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

Abstract

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 challenge–skill balance and antecedents of flow: A meta-analytic investigation
Carlton J. Fong
a,1
*, Diana J. Zaleski
b
and Jennifer Kay Leach
c,2
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
(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 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.
Keywords: flow; challenge–skill balance; antecedents; meta-analysis; intrinsic motivation
Csikszentmihalyi’s claim that
in flow, the demands of a situation match the individ-
ual’s ability, and the individual is engaged fully in the
act of doing the activity. In flow, 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 zone’or ‘in flow’–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 ‘intrinsically’motivated behavior as consisting of a
flow 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)defined
optimal experience or flow 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 identified flow experiences in the lives of people
from diverse cultural and economic backgrounds (see
Csikszentmihalyi & Csikszentmihalyi, 1988; Massimini &
Delle Fave, 2000). Also, the importance of flow has
spread to fields such as education (e.g. Bassi & Della
Fave, 2012) or work (e.g. Moneta, 2012) given that flow
can lead to greater concentration, determination, persis-
tence, and motivation, which in turn contributes to
increased performance (see Aube, Brunelle, & Rousseau,
2014).
Theoretically, flow should be related to enhanced per-
formance for numerous reasons. First, flow is a highly
functional state, which should in itself foster higher per-
formance. Second, individuals experiencing flow are
intrinsically motivated to re-engage in future activities
(Engeser & Rheinberg, 2008). In addition, in order to
experience flow again, there is greater desire to take on
more challenging tasks (Nakamura & Csikszentmihalyi,
2005). Thus, flow 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
flow 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 flow has had a prominent status in the field of positive
*Corresponding author: Email: carlton.fong@utexas.edu
1
Department of Educational Administration, The University of Texas at Austin, One University Station D5400, Austin, TX 78712,
USA.
2
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
flow experiences (Keller, Bless, Blomann, & Kleinbohl,
2011); however, much debate exists regarding the
existence and strength of flow’s antecedents.
The concept of flow: the challenge–skill balance and
other antecedents
The concept of flow has been difficult to define and op-
erationalize (Lovoll & Vitterso, 2012). Csikzentmihalyi
(1990) himself cautioned defining flow too precisely lest
it break the spirit of this dynamic construct. Yet one of
the most common and accepted conceptualizations of
flow is ‘the balance between perceived challenges and
perceived skills’(Csikzentmihalyi, 2009, p. 398).
Csikzentmihalyi argued that the challenge–skill balance
leads to the optimal experience and maintaining such
balance in itself is intrinsically rewarding.
Flow’s dynamic structure of the perceived match
between high challenge and adequate personal skill has
been described by four channels of daily experience:
flow (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 difficult in comparison to one’s
skill level, the experience of flow will be weak. In the
state of flow, one feels optimally challenged and confi-
dent. This has a strong functional aspect and explains
why people in flow 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
challenge–skill balance to the induction of flow. In an
experimental study, Keller and Bless (2008) supported
the challenge–skill 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 challenge–skill balance had a positive effect on ado-
lescents’perceptions of concentration, wishing to do the
activity, involvement, and happiness. However, these
findings were not found within all contexts and domains,
and across all dimensions of experience. For example,
the challenge–skill 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 significant
amount (47%) of the variance in self-reported concentra-
tion was explained by the balance of skills and
challenges.
On the other hand, there has also been a great deal
of research that suggests that the challenge–skill balance
is not a salient predictor of flow experiences. Some stud-
ies have shown that the challenge–skill balance explains
as little as 2–4% 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 one’s skills.
Other arguments have contested the original opera-
tional definition of flow as a balance between skill and
challenge (Engeser & Rheinberg, 2008). One of the first
problems is that people vary in the extent to which one’s
skills and the perception of challenge are related.
Furthermore, the construct of perceived challenge com-
pounds both perceived difficulty 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 difficulty-skill yielded no substantial
differences (Pfister, 2002).
