Model of
Flow (Quinn, 2005), certain dimensions may be required
in order to enter the ow state (i.e. challengeskill bal-
ance, clear goals, and unambiguous feedback ), while
others are necessary characteristics of being in a ow
state (i.e. concentration, merging of action and aware-
ness, sense of control, loss of self-consciousness, and
transformation of time), or the result of the ow experi-
ence (i.e. autotelic experience). These additional anteced-
ents and components support how the challengeskill
balance may not be the most salient contributor to
achieving a ow state (Shin, 2006; Wang & Hsiao,
2012), despite receiving the greatest attention among
conditions for entering ow according to the literature.
Intrinsic motivation, ow, and the challengeskill
Intrinsic motivation, the propensity to engage in a task
out of interest or enjoyment, for its own sake, or without
any external incentive or reward (e.g. Ryan & Deci
2000), has been shown to be highly related to ow
(Csikszentmihalyi & LeFevre, 1989; Keller, Ringelhan,
& Blomann, 2011; Jackson, 1995). By denition, ow is
understood as an intrinsically motivating state; in fact,
some researchers have coined ow to be a model of
intrinsic motivation (Keller & Bless, 2008). Moreover,
individuals who experience a challengeskill balance are
more likely to freely choose to reengage in activities, a
behavioral indicator of intrinsic motivation. In an experi-
mental paradigm, Keller, Ringelhan, et al. (2011) found
that compared with individuals not in ow, individuals
in ow were more intrinsically motivated to perform a
free-choice activity. They found that the degree to
which they indicated interest (self-reported measure of
intrinsic motivation) mediated the extent to which they
engaged in the activity (behavioral measure of intrinsic
Self-determination theory (SDT; Deci & Ryan,
1985), a prominent view of intrinsic motivation, has
been linked with ow. SDT posits that feelings of com-
petence, autonomy, and relatedness undergird intrinsic
motivation, and research has supported the link between
these three determinants and ow (Kowal & Fortier,
1999). In a study of Canadian swimmers, Kowal and
Fortier found that intrinsic motivation and two determi-
nants, competence and relatedness, were signicantly
positively correlated with ow and with challengeskill
balance as well. Bassi and Della Fave (2012) argued that
optimal challenge supports the self-determination
perspective given the competence need as a basis for
intrinsic motivation.
Given the theoretical and empirical relationship
between intrinsic motivation, ow, and the challenge
skill balance, we also wanted to assess its magnitude and
direction in the present study. Inconsistent results
reported in the above literature, issues in operationalizing
challenge and skill, and the alternate antecedent models
of ow call for further understanding of how the chal-
lenge and skill balance really predict ow experiences.
In addition, a meta-analysis has yet to be conducted
examining this seminal yet debatable topic. Moreover,
systematic variants or moderators to this relationship
have not been assessed across a larger body of research.
Moderators to the challengeskill balance
Additional variables may also differentially impact how
the challengeskill balance inuences ow experiences.
In the present study, we systematically explored theoreti-
cal and methodological factors that may moderate this
relationship. First, individual differences, such as
achievement motivation, have been found to moderate
this dynamic (Engeser & Rhineberg, 2008). For example,
individuals with low need for achievement perceive
moderately difcult tasks as daunting. For the highly
achievement-motivated individuals, they prefer tasks of
medium challenge, or when there is an optimal balance
of difculty and skill. Similarly, Moneta and
Csikszentmihalyi (1999) argued that individuals of high
ability or talent are expected to express the closest
approximation to the theoretical model,that is, the chal-
lengeskill balance predicting ow (p. 630).
Csikszentmihalyi (1975) even acknowledged the possi-
bility of an autotelic personality. Autotelic individuals
often have greater curiosity about life, engaging in
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activity for their own sake rather driven by external pres-
sure. This characteristic has obvious consequences to
their response to ow states and its antecedents.
Demographic characteristics such as age may inuence
the relationship between ow and the challengeskill
balance. In a study comparing subjective experiences of
younger and older individuals, results indicated that the
older participants were more alert and able to concentrate
than younger participants (Prescott, Csikszentmihalyi, &
Graef, 1981). With regard to domain, younger partici-
pants were more relaxed in leisure settings such as the
home compared to the older group; whereas older partic-
ipants were more interested and relaxed at work contexts
compared to the younger group. One explanation could
be the development of career challenges for the younger
participants and less enjoyment of leisure or recreation
for the older participants. Alternatively, some research
has indicated that age may not differentiate the dynamics
of ow. Bye, Pushkar, and Conway (2007) revealed that
younger traditional age college students had the same
levels of intrinsic and extrinsic motivation as older
non-traditional age college students, suggesting the same
difculty in experiencing ow across age groups.
Although these study outcomes do not directly tap into
the ow construct, they provide evidence that age might
play a moderating role in the relationship between
challengeskill balance, ow, and intrinsic motivation.
Culture may play a moderating role in the experience of
ow (Delle Fave, Massimini, & Bassi, 2011). Early criti-
cism of the ow concept came from its supposedly bias
toward Western culture as ow focused on more active
and goal-directed processes, suggesting that ow may
operate differently among various cultures. For example,
in a study comparing Chinese college students with
Grade 12 students from the USA, Moneta (2004) found
a cultural variation in which the Chinese students were
more motivated when there was an imbalance of chal-
lenge and skill, favoring lower challenges. He suggested
that it was partially due to the Chinese students internal-
izing collectivist values. However, Csikszentmihalyi and
Csikszentmihalyi (1988) argued that what causes ow
may differ from culture to culture, but the dynamics of
the ow experience are universal.
Also, an important moderator to examine is whether the
challengeskill balance relationship with ow varies
depending on domain or context. Given how ow has
been studied in numerous contexts, assessing whether
work/academic contexts vs. leisure contexts is a critical
issue for applied researchers when examining ow and
practitioners who want to increase ow experiences.
Csikszentmihalyi and LeFevre (1989) found that the
great majority of adults were experiencing ow when
working and not in leisure despite being more motivated
in leisure. Boredom and lack of engagement is a chronic
issue in the workplace and classroom, and the applicabil-
ity of ow to working and learning environments is dif-
cult given the compulsory nature of job and learning
activities (Kiili & Lainema, 2008; Marzalek, 2006;
Shernoff et al., 2003). Other contextual factors such as
environments that support autonomy or that aid in focus-
ing attention or removing distractions can foster more
ow-related activities (Nakamura & Csikszentmihalyi,
2005; Schmidt, Shernoff, & Csikszentmihalyi, 2007).
Studies also assess ow during personal activities that
individuals indicate are meaningful or salient to them in
their everyday experience.
Lastly, there are methodological characteristics that we
want to examine as potential moderators. How researchers
have formalized the challengeskill balance has varied
from study to study (see Moneta & Csikszentmihalyi,
1999), and results revealed a differential impact on ow
experiences depending on how the skill-challenge balance
variable is calculated. For example, in a study with tal-
ented high school students, Moneta and Csikszentmihalyi
(1999) compared three methods of calculating the
skill-challenge balance: cross-product, absolute difference,
and quadratic effects following a rotation of the predictor
axes. Their results indicated that the cross-product and the
absolute difference models were preferable (determined by
model t).
Another methodological concern is how ow is oper-
ationalized and measured (see Martin & Jackson, 2008).
A study may use experience sampling method (ESM;
see Csikszentmihalyi & Larson, 1987), which records
multiple temporal measurements of ow over a period of
time. More frequently, a single measurement is used such
as the Flow State Scale (Jackson & Marsh, 1996)or
Dispositional Flow Scale (Marsh & Jackson, 1999),
which includes the nine antecedents of ow. Other self-
reported measures include just one or two items assess-
ing concentration or related topics. Given the range of
methods to assess ow states, a moderator analysis may
further distinguish the validity of such techniques.
In addition, as described earlier as an autotelic per-
sonality, ow can be conceptualized as a trait or a state
(see Marsh & Jackson, 1999). Flow as a state involves
feeling certain subjective experiences after engaging in
an activity; however, ow as a trait, involves a more
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enduring sense of ow, often measured by how often an
individual experiences ow. Whether the challengeskill
balance is more strongly related to ow as a state or trait
is both a theoretical and methodological concern.
The present study
Over 30 years of research has accumulated on the
construct of ow across a variety of domains (see
Csikszentmihalyi, 1990). Flow theory posits that intrin-
sic motivation peaks in activities characterized by the
simultaneous perception of high challenge and skill. In
particular, the challengeskill balance hypothesis of
ow theory has been a center of much debate with
empirical evidence supporting both sides (see Engeser
& Rhineberg, 2008). Pockets of research have con-
cluded that the subjectively perceived t between the
challenge of an activity and the skills of the individual
is the most important prerequisite to experiencing ow
(e.g. Schiefele & Raabe, 2011). Therefore, a meta-
analysis on the relationship between the challengeskill
balance and ow is not only timely, but also essential
in empirically assessing the overall theoretical basis of
this important ow construct, its relation to intrinsic
motivation, and the moderators that inuence these
relationships. Second, assessing how strongly the
challengeskill balance relates to ow in comparison to
other factors (i.e. nine-factor model) was measured.
Lastly, we also examined the relationship between the
challengeskill balance and intrinsic motivation.
The following section describes the procedures used to
conduct this meta-analysis, including subsections
addressing study inclusion criteria, literature search and
information retrieval, coding procedures, effect size
calculations, data integration, search outcomes, and
moderator analyses.
Literature search procedures
Studies were collected from a wide variety of sources
and included search strategies meant to uncover both
published and unpublished research. In order to locate
the most exhaustive set of studies, we searched ERIC,
PsycINFO,Proquest Dissertation and Theses Full Text,
Social Science Citation Index,andGoogle Scholar elec-
tronic databases using a broad array of subject terms
including owand optimal experience,while exclud-
ing keywords cash ow,’‘optic ow,and blood ow
to reduce the number of irrelevant results. The reference
sections of relevant documents were examined to deter-
mine if any cited works might be relevant to our topic.
In addition, Social Sciences Citation Index was searched
for documents that had cited several seminal works on
ow: Csikszntmihalyi, 1975,1990. These searches com-
bined located a total of 355 unique, potentially relevant
Each title and abstract was examined by the authors.
