Flow, performance and moderators of challenge-skill balance
Stefan Engeser Æ Falko Rheinberg
Springer Science+Business Media, LLC 2008
Abstract The concept of ﬂow is brieﬂy reviewed and
several theoretical and methodological problems related to
ﬂow research are discussed. In three studies, we attempted
to avoid these problems by measuring the experience of
ﬂow in its components, rather than operationally deﬁning
ﬂow in terms of challenge and skill. With this measure, we
tested the assumption that experience of ﬂow substantially
depends on the balance of challenge and skill. This
assumption could only be partially supported, and, as
expected, this relationship was moderated by the (per-
ceived) importance of the activity and by the achievement
motive. Furthermore, ﬂow predicted performance in two of
the three studies.
Keywords Flow Challenge Skill Performance
Balance Achievement motive Instrumentality
Csikszentmihalyi (1975) began his research on ﬂow with
the rather simple question of why people are often highly
committed to activities without obvious external rewards.
Other researchers at that time also tried to understand the
reasons for such ‘‘intrinsically’’ motivated behavior
(McReynolds 1971; Berlyne 1960; DeCharms 1968; Deci
and Ryan 1980; Hebb 1955; White 1959). In interview
studies, Csikszentmihalyi found that such activities share a
common aspect, which he labeled ‘‘ﬂow state’’ or ‘‘ﬂow
experience’’. According to Csikszentmihalyi (1975) and
Rheinberg (2008), ﬂow state can be characterized by the
following components: (1) A balance between perception
of one’s skills and the perception of difﬁculty of the
activity (task demand). In this state of balance, one feels
both optimally challenged and conﬁdent that everything is
under control. (2) The activity has coherence, contains no
contradictory demands, and provides clear, unambiguous
feedback. (3) The activity seems to be guided by an inner
logic. (4) A high degree of concentration on the activity
due to undivided attention to a limited stimulus ﬁeld. (5) A
change in one’s experience of time. (6) The self and the
activity are not separated, leading to a merging of the self
and the activity and the loss of self-consciousness.
As can be seen form the components, the ﬂow state has a
strong functional aspect, in that individuals experiencing
ﬂow are highly concentrated and optimally challenged
while being in control of the action. This functional state has
positive valence and explains why people are highly com-
mitted to tasks lacking external rewards. Csikszentmihalyi
and LeFevre (1989) even called the ﬂow experience the
‘‘optimal experience’’. This holds true to an even greater
degree when taking into account later descriptions, which
include happiness as part of ﬂow: ‘‘Flow is deﬁned as a
psychological state in which the person feels simultaneously
cognitively efﬁcient, motivated, and happy’’ (Moneta and
Csikszentmihalyi 1996, p. 277).
S. Engeser (&)
r Psychologie, Technische Universita
Lothstr. 17, 80335 Mu
University of Potsdam, Potsdam, Germany
Later on, other authors separated some of the components and
considered ‘‘autotelic’’ or ‘‘intrinsically rewarding’’ experience as a
component of ﬂow (e.g. Jackson and Eklund 2002; Nakamura and
Csikszentmihalyi 2005). We also consider ﬂow as a rewarding
experience for which people strive but do not consider it as a separate
or additional component (see also the last two paragraphs discussion
An early, similar description of the ﬂow state can be
found in Woodworth (1918, p. 69f; cf. Rheinberg 2008),
who placed special attention on the absorption of adults
and children in an activity and referred to the absorption as
being of particular interest motivationally. Recent support
that ﬂow is a psychologically meaningful state is reported
in neurological work clearly indicating that brain structures
related to self-reﬂective introspection were inhibited when
task demand was high (Goldberg et al. 2006). The authors
conclude that: ‘‘Thus, the common idiom ‘losing yourself
in the act’ receives here a clear neurophysiological
underpinning’’ (p. 330).
After the qualitative description of ﬂow by
Csikszentmihalyi, he and others started to study daily
experience with the quantitatively based experience sam-
pling method (ESM, Csikszentmihalyi et al. 1977). The
ESM captures participants’ immediate conscious experi-
ence via self-reports in response to electronic signals at
random times throughout each day. This seems an espe-
cially suitable methodological approach to measure ﬂow,
which is characterized by a loss of self-consciousness, and
retrospectively given statements are biased (retrospec-
tively, the affect of the ﬂow experience was remembered
more positively; Aellig 2004). In the self-report forms,
perceived skills and challenge were measured with single
items, and participants were also asked about concentra-
tion. In addition to these two components of ﬂow, affect
and the wish to do the activity were assessed.
Instead of measuring all components in these studies,
ﬂow was deﬁned operationally according to the ﬂow model
by Csikszentmihalyi (1975). This model proposes that ﬂow
occurs when the actor perceives a balance between the
challenge of the activity and his or her own skill (see left-
hand side of Fig. 1). Due to theoretically inconsistent
results, this model was reformulated by Csikszentmihalyi
and Csikszentmihalyi (1988). The revised model proposes
that ﬂow is experienced only when challenge and skill are
both high. While this model is sometimes referred to as the
‘‘four channel model’’, we refer to it as the quadrant model
(see right-hand side of Fig. 1).
There are several problems with the operational deﬁni-
tion of ﬂow according to the ﬂow models. Even if ﬂow is
indeed characterized by the perceived balance between
challenge and skills, this does not necessarily mean that
ﬂow is always experienced when this balance is present. In
addition, persons differ in the extent to which challenge
and skills are related to each other (Pﬁster 2002). Ellis et al.
(1994) further point out that little has been done to examine
the construct validity of the indicators of ﬂow; instead, the
ESM data are considered to be ecologically valid. In
summary, it would be desirable to measure all components
of ﬂow and to further examine the external validity of the
ﬂow concept. This problem has been recognized and
instruments to measure all components of ﬂow have been
developed for the areas of sports (Jackson and Eklund
2002) and computer activity (Remy 2000).