Another problematic issue to the challenge–skill
balance and flow relationship is that some people more
frequently experience flow 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 flow. Empirically,
Moneta and Csikszentmihalyi (1996) found that the
challenge–skill balance was not compatible with certain
flow 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 flow’s original conception, Csikszentmihalyi
(1990) has also identified eight other dimensions of the
flow experience beyond the challenge–skill balance, with
nine antecedents all together: (a) challenge–skill balance
or engaging in challenges that meet one’s 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 challenge–skill
balance refers to the perception that an activity’s chal-
lenge is matched or balanced with one’s ability. Action-
awareness merging is involvement in the flow activity to
a point of spontaneity or automaticity. Clear goals refer
to one’s perception of the goals of the activity before or
during the activity. Unambiguous feedback refers to the
monitoring of one’s 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 flow state (i.e. challenge–skill bal-
ance, clear goals, and unambiguous feedback ), while
others are necessary characteristics of being in a flow
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 flow experi-
ence (i.e. autotelic experience). These additional anteced-
ents and components support how the challenge–skill
balance may not be the most salient contributor to
achieving a flow state (Shin, 2006; Wang & Hsiao,
2012), despite receiving the greatest attention among
conditions for entering flow according to the literature.
Intrinsic motivation, flow, and the challenge–skill
balance
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 flow
(Csikszentmihalyi & LeFevre, 1989; Keller, Ringelhan,
& Blomann, 2011; Jackson, 1995). By definition, flow is
understood as an intrinsically motivating state; in fact,
some researchers have coined flow to be a model of
intrinsic motivation (Keller & Bless, 2008). Moreover,
individuals who experience a challenge–skill 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 flow, individuals
in flow 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
motivation).
Self-determination theory (SDT; Deci & Ryan,
1985), a prominent view of intrinsic motivation, has
been linked with flow. SDT posits that feelings of com-
petence, autonomy, and relatedness undergird intrinsic
motivation, and research has supported the link between
these three determinants and flow (Kowal & Fortier,
1999). In a study of Canadian swimmers, Kowal and
Fortier found that intrinsic motivation and two determi-
nants, competence and relatedness, were significantly
positively correlated with flow and with challenge–skill
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, flow, 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 flow call for further understanding of how the chal-
lenge and skill balance really predict flow 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 challenge–skill balance
Additional variables may also differentially impact how
the challenge–skill balance influences flow 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 difficult tasks as daunting. For the highly
achievement-motivated individuals, they prefer tasks of
medium challenge, or when there is an optimal balance
of difficulty 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-
lenge–skill balance predicting flow (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 flow states and its antecedents.
Age
Demographic characteristics such as age may influence
the relationship between flow and the challenge–skill
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 flow. 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
difficulty in experiencing flow across age groups.
Although these study outcomes do not directly tap into
the flow construct, they provide evidence that age might
play a moderating role in the relationship between
challenge–skill balance, flow, and intrinsic motivation.
Culture
Culture may play a moderating role in the experience of
flow (Delle Fave, Massimini, & Bassi, 2011). Early criti-
cism of the flow concept came from its supposedly bias
toward Western culture as flow focused on more active
and goal-directed processes, suggesting that flow 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 flow
may differ from culture to culture, but the dynamics of
the flow experience are universal.
Domain
Also, an important moderator to examine is whether the
challenge–skill balance relationship with flow varies
depending on domain or context. Given how flow has
been studied in numerous contexts, assessing whether
work/academic contexts vs. leisure contexts is a critical
issue for applied researchers when examining flow and
practitioners who want to increase flow experiences.
Csikszentmihalyi and LeFevre (1989) found that the
great majority of adults were experiencing flow 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 flow to working and learning environments is diffi-
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
flow-related activities (Nakamura & Csikszentmihalyi,
2005; Schmidt, Shernoff, & Csikszentmihalyi, 2007).
Studies also assess flow during personal activities that
individuals indicate are meaningful or salient to them in
their everyday experience.
Methodology
Lastly, there are methodological characteristics that we
want to examine as potential moderators. How researchers
have formalized the challenge–skill balance has varied
from study to study (see Moneta & Csikszentmihalyi,
1999), and results revealed a differential impact on flow
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 fit).
Another methodological concern is how flow 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 flow 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 flow. Other self-
reported measures include just one or two items assess-
ing concentration or related topics. Given the range of
methods to assess flow states, a moderator analysis may
further distinguish the validity of such techniques.