If the abstract provided and indicated that the document
contained data relevant to the relationship on ow and
the challengeskill balance, the full document was
obtained for further examination.
Criteria for including studies
To be included in the meta-analysis, a study was
required to meet several criteria. First, studies need to
have reported data to derive the bivariate relationship
between the challenge and skill balance and a measure
of ow or intrinsic motivation. Many studies have
included measures of both perceived skill and challenge,
but did not calculate a match or balance between the
two; these studies were not included (e.g. Abuhamdeh &
Csikszentmihalyi, 2012). Research studies conducted in
any context with participants of any age were included.
Information retrieved from studies
Numerous different characteristics of each study will be
included in our data. These characteristics encompassed
six broad distinctions among studies: (a) publication sta-
tus (published or unpublished); (b) the ow variable
(how ow was measured and/or calculated); (c) the
domain (work/education-related activities, leisure activi-
ties, or self-selected personally salient activities related
to ones identity; (e) the sample characteristics (age and
country of origin); (f ) the measure of the challengeskill
balance; and (g) the estimate of the relationship.
Methods of data integration
Before conducting any statistical integration of the effect
sizes, the number of positive and negative effects was
counted. Next, the range of estimated relationships was
calculated. We examined the distribution of sample sizes
and effect sizes to determine whether any studies con-
tained statistical outliers. Grubbss(
1950) test was
applied and if outliers were identied, these values were
set at the value of their next nearest neighbor.
Both published and unpublished studies were
included in the synthesis. There is still the possibility
that not all studies investigating the relationship between
ow and challengeskill balance were obtained. There-
fore, Duval and Tweedies(
2000) trim-and-ll procedure
was employed. The trim-and-ll procedure tests whether
the distribution of effect sizes used in the analyses was
consistent with that expected if the estimates were
normally distributed.
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Effect size calculation
We collected correlation coefcients between challenge
skill balance and ow (often represented by ror the
Pearson product moment coefcient). When only means
and standard deviations were provided for a ow group
and a non-ow group, we estimated a correlation. Since
some of the studys sample sizes were small, and we
wanted to improve normality, we conducted Fishers
r-to-z transformations, a rather effective normalizing
transformation (see Meng, Rosenthal, & Rubin, 1992).
Meta-analytic methods assume that the sampling distri-
bution of the observed outcomes is (at least approxi-
mately) normal. Weighted procedures were used to
calculate average effect sizes across all comparisons in
which each independent effect size is rst multiplied by
the inverse of its variance and then the sum of these
products is then divided by the sum of the inverses (see
Cooper, Hedges, & Valentine, 2009). Also, 95% con-
dence intervals were calculated for average effects to
assess signicance.
One problem that arises in calculating average effect
sizes involves deciding what constitutes an independent
estimate of effect. Here, we used a shifting-unit-of-analysis
approach (Cooper, 1998). This approach involves coding
as many effect sizes from each study that exist as a result
of variations in characteristics of the intervention, sample,
setting, and outcomes within the study. However, when
calculating the overall effect size, the multiple effect sizes
were averaged to create a single effect size for each study.
To calculate an overall effect size of the intervention, a
weighted average of all effect sizes was computed and
entered prior to analysis, so that the study will only con-
tribute one effect to the assessment of the overall effects of
the intervention on achievement. The shifting-unit-of-anal-
ysis approach maximizes the amount of data from each
study without violating the assumption of independent
data points.
Moderator analyses
We conducted moderator analyses when tested using
homogeneity analyses (Cooper et al., 2009). Effect sizes
may vary even if they estimate the same underlying pop-
ulation value; therefore, homogeneity analyses were
needed to determine whether sampling error alone
accounted for this variance compared to the observed
variance caused by features of the studies. We tested
homogeneity of the observed set of effect sizes using a
within-class goodness-of-t statistic (Q
), which follows
a chi-square distribution with k1degrees of freedom
(kequals the number of effect sizes). A signicant Q
statistic suggests that sampling variation alone cannot
adequately explain the variability in the effect size esti-
mation; it follows that moderator variables should be
examined (Cooper, 1998). Similarly, the Q
indicates that average effect sizes vary between catego-
ries of the moderator variables more than predicted by
sampling error alone.
Analyses were conducted using both xed- and ran-
dom-error assumptions (Cooper et al., 2009). In a xed-
effects model of error, each effect sizes variance is
assumed to reect only sample error or differences
among participants in the study. In a random-effects
model of error, a study-level variance component also is
assumed to be an additional source of random variation.
Due to the potential to over- or underestimated error
variance in moderator analysis (Hedges & Vevea, 1998),
we conducted all the analyses twice using both models
of error in order for sensitivity analyses to examine the
effect of different assumptions (Greenhouse & Iyengar,
1994). All statistical analyses were conducted using the
Comprehensive Meta-Analysis statistical software pack-
age (Borenstein, Hedges, Higgins, & Rothstein, 2005).
Overall ndings
The literature search uncovered 28 studies that reported
a relationship between optimal challengeskill balance
and ow and 18 studies that provided a relationship
between challengeskill balance and intrinsic motivation.
For ow, the 28 studies reported 37 effect sizes based on
34 separate samples with a total N of 9620 participants.
For the relationship between challengeskill balance and
intrinsic motivation, the pool of 18 studies reported 51
effect sizes from 25 samples with a total N of 4270. The
characteristics of the included studies are reported in
Table 1.
Regarding the pool of studies that assessed challenge
skill balance and ow, the studies were published between
the years 1996 and 2013. The sample sizes ranged from 51
to 1231, with a median sample size of 270. The average
sample size was 277.9, with a standard deviation of 230.8,
suggesting a normal distribution. Two included studies uti-
lized ESM (Chen, 2000; Fullager, Knight, & Sovern,
2013). Chen (2000) assessed three time points for
each participant, yielding 1215 momentary assessments.
Fullager et al. (2013) measured 1031 momentary assess-
ments. There were also no signicant outliers among the
correlations, so all were retained for analysis as reported.
The effect sizes of the correlations (Fishersz) ranged from
0.25 to 1.42. They were all positive correlations, except
for one.
Under a xed-error model, the overall relationship
between challengeskill balance and ow (a normally
distributed and weighted correlation or Fishersz) was
0.56 with a 95% CI from 0.55 to 0.58, indicating a mod-
erate relationship (see Table 2a). Under a random-error
model, the weighted average correlation was 0.52 with a
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Table 1. Characteristics of included studies.
Author (year)
Type of
(ESM) Age Country Culture
measure Domain
calculation Fishersz
Bakker (2005) J 120 41 the
I Survey Trait Scale Work/Educ Separate F: 0.22
IM: 0.23
605 19 F: 0.04
IM: 0.07
Bassi and Delle Fave (2012) J 268
17 Italy I ESM
. High/High
Work/Educ Separate IM: 0.29
Ceja and Navarro (2011) J 60 (698) 38 Spain C ESM S × C Work/Educ Separate IM: 0.21
IM: 0.31
S + C IM: 0.21
60 IM: 0.37
S × C IM: 0.37
IM: 0.43
S + C IM: 0.49
IM: 0.55
Chan and Ahern (1999) J 80 Over
USA I Survey State Subscale Work/Educ Subscore-
F: 1.29
Chen (2000) D 405 31 USA I ESM State Scale Leisure Separate F: 0.04
Collins (2006) D 55 77.64 USA I ESM State Subscale Personal Separate F: 1.04
Csikzentmihalyi and Fevre (1989)J 78
36.5 USA I ESM
. High/High
Work/Educ Separate IM: 0.20
Deichter (2011) T 186 39 Canada I Survey Trait Subscale Work/Educ Subscore-
F: 0.78
Fullagar et al. (2013) J 27 21.71 USA I ESM State | S C | Work/Educ Separate F: 0.73
Hodge et al. (2009) J 51 22.9 Canada I Survey Trait Subscale Work/Educ Subscore-
F: 0.91
Jackson (1996) J 394 22 USA I Survey State Subscale Leisure Subscore-
F: 1.37
Kawabata and Mallet (2011) J 635 20.5 Japan C Grouping State Subscale Leisure Subscore-
F: 0.95
413 20.4 F: 0.91
Keller and Bless (2008) J 72 20 Germany I Survey . Subscale Work/Educ Separate IM: 0.46
Keller and Blomann (2008) J 72 20 Germany I Grouping . High/High
Work/Educ Means/SD IM: 0.42
IM: 0.44
Keller, Bless et al. (2011) J 102 20 Germany I Grouping . High/High
Work/Educ Means/SD IM: 0.23
IM: 0.26
84 IM: 0.81
IM: 0.37
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Table 1. (Continued).
Author (year)
Type of
(ESM) Age Country Culture
measure Domain
calculation Fishersz
Kiili and Lainema (2008) J 92 2030 Finland I Survey State Scale Work/Educ Separate F: 0.79
Kowal and Fortier (1999) J 203 36.4 Canada I Survey . Subscale Work/Educ Separate IM: 0.60
Lee (2005) J 262 20.02 Korea C Survey . Subscale Work/Educ Separate IM: 0.31
Lee and LaRose (2007) J 388 19 US I Survey State Median
Leisure Separate F: 0.48
Lovoll and Vitterso (2012) J 64 (698) 21.2 Norway I Grouping . High/High
Leisure Means/SD IM: 0.24
IM: 0.11
26 IM: 0.26
(260) 23.5 IM: 0.12
IM: 0.15
IM: 0.19
Marsh and Jackson (1999) J 385 NA Australia I Survey State
Subscale Leisure Subscore-
F: 0.29
F: 0.54
Marzalek (2006) D 134128 13 USA I Survey Trait
Subscale Work/educ Subscore-
F: 0.98
F: 0.25
Murica, Gimeno. and Gonzales
J 413 13.7 Spain C Survey Trait Subscale Leisure Subscore-
F: 1.02
IM: .48
Nah et al. (2010) J 211 22 USA I Survey State S + C Leisure Separate F: 0.16
Payne et al. (2011) J 197 72.1 USA I Survey State Subscale Personal Subscore-
F: 0.76
Rezabek (1994) D 108 20 USA I Grouping . High/High
Work/Educ Means/SD IM: 0.40
Robinson et al. (2012) J 30 (349) 51 Ireland I Grouping . High/High
Personal Means/SD IM: 0.25
IM: 0.25
IM: 0.26
Rodriguez-Sanchez et al. (2011) J 258 40.2 Spain C Survey State S × C Work/Educ Separate F: 0.63
Saville (2006) D 37 25.5 USA I Survey Trait Subscale Work/Educ Separate IM: 0.25
IM: 0.56
IM: 0.52
State IM: 0.26
IM: 0.41
IM: 0.54
Schiefele and Raabe (2011) J 89 23.7 Germany I Survey State Two items Work/Educ Separate F: 0.51
Schuler (2007) J 57 25 Switzerland I Survey State Single item Work/Educ Separate F: 0.05
395 F: 0.03
Schwartz and Waterman (2006) J 87 18.9 USA I Survey State S + C Personal Separate F: 0.38
F: 0.34
F: 0.30
IM: 0.16
IM: 0.28
IM: 0.19
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Table 1. (Continued).