An additional problem might be seen in the fact that
instead of being asked about the perceived difﬁculty of the
task, the person has to indicate the perceived challenge.
Challenge already compounds perceived difﬁculty and skill
(an easy task, for example, could be highly challenging
given a lack of skill). Pﬁster (2002) also regarded this as a
problematic issue and empirically compared the opera-
tional deﬁnition of challenge-skill with difﬁculty-skill
balance, but found no differences. Whether there was a
balance of challenge-skill or of difﬁculty-skill, the partic-
ipants reported similar experiences, and one could
therefore argue that it makes no (empirical) difference
whether one asks about challenge or difﬁculties. Future
research should tackle this problem. For example, task
difﬁculty could be manipulated and skills could be objec-
tively measured and then related to subjective experience
of challenge and difﬁculty (e.g. Keller and Bless 2008).
Studies conducted thus far with ﬂow indicators were able
to ﬁnd support for the ﬂow models. In line with the expec-
tations of the quadrant model, affect, concentration, and the
wish to do the activity were high in the ﬂow quadrant
(Csikszentmihalyi and LeFevre 1989; Schallberger and
Pﬁster 2001). However, the differences between the ﬂow
quadrant and the boredom quadrant were not found in all
studies (e.g. Clarke and Haworth, 1994; Csikszentmihalyi
and Csikszentmihalyi 1988; Ellis et al. 1994). This ﬁnding
has led to a changing of the name from ‘‘boredom quadrant’’
to ‘‘relaxation quadrant’’ (Csikszentmihalyi 1997, p. 152).
Ellis et al. (1994); Moneta and Csikszentmihalyi (1996,
1999); Pﬁster (2002) also support the claim that the inter-
action of challenge and skill inﬂuences ﬂow indicators, but
the empirical effect sizes were small. The results also indi-
cate that situations in which individual skill exceeded task
challenge led to positive affect and concentration (this would
correspond to boredom/relaxation in the quadrant model).
One possible reason for the unsatisfactory support for
the ﬂow model is that it might be only applicable under
certain circumstances or for certain kinds of activity. We
argue that for activities perceived as unimportant and as
having no further important consequences (activities with
low importance), the balance between difﬁculty and skill
should lead to ﬂow experiences. If the task is considered to
have very important consequences, ﬂow should only be
experienced when skill exceeds difﬁculty. The rationale for
this is that in the case of highly important consequences,
Massimini and colleagues proposed an eight- and even 16-channel
model (e.g. Massimini and Carli 1988). The models are reﬁned
extensions of the quadrant model, having the same theoretical
the threat of potential failure will hinder the experience of
ﬂow. However, if skill is higher than difﬁculty, a person
feels more comfortable and this should make ﬂow more
likely. This would explain why ﬂow indicators were high in
the ‘‘ﬂow quadrant’’ as well as in the ‘‘boredom quadrant/
relaxation quadrant’’ (e.g. when individual skill exceeded
The second reason for the unsatisfactory support for the
ﬂow models has been discussed since the beginning of the
research on ﬂow. It has been argued that some people are
more likely to experience ﬂow and are more likely to
experience it in challenging activities. Csikszentmihalyi
(1975, 1990) has described such persons as having an
autotelic personality. Empirical evidence reported by
Moneta and Csikszentmihalyi (1996) also points to indi-
vidual differences. They found that the balance of
challenge and skill does not go hand in hand with high
values in the ﬂow indicators such as high concentration for
all individuals (cf. Pﬁster 2002). When looking at the
achievement motivation research, the individual differ-
ences could easily be explained by differences in the
achievement motive: The assumption that some people
experience balance as positive and some as negative forms
the core of the risk-taking model of Atkinson (1957);
Brunstein and Heckhausen (2008). According to this
model, highly achievement-motivated individuals prefer
tasks of medium difﬁculty (e.g. tasks in which the balance
of difﬁculty and skill is present). In contrast to this hope of
success aspect of the achievement motive, individuals with
a strong motive of fear of failure even avoid tasks of
medium difﬁculty. The assumption that the achievement
motive moderates the effects of the balance seems even
more plausible considering that ‘‘the ﬂow model may be
more applicable to social contexts and activities where
achievement plays a dominant role…’’ (Moneta and
Csikszentmihalyi 1996, p. 303). In ﬂow research, ﬁrst
support for the moderating role of the achievement motive
was presented by Eisenberger et al. (2005); Schu
see also Clarke and Haworth (1994).
In empirical studies testing the risk-taking model, the
achievement motive (hope of success) was measured with
the projective measure of the Thematic Apperception Test
(TAT, McClelland et al. 1953). Fear of failure was mea-
sured with the Test Anxiety Questionnaire (TAQ; cf.
Brunstein and Heckhausen 2008). According to the con-
temporary understanding, the TAT measures the ‘‘need
achievement’’ or the ‘‘implicit achievement motive’’ and
the TAQ the ‘‘self-attributed need achievement’’ or the
‘‘explicit achievement motive’’ (McClelland et al. 1989;
Brunstein and Heckhausen 2008). For implicit and explicit
motives, hope of success and fear of failure could be dif-
ferentiated. The research of the risk-taking model therefore
captured the implicit motive of hope of success and the
explicit motive of fear of failure. Both personality aspects
inﬂuence whether or not individuals prefer a balance of
challenge and skill.
Since the beginning of the ﬂow research, it has been
expected that ﬂow is related to performance, and several
studies have indeed reported this relationship. On a con-
ceptual basis, ﬂow should be associated with better
performance for two reasons. First, ﬂow is a highly func-
tional state which should in itself foster performance.