In addition, as described earlier as an autotelic per-
sonality, flow 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, flow as a trait, involves a more
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enduring sense of flow, often measured by how often an
individual experiences flow. Whether the challenge–skill
balance is more strongly related to flow 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 flow 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 challenge–skill balance hypothesis of
flow 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 fit between the
challenge of an activity and the skills of the individual
is the most important prerequisite to experiencing flow
(e.g. Schiefele & Raabe, 2011). Therefore, a meta-
analysis on the relationship between the challenge–skill
balance and flow is not only timely, but also essential
in empirically assessing the overall theoretical basis of
this important flow construct, its relation to intrinsic
motivation, and the moderators that influence these
relationships. Second, assessing how strongly the
challenge–skill balance relates to flow in comparison to
other factors (i.e. nine-factor model) was measured.
Lastly, we also examined the relationship between the
challenge–skill balance and intrinsic motivation.
Method
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 ‘flow’and ‘optimal experience,’while exclud-
ing keywords ‘cash flow,’‘optic flow,’and ‘blood flow’
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
flow: Csikszntmihalyi, 1975,1990. These searches com-
bined located a total of 355 unique, potentially relevant
documents.
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 flow and
the challenge–skill 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 flow 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 flow variable
(how flow was measured and/or calculated); (c) the
domain (work/education-related activities, leisure activi-
ties, or self-selected personally salient activities related
to one’s identity; (e) the sample characteristics (age and
country of origin); (f ) the measure of the challenge–skill
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. Grubbs’s(
1950) test was
applied and if outliers were identified, 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
flow and challenge–skill balance were obtained. There-
fore, Duval and Tweedie’s(
2000) trim-and-fill procedure
was employed. The trim-and-fill 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 coefficients between challenge–
skill balance and flow (often represented by ror the
Pearson product moment coefficient). When only means
and standard deviations were provided for a flow group
and a non-flow group, we estimated a correlation. Since
some of the study’s sample sizes were small, and we
wanted to improve normality, we conducted Fisher’s
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 first 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% confi-
dence intervals were calculated for average effects to
assess significance.
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-fit statistic (Q
w
), which follows
a chi-square distribution with k−1degrees of freedom
(kequals the number of effect sizes). A significant Q
w
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
b
statistic
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 fixed- and ran-
dom-error assumptions (Cooper et al., 2009). In a fixed-
effects model of error, each effect size’s variance is
assumed to reflect 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).
Results
Overall findings
The literature search uncovered 28 studies that reported
a relationship between optimal challenge–skill balance
and flow and 18 studies that provided a relationship
between challenge–skill balance and intrinsic motivation.
For flow, the 28 studies reported 37 effect sizes based on
34 separate samples with a total N of 9620 participants.
For the relationship between challenge–skill 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 flow, 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 significant outliers among the
correlations, so all were retained for analysis as reported.
The effect sizes of the correlations (Fisher’sz) ranged from
−0.25 to 1.42. They were all positive correlations, except
for one.
Under a fixed-error model, the overall relationship
between challenge–skill balance and flow (a normally
distributed and weighted correlation or Fisher’sz) 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
document
Sample
size
(ESM) Age Country Culture
Flow
measure
Flow
type
Balance
measure Domain
Correlation
calculation Fisher’sz
Bakker (2005) J 120 41 the
Netherlands
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
(3432)
17 Italy I ESM
Grouping
. High/High
ratio
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
18
USA I Survey State Subscale Work/Educ Subscore-
Global
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
(3432)
36.5 USA I ESM
Grouping
. High/High
ratio
Work/Educ Separate IM: 0.20
Deichter (2011) T 186 39 Canada I Survey Trait Subscale Work/Educ Subscore-
Global
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-
Global
F: 0.91
Jackson (1996) J 394 22 USA I Survey State Subscale Leisure Subscore-
Global
F: 1.37
Kawabata and Mallet (2011) J 635 20.5 Japan C Grouping State Subscale Leisure Subscore-
Global
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
ratio
Work/Educ Means/SD IM: 0.42
IM: 0.44
Keller, Bless et al. (2011) J 102 20 Germany I Grouping . High/High
ratio
Work/Educ Means/SD IM: 0.23
IM: 0.26
84 IM: 0.81
IM: 0.37
(Continued)
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Table 1. (Continued).