Author (year)
Type of
(ESM) Age Country Culture
measure Domain
calculation Fishersz
Shin (2006) J 525 1822 Korea C Survey State S C Work/Educ Separate F: 0.21
Snow (2010) D 176 Over
USA I Survey State Subscale Leisure Separate F: 1.04
Stavrou et al. (2007) J 220 19.95 Greece C Survey State Subscale Leisure Subscore-
F: 1.16
van Schaik et al. (2012) J 83 25 Japan C Survey State Subscale Work/Educ Subscore-
F: 0.55
Vlachopoulous, Karageorghis and
Terry (2000)
J 1231 31.43 England I Survey State Subscale Leisure Subscore-
F: 1.42
Wang and Hsiao (2012) J 122 Varied USA I Grouping . High/High
Lesiure Means/SD IM: 0.11
136 IM: 0.17
102 IM: 0.49
Waterman et al. (2003) J 348 20 USA I Survey State S + C Personal Separate F: 0.35
IM: 0.41
270 F: 0.44
IM: 0.19
Waterman et al. (2008) J 217 20 USA I Survey State S + C Personal Separate F: 0.38
IM: 0.54
202 F: 0.28
IM: 0.50
218 F: 0.35
IM: 0.39
Notes: J = Journal article, T: Masters Thesis, D: Doctoral Dissertation; I: Individualistic or independent self-construal; C: Collectivistic or interdependent self-construal; S = Skill, C = Challenge;
F: Flow, IM: Intrinsic motivation.
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95% CI from 0.38 to 0.62. Additionally, the tests of the
distribution of effect sizes revealed that the hypothesis
that the effects were estimating the same underlying pop-
ulation could be rejected (Q(36) = 2282.4, p< 0.001), or
that the averaged correlation was greater than zero
potentially explained by the existence of moderators of
this relationship. Next, trim-and-ll analyses were con-
ducted. With both a xed-effects model and a random-
effects model, there was no evidence that effect sizes
might have been missing in the sample of studies.
Studies that assessed challengeskill balance and
intrinsic motivation were published between the years
1989 and 2012. The sample sizes ranged from 26 to
605, with a median sample size of 163. The average
sample size was 191.56, with a standard deviation of
135.92, suggesting a normal distribution. Five of the
included studies utilized experience sampling methodol-
ogy (Bassi & Delle Fave, 2012; Ceja & Navarro, 2011;
Csikszentmihalyi & Fevre, 1989; Lovoll & Vitterso,
2012; Robinson, Kennedy, & Harmon, 2012). The
included studies widely varied in the number of momen-
tary assessments: 5985 assessments (Bassi & Delle Fave,
2012); 698 assessments (Ceja & Navarro, 2011); 3432
assessments (Csikszentmihalyi & Fevre, 1989); 698
assessments in Study 1 and 260 assessments in Study 2
(Lovoll & Vitterso, 2012); 349 assessments (Robinson
et al., 2012). There were also no signicant outliers
among the correlations, so all were retained for analysis
as reported. The effect sizes of the correlations (Fishersz)
ranged from 0.236 to 1.02. They were all positive
correlations, except for two.
Under a xed-error model, the overall relationship
between challengeskill balance and intrinsic motivation
was 0.24 with a 95% CI from 0.22 to 0.25, indicating a
small relationship (see Table 2b). Under a random-error
model, the weighted average correlation was 0.32 with a
95% CI from 0.25 to 0.39. Additionally, the tests of the
distribution of effect sizes revealed that the hypothesis
that the effects were estimating the same underlying pop-
ulation could be rejected (Q(24) = 526.94, p< 0.001).
Next, trim-and-ll analyses revealed no evidence that
effect sizes might have been missing in the sample of
Findings of the moderator analyses
Since the overall relationships between challengeskill
balance and ow and intrinsic motivation were found to
be statistically heterogeneous, a series of moderator anal-
yses were conducted to help explain variation among
effect sizes. Table 3a and bpresents the ndings from
the moderator analyses.
Publication status
First, we assessed the publication status (published vs.
unpublished status) of the study report. For the ow
moderator analysis, 22 of the studies had been published
as journal articles, and their results were compared to the
ve studies that had appeared in dissertations, conference
papers, and master theses. Under the xed-error model,
correlations from the unpublished reports, z= 0.43 (95%
CI from 0.38 to 0.48), were just signicantly different
from those from published sources, z= 0.58 (95% CI
from 0.53 to 0.56), Q(1) = 37.50, p< 0.001. Under the
random-error model, there was no difference between
published and unpublished reports, Q(1) = 0.04,
p> 0.05.
Table 2a. Results of main analysis examining the relationship between ow and the challengeskill balance.
95% condence interval
kzLow estimate High estimate Q
Challengeskill balance 37 2282.37
Fixed model 0.56 0.55 0.58
Random model 0.52 0.38 0.62
Note: All effect sizes were signicantly different from 0 at a p< 0.001 value unless specied.
***p< 0.001.
Table 2b. Results of main analysis examining the relationship between intrinsic motivation and the challengeskill balance.
95% condence interval
kzLow estimate High estimate Q
Challengeskill balance 25 526.94
Fixed model 0.24 0.22 0.25
Random model 0.32 0.25 0.39
Note: All effect sizes were signicantly different from 0 at a p< 0.001 value unless specied.
***p< 0.001.
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For the intrinsic motivation moderator analysis, pub-
lished reports (k= 23, z= 0.24) had a signicantly smal-
ler relationship than unpublished reports (k=2,z= 0.39)
under xed-error model (Q(1) = 9.17, p< 0.01). There
were no differences under the random-error model, Q(1)
= 1.37, p> 0.05.
We next examined whether age would moderate the
challengeskill balance and ow relationship. We coded
age dichotomously and continuously. First, we formed
two groups age 30 and above, and below 30. Separat-
ing the two groups at the 30-year mark followed previous
literature examining age groups and ow (e.g. Prescott
et al., 1981). Second, we examined age as a continuous
variable to assess any linear trends, using the average age
of the sample if only a range was reported. Only two
studies did not report age characteristics of their samples.
First, our ndings indicated that for older participants
(k= 7), the correlation between skill-challenge balance
and ow was z= 0.73 (95% CI = 0.710.74) compared
to z= 0.50 (95% CI = 0.480.52) for younger participants
(k= 27) under the xed-error model. This comparison
was signicantly different (Q(1) = 260.14, p< 0.001).
Under the random-error model, there was no signicant
difference (Q(1) = 0.70, p> 0.05). Second, we conducted
a meta-regression analysis to assess the impact of age as
a continuous variable. Using maximum likelihood estima-
tion, we found that age was only contributing a small
non-signicant linear effect of 0.007 (slope coefcient)
on the relationship between skill-challenge balance and
ow. So as age increases, the correlation was very
slightly increasing as well. The two age ndings do not
seem to reconcile together, suggesting a potential non-lin-
ear relationship with age.
For the intrinsic motivation moderator analysis, there
were no signicant differences between age groups in
xed or random model of error. Similarly, the meta-
regression indicated no signicant age moderation on
challengeskill balance and intrinsic motivation.
Cultural characteristics
We next assessed the moderation of country and cultural
characteristics. We rst compared samples from the USA
Table 3a. Results of moderator analyses for ow and challengeskill balance.
95% condence interval
kzLow estimate High estimate Q
Publication status 37.50
Published 29 0.58 (.51) 0.56 (0.36) 0.59 (0.63) (0.04)
Unpublished 6 0.43 (0.54
) 0.38 (0.15) 0.48 (0.79)
Age 260.14
Under 30 27 0.50 (0.48) 0.48 (0.35) 0.52 (0.60) (0.70)
30 and over 7 0.73 (0.63
) 0.71 (0.27) 0.74 (0.83)
Country 88.76
USA 18 0.47 (0.47) 0.45 (0.29) 0.50 (0.62) (0.47)
Non-USA 17 0.61 (0.55) 0.60 (0.36) 0.63 (0.70)
Culture 59.14
Individualistic 28 0.53 (0.46) 0.51 (0.30) 0.55 (0.61) (2.60)
Collectivistic 7 0.64 (0.65) 0.62 (0.48) 0.66 (0.77)
Domain 688.00
11 0.73 (0.67) 0.71 (0.47) 0.74 (0.80) (5.10)
16 0.32 (0.40) 0.29 (0.23) 0.36 (0.55)
8 0.40 (0.44) 0.36 (0.33) 0.44 (0.54)
Type of ow 39.28
State 29 0.58 (0.48) 0.57 (0.34) 0.60 (0.62) (0.21)
Trait 7 0.47 (0.56) 0.43 (0.29) 0.50 (0.75)
Measurement 182.96
ESM 2 0.00
)0.10 (0.79) 0.09 (0.41) (5.62)
Single measure 34 0.58 (0.54) 0.57 (0.42) 0.60 (0.65)
Balance measure 1118.60
Globalsubscore 15 0.75 (0.70) 0.74 (0.57) 0.76 (0.80) (16.29)
Separate 20 0.30 (0.33) 0.28 (0.22) 0.33 (0.44)
Notes: All effect sizes were signicantly different from 0 at a p< .001 value unless specied. Fixed-effects values are presented outside of parentheses
and random-effects values are within parentheses.