Second, individuals experiencing ﬂow are more motivated
to carry out further (learning) activities, and in order to
experience ﬂow again, they will set themselves more
challenging tasks. Thus, ﬂow could be seen as a motivating
force for excellence. Although several studies document
the relationship between ﬂow and performance (Nakamura
and Csikszentmihalyi 2005), some of them share the
aforementioned methodological problems, making this
evidence less convincing (Csikszentmihalyi 1988; Mayers
1978, unpublished; Nakamura 1988). Others were corre-
lational studies or did not control for basic or prior
performance (Jackson et al. 2001; Puca and Schmalt 1999;
ler 2007). Therefore, it could be argued that ﬂow is
related to higher performance, but does not necessarily
cause it. Many activities require higher expertise in order to
get into the smooth performance state typical of ﬂow. Thus,
Fig. 1 Original Flow Model
Csikszentmihalyi 1975) and
reformulated quadrant Model of
Flow (right-hand side;
it is likely that individuals with higher ability have higher
ﬂow values (expertise effect; Rheinberg 2008). This would
mean that the correlation between ﬂow and performance
arises simply because expertise leads to more ﬂow, instead
of ﬂow fostering performance, as was argued above. To
resolve this empirically, it would be helpful to control for
differences in expertise as well as ability in order to
ascertain whether ﬂow will actually lead to better
The present research
To avoid one central problem of quantitative ﬂow research,
we measured all components of ﬂow in the studies reported
here. To empirically evaluate the ﬂow model, we also
measured perceived difﬁculty and skill. In addition, we
assessed the subjective balance between challenge and skill
by asking whether the demands of the task are too low, just
right or too high. This was carried out in response to the
ﬁndings indicating that the relationship between challenge/
difﬁculty and skill varies greatly among individuals (Pﬁster
2002). Furthermore, individuals may be able to report this
perceived balance more accurately than the two quite
abstract variables of difﬁculty and skill (Ellis et al. 1994).
Moreover, the combination of two variables leads to an
unreliable measure due to the combination of measurement
errors (McClelland and Judd 1993).
As we argued above, the importance of the activity
should inﬂuence whether the balance of difﬁculty and skill
will lead to ﬂow. In all studies, we measured the perceived
importance of the activity. Thus, our ﬁrst hypothesis is that
for a low perceived importance, balance will lead to ﬂow;
otherwise, ﬂow will be experienced when skill exceeds
difﬁculty. We also compare activities with objectively
different importance, expecting results analogous to the
As a second potential moderator we discussed the
achievement motive. By trying to replicate the ﬁndings of
the risk-taking model with the dependent variable of ﬂow,
we expect in our second hypothesis that when balance is
present, ﬂow will be more intense for highly implicit
achievement-motivated (hope of success) individuals and
less intense for individuals high in fear of failure in terms
of the explicit achievement motive. The latter should be
threatened when confronted with balance, which has a
negative impact on ﬂow. We had no expectations regarding
the implicit motive of fear of failure and the explicit
achievement motive of hope of success.
Finally, we studied the relationships between ﬂow and
performance. We expect in our third hypothesis that ﬂow
will be related to performance even when prior performance
and ability are controlled for. The test also seeks to validate
the concept of ﬂow and the ﬂow measure employed.
We conducted three studies. In the ﬁrst study, we tested
all three hypotheses. The other two studies were less
complex and did not include the achievement motive
measure. Here, we focused on testing hypotheses 1 and 3
further. Finally, we conducted a meta-analysis of all studies
to test hypothesis 1 by comparing activities with objec-
tively different importance in one analysis.
Study 1: Flow during learning for an obligatory course
Basic statistics is an obligatory part of studying psychology
in Germany. Psychology students must pass a ﬁnal statistics
exam at the end of their ﬁrst semester in order to continue
studying psychology. Therefore, this exam is very important.
About 273 participants took part in the study, which was
conducted at the University of Potsdam and the Technical
University of Berlin during two consecutive years (ﬁrst
year N = 71 and 73, second year 63 and 66). Seven par-
ticipants were not measured for the implicit achievement
motive and 11 participants dropped out before ﬂow was
measured. These participants were excluded from the
analysis (for a detailed description of the dropouts, see
Engeser 2005). Of the remaining 246 participants, 197
were women and 49 were men. Their ages ranged from 18
to 54 years, with a mean of M = 22.4 (SD = 4.73). A total
of 22 participants did not participate in the ﬁnal exam.
Their missing values were estimated with the Expectation
Maximization Method in SPSS (Verleye et al. 1998).
Participants obtained course credit for participation.
The longitudinal study started at the beginning of the winter
semester and ended with the ﬁnal exam at the end of the
semester. The study was part of a larger project attempting
to explain learning activities and performance in statistics
2005). At the ﬁrst assessment, age, gender, math
grades in school, prior knowledge, and implicit and explicit
achievement motives were measured. One week before the
exam, participants were asked to work on a statistical task
they would have typically worked on to prepare for the ﬁnal
exam. They were also instructed to set an alarm clock to
ring ten minutes after they had started the task. At this point
they should ﬁll out the ﬂow measure. Finally, participants
consented that their scores on the exam could be obtained
from the teachers of the statistics course.
The prior knowledge relevant for the statistics course was
measured with the Questionnaire of Probability Theory by
Nachtigal and Wolf (2001). The questionnaire contains two
parallel forms with seven different topics from probability
theory. Each topic is measured with two items. Three of the
most difﬁcult items were not used because we wanted to
avoid the students feeling frustrated.
The implicit achievement motive was assessed by pre-
senting participants with ﬁve pictures and having them
write an imaginative story about each picture (TAT or
Picture Story Exercise, PSE; Pang and Schultheiss 2005).
The stimuli pictures were ‘‘architect at a desk’’, ‘‘two
women in lab coats in a laboratory’’, ‘‘trapeze artists’’,
‘‘two men (‘inventors’) in a workshop’’, and ‘‘gymnast on
balance beam’’ (Smith 1992). In the study of the ﬁrst year,
the ﬁrst picture was ‘‘boy with vague operation scene in
background’’ (McClelland et al. 1953). The use of different
pictures was due to cooperation with other researchers. The
instruction was based on Atkinson (1958). Stories were
later coded for motivational imagery by two trained scorers
using Heckhausen’s (1963) scoring manual for ‘‘hope of
success’’ and ‘‘fear of failure’’.