Author (year)
Type of
document
Sample
size
(ESM) Age Country Culture
Flow
measure
Flow
type
Balance
measure Domain
Correlation
calculation Fisher’sz
Kiili and Lainema (2008) J 92 20–30 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
split
Leisure Separate F: 0.48
Lovoll and Vitterso (2012) J 64 (698) 21.2 Norway I Grouping . High/High
ratio
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
Trait
Subscale Leisure Subscore-
global
F: 0.29
F: 0.54
Marzalek (2006) D 134128 13 USA I Survey Trait
State
Subscale Work/educ Subscore-
global
F: 0.98
F: −0.25
Murica, Gimeno. and Gonzales
(2006)
J 413 13.7 Spain C Survey Trait Subscale Leisure Subscore-
Global
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-
Global
F: 0.76
Rezabek (1994) D 108 20 USA I Grouping . High/High
ratio
Work/Educ Means/SD IM: 0.40
Robinson et al. (2012) J 30 (349) 51 Ireland I Grouping . High/High
ratio
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
(Continued)
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Table 1. (Continued).
Author (year)
Type of
document
Sample
size
(ESM) Age Country Culture
Flow
measure
Flow
type
Balance
measure Domain
Correlation
calculation Fisher’sz
Shin (2006) J 525 18–22 Korea C Survey State S –C Work/Educ Separate F: 0.21
Snow (2010) D 176 Over
18
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-
Global
F: 1.16
van Schaik et al. (2012) J 83 25 Japan C Survey State Subscale Work/Educ Subscore-
Global
F: 0.55
Vlachopoulous, Karageorghis and
Terry (2000)
J 1231 31.43 England I Survey State Subscale Leisure Subscore-
Global
F: 1.42
Wang and Hsiao (2012) J 122 Varied USA I Grouping . High/High
ratio
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: Master’s 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-fill analyses were con-
ducted. With both a fixed-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 challenge–skill 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 significant outliers
among the correlations, so all were retained for analysis
as reported. The effect sizes of the correlations (Fisher’sz)
ranged from −0.236 to 1.02. They were all positive
correlations, except for two.
Under a fixed-error model, the overall relationship
between challenge–skill 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-fill analyses revealed no evidence that
effect sizes might have been missing in the sample of
studies.
Findings of the moderator analyses
Since the overall relationships between challenge–skill
balance and flow 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 findings from
the moderator analyses.
Publication status
First, we assessed the publication status (published vs.
unpublished status) of the study report. For the flow
moderator analysis, 22 of the studies had been published
as journal articles, and their results were compared to the
five studies that had appeared in dissertations, conference
papers, and master theses. Under the fixed-error model,
correlations from the unpublished reports, z= 0.43 (95%
CI from 0.38 to 0.48), were just significantly 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 flow and the challenge–skill balance.
95% confidence interval
kzLow estimate High estimate Q
Challenge–skill 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 significantly different from 0 at a p< 0.001 value unless specified.
***p< 0.001.
Table 2b. Results of main analysis examining the relationship between intrinsic motivation and the challenge–skill balance.
95% confidence interval
kzLow estimate High estimate Q
Challenge–skill 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 significantly different from 0 at a p< 0.001 value unless specified.
***p< 0.001.
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For the intrinsic motivation moderator analysis, pub-
lished reports (k= 23, z= 0.24) had a significantly smal-
ler relationship than unpublished reports (k=2,z= 0.39)
under fixed-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.
Age
We next examined whether age would moderate the
challenge–skill balance and flow 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 flow (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 findings indicated that for older participants
(k= 7), the correlation between skill-challenge balance
and flow was z= 0.73 (95% CI = 0.71–0.74) compared
to z= 0.50 (95% CI = 0.48–0.52) for younger participants
(k= 27) under the fixed-error model. This comparison
was significantly different (Q(1) = 260.14, p< 0.001).
Under the random-error model, there was no significant
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-significant linear effect of 0.007 (slope coefficient)
on the relationship between skill-challenge balance and
flow. So as age increases, the correlation was very
slightly increasing as well. The two age findings do not
seem to reconcile together, suggesting a potential non-lin-
ear relationship with age.
For the intrinsic motivation moderator analysis, there
were no significant differences between age groups in
fixed or random model of error. Similarly, the meta-
regression indicated no significant age moderation on
challenge–skill balance and intrinsic motivation.
Cultural characteristics
We next assessed the moderation of country and cultural
characteristics. We first compared samples from the USA
Table 3a. Results of moderator analyses for flow and challenge–skill balance.