*p> 0.05; **p< 0.01; ***p< 0.001;
p< 0.10;
p> 0.05.
Shared superscripts indicate signicant pairwise comparisons:
pairwise comparison is signicant under both xed and random models of error.
The Journal of Positive Psychology 11
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(k= 18) and outside the USA (k= 17). Results revealed
that samples from the USA had a correlation of z= 0.47
(95% CI = 0.450.50) compared to samples outside the
USA, which had a correlation of z= 0.61 (95%
CI = 0.600.63). The international difference was signi-
cant under the xed-error model (Q(1) = 88.76,
p< 0.001), but not under the random-error model (Q(1)
= 0.47, p= 0.49). International samples appeared to have
a stronger relationship between challenge-skill and ow
compared to USA samples.
To further understand this moderation, we coded
each sample as either a collectivistic or individualistic
culture based on the country of origin. We determined
such categorizations by previous research on collectiv-
ismindividualism (e.g. Hofstede, 2001). For example,
countries such as the USA, Canada, the Netherlands,
Finland, Switzerland, and Germany were considered
individualistic (k= 28), whereas China, Japan, Korea,
Greece, and Spain were coded as collectivistic (k= 7).
Results indicated that collectivistic samples reported a
higher correlation of z= 0.64 (95% CI = 0.620.66) than
individualist samples (z= 0.53; 95% CI = 0.510.55).
However, this difference was only signicant under the
xed-error model (Q(1) = 59.14, p< 0.001), but close
to marginally signicant under the random-error model
(Q(1) = 2.60, p= 0.098). Similarly, the correlation
between challengeskill balance and intrinsic motivation
was higher for collectivistic cultures (k =3; z= 0.36)
compared to individualistic cultures (k= 22; z= 0.20)
under xed effects only (Q(1) = 80.71, p< 0.001).
However, there were no signicant differences between
US and non-US countries under both models of error for
intrinsic motivation.
Next, the domain of each study was assessed as a mod-
erator comparing studies of ow in a leisure context,
work/education context, or a personal setting. For exam-
ple, leisure contexts included online activity (e.g. surng
the web), recreational sports, and video gaming. Work or
education contexts involved job settings, school settings,
professional sports, and taking exams. Flow in personal
contexts typically involved participants choosing a few
salient or meaningful activities that take place throughout
a given day. Overall for ow, there were signicant dif-
ferences among leisure (k= 11), work/education
(k= 16), and personal settings ( k= 8) under a xed-error
model (Q(2) = 688.00, p< 0.001) and marginally signi-
cant under a random-error model (Q(2) = 5.10,
p= 0.083. Additional pairwise comparisons indicated
that leisure contexts had a correlation z= 0.73 (95%
CI = 0.710.74), and work/education contexts had a
correlation of z= 0.32 (95% CI = 0.290.35). This dif-
ference was signicant under both the xed-error model
(Q(1) = 650.33, p< 0.001) and the random-error model
(Q(1) = 4.63, p= 0.031). Compared to leisure contexts,
studies examining personal contexts had a weighted
Table 3b. Results of moderator analyses for intrinsic motivation and challengeskill balance.
95% condence interval
kzLow estimate High estimate Q
Publication status 9.17
Published 23 0.24 (0.32) 0.22 (0.24) 0.25 (0.39) (1.37)
Unpublished 2 0.39 (0.39) 0.29 (0.29) 0.48 (0.48)
Age 1.06
Under 30 19 0.23 (0.34) 0.21 (0.22) 0.25 (0.44) (0.66)
30 and over 6 0.25 (0.28) 0.23 (0.22) 0.27 (0.35)
Country 0.38
USA 10 0.24 (0.31) 0.22 (0.25) 0.27 (0.37) (0.13)
Non-USA 15 0.23 (0.33) 0.22 (0.22) 0.35 (0.44)
Culture 80.71
Individualistic 22 0.20 (0.30) 0.18 (0.23) 0.22 (0.36) (0.96)
Collectivistic 3 0.36 (0.49) 0.33 (0.07) 0.38 (0.76)
Domain 44.22
3 0.15 (0.36^) 0.11 (0.15) 0.18 (0.72) (0.78)
13 0.24 (0.33) 0.29 (0.25) 0.36 (0.40)
9 0.29 (0.29) 0.26 (0.26) 0.31 (0.33)
Measurement 119.98
ESM 6 0.18 (0.19) 0.16 (0.09) 0.20 (0.29) (6.20)
Single measure 19 0.36 (0.37) 0.33 (0.27) 0.38 (0.46)
Notes: All effect sizes were signicantly different from 0 at a p< 0.001 value unless specied. Fixed-effects values are presented outside of parenthe-
ses and random-effects values are within parentheses.
*p< 0.05; **p< 0.01; ***p< 0.001; ^p> 0.05.
Shared superscripts indicate signicant pairwise comparisons under xed models of error.
12 C.J. Fong et al.
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average correlation of z= 0.40 (95% CI = 0.360.44).
This was also signicantly lower than leisure contexts
under both the xed-error model (Q(1) = 311.33,
p< 0.001) and the random-error model (Q(1) = 4.07,
p= 0.044). For only the xed-error model, results
showed that relationship between challengeskill balance
and ow signicantly varied by whether the activity was
in a work/education context or personal context, Q(1) =
467.004, p< 0.01. In sum, the relationship between
challengeskill balance and ow is strongest for leisure
contexts, then personal contexts, followed by work or
education contexts.
The domain was a signicant moderator for the rela-
tionship between challengeskill balance and intrinsic
motivation, but only under xed effects (Q(2) = 44.22,
p< 0.001). Personal activities (k= 9) had the highest
correlation (z= 0.29), then work or education-related
activities (k= 13, z= 0.24), followed by leisure activities
(k= 3; z = 0.15). Interestingly, leisure activities had the
smallest correlation out of the three domains, in contrast
with the high correlation in the previous analysis.
Methodological characteristics
We next considered the inuence of various methodologi-
cal characteristics present in the included studies. Unfortu-
nately, some of the moderators that we believe were
practically and theoretically relevant to the ow literature
did not report enough data or used methods too heteroge-
neous to meaningfully aggregate. For example, how stud-
ies calculated the challengeskill balance varied too
widely. Studies measured the challengeskill balance in
various ways: a single-item assessing balance (e.g.
Schuler, 2007), a scale (e.g. Bakker, 2005), the product of
a challenge measure and skill measure (e.g. Rodriguez-
Sanchez, Salanova, Cifre, & Schaufeli, 2011), an absolute
difference between a challenge measure and skill measure
(e.g. Fullager et al., 2013), or a sum of challenge and skill
(e.g. Waterman, Schwartz, & Conti, 2008). Although the
most common form of operationalizing challengeskill
balance was to use a separate scale of subscale, other stud-
ies computed the balance as either a difference, sum, or
product of challenge and skill. Due to the large amount of
heterogeneity, a formal moderator analysis could not be
conducted in the present study. See Table 1for an
overview of how ow was measured.
ESM vs. single measurements. In the database of studies,
ow was operationalized using a single survey (either
the sum of ow antecedents or a separate ow measure
which included items assessing engrossment or involve-
ment) or multiple momentary assessments using ESM.
One methodological concern was comparing whether
ow assessed using a survey differs from ow assessed
using ESM, which would provide a more real-time
Table 4a. Relations between moderator variables for ow and challengeskill balance.
Moderator variable Age Country Culture Domain Type of ow
Country χ
(2, N= 35) = 0.06
p= 0.80
Culture χ
(1, N= 35) = 0.21 χ
(1, N= 36) = 9.27
p= 0.64 p= 0.002
Domain χ
(2, N= 35) = 1.28 χ
(2, N= 36) = 10.32 χ
(2, N= 36) = 3.86
p= 0.53 p= 0.006 p= 0.15
Type of ow χ
(2, N= 36) = 0.28 χ
(1, N= 36) = 3.50 χ
(1, N= 36) = 0.05 χ
(2, N= 36) = 4.40
p= 0.87 p= 0.06 p= 0.82 p= 0.11
Balance measure χ
(1, N= 35) = .578 χ
(1, N= 35) = 1.37 χ
(1, N= 35) = 2.97 χ
(2, N= 35) = 4.96 χ
(1, N= 36) = 2.56
p= 0.45 p= 0.24 p= 0.09 p= 0.08 p= 0.11
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measurement of optimal experience. Flow research has
moved toward ESM to assess momentary variation in
subjectively reported experiences in order to examine
ow. Some researchers argued that this is more accurate
assessment of the ow state (Csikszentmihalyi & Larson,
1987). There were only two studies that employed ESM
when assessing the challengeskill balance and ow. In
a 10-week longitudinal study, Fullagar et al. (2013) mea-
sured ow during every practice session for 27 musi-
cians, assessing their momentary subjective experiences.
They found that the relationship between challengeskill
balance and ow was quite robust with an average
weighted correlation z= 0.73. Interestingly, the average
weighted correlation in the second ESM study was much
lower. In Chens(
2000) study, participants engaged in
online activities and web browsing with a repeated pop-
up questionnaire assessing ow. The average weighted
correlation zbetween challengeskill balance and ow
was only z= 0.04. The variability in measurements in
the ESM studies suggests further research examining
ESM vs. survey measurements of ow. Despite the
paucity of studies using ESM, we attempted to test
this moderator. Single measurements of ow and the
challengeskill balance (k= 34) had a signicantly larger
correlation than ESM correlations (k= 2) under both
xed effects (Q(1) = 182.96, p< 0.001) and random
effects (Q(1) = 5.62, p< 0.05).
Similarly, the average correlation between challenge
skill balance and intrinsic motivation was signicantly
smaller for ESM studies (k= 6) than single-measurement
studies (k= 19). The average correlation for ESM studies
was 0.14 under xed effects and 0.27 under random
effects, whereas for single measurement studies, the aver-
age correlation was 0.28 and 0.35, respectively.
Subscoreglobal vs. separate measurements. A fair
number of the correlations between skill-challenge and
ow were calculated by comparing a challengeskill bal-
ance subscore to a global ow score (k= 15). This type
of correlation contains some shared variance because the
challengeskill balance is a part of the ow measure.