In line with the terminol-
ogy of McClelland et al. (1989), the implicit measure of
hope of success was labeled ‘‘need hope of success’’ (nHS)
and for the fear of failure ‘‘need fear of failure’’ (nFF). The
interrater correlation was r [ 0.94 for nHS and nFF. On
average, participants wrote 453 (SD = 107) words, con-
taining M = 6.46 (SD = 3.04) images related to hope of
success and M = 2.95 (SD = 2.24) images related to fear
of failure. We adjusted for protocol length by multiplying
by 1000 and dividing by word count. The different picture
stimuli were corrected by z-standardizing the motive val-
ues for each consecutive year. The correlation between
nHS and nFF was r = 0.15 (p = 0.02).
The explicit or self-attributed need of achievement was
measured with the German version (Dahme et al. 1993)of
the Achievement Motives Scale (AMS; Gjesme and Nygard
1970, unpublished). This scale measures ‘‘hope for success’’
(sanHS) and ‘‘fear of failure’’ (sanFF; Heckhausen et al.
1985). Both scales consist of 15 items to be answered on a 4-
point scale, ranging from (1) strongly disagree to (4)
strongly agree. The AMS is widely used in Scandinavia and
Germany and has been established as a reliable and valid
instrument (e.g. Dahme et al. 1993; Rand 1987). The con-
sistency of sanHS was a = 0.82 and the consistency of
sanFF was a = 0.91. The mean of sanHS was M = 3.06
(SD = 0.36) and the mean of sanFF M = 2.12 (SD = 0.50).
The correlation between sanHS and sanFF was r =-0.44
(p \ 0.01). The explicit achievement motive (sanHS and
sanFF) did not signiﬁcantly correlate with the implicit
motive (nHS and nFF); rs \ |0.11|, ps [ 0.11.
Flow was measured with the Flow Short Scale (Rhein-
berg et al. 2003). This scale measures all components of
ﬂow experience with ten items and was used to measure
ﬂow during all activities (7-point scale; see Appendix). The
scale also contains three additional items to measure the
perceived importance (‘‘Something important to me is at
stake here’’, ‘‘I won’t make any mistakes here’’, and ‘‘I am
worried about failing’’). The experienced difﬁculty of the
task, perceived skill and perceived balance were measured
on a 9-point scale (see Appendix). The Flow Short Scale
has been validated and successfully used in various appli-
cations ranging from experimental and correlational studies
(see Rheinberg et al. 2003; Schu
ler 2007) to the experi-
ence-sampling method (Rheinberg et al. 2007). The factor
structure of the Flow Short Scale parallels those now
reported for this study (rotated principal factor analysis).
An investigation of the scree plot and the application of the
parallel analysis method (Zwick and Velicer 1986) indi-
cated a two-factor solution (eigenvalues: 5.86, 2.24, 1.00,
0.76, 0.59) with items for ﬂow and perceived importance
falling on separate factors. The internal consistencies were
a = 0.92 for the ﬂow score and a = 0.76 for importance,
and the two were virtually uncorrelated (r =-0.03,
p = 0.65). We use the mean values of the two factors
throughout this paper. If three factors were extracted, the
ﬂow items fell into factors named ‘‘ﬂuency of perfor-
mance’’ (items 2, 4, 5, 7, 8, 9) and ‘‘absorption by activity’’
(items 1, 3, 6, 10). The internal consistencies were a= 0.93
and a = 0.78, respectively, and the mean values according
to these two factors correlate at r = 0.65 (p \ 0.01).
The meanlevel of ﬂow was M = 4.60 (SD = 1.16) and the
mean for perceived importance was M = 3.45 (SD = 1.44).
Compared to scores attained with various activities and across
various studies (Rheinberg 2004), the ﬂow score lies slightly
below the overall mean (T = 47), and importance is slightly
above the mean (T = 55). The mean level for difﬁculty was
M = 5.18 (SD = 1.79); for skill, M = 4.68 (SD = 1.71);
and for perceived balance, M = 5.42 (SD = 1.32).
The content and difﬁculty of the ﬁnal exam were similar
between universities and consecutive years. The scores of the
ﬁnal exams were z-standardized within each year and uni-
versity to eliminate scaling differences (for details on how we
ensured that the exams were comparable, see Engeser 2005).
We ﬁrst conducted a regression analysis on ﬂow, with
difﬁculty, skill, and the interaction terms of both variables
(difﬁculty and skill were centered before the interaction
term was calculated). There was a marginally signiﬁcant
Hope of success is equivalent to the achievement motive as
measured by Atkinson (1957).
main effect for difﬁculty, b =-0.11, t(244) =-1.86
p = 0.07, and a signiﬁcant main effect for skill, b = 0.59,
t(243) = 10.44, p \ 0.01. The interaction of difﬁculty and
skill was not signiﬁcant, b = 0.03, t(242) = 0.56,
p = 0.58. This indicates that ﬂow depends on skill, and on
difﬁculty (marginally signiﬁcant), but not on the interaction
between difﬁculty and skill. Thus, neither the channel
model nor the quadrant model was empirically supported,
and difﬁculty even had a negative inﬂuence on ﬂow. This
also contradicts existing empirical results, which found
weak but reliable interaction effects with ﬂow indicators
(Moneta and Csikszentmihalyi 1996, 1999; Pﬁster 2002).
On the other hand, the results are in accordance with
empirical ﬁndings showing positive experiences for the
boredom/relaxation quadrant (Csikszentmihalyi and
Csikszentmihalyi 1988) and are in line with our reasoning
for the ﬁrst hypothesis.
Next, we present the descriptive results with the direct
measure of balance. Table 1 presents the mean values of
ﬂow for each value of the measure of balance (the number of
participants are given in brackets). The results indicate that
ﬂow was more intense when demand was low or just right.