95% confidence interval
kzLow estimate High estimate Q
b
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
***
Leisure
a,c
11 0.73 (0.67) 0.71 (0.47) 0.74 (0.80) (5.10)
+
Work/educ
a,b
16 0.32 (0.40) 0.29 (0.23) 0.36 (0.55)
Personal
b,c
8 0.40 (0.44) 0.36 (0.33) 0.44 (0.54)
Type of flow 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
^
(-0.31
^
)−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
***
Global–subscore 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 significantly different from 0 at a p< .001 value unless specified. 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 significant pairwise comparisons:
a
and
b
pairwise comparison is significant under both fixed and random models of error.
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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 = 0.45–0.50) compared to samples outside the
USA, which had a correlation of z= 0.61 (95%
CI = 0.60–0.63). The international difference was signifi-
cant under the fixed-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 flow
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-
ism–individualism (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 = 0.62–0.66) than
individualist samples (z= 0.53; 95% CI = 0.51–0.55).
However, this difference was only significant under the
fixed-error model (Q(1) = 59.14, p< 0.001), but close
to marginally significant under the random-error model
(Q(1) = 2.60, p= 0.098). Similarly, the correlation
between challenge–skill balance and intrinsic motivation
was higher for collectivistic cultures (k =3; z= 0.36)
compared to individualistic cultures (k= 22; z= 0.20)
under fixed effects only (Q(1) = 80.71, p< 0.001).
However, there were no significant differences between
US and non-US countries under both models of error for
intrinsic motivation.
Domain
Next, the domain of each study was assessed as a mod-
erator comparing studies of flow in a leisure context,
work/education context, or a personal setting. For exam-
ple, leisure contexts included online activity (e.g. surfing
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 flow, there were significant dif-
ferences among leisure (k= 11), work/education
(k= 16), and personal settings ( k= 8) under a fixed-error
model (Q(2) = 688.00, p< 0.001) and marginally signifi-
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 = 0.71–0.74), and work/education contexts had a
correlation of z= 0.32 (95% CI = 0.29–0.35). This dif-
ference was significant under both the fixed-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 challenge–skill balance.
95% confidence interval
kzLow estimate High estimate Q
b
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
***
Leisure
a
3 0.15 (0.36^) 0.11 (−0.15) 0.18 (0.72) (0.78)
Work/educ
a
13 0.24 (0.33) 0.29 (0.25) 0.36 (0.40)
Personal
a
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 significantly different from 0 at a p< 0.001 value unless specified. 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.
a
Shared superscripts indicate significant pairwise comparisons under fixed models of error.
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average correlation of z= 0.40 (95% CI = 0.36–0.44).
This was also significantly lower than leisure contexts
under both the fixed-error model (Q(1) = 311.33,
p< 0.001) and the random-error model (Q(1) = 4.07,
p= 0.044). For only the fixed-error model, results
showed that relationship between challenge–skill balance
and flow significantly 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
challenge–skill balance and flow is strongest for leisure
contexts, then personal contexts, followed by work or
education contexts.
The domain was a significant moderator for the rela-
tionship between challenge–skill balance and intrinsic
motivation, but only under fixed 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 influence 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 flow literature
did not report enough data or used methods too heteroge-
neous to meaningfully aggregate. For example, how stud-
ies calculated the challenge–skill balance varied too
widely. Studies measured the challenge–skill 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 challenge–skill
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 flow was measured.
ESM vs. single measurements. In the database of studies,
flow was operationalized using a single survey (either
the sum of flow antecedents or a separate flow measure
which included items assessing engrossment or involve-
ment) or multiple momentary assessments using ESM.
One methodological concern was comparing whether
flow assessed using a survey differs from flow assessed
using ESM, which would provide a more ‘real-time’
Table 4a. Relations between moderator variables for flow and challenge–skill balance.
Moderator variable Age Country Culture Domain Type of flow
Country χ
2
(2, N= 35) = 0.06
p= 0.80
Culture χ
2
(1, N= 35) = 0.21 χ
2
(1, N= 36) = 9.27
p= 0.64 p= 0.002
Domain χ
2
(2, N= 35) = 1.28 χ
2
(2, N= 36) = 10.32 χ
2
(2, N= 36) = 3.86
p= 0.53 p= 0.006 p= 0.15
Type of flow χ
2
(2, N= 36) = 0.28 χ
2
(1, N= 36) = 3.50 χ
2
(1, N= 36) = 0.05 χ
2
(2, N= 36) = 4.40
p= 0.87 p= 0.06 p= 0.82 p= 0.11
Balance measure χ
2
(1, N= 35) = .578 χ
2
(1, N= 35) = 1.37 χ
2
(1, N= 35) = 2.97 χ
2
(2, N= 35) = 4.96 χ
2
(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
flow. Some researchers argued that this is more accurate
assessment of the flow state (Csikszentmihalyi & Larson,
1987). There were only two studies that employed ESM
when assessing the challenge–skill balance and flow. In
a 10-week longitudinal study, Fullagar et al. (2013) mea-
sured flow during every practice session for 27 musi-
cians, assessing their momentary subjective experiences.