Other studies compared two separate measurements of
ow and challengeskill balance, respectively (k= 20).
Under xed effects, moderator analyses revealed that
studies with a subscoreglobal correlation (z= 0.75; 95%
CI = 0.740.76) had signicantly higher correlations than
studies that calculated separate measurements of ow
and the skill-challenge balance (z= 0.30; 95%
CI = 0.280.33). This was signicant under both xed-
(Q(1) = 1211.86, p< 0.001) and random-error models
(Q(1) = 16.02, p< 0.001).
State vs. trait ow. Flow has been understood as either a
state of subjective experience measured after engaging in
an activity or the frequency of activity-specicow,
enduring over time (Jackson, 1996). The difference
between trait (k= 7) and state ( k= 30) types of ow was
assessed. The correlation between ow state and chal-
lengeskill balance was signicantly lower in trait ow
(z= 0.47; 95% CI = 0.430.50) than in state ow
(z= 0.58; 95% CI = 0.570.60) under the xed model of
error (Q(1) = 39.11, p< 0.001). This was not signicant
under the random model of error (Q(1) = 0.30, p= 0.58).
Relations between moderator variables
The moderator analyses indicated that many variables
signicantly inuenced the relationship between chal-
lengeskill balance and ow. However, when moderators
are tested individually, they might be confounded with
one another (see Cooper, 1998; Patall, Cooper, &
Robinson, 2008). For example, both study location of
USA or non-USA as well as cultural characteristics of
individualistic or collectivistic self-construal were signi-
cant moderators, but it is possible that non-US countries
are more likely to be collectivistic whereas the USA is
individualistic. Therefore, we assessed the pairwise rela-
tionships between the following signicant moderators:
age, country, culture, domain, type of ow, and correla-
tion calculation. Using effect sizes as the unit of analy-
sis, we conducted a series of chi-square tests for
pairwise comparisons of each of the moderators since
they were all categorical. The results of all tests for chal-
lengeskill balance and ow are reported in Table 4a
and for intrinsic motivation in Table 4b.
Using a conservative p-value of 0.01, we found one
cluster of confounded variables involving country, cul-
ture, domain, and type of ow for the ow moderators.
One way to describe this cluster of confounded study
variables is as follows: Compared to studies in non-US
Table 4b. Relations between moderator variables for intrinsic motivation and challengeskill balance.
Moderator variable Culture Domain Measurement
Domain χ
(2, N= 25) = 1.53
p= 0.47
Measurement χ
(1, N= 25) = 0.16 χ
(2, N= 25) = 3.54
p= 0.64 p= 0.17
Balance measure χ
(1, N= 25) = 1.92 χ
(2, N= 25) = 2.00 χ
(1, N= 25) = 0.672
p= 0.17 p= 0.37 p= 0.412
14 C.J. Fong et al.
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countries, studies in the USA were more likely to repre-
sent a culture with individualistic self-construal, were
more likely to measure ow in leisure settings, and were
more likely to assess ow as a state, rather than a trait.
For intrinsic motivation, there were no confounds for the
signicant moderators.
Comparing the challengeskill balance to other ow
Among the studies that met our initial inclusion criteria,
13 studies also measured the correlations between ow
and the other eight following factors of the nine-factor
model of ow: merging of action and awareness, clear
goals, unambiguous feedback, concentration, sense of
control, loss of self-consciousness, transformation of
time, and autotelic experience. In order to assess the
relationship between challengeskill balance and ow
relative to the other antecedents, we also meta-analyzed
the correlations between ow and each of the eight other
factors. Results are presented in Table 5.
Comparing the within-sample correlations to each
other (see Meng et al., 1992), we found that the
challengeskill balance is a relatively robust ow anteced-
ent compared to the other eight factors. Under the xed
model of error, its correlation to ow (z= 0.76) was signif-
icantly larger at the p< 0.001 level than merging of action
and awareness (z= 0.56; t= 10.42), concentration
(z= 0.65; t= 5.83), loss of self-consciousness (z= 0.46;
t= 14.11), transformation of time (z= 0.33; t= 19.18),
and autotelic experience (z= 0.59; t= 9.59). The
challengeskill balance was also larger than unambiguous
feedback (z= 0.72; t= 2.48, p= 0.02), but to a lesser
degree. The relationship between ow and having clear
goals (z= 0.75) and a sense of control (z= 0.79) were not
signicantly different from the challengeskill balance. It
is worth noting that sense of control was the most highly
correlated antecedent with ow.
In addition, we conducted another moderator analysis
to assess whether ow measured as a state or trait mod-
erated the correlations of all the antecedents. Overall,
measured as states, ow correlations with most of the
antecedents were signicantly larger compared to being
measured as a trait only under xed model of error. The
few exceptions were that concentration and a loss of
self-consciousness were not signicantly different from
each other under both xed and random models of error,
and trait transformation of time (z= 0.45) was signi-
cantly higher than as a state (z= 0.27; Q= 37.5,
p< 0.001) under the xed model of error.
The results indicated that the relationship between
challengeskill balance and ow was moderate, and this
relationship was inuenced by a number of moderating
variables. This moderately large correlation reveals that
Table 5. Results of comparing correlations between ow and its antecedents and assessing trait vs. state moderator analysis.
Overall Z95% CI Flow as state 95% CI Flow as trait 95% CI Q
(k= 13)
(k=9) (k=5)
Challengeskill balance 0.76 0.75, 0.78 0.79 0.78, 0.81 0.67 0.64, 0.7 56.64
(0.70) (0.53, 0.82) (0.68) (0.39, 0.84) (0.69) (0.55, 0.79) (0.004)
Merging of action & awareness
0.56 0.54, 0.58 0.59 0.57, 0.62 0.49 0.44, 0.53 17.31
(0.54) (0.41, 0.65) (0.55) (0.34, 0.71) (0.50) (0.33, 0.64) (0.166)
Clear goals 0.75 0.74, 0.77 0.79 0.78, 0.81 0.63 0.59, 0.66 94.07
(0.69) (0.50, 0.82) (0.68) (0.38, 0.85) (.65) (0.49, 0.77) (0.059)
Unambiguous feedback
0.72 0.70, 0.73 0.75 0.73, 0.76 0.64 0.60, 0.67 35.31
(0.69) (0.53, 0.80) (0.68) (0.45, 0.83) (0.64) (0.42, 0.79) (0.106)
0.65 0.63, 0.67 0.65 0.63, 0.67 0.64 0.61, 0.67 0.225
(0.64) (0.53, 0.74) (0.64) (0.48, 0.76) (0.62) (0.41, 0.76) (0.05)
Sense of control 0.79 0.78, 0.80 0.83 0.82, 0.85 0.66 0.62, 0.69 136.45
(0.73) (0.55, 0.85) (0.74) (0.49, 0.88) (0.65) (0.43, 0.80) (0.453)
Loss of self-consciousness
0.46 0.44, 0.49 0.46 0.43, 0.49 0.47 0.42, 0.51 0.22
(0.52) (0.39, 0.63) (0.50) (0.33, 0.64) (0.48) (0.25, 0.66) (0.03)
Transformation of time
0.33 0.30, 0.35 0.27 0.23, 0.30 0.45 0.41, 0.50 37.5
(0.40) (0.24, 0.54) (0.30) (0.10, 0.47) (0.50) (0.26, 0.68) (1.79)
Autotelic experience
0.59 0.57, 0.61 0.61 0.59, 0.64 0.55 0.51, 0.59 6.82
(0.62) (0.59, 0.64) (0.63) (0.45, 0.76) (0.55) (0.45, 0.76) (0.39)
Overall kdoes not add up to 13 because one study measured both trait and state ow with the same sample, so the independence assumption was not
Note: Included studies were those indicated in Table in the column Correlation Calculation as Subscore-Global.All effect sizes were signicantly
different from 0 at a p< 0.001 value unless specied. Fixed-effects values are presented outside of parentheses and random-effects values are within
**p< 0.01; ***p< 0.001.
Superscripts denote statistically signicant differences between challengeskill balance and other antecedents.
p< 0.001;
p< 0.05.
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there is evidence for the fundamental notion that match-
ing skill and challenge is an important ow indicator.
However, the lack of a strong, robust relationship sup-
ports the possibility of other theoretical antecedents of
ow. The other eight theorized antecedents to ow var-
ied in its relationship with ow relative to the strength
of challengeskill balance. Additionally, the lack of rela-
tionship between skill and challenge and the difculty of
operationalizing challenge (Engeser & Rhineberg, 2008)
may explain this weaker than expected relationship
between challengeskill balance and ow. Overall, there
is adequate support that matching skill and challenge is
robustly related with feelings of ow or optimal experi-
ence. There is a similar nding with the intrinsic motiva-
tion studies as well.
It is important to note that some of the meta-analytic
ndings were based on a small number of effect sizes
and studies. Caution should be taken when interpreting
the specic magnitude of the effects. Surprisingly, the
majority of studies that reported correlational relationship
between challenge-skill and ow were single survey
measurements of the related constructs with hardly any
experimental designs where various levels of the
challengeskill balance were manipulated. The studies
that measured challengeskill balance and intrinsic moti-
vation represented a much more diverse set of designs,
including ESM studies and experimental studies (see
Table 1). Other studies reported regression coefcients
controlling for a diverse amount of variables but could
not be statistically integrated together. Although these
studies also investigated important questions related to
the present study, their data could not be practically
aggregated in the meta-analysis.
Driven by theoretical and methodological concerns in the
literature, moderator analyses revealed that the relation-
ship between challengeskill balance and ow varied by
individual characteristics, setting, and methodological
characteristics. For example, published studies had a sig-
nicantly higher averaged correlation compared to
unpublished studies. There may be a bias in how this
construct is represented in the eld.
Assessing age dichotomously, we found that studies with
subjects aged 30 and over had a much stronger relation-
ship between ow and the challengeskill balance, but
this effect was only signicant under xed effects.
Although ow in the majority of the literature seems to
transcend any age group, our exploratory analysis sug-
gested that as individuals get older, having their skills
match the level of challenge is more related with ow.