When the demand was too high (e.g. if difﬁculty exceeds
skill), ﬂow was less intense.
In order to go beyond
descriptive analysis, a regression analysis was conducted.
Balance and squared balance were used as predictors (bal-
ance was centered before being squared). We found a
reliable main effect for balance, b =-0.45, t(244) =
-8.24, p \ 0.01 and a reliable quadratic relationship,
b =-0.23, t(243) =-4.14, p \ 0.01. The signiﬁcant
negative quadratic relationship lends support to the ﬂow
model, but the linear relationship is stronger still (the strong
linear relationship was expected for the highly instrumental
activity of learning statistics).
We then tested whether the perceived importance of the
activity moderates the relationship between balance and
ﬂow. Once again, all variables were centered before cal-
culating the interaction terms. There was a main effect for
balance b =-0.48, t(244) = 8.79, p \ 0.01 and no reli-
able main effect of importance b =-0.01, t(243) =
-0.18, p = 0.85. The quadratic balance term was also
signiﬁcant, b =-0.27, t(242) =-4.84, p \ 0.01. The
interaction of importance and balance was not signiﬁcant,
b = 0.07, t(241) = 1.22, p = 0.23. Most importantly, the
interaction of quadratic balance and importance was sig-
niﬁcant, b = 0.19, t(240) = 3.08, p \ 0.01. Values for one
standard deviation above the mean, the mean itself and one
standard deviation below the mean were used to illustrate
this result. As can be seen in Fig. 2a, the quadratic rela-
tionship between balance and ﬂow can only be found for
low perceived importance. This result is fully in line with
our expectation according to the ﬁrst hypothesis that the
perceived importance moderates the relationship between
balance and ﬂow; the lower the perceived importance, the
stronger the quadratic relationship between balance and
For our second hypothesis, we tested whether hope of
success for the implicit achievement motive (nHS) and fear
of failure for the explicit achievement motive (sanFF)
moderate the relationship between perceived balance and
ﬂow. Separate regression analyses for the nHS and sanFF
achievement motives revealed that both aspects of the
achievement motive are moderators. The analysis showed a
main effect for nHS, b = 0.21, t(244) = 3.46, p \ 0.01,
and for balance, b =-0.49, t(243) =-8.87, p \ 0.01.
The quadratic balance term was also signiﬁcant, b =
-0.17, t(242) =-3.08, p \ 0.01. The interaction of nHS
and balance was only marginally signiﬁcant, b = 0.10,
t(241) = 1.78, p = 0.08. The interaction of quadratic bal-
ance and nHS was signiﬁcant, b =-0.16, t(240) =
-2.50, p = 0.01. As can be seen in Fig. 3, the quadratic
relationship for balance only held for people with higher
values of nHS, supporting our expectation (also, the gen-
erally strong linear relationship beyond the moderation of
the achievement motive is still present).
The analogous regression analysis with sanFF yielded a
marginally signiﬁcant main effect for sanFF, b =-0.13,
Table 1 Flow values (number of cases) for the direct measure of balance
Direct measure of balance (demand)
Too low Just right Too high
Study 1: Statistics course 4.2 (2) 6.2 (2) 5.0 (7) 5.0 (30) 5.1 (114) 4.2 (48) 4.0 (22) 3.2 (15) 2.4 (6)
Study 2: Pac-Man Time 1 4.1 (2) – (0) 5.3 (7) 5.3 (6) 5.3 (25) 4.8 (8) 4.2 (3) 3.1 (6) 2.9 (3)
Time 2 – (0) 3.5 (1) 4.6 (9) 5.2 (12) 5.9 (17) 5.5 (11) 5.1 (7) 3.6 (2) 2.5 (1)
Study 3: French course Time 1 – (0) 3.7 (4) 3.1 (1) 4.9 (4) 4.5 (30) 4.2 (9) 3.6 (10) 2.3 (2) 2.8 (1)
Time 2 – (0) 2.7 (1) 4.8 (5) 4.0 (8) 4.2 (27) 3.8 (10) 4.0 (6) 3.4 (1) 3.0 (3)
Regression analysis with difﬁculty and skill showed a total explained
variance of the perceived balance of 54%. There was a reliable main
effect of difﬁculty and skill of similar magnitude, b =-0.42,
t(244) =-8.32, p \ 0.01, b = 0.36, t(243) = 7.16 p \ 0.01, and a
signiﬁcant interaction, b =-0.25, t(242) = 5.67, p \ 0.01.
t(244) =-2.07, p = 0.04, and a main effect for balance
and quadratic balance, b =-0.49, t(243) =-8.36,
p \ 0.01 and b =-0.25, t(242) =-4.31, p \ 0.01. The
interaction of sanFF and balance was not signiﬁcant,
b = 0.03, t(241) = 0.53, p = 0.60. The interaction of
quadratic balance and sanFF was signiﬁcant, b = 0.16,
t(240) = 2.37, p = 0.02. In Fig. 3 it can be seen that the
quadratic relationship for balance only held for people with
lower values of fear of failure, as we expected. Both
moderation effects of nHS and sanFF have been derived
from the risk-taking model. The parallel effects to the risk-
taking model also hold when the resultant achievement
motive (subtracting sanFF from nHS—as has customarily
been used in the research tradition of the risk-taking model)
was considered. Furthermore, we did not form hypotheses,
but conducted analyses with fear of failure of the implicit
motive (nFF) and hope of success of the explicit motive
(sanHS). Results revealed no moderation of the quadratic
relationship of perceived balance (ps [ 0.41).
Finally, we tested our assumption that ﬂow is related to
academic performance when basic abilities and prior
knowledge are controlled for. In order to control for basic
or prior skill, math grades and prior knowledge were
included in a hierarchical regression analysis. Age had a
substantial inﬂuence on the performance on the ﬁnal exam,
so we also included it as a predictor in the regression
analysis. Table 2 shows the results of the regression anal-
ysis. Age and math grades signiﬁcantly inﬂuenced
performance on the ﬁnal exam. Prior knowledge only
showed a marginally signiﬁcant inﬂuence. Flow explained
an additional 4% of the variance of the ﬁnal exam results.