They found that the relationship between challenge–skill
balance and flow 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 Chen’s(
2000) study, participants engaged in
online activities and web browsing with a repeated pop-
up questionnaire assessing flow. The average weighted
correlation zbetween challenge–skill balance and flow
was only z= 0.04. The variability in measurements in
the ESM studies suggests further research examining
ESM vs. survey measurements of flow. Despite the
paucity of studies using ESM, we attempted to test
this moderator. Single measurements of flow and the
challenge–skill balance (k= 34) had a significantly larger
correlation than ESM correlations (k= 2) under both
fixed 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 significantly
smaller for ESM studies (k= 6) than single-measurement
studies (k= 19). The average correlation for ESM studies
was 0.14 under fixed effects and 0.27 under random
effects, whereas for single measurement studies, the aver-
age correlation was 0.28 and 0.35, respectively.
Subscore–global vs. separate measurements. A fair
number of the correlations between skill-challenge and
flow were calculated by comparing a challenge–skill bal-
ance subscore to a global flow score (k= 15). This type
of correlation contains some shared variance because the
challenge–skill balance is a part of the flow measure.
Other studies compared two separate measurements of
flow and challenge–skill balance, respectively (k= 20).
Under fixed effects, moderator analyses revealed that
studies with a subscore–global correlation (z= 0.75; 95%
CI = 0.74–0.76) had significantly higher correlations than
studies that calculated separate measurements of flow
and the skill-challenge balance (z= 0.30; 95%
CI = 0.28–0.33). This was significant under both fixed-
(Q(1) = 1211.86, p< 0.001) and random-error models
(Q(1) = 16.02, p< 0.001).
State vs. trait flow. Flow has been understood as either a
state of subjective experience measured after engaging in
an activity or the frequency of activity-specificflow,
enduring over time (Jackson, 1996). The difference
between trait (k= 7) and state ( k= 30) types of flow was
assessed. The correlation between flow state and chal-
lenge–skill balance was significantly lower in trait flow
(z= 0.47; 95% CI = 0.43–0.50) than in state flow
(z= 0.58; 95% CI = 0.57–0.60) under the fixed model of
error (Q(1) = 39.11, p< 0.001). This was not significant
under the random model of error (Q(1) = 0.30, p= 0.58).
Relations between moderator variables
The moderator analyses indicated that many variables
significantly influenced the relationship between chal-
lenge–skill balance and flow. 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 signifi-
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 significant moderators:
age, country, culture, domain, type of flow, 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-
lenge–skill balance and flow 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 flow for the flow 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 challenge–skill balance.
Moderator variable Culture Domain Measurement
Domain χ
2
(2, N= 25) = 1.53
p= 0.47
Measurement χ
2
(1, N= 25) = 0.16 χ
2
(2, N= 25) = 3.54
p= 0.64 p= 0.17
Balance measure χ
2
(1, N= 25) = 1.92 χ
2
(2, N= 25) = 2.00 χ
2
(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 flow in leisure settings, and were
more likely to assess flow as a state, rather than a trait.
For intrinsic motivation, there were no confounds for the
significant moderators.
Comparing the challenge–skill balance to other flow
antecedents
Among the studies that met our initial inclusion criteria,
13 studies also measured the correlations between flow
and the other eight following factors of the nine-factor
model of flow: 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 challenge–skill balance and flow
relative to the other antecedents, we also meta-analyzed
the correlations between flow 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
challenge–skill balance is a relatively robust flow anteced-
ent compared to the other eight factors. Under the fixed
model of error, its correlation to flow (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
challenge–skill balance was also larger than unambiguous
feedback (z= 0.72; t= 2.48, p= 0.02), but to a lesser
degree. The relationship between flow and having clear
goals (z= 0.75) and a sense of control (z= 0.79) were not
significantly different from the challenge–skill balance. It
is worth noting that sense of control was the most highly
correlated antecedent with flow.