Perhaps as adults begin work, the initial excitement of a
new job and career may dissipate as routine sets in.
Wolfe and Kolb (1980) theorized that as individuals
become more specialized in their elds, they experience
the onset of routine and tasks becoming less challenging,
and ultimately less satisfying. This is especially evident
when individuals reach the mid-life transition or crisis
(see Brim, 1976). It follows that for older individuals to
experience ow, the importance of the challengeskill
balance seems more salient. On the other hand, when we
assessed age continuously in a meta-regression analysis,
there was essentially no linear trend between age and
correlation effect size. Either age really does have no
effect on how related the challengeskill balance is with
ow, or there is possibly a nonlinear relationship.
Because of the uneven distribution of ages in our sample
of studies, creating equal groups to assess quadratic or
higher order function curves was unfeasible. Moreover,
age did not moderate the relationship between chal-
lengeskill balance and intrinsic motivation, suggesting a
consistent inuence of challengeskill balance on intrin-
sic motivation across the developmental lifespan.
Cultural characteristics
Because of the international scope of research on ow,
we conducted two moderator analyses assessing whether
country and self-construal affected the magnitude of the
challengeskill balance and ow relationship. First, stud-
ies with USA-based samples had a weaker relationship
between challengeskill balance and ow compared to
non-USA countries. This followed previous research
such as a cross-cultural study comparing USA and
Italian adolescents (Carli, Delle Fave, & Massimini,
1987). They found that ow was much more congruent
to challengeskill balance in the Italian sample, but more
diffused and less polarized in the USA sample. As dis-
cussed earlier, this contrast was confounded by domain,
type of ow, and self-construal. Self-construal contrasts
indicated that the effect sizes were larger in samples
from collectivistic cultures compared to individualistic
cultures. Our ndings extended other cross-cultural
research by adding other nations such as Spain and
Greece, instead of limiting collectivistic nations to sim-
ply China or other Asian countries. Previous research
actually showed that collectivistic nations might have a
more prudent approach to challenges, and thereby bias
personal skill in their optimal challenge/skill ratio com-
pared to cultures with an independent or individualistic
self-construal (Moneta, 2004). Our ndings were con-
trary to this: Collectivistic samples had a higher correla-
tion with optimal balance and both outcomes of ow
and intrinsic motivation compared to the individualistic
samples, which should theoretically be less challenge-
avoidant. There is still little research in this area, and
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future directions regarding this personal and cultural
moderation are suggested.
Given that owbalance relationship is stronger in leisure
contexts, the weaker relationship in work/education envi-
ronments and personal situations might be explained by a
variety of reasons. Abuhamdeh and Csikszentmihalyi
(2012) discussed that many everyday activities such as
school-related activities are not typically engaged in vol-
untarily. Instead, students, out of obligation or necessity,
might participate in academic or work-related activities
(Graef, Csikszentmihalyi, & McManama Gianinno,
1983). Therefore, optimal challenges do not seem to be
as ow inducing in academic contexts than leisure con-
texts (Bassi & Delle Fave, 2012). Csikszentmihalyi and
LeFevre (1989) described that optimal experiences during
work or school work involve low levels of happiness,
freedom, and intrinsic motivation. In contrast, other
research pointed to how important one perceives the
activity to moderate how the challengeskill balance
inuences ow (Engeser & Rhineberg, 2008); however,
assuming that work/education contexts are valued as
more important despite being less intrinsically motivating,
the inuence of task importance or value seems reversed
according to the results. In addition, for work/education
activities, the lower correlation between ow and chal-
lengeskill balance may be explained by the presence of
greater challenge in this context. The high levels of chal-
lenge reduce the likelihood for skill and challenge to
match; instead of ow being induced, anxiety may be
present. This demands greater attention on how to t
together appropriately challenging assignments to the
skill level of students and employees. However, even in
the typically highly extrinsically motivated classroom or
working contexts, it is possible for individuals to feel
more motivated while engaged in very challenging activi-
ties (Csikszentmihalyi et al., 1993). How to create situa-
tions in academic contexts that provide the types of
experiences found in intrinsically motivated, goal-directed
activities is the challenge confronting intrinsic motivation
research. Interestingly, leisure activities, such as web-
browsing (see Shin, 2006), may be intrinsically motivat-
ing, but the perceptions of challenge for such an activity
are dubious. There is no built-in pursuit of goals;
therefore, a sense of challenge is less relevant for task
absorption or ow (Abuhamdeh & Csikszentmihalyi,
Personal settings that consist of the activities salient
to their identity or chosen by participants to be meaning-
ful or important had signicantly higher optimal balance
to ow and intrinsic motivation correlations than work
and education activities. Practically, individuals most
likely will choose personal activities that include both
leisure activities and work/education activities, which
may explain this order of magnitude across domains.
This includes both discretionary and obligatory activities
throughout a given day.
Methodological characteristics
The correlation between challengeskill balance and ow
was higher when ow was measured as a state vs. trait.
One potential explanation for this difference is with ow
state, measured by subjective experiences after a particu-
lar task, individuals can more immediately, and arguably
more reliably, respond to ow antecedents. With ow
trait, individuals are responding to how often they expe-
rience ow antecedents, essentially describing a more
enduring autotelic personality. Even in comparison with
the other ow antecedents, the trait correlations overall
were smaller or equal to state correlations because traits
are not exact, but based on situational factors and depen-
dent on context (see Fridhandler, 1986). For example,
there may be some contexts where an autotelic individ-
ual may enjoy doing a task for its own sake, but also
engage in other behavior out of duty or necessity. Such
transient factors associated with trait measures may
explain why trait correlations were smaller overall. Inter-
estingly, concentration and loss of consciousness had
similar correlations when assessed as state vs. trait. One
notable commonality between these two antecedents is
they are both aspects of being in ow, rather than a pre-
cursor or outcome of ow based on the Quinn Model. It
follows that these antecedents are equally likely to be
salient as a trait, or the autotelic personality, compared to
as a state. Csikzentmihalyi (1990) described autotelic
individuals as more involved with everything around
them because they are fully immersed in the current of
life(p. 84). Concentration and loss of self-conscious-
ness are also highly related as one is focused in the task,
forgetting irrelevant concerns in a way, to be truly con-
centrated is to lose ones sense of self. In addition, some
researchers argued that concentration would be correlated
with both ow as a state or trait because owsdenition
is so inextricably tied to intense-focused concentration.
Another interesting nding was that transformation
of time was signicantly higher when measured as a trait
instead of a state. In a similar way to loss of conscious-
ness, the sense that time ies byis a natural result of
being fully immersed in an activity. Thus, transformation
of time seems more related to the autotelic personality
(trait) vs. the feelings right after an activity.
One of the most robust ndings was the calculation
of the correlation between ow and optimal balance,
using a global ow scale and one of its subscales of
challenge-skill t has a much larger correlation than if
they are two separate, unrelated measures. Although we
expected shared variance between the global score and a
The Journal of Positive Psychology 17
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subscale, the average weighted correlation was unexpect-
edly high, and articially inated the average weighted
effect size. Examining the range of correlations between
global scales and subscores, we found some studies
reporting low correlations (z = 0.55; van Schaik, Martin,
& Vallance, 2012) and even negative correlations
(z = 0.25; Marzalek, 2006). Not every inated correla-
tion appeared extremely high, suggesting adequate vari-
ance in using this type of calculation. Also, many of the
intercorrelations consist of one-ninth of the scale being
correlated to the global ow measure. However, in order
to more fairly compare apples to oranges, we examined
all the global to subscale studies that used all nine ante-
cedents. In sum, the most conservative estimate of the
relationship between challengeskill balance and ow is
using the studies that used separate measurements of
both; this results in a much smaller correlation of
z= 0.30, similar to the correlation of the challengeskill
balance and intrinsic motivation, which suggests that
ow and intrinsic motivations are comparably related
with an optimal balance of challenge and skill.
Lastly, the use of ESM was compared with single
measurements of the challengeskill balance. When
assessing both intrinsic motivation and ow, ESM stud-
ies on average reported lower correlations. This nding
implies that single measurements may inate the inu-
ence of the challengeskill balance compared to momen-
tary assessments, which may more accurately assess the
levels of ow and their antecedents.
Other antecedents
Compared to the other eight ow antecedents, challenge
skill balance remains a powerful precursor to ow. This
nding supported original conceptions of ow where
challengeskill balance must be met in order for ow to
occur. However, the challengeskill balance as the sole
catalyst of ow is also brought into question, and consid-
eration for other antecedents is recommended. Results of
the meta-analyses indicated that clear goals and sense of
control were as powerful as challengeskill balance as
sources of ow. Whereas the other antecedents (e.g. con-
centration, merging of action and awareness, and feed-
back) appear to be more cognitive of nature either
inuencing their thought processes or learning, sense of
control and clear goals are more directly related to
motivation. Sense of control, or a sense of autonomy is
one of the central components to SDT (Ryan & Deci,
2000), and the importance of goals has been underscored
in a variety of motivation theories (e.g. Carver & Scheier,
1982; Nicholls, 1975). Bassi and Delle Fave (2012)
conducted a study of high school students using ESM to
examine optimal experience and self-determination. They
found that ow was associated with low levels of
self-determination, but that the quality of the experience
was better with moderate to high self-determination.
Hodge et al. (2009) found that intrinsic motivation that
needs satisfaction (competence, autonomy, and related-
ness) were signicant predictors of dispositional ow.
Regarding goals, Novak, Hoffman, and Duhachek (2003)
revealed that online users experienced greater ow when
they were engaged in goal-directed activities vs. experi-
ential activities, suggesting the importance of goals when
experiencing ow. In sum, among the nine theorized ow
antecedents, the challengeskill balance is highly corre-
lated with ow among other motivational antecedents
such as control and clear goals.