Thus, ﬂow can be seen as a predictor of performance rather
than just being part of high performance. In total, 28% of
the variance is explained by all predictors.
To avoid a central problem of quantitative ﬂow research in
this study, ﬂow was measured in its components. With this
measure, it was revealed that ﬂow depends on difﬁculty and
skill, and not—as predicted by both ﬂow models—on the
SD = -1 SD = 0 SD = 1
SD = -1 SD = 0 SD = 1
French course (T1)
SD = -1 SD = 0 SD = 1
Perceived Importance SD = 1
Perceived Importance SD = -1
Fig. 2 Interaction of perceived
importance and balance on Flow
(Studies 1, 2, and 3)
SD = -1 SD = 0 SD = 1
nHS SD = -1
nHS SD = 1
sanFF SD = -1
sanFF SD = 1
Fig. 3 Interaction of hope of success of implicit achievement motive
(nHE) and fear of failure forms the explicit achievement motive
(sanFF) and balance on Flow
Table 2 Predicting ﬁnal exam performance with hierarchical
regression including ﬂow (study 1, statistics course)
DF b tdfr
Age 0.170 50.0* -0.31 -5.35* 244 -0.41*
Math grade 0.063 19.8* 0.19 3.22* 243 0.35*
Prior knowledge 0.011 3.58** 0.11 1.92** 242 0.23*
Flow 0.040 13.5* 0.21 3.68* 241 0.31*
Note: N = 246; * p \ 0.05, ** p \ 0.10
interaction between these two variables. On the other hand,
analyses with the additional direct measure of the balance
between difﬁculty and skill validated one aspect of the ﬂow
model, namely that ﬂow decreases when task demand is too
high. The ﬁnding that ﬂow is still high when the task demand
is too low is in accordance with our expectations. For highly
important activities, i.e. activities with high importance,
individuals experience ﬂow even if skill exceeds difﬁculty.
Analyses looking at the perceived importance point in the
same direction. The importance moderates the inﬂuence of
balance on ﬂow in the hypothesized way. Also as expected,
when demand is ‘‘just right’’ (i.e. in tasks of medium chal-
lenge), ﬂow is higher for individuals high in the implicit
achievement motive ‘‘hope of success’’. The reverse pattern
holds true for the explicit achievement motive of ‘‘fear of
failure’’. This pattern of results of the components of the
implicit and explicit achievement motive is exactly what was
expected from the risk-taking model. Furthermore, ﬂow was
related to performance on the ﬁnal exam.
Taking these results into consideration, it can be argued
that the reliance of much of the research on ﬂow merely on
difﬁculty and skill level is not completely justiﬁed. Flow
should be measured, and not inferred when difﬁculty/
challenge matches skill (on high levels). This is even more
important when bearing in mind that the achievement
motive moderates how balance affects the experience of
ﬂow, at least when learning statistics. Taking into account
also the results of other studies (Eisenberger et al. 2005;
ler 2007), we can conclude that the ﬂow model is
more applicable for some individuals and less so for others.
To ﬁnd further support for our ﬁrst hypothesis, the next
study was conducted with an activity—in contrast to the
ﬁrst study—of very low importance. In this case, we expect
ﬂow to be low when the activity is either not demanding
enough or too demanding. We again tested the hypothesis
that ﬂow relates to performance when prior performance is
Study 2: Flow during a computer game
We chose the computer game Pac-Man due to its friendly
nature and because the difﬁculty levels are easy to manip-
ulate. Participants were told that we wanted to evaluate
feelings and thoughts while playing computer games and
that performance in the game itself was of no consequence.
About 60 participants took part in this study. The mean age
was M = 22.6 (SD = 4.22) with a range from 14 to 49; 48
of the participants were women. The participants were
either paid or received course credit.
After receiving instructions, the participants played three
preliminary rounds lasting for two minutes each in order to
get used to the game and provide a baseline measure of
playing ability. After playing four rounds of ﬁve minutes
each, participants were asked to ﬁll out the Flow Short
Scale. The ﬁrst and third round was set at a medium dif-
ﬁculty level, providing a challenging situation for most of
the participants. The second round was very difﬁcult and
the fourth round was very easy. Only the results regarding
our hypotheses of the two rounds played at medium difﬁ-
culty are reported here (for ease of presentation, these two
rounds are labeled ﬁrst and second time measure; only the
mean values of ﬂow for the very difﬁcult and very easy
rounds are given). After the ﬁnal round, participants were
thanked for their participation and debriefed.
Flow was again measured with the Flow Short Scale. In
this study, only subjectively perceived balance was mea-
sured. The internal consistency of the Flow Short Scale for
the two measures was a = 0.87 and a = 0.87, and for the
perceived importance a = 0.63 and a = 0.85. Flow and
importance were only weakly and not signiﬁcantly corre-
lated (r =-0.12, p = 0.37 and r = 0.06, p = 0.65). The
mean level of ﬂow was M = 4.68 (SD = 1.18) for Time 1
M = 5.21 (SD = 1.03) for Time 2 (for the very difﬁ-
cult and very easy rounds, the means were M = 3.08,
SD = 0.69 and M = 3.83, SD = 0.92). For perceived
importance, the mean level was M = 1.65 (SD = 0.86)
and M = 1.43 (SD = 0.83). The values for importance are
considerably lower than in the ﬁrst study, supporting our
reasoning that the importance of the computer game is
lower than that of the statistics exam in the ﬁrst study.
Compared to values attained from various activities
(Rheinberg 2004), the ﬂow values here are around the
overall mean (T values were 48 and 52) and importance
values are well below the mean (T values were 44 and 42).