In addition, we conducted another moderator analysis
to assess whether flow measured as a state or trait mod-
erated the correlations of all the antecedents. Overall,
measured as states, flow correlations with most of the
antecedents were significantly larger compared to being
measured as a trait only under fixed model of error. The
few exceptions were that concentration and a loss of
self-consciousness were not significantly different from
each other under both fixed and random models of error,
and trait transformation of time (z= 0.45) was signifi-
cantly higher than as a state (z= 0.27; Q= 37.5,
p< 0.001) under the fixed model of error.
Discussion
The results indicated that the relationship between
challenge–skill balance and flow was moderate, and this
relationship was influenced by a number of moderating
variables. This moderately large correlation reveals that
Table 5. Results of comparing correlations between flow and its antecedents and assessing trait vs. state moderator analysis.
Overall Z95% CI Flow as state 95% CI Flow as trait 95% CI Q
b
(k= 13)
1
(k=9) (k=5)
Challenge–skill 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
a
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
b
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)
Concentration
a
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
a
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
a
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
a
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)
1
Overall kdoes not add up to 13 because one study measured both trait and state flow with the same sample, so the independence assumption was not
violated.
Note: Included studies were those indicated in Table in the column Correlation Calculation as “Subscore-Global.”All effect sizes were significantly
different from 0 at a p< 0.001 value unless specified. Fixed-effects values are presented outside of parentheses and random-effects values are within
parentheses
**p< 0.01; ***p< 0.001.
Superscripts denote statistically significant differences between challenge–skill balance and other antecedents.
a
p< 0.001;
b
p< 0.05.
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there is evidence for the fundamental notion that match-
ing skill and challenge is an important flow indicator.
However, the lack of a strong, robust relationship sup-
ports the possibility of other theoretical antecedents of
flow. The other eight theorized antecedents to flow var-
ied in its relationship with flow relative to the strength
of challenge–skill balance. Additionally, the lack of rela-
tionship between skill and challenge and the difficulty of
operationalizing challenge (Engeser & Rhineberg, 2008)
may explain this weaker than expected relationship
between challenge–skill balance and flow. Overall, there
is adequate support that matching skill and challenge is
robustly related with feelings of flow or optimal experi-
ence. There is a similar finding with the intrinsic motiva-
tion studies as well.
It is important to note that some of the meta-analytic
findings were based on a small number of effect sizes
and studies. Caution should be taken when interpreting
the specific magnitude of the effects. Surprisingly, the
majority of studies that reported correlational relationship
between challenge-skill and flow were single survey
measurements of the related constructs with hardly any
experimental designs where various levels of the
challenge–skill balance were manipulated. The studies
that measured challenge–skill 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 coefficients
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.
Moderators
Driven by theoretical and methodological concerns in the
literature, moderator analyses revealed that the relation-
ship between challenge–skill balance and flow varied by
individual characteristics, setting, and methodological
characteristics. For example, published studies had a sig-
nificantly higher averaged correlation compared to
unpublished studies. There may be a bias in how this
construct is represented in the field.
Age
Assessing age dichotomously, we found that studies with
subjects aged 30 and over had a much stronger relation-
ship between flow and the challenge–skill balance, but
this effect was only significant under fixed effects.
Although flow 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 flow.
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 fields, 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 flow, the importance of the challenge–skill
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 challenge–skill balance is with
flow, 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-
lenge–skill balance and intrinsic motivation, suggesting a
consistent influence of challenge–skill balance on intrin-
sic motivation across the developmental lifespan.
Cultural characteristics
Because of the international scope of research on flow,
we conducted two moderator analyses assessing whether
country and self-construal affected the magnitude of the
challenge–skill balance and flow relationship. First, stud-
ies with USA-based samples had a weaker relationship
between challenge–skill balance and flow 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 flow was much more congruent
to challenge–skill 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 flow, and self-construal. Self-construal contrasts
indicated that the effect sizes were larger in samples
from collectivistic cultures compared to individualistic
cultures. Our findings 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 findings were con-
trary to this: Collectivistic samples had a higher correla-
tion with optimal balance and both outcomes of flow
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.