This study is not without limitations. Mainly, limitations
to generalizability are present. It is also important to note
that synthesis-generated evidence should not be inter-
preted as supporting statements about causality (see
Cooper, 1998). Thus, when exploratory moderators are
found to be associated with the effect sizes, these nd-
ings should be used to direct future researchers to exam-
ine these factors. Finally, there were a number of
potentially interesting and theoretically relevant variables
that could not be examined as moderators. Gender as
well as other individual and personality variables would
be interesting to examine. Although we assessed age
moderation to some degree, a lack of data as well as a
bias toward older populations prevented further modera-
tor analyses to measure curvilinear effect. Also, as noted
earlier, there was a cluster of confounded moderator vari-
ables. This prevents interpreting the effects of country
origin from cultural characteristics, domain, and whether
ow was measured as a trait or state. In addition, some
of the correlations in the study sample represented inter-
correlations between the challengeskill balance and ow
measure, which caused some ination in the effect sizes.
Assessing the differences between xed- and ran-
dom-error models, we found that most moderator analy-
ses were signicant under the xed-error model, but not
so in the random-error model. We caveat such ndings
as limited in their generalizability of these particular
moderator variables (see Cooper, 1998).
Implications of ow antecedents in the real world
Every day, whether in work or school, in leisure activi-
ties, or engaging in daily tasks, people prefer an optimal
experience of positive affect and attempt to avoid feel-
ings of boredom, anxiety, and apathy. Across the life-
span, the pursuit of happiness has become nearly
axiomatic, and the importance of ow induction is inex-
tricably a part of this ubiquitous endeavor (see Seligman
& Csikszentmihalyi, 2000). How to create ow states
and to instill intrinsic motivation has been an important
18 C.J. Fong et al.
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question for researchers and practitioners as well as all
individuals who desire optimal experience, and our
meta-analytic investigation shows that promoting a
challengeskill balance remains to be a robust contribu-
tor. Assessing ones set of personal skills or perceived
competence is critical as well as appropriately nding
challenges or scaffolding activities and tasks to match a
precise balance between the two. Other antecedents are
important as well to engender ow: Clear goals and a
sense of control are also signicant factors to consider.
Goal-directed activities with clear instruction as well as
support and environments where the individual feels
autonomous and self-determined (e.g. providing choices)
are motivating as well as ow-inducing (e.g. Patall et al.,
2008; Su & Reeve, 2011).
When trying to create motivating and optimal experi-
ences, what are some important factors to consider? The
results of this meta-analysis suggested that nding a
balance of challenge and skill is an important factor to
consider. Moreover, our ndings indicated that this rela-
tionship may be diminished with younger individuals,
those with more of an individualistic self-construal, in
work or educational contexts. Overall, there are impor-
tant and profound implications for the promotion of
human motivation, happiness, and thriving. Decision-
makers might consider how to appropriately design chal-
lenges, provide goals, and support autonomy to enhance
ow experiences for all individuals. We encourage more
scholarship in this eld to add greater validation and
assessment of this important construct and how to best
inform practice in creating intrinsically motivating activi-
ties in a diverse array of contexts.
We want to specially thank Erika A. Patall and Dale H. Schunk
for their helpful comments on previous versions of this
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... More importantly, prior work suffers from significant methodological issues. First, existing studies use a wide variety of measures and calculations for difficulty, skill and difficulty-skill balance as independent variables, which the most recent metaanalysis found simply 'too heterogeneous to meaningfully aggregate' [23]. This goes hand in hand with a similar breadth of dependent variable measures for enjoyment and engagement, spanning different scales and subscales of flow, intrinsic motivation or unvalidated scales [23]. ...
... First, existing studies use a wide variety of measures and calculations for difficulty, skill and difficulty-skill balance as independent variables, which the most recent metaanalysis found simply 'too heterogeneous to meaningfully aggregate' [23]. This goes hand in hand with a similar breadth of dependent variable measures for enjoyment and engagement, spanning different scales and subscales of flow, intrinsic motivation or unvalidated scales [23]. Such measurement flexibility opens massive researcher degrees of freedom and makes comparing and aggregating results hard [24]. ...
... Such measurement flexibility opens massive researcher degrees of freedom and makes comparing and aggregating results hard [24]. Second, the majority of studies (every single one from 28 studies identified in the cited meta-analysis [23]) measure difficulty, skill and difficulty-skill balance with self-report scales, which have come under increasing critique especially in media effects research for their systematic biases and high variance [25]. Third, existing self-report studies operationalize balance either with unvalidated scales-showcasing a 'measurement schmeasurement' attitude [26] that threatens construct and overall study validity-or with a subscale of the same flow scale which is then used as the dependent variable, inducing significant spurious correlations [23]. ...
Full-text available
How does the difficulty of a task affect people's enjoyment and engagement? Intrinsic motivation and flow theories posit a 'goldilocks' optimum where task difficulty matches performer skill, yet current work is confounded by questionable measurement practices and lacks scalable methods to manipulate objective difficulty-skill ratios. We developed a two-player tactical game test suite with an artificial intelligence (AI)-controlled opponent that uses a variant of the Monte Carlo Tree Search algorithm to precisely manipulate difficulty-skill ratios. A pre-registered study (n = 311) showed that our AI produced targeted difficulty-skill ratios without participants noticing the manipulation, yet different ratios had no significant impact on enjoyment or engagement. This indicates that difficulty-skill balance does not always affect engagement and enjoyment, but that games with AI-controlled difficulty provide a useful paradigm for rigorous future work on this issue.
... More recently, necessary conditions have been relabelled antecedents, and researchers (e.g., Barthelmäs & Keller, 2021;Fong et al., 2015;Kennedy et al., 2014;Moneta, 2021) have separated antecedents from the characteristics of the flow experience itself. Moneta (2021) described the antecedents of flow as internal states that are theorised to promote either the frequency and/or the intensity of flow experiences. ...
... Separating the experience of flow from its antecedents is beneficial as it helps researchers to identify key antecedents that might be targeted in interventions. Purported antecedents of flow that have been investigated include two of the nine dimensions of flow: challenge/skill balance and clear goals (Fong et al., 2015;Stein et al., 1995). However, several other antecedents of flow have also been investigated (e.g., Barthelmäs & Keller, 2021;Hoffman & Novak, 2009;Moneta, 2021). ...
... In the guided adventure recreation context, Løvoll and Vittersø (2014) similarly found that a balance between skill and challenge did not correlate with indicators of flow. Nevertheless, Fong et al. (2015) have provided compelling evidence, in the form of a meta-analysis, for the importance of the skillchallenge balance in a variety of contexts (e.g., work, leisure) and found a moderate relationship between flow and a skill-challenge balance. Future research is therefore warranted in the adventure recreation context to reveal if a causal relationship exists between skill-challenge balance and a subsequent flow experience. ...
Full-text available
Adventure recreation participants, such as rock-climbers, skydivers, and free-style skiers have reported that one of the most important reasons for continued participation in adventure recreation is a state of mind focused on the present moment. Most psychologists have referred to this state as flow. More recently, sport and exercise psychology researchers have proposed another optimal state called clutch. However, the majority of optimal psychological states research in adventure recreation contexts has generally made use of flow models that treat optimal psychological states as a singular state. Thus, there is a need to better understand if and how distinct optimal psychological states, such as flow and clutch, function in adventure recreation contexts. This project is an investigation of flow and clutch states with a focus on the adventure recreation context. To understand the antecedents, characteristics, and consequences of flow and clutch states, the following three studies were completed: a systematic review of flow states in adventure recreation (Study One), a mixed method study with advanced rock-climbers in outdoor and indoor settings (Study Two), and a qualitative study with a diverse group of adventure recreation participants (Study Three).
... In games, difficulty is understood to be an intrinsic and salient component of the gameplay experience [22,58]-and in fact, the experience of challenge (and of overcoming said challenge) has been identified as a primary motivator for why we play games [3]. Challenge not only emerges through mechanical demands placed on the player-for example, performing a powerful combo quickly enough to subdue an enemy-but also through cognitive demand. ...
... Further, games may also employ dynamic difficulty adjustment: reactive game systems that adapt dynamically to player performance, allowing for the maintenance of challenge-skill balance (wherein the skill of the player is matched by the challenge of the game) [32]. Some literature proposes that optimal challenge represents an antecedent to-or is, at least, predictive of-the experience of flow [22,38]: an optimal state characterised by holistic absorption in, and enjoyment of, an activity (and often an end goal of game design) [15]. ...
This work explores the use of functional Near Infrared Spectroscopy (fNIRS) to assess cognitive activity during videogame play, and compare it to cognitive activity during cognitive tasks that assess executive control. To this end, we assessed haemodynamic response to videogame and cognitive tasks in the prefrontal cortex, each manipulated on a spectrum of difficulty. In our study (n = 37), we find that mental effort expended during videogame play did not differ from mental effort expended during cognitive tasks---and speculate that regional cognitive activity during gameplay is indicative of functions pertaining to memory encoding and retrieval, planning, and sustainment of attention. Our findings suggest the utility of fNIRS as a means to understand challenge as part of the player experience, and contest the popular conception of videogame play as cognitively undemanding entertainment. Further, we were successful in distinguishing between difficulty levels in the gameplay tasks, situating fNIRS as broadly useful for granular assessment of gameplay difficulty. As such, we contend that fNIRS is an effective and useful tool for generating high-resolution insights regarding cognition (and particularly the experience of difficulty) during gameplay.
... Faiola et al. (2013Faiola et al. ( : 1113 address it as "a highly enjoyable state of consciousness that occurs when our skills match the challenges that we are undertaking." Pondering these and other conceptualizations, we first conclude that the Flow concept has been developed to understand intrinsically rewarding and enjoyable autotelic activities (Csikszentmihalyi, 2014;Fong et al., 2015;Nakamura and Csikszentmihalyi, 2014). Secondly, that Flow is related to individual skills and tasks, activities, or actions being performed, and its duration is most likely very short (Riva et al., 2017). ...
Full-text available
Drawing on Flow theory and memorable tourism experience, this study explores the relationships between Flow, novelty, and interpersonal interaction and the impact on travelers' satisfaction and memories. By applying a mixed-method approach and the critical incident technique to a once-in-a-lifetime leisure experience, 550 tourists recalled their experiences. The results reveal that Flow has a direct and positive influence on tourists' satisfaction and memories of the experience. The findings highlight the role of novelty and interpersonal interaction in producing positive Flow. However, novelty and interpersonal interaction did not come out as significant to predict memories and satisfaction.