The mean levels for perceived balance were M = 5.27
(SD = 1.76) and M = 5.03 (SD = 1.48).
Pac-Man, created in 1980, was one of the ﬁrst computer
games. The player has to maneuver Pac-Man, a yellow
circle with a mouth, through a maze while eating small dots
and being hunted by ghosts. Eating power pellets gives
Pac-Man the temporary ability to eat the ghosts himself and
gain additional points. The mean for the baseline was
M = 168 (SD = 42.5). The points for the ﬁnal rounds were
M = 378 (SD = 169) and M = 423 (SD = 173).
Table 1 presents the mean values of ﬂow for the direct
measure of balance. The results indicate that ﬂow is more
intense when demand is just right and less intense other-
wise. Thus, for computer games without serious
consequences (e.g. low importance), the ﬂow model seems
to ﬁt the data.
To go beyond descriptive analysis, balance and squared
balance were used as predictors in a regression analysis.
For the Time 1 measure, we found a reliable main effect for
perceived balance, b =-0.30, t(58) =-2.90, p \ 0.01,
and an even stronger quadratic relationship, b =-0.54,
t(57) =-5.28, p \ 0.01. For the Time 2 measure of ﬂow,
the linear relationship between balance and ﬂow was not
signiﬁcant, b = 0.14, t(58) = 1.37, p = 0.17, but a strong
quadratic relationship was found, b =-0.68, t(57) =
-6.63, p \ 0.01. This is in support of our ﬁrst hypothesis
that for activities with low importance, a quadratic rela-
tionship will be found according to the ﬂow model.
Next, we tested whether the perceived importance of the
activity moderates the relationship between balance and
ﬂow. All variables were centered before calculating the
interaction terms. For the ﬁrst measure, there was a main
effect of balance and of importance, b =-0.24,
t(244) = 2.45, p = 0.02 and b =-0.39, t(243) =-3.45,
p \ 0.01. The quadratic balance term was also signiﬁcant,
b =-0.43, t(242) =-4.20, p \ 0.01. The interaction of
importance and balance was not signiﬁcant, b = 0.08,
t(241) = 0.78, p
= 0.44. Most importantly, the interaction
of quadratic balance and importance was signiﬁcant,
b = 0.37, t(240) =-2.94, p \ 0.01. One standard devia-
tion above the mean, the mean itself and one standard
deviation below the mean were used to illustrate this result.
As can be seen in Fig. 2b, the quadratic relationship
between balance and ﬂow is stronger the lower the per-
For the second measure, there was a main effect of
balance and of importance, although these were not sig-
niﬁcant, b = 0.18, t(244) = 1.66, p = 0.10 and b =
-0.02, t(243) =-0.15, p = 0.89. The quadratic balance
term was signiﬁcant, b =-0.69, t(242) =-6.09,
p \ 0.01. Neither the interaction of importance and bal-
ance, b = 0.14, t(241) = 1.12, p = 0.24, nor the
interaction of quadratic balance and importance, b = 0.01,
t(240) = 0.07 p = 0.94 was signiﬁcant. Thus, for the
second measure, importance does not reliably moderate the
strong quadratic relationship.
Finally, we tested our assumption that ﬂow relates to
performance. Performance baseline measures in Pac-Man
served to control for baseline performance, and ﬂow Time
1 and Time 2 were summed to form a single predictor.
There was a main effect for baseline, b = 0.52,
t(58) = 3.85, p \ 0.01. This baseline measure explains
51% of the variance of the performance. Flow explained an
additional 3%, but this effect is only marginally signiﬁcant,
b = 0.27, t(57) = 1.98,
p = 0.052.
As expected for an activity with low importance, a qua-
dratic relationship of balance and ﬂow was found: Flow
was high when balance was present and low when the
demand was too high or too low. The individual measure of
perceived importance also moderated the relationship as
expected for the ﬁrst measurement point. Only when the
perceived importance was low could the quadratic rela-
tionship be found. For the second measure, no reliable
moderation of the perceived importance was found. The
expectation that ﬂow relates to performance beyond ability
could not be supported, as its inﬂuence beyond the baseline
measure was only marginally signiﬁcant.
For the second measure, the perceived importance was
low, and indeed lower than for the ﬁrst measure. This
might explain the fact that importance did not act as a
moderator here. The absence of a linear trend and a
stronger quadratic relationship for the second measure also
lends credence to this explanation: when there is no (or
little) perceived importance, only the quadratic relation-
ships are found and the ﬂow model is warranted for these
situations. Perceived importance has to be at a minimum
level in order for its effect to be apparent (at least
Regarding the relationship between ﬂow and perfor-
mance, we argued that ﬂow leads to better performance for
two reasons: (1) a better functional state is achieved during
ﬂow and (2) there is a higher motivation to perform the
activity again. Only the ﬁrst reason applies to this study,
because the experimental situation was standardized and
thus did not allow for additional practice. In learning sta-
tistics, this second reason could have played a major role.
This might also be the case in our third study, in which we
examined the activity of learning French. Therefore, we
expect that ﬂow will be a predictor of performance again.
Regarding our ﬁrst hypothesis, we expect the relationship
between balance and ﬂow to again be moderated by the
importance of the activity and the perceived importance.
Study 3: Flow during learning in a voluntary French
French courses are offered by the university to regular
students who want to improve their language skills.
Although these courses are not a regular part of the studies,
students receive a certiﬁcate which could be useful in
applying for scholarships and jobs. The importance of
learning French could therefore be considered to be greater
than that of playing Pac-Man, but less than that of learning
for the (obligatory) statistics exam.
About 61 participants took part in the study. The study was
conducted at the language center of the University of
Potsdam. The mean age of the participants was M = 22.6
(SD = 2.04) with a range from 19 to 28; thirty-ﬁve of the
participants were women. About 13 participants (seven of
them women) did not take the ﬁnal exam. Due to the high
dropout rate, these values were not replaced and these
participants were excluded from the analysis concerning
performance. Every participant took part without being
paid or receiving course credit.