Domain
Given that flow–balance 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 flow 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 challenge–skill balance
influences flow (Engeser & Rhineberg, 2008); however,
assuming that work/education contexts are valued as
more important despite being less intrinsically motivating,
the influence of task importance or value seems reversed
according to the results. In addition, for work/education
activities, the lower correlation between flow and chal-
lenge–skill 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 flow being induced, anxiety may be
present. This demands greater attention on how to fit
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 flow (Abuhamdeh & Csikszentmihalyi,
2012).
Personal settings that consist of the activities salient
to their identity or chosen by participants to be meaning-
ful or important had significantly higher optimal balance
to flow 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 challenge–skill balance and flow
was higher when flow was measured as a state vs. trait.
One potential explanation for this difference is with flow
state, measured by subjective experiences after a particu-
lar task, individuals can more immediately, and arguably
more reliably, respond to flow antecedents. With flow
trait, individuals are responding to how often they expe-
rience flow antecedents, essentially describing a more
enduring autotelic personality. Even in comparison with
the other flow 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 flow, rather than a pre-
cursor or outcome of flow 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 one’s sense of self. In addition, some
researchers argued that concentration would be correlated
with both flow as a state or trait because flow’sdefinition
is so inextricably tied to intense-focused concentration.
Another interesting finding was that transformation
of time was significantly higher when measured as a trait
instead of a state. In a similar way to loss of conscious-
ness, the sense that time ‘flies by’is 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 findings was the calculation
of the correlation between flow and optimal balance,
using a global flow scale and one of its subscales of
challenge-skill fit has a much larger correlation than if
they are two separate, unrelated measures. Although we
expected shared variance between the global score and a
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subscale, the average weighted correlation was unexpect-
edly high, and artificially inflated 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 inflated 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 flow 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 challenge–skill balance and flow 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 challenge–skill
balance and intrinsic motivation, which suggests that
flow 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 challenge–skill balance. When
assessing both intrinsic motivation and flow, ESM stud-
ies on average reported lower correlations. This finding
implies that single measurements may inflate the influ-
ence of the challenge–skill balance compared to momen-
tary assessments, which may more accurately assess the
levels of flow and their antecedents.
Other antecedents
Compared to the other eight flow antecedents, challenge–
skill balance remains a powerful precursor to flow. This
finding supported original conceptions of flow where
challenge–skill balance must be met in order for flow to
occur. However, the challenge–skill balance as the sole
catalyst of flow 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 challenge–skill balance as
sources of flow. Whereas the other antecedents (e.g. con-
centration, merging of action and awareness, and feed-
back) appear to be more cognitive of nature either
influencing 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 flow 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 significant predictors of dispositional flow.
Regarding goals, Novak, Hoffman, and Duhachek (2003)
revealed that online users experienced greater flow when
they were engaged in goal-directed activities vs. experi-
ential activities, suggesting the importance of goals when
experiencing flow. In sum, among the nine theorized flow
antecedents, the challenge–skill balance is highly corre-
lated with flow among other motivational antecedents
such as control and clear goals.
Limitations
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 find-
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
flow was measured as a trait or state. In addition, some
of the correlations in the study sample represented inter-
correlations between the challenge–skill balance and flow
measure, which caused some inflation in the effect sizes.
Assessing the differences between fixed- and ran-
dom-error models, we found that most moderator analy-
ses were significant under the fixed-error model, but not
so in the random-error model. We caveat such findings
as limited in their generalizability of these particular
moderator variables (see Cooper, 1998).
Implications of flow 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 flow induction is inex-
tricably a part of this ubiquitous endeavor (see Seligman
& Csikszentmihalyi, 2000). How to create flow states
and to instill intrinsic motivation has been an important
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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
challenge–skill balance remains to be a robust contribu-
tor. Assessing one’s set of personal skills or perceived
competence is critical as well as appropriately finding
challenges or scaffolding activities and tasks to match a
precise balance between the two. Other antecedents are
important as well to engender flow: Clear goals and a
sense of control are also significant 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 flow-inducing (e.g. Patall et al.,
2008; Su & Reeve, 2011).
Conclusion
When trying to create motivating and optimal experi-
ences, what are some important factors to consider? The
results of this meta-analysis suggested that finding a
balance of challenge and skill is an important factor to
consider. Moreover, our findings 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
flow experiences for all individuals. We encourage more
scholarship in this field 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.
Acknowledgments
We want to specially thank Erika A. Patall and Dale H. Schunk
for their helpful comments on previous versions of this
manuscript.
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