... In other words, if game mechanics can provide players with specific and clear goals and immediate feedback, they will be able to highly focus on the game and enjoy the flow experience and learning benefits brought by GBL (Whitton, 2011). Related research has also verified that when students are in a flow state, they feel confident and interested, in turn increasing their engagement in a game (Chen & Hsu, 2020;Fong et al., 2015). As a result, students' learning results have a positive relationship with flow experience (Hung et al., 2015;Schweickle et al., 2017). ...
... Proper challenge-skill balance has been shown to enable a motivating state of ''flow'' when there is an appropriate level of challenge to match a person's skill. 16 In the field of positive psychology, this optimal state of focused concentration resulting from proper matching of challenge and skill is referred to as flow theory. 31 Flow theory has been studied under the scope of education, revealing strong correlations between the state of flow and heightened motivation, self-efficacy, and satisfaction. ...
Full-text available
Commercial escape rooms have grown in popularity as an enjoyable experience that also doubles as an exercise in communication and collaboration. Educators can take advantage of these natural qualities to engage and support students in a low-stress learning environment. The primary goal of this study is to share the development and application of an educational escape room as a tool to provide biomedical engineering (BME) students with an immersive and practical experience. A BME laboratory course-specific escape room was developed and beta-tested on an initial group of BME students. The first set of feedback enabled improvements to the design and difficulty of the escape room, which was followed by the final release of the activity for the intended undergraduate BME course. Across an academic year, 74 participants agreed to provide survey feedback for this study. Despite a moderate escape rate (29%), students reported high satisfaction and enthusiasm for the activity. Student survey responses indicated that participants were engaged and empowered to successfully escape even without external motivators. Responses supported the effectiveness of the escape room as a BME learning environment, allowing students to practice and retain course-related knowledge in a challenging but low-risk activity. The foundational structure of escape rooms offers a beneficial environment for experiential knowledge application. We conclude that educational escape rooms show promise as a pedagogical tool in promoting enhanced knowledge retention through immersive, game-based learning. Supplementary information: The online version contains supplementary material available at 10.1007/s43683-022-00089-w.
... First, we argue that when adults play an IGA, they would feel more engaged in the game than when they play an advergame. This would happen GHOSH ET AL. | 7 because an IGA, as compared to an advergame, is more complex (Cauberghe & De Pelsmacker, 2010; and offers higher congruence between the difficulty level of the game and the cognitive skills of adults, which would lead to better playability and flow experience (Csikszentmihalyi & LeFevre, 1989;Fong, et al., 2015). In other words, since adults have fullydeveloped attentional resources and cognitive capabilities, they would be able to deploy these capabilities in an IGA to handle its high level of complexity and challenge and, therefore, feel more immersed (i.e., flow) and engaged in the medium. ...
Full-text available
Although a rich body of knowledge exists in the domain of gamification of advertising, no research emphasis has been given to compare the persuasive effects of two well‐known gamification formats—in‐game advertising and advergame. Also, we do not know much about their comparative effects on child and adult gamers. The present research fills these gaps by conducting three experiments in which we examine the effects of gamification format (advergame vs. in‐game advertising) and age of consumers (children vs. adults) on attitude toward fictitious and real brands (Studies 1 and 2) and purchase intention of fictitious brands (Study 3). The findings reveal that children have more favorable attitude and purchase intention when the brand is advertised in an advergame than in an in‐game advertising format, while adults demonstrate higher brand attitude and purchase intention in the latter as compared to the former gamification format. Also, brand familiarity differentially moderates the relationship between gamification format, age, and brand attitude (Study 2). Finally, consumers' engagement in the game positively mediates the relationship between the independent variables and purchase intention (Study 3). Our research contributes to academia by advancing the literature on gamification of advertising through a granular evaluation of persuasive efficacy of IGA and advergame played by adults and children. It also informs managers to effectively persuade consumers of different age groups by the usage of the right gamification format.
... This flow experience may be a decisive characteristic of the human's experience of a oneness with the machine in a human-machine symbiosis. In such a flow experience the highest overall quality of subjective experience is characterized by a feeling of being cognitively efficient, motivated, happy, and fully involved in the activity (e.g., Csikszentmihalyi and LeFevre, 1989;Moneta and Csikszentmihalyi, 1996, for a meta-analysis, see Fong et al., 2015). Flow states have been observed to have a great influence on the quality of our experiences irrespective of whether we are working or leisuring (Csikszentmihalyi and LeFevre, 1989), to predict performance (Engeser and Rheinberg, 2008), and they may optimize energy expenditure (Peifer et al., 2020). ...
The notion of symbiosis has been increasingly mentioned in research on physically coupled human-machine systems. Yet, a uniform specification on which aspects constitute human-machine symbiosis is missing. By combining the expertise of different disciplines, we elaborate on a multivariate perspective of symbiosis as the highest form of interaction in physically coupled human-machine systems, characterized by a oneness of the human and the machine. Four dimensions are considered: Task, interaction, performance, and experience. First, human and machine accomplish a common objective by completing tasks conceptualized on a decomposition, a decision and an action level (task dimension). Second, each partner possesses an internal representation of the oneness they form, including the partner’s inner states (e. g. experiences) and their joint influence on the environment. This representation constitutes the “symbiotic understanding” between both partners, being the basis of a joint and highly coordinated action (interaction dimension). Third, the symbiotic interaction leads to synergetic effects regarding the complementary strengths of the partners, resulting in a higher overall performance (performance dimension). Fourth, symbiotic systems specifically change the user’s experiences, like flow, acceptance, sense of agency, and embodiment (experience dimension). Our multivariate perspective allows a clear description of symbiotic human-machine systems and helps to bridge barriers between different disciplines.
The article describes the theoretical premises and the logic of the emergence of the Flow concept and its development in almost half of the century. The article presents an overview of the current state of arts in Flow theory being developed by M. Csikszentmihalyi and his followers. Different models of Flow are described, main directions of Flow research are analyzed, an overview of research methods and techniques are highlighted, including qualitative (interviews) and quantitative methods (questionnaires, experience sampling method (ESM). The possibilities and directions of further development of the Flow ideas and research, including interdisciplinary ones, are discussed. The ideas and concepts of representatives of modern Russian psychology, most close to the ideas of M. Csikszentmihalyi and their contribution to flow understanding are listed.
Purpose: This inquiry is the first comprehensive, empirical analysis of the nature and measurement of flow in elementary teachers. The clearest sign of flow is the merging of action and awareness, that is, the degree to which an activity becomes spontaneous and automatic and individuals lose conscious awareness of themselves as they perform a task such as teaching. The basic objective of the research was to examine the theoretical structure and measurement of flow in elementary teachers. Research Methods: A typical sample of 260 elementary teachers from rural, urban, and suburban elementary schools in Ohio was used to test two rival explanations about the nature of flow. Structural equation modeling was used to assess the goodness of fit of the two models. Findings: Two rival explanations of flow, the Jackson-Marsh and the Quinn models, were evaluated using confirmatory factor analysis and structural equation modeling. The Jackson-Marsh model of flow proved to be a better explanation of flow of elementary teachers than the Quinn model. Consistent with Csikszentmihalyi’s explanation of flow, the construct consists of nine elements, all of which form an integrated whole. In addition, and as predicted, optimism was positively related to flow. Discussion: Because most flow research has been in sports and leisure, Quinn’s research in knowledge work seemed especially relevant for the study of flow in teachers. In spite of that fact, the Jackson and Marsh model was the better fit; the Quinn model provided insight into the dynamics of flow in elementary schools. Finally, although optimism was positively related to flow, it was academic optimism, not dispositional optimism, that was a strong predictor of flow in elementary teachers. Conclusions: The nature of flow in schools is instructive, offering insight into the elements of flow and how they collectively and individually inform us in pursuit of optimal teaching and learning conditions, but much more research remains to be done.
Biological and cultural inheritance deeply influence daily human behavior. However, individuals actively interact with bio-cultural information. Throughout their lives, they preferentially cultivate a limited subset of activities, values, and personal interests. This process, defined as psychological selection, is strictly related to the quality of subjective experience. Specifically, cross-cultural studies have highlighted the central role played by optimal experience or flow, the most positive and complex daily experience reported by the participants. It is characterized by high involvement, deep concentration, intrinsic motivation, and the perception of high challenges matched by adequate personal skills. The associated activities represent the basic units of psychological selection. Flow can therefore influence the selective transmission of bio-cultural information and the process of bio-cultural evolution.
I: Background.- 1. An Introduction.- 2. Conceptualizations of Intrinsic Motivation and Self-Determination.- II: Self-Determination Theory.- 3. Cognitive Evaluation Theory: Perceived Causality and Perceived Competence.- 4. Cognitive Evaluation Theory: Interpersonal Communication and Intrapersonal Regulation.- 5. Toward an Organismic Integration Theory: Motivation and Development.- 6. Causality Orientations Theory: Personality Influences on Motivation.- III: Alternative Approaches.- 7. Operant and Attributional Theories.- 8. Information-Processing Theories.- IV: Applications and Implications.- 9. Education.- 10. Psychotherapy.- 11. Work.- 12. Sports.- References.- Author Index.
A fundamental issue pursued by researchers in positive psychology involves defining what constitutes a good life and understanding how individuals can create one. From the perspective of flow theory, a good life is one that is characterized by complete absorption in what one does (Nakamura and Csikszentmihalyi in Handbook of positive psychology. Oxford, New York, 2002). Born out of a desire to understand intrinsically motivated activity, flow refers to a state of optimal experience characterized by total absorption in the task at hand: a merging of action and awareness in which the individual loses track of both time and self, The flow state is experientially positive, and out of the flow experience emerges a desire to replicate the experience. Over the past three decades, Csikszentmihalyi and colleagues have developed theoretical constructs and empirical research tools to better understand the nature, origins, and consequences of this state of optimal experience called flow. In this chapter, we describe the flow model and then present data analyses in which we explore the personal traits and contextual conditions associated with the experience of flow among adolescents in the United States. We demonstrate the utility of hierarchical linear modeling (HLM) for exploring flow using a complex data set characterized by repeated measures. © 2014 Springer Science+Business Media Dordrecht. All rights reserved.