The longitudinal study started at the beginning of the
winter term and ended with the ﬁnal exam at the end of the
semester. Before the course started, the language center
conducted a placement or ability test to allocate the par-
ticipants to the appropriate course level. The course was
taught every week for two hours. Flow was measured after
60 min of class time at two points: one during the ﬁrst half
of the semester, and one during the second half. At the ﬁrst
point, age and gender were also measured. At the end of the
semester, every student received a mark for his or her
In the ability test the participants could earn a maximum of
100 points. The scores ranged from 31 to 76, with a mean
level of M = 54.4 (SD = 12.0). Students earning less than
55 points were allocated to the level 1 course, while all
others were placed in the level 2 course. For the analysis
conducted below, baseline ability was z-standardized
within each ability level.
Flow was again measured with the Flow Short Scale. As
in study 2, only the subjectively perceived balance was
measured. In this study, the internal consistency of ﬂow
was a = 0.87 for both times and a = 0.87 and a = 0.88
for perceived importance. Flow and importance were only
weakly and not signiﬁcantly correlated (r =-0.20,
p = 0.13 and r =-0.11, p = 0.39). The mean level of
ﬂow was M = 4.12 (SD = 1.10) for Time 1, and M = 4.04
(SD = 1.07) for Time 2. For importance, the mean level
was M = 2.45 (SD = 1.46) and M = 2.43 (SD = 1.33).
Compared to values attained in various activities (Rhein-
2004), the ﬂow values are below the overall mean (T
values are 43 and 44), while values for importance are
slightly below the mean (Ts = 48) and in-between those
for statistics and Pac-Man. The mean level for perceived
balance was M = 5.34 (SD = 1.41) and M = 5.26
(SD = 1.44).
The ﬁnal marks are based on oral participation (one
third) and on the results of the ﬁnal exam (two thirds). The
marks ranged from 1.5 to 4.3, with a mean level of
M = 2.73 (SD = 0.70; here, lower marks indicate better
performance). For the analysis conducted below, the marks
were reversed and z-standardized within each ability level.
On a descriptive level, Table 1 shows that for Times 1 and
2, ﬂow was more intense when demand was just right, but
still relatively high when demand was too low (e.g. when
skill exceeds difﬁculty). If demand was perceived as being
too high (e.g. if difﬁculty exceeds skill), ﬂow was less
intense. To go beyond descriptive analysis, regression
analyses were conducted. For Time 1, a reliable main effect
of perceived balance, b =-0.29, t(59) =-2.48,
p = 0.02, and of the quadratic relationship, b =-0.40,
t(58) =-3.39, p \ 0.01, were found. For Time 2, we
found no reliable main effect of perceived balance, b =-
0.18, t(59) =-1.33, p = 0.19, and no reliable quadratic
relationship, b =-0.15, t(58) =-1.07, p = 0.29. Thus,
the moderate linear and quadratic relationship is in line
with our ﬁrst hypothesis only for the ﬁrst measure. For
Time 2, no reliable effect of perceived balance could be
Next, we tested whether the perceived importance of the
activity moderates the relationship of balance and ﬂow.
Again, all variables were centered before calculating the
interaction terms. For the ﬁrst measure, there was a main
effect of balance b =-0.45, t(244) = 2.88, p \
no reliable main effect of importance b =-0.11,
t(243) =-0.74, p = 0.47. The quadratic balance term was
also signiﬁcant, b =-0.45, t(242) =-3.10, p \ 0.01.
The interaction of importance and balance was not signif-
icant, b =-0.10, t(241) = 0.64, p = 0.52. Most
importantly, the interaction of quadratic balance and
importance was signiﬁcant, b = 0.39, t(240) = 2.20,
p = 0.032. Values for one standard deviation above the
mean, the mean itself and one standard deviation below the
mean were used to illustrate this result. As can be seen in
Fig. 2c, the quadratic relationship between balance and
ﬂow is only found for low perceived importance. This
result is fully in line with our expectation in the ﬁrst
hypothesis that the importance moderates the relationship
between balance and ﬂow. For the second measure, no
reliable effects could be found (ps [ 0.20). Thus, we were
able to support our hypothesis with the ﬁrst but not the
second measure of ﬂow.
Finally, we tested whether ﬁnal marks were dependent
on ﬂow when controlling for language ability as measured
before the course. We therefore conducted a regression
analysis with the ability test as one predictor and ﬂow
Times 1 and 2 summed for a single predictor. There was a
main effect of basic ability, b = 0.48, t(46) = 3.87,
p \ 0.01. This measure explains 26% of the variance of the
ﬁnal marks. Flow explained an additional 7% and this
effect was signiﬁcant, b = 0.28, t(45) = 2.24, p = 0.03.
As expected for an activity with medium importance, the
relationship between balance and ﬂow showed a linear
relationship and a substantial quadratic relationship. The
pattern of this relationship could be seen as lying in
between learning statistics and playing Pac-Man, which
were of especially high and low importance, respectively.
However, this only holds true for the ﬁrst measure of ﬂow;
for the second measure, balance had no reliable effect on
ﬂow. Our expectation regarding perceived importance
could also only be found in the ﬁrst time measure. The
assumption that ﬂow relates to performance beyond basic
ability was again supported for this learning activity, as
was the case for learning statistics.
The fact that no reliable effects were found for the
second measure might possibly be explained by the gen-
erally low ﬂow values. When an activity has low overall
ﬂow values, ﬂow might not even be experienced when
there is balance. Here, ﬂow might be hindered by other
aspects or due to special circumstances (e.g. instruction
method or tensions between students). However, this is
only a tentative explanation. We were not able to validate
this reasoning with data as we did not measure such
aspects. Future research should therefore be more sensitive
to such variables that possibly further restrict the ﬂow
Thus far, the comparison of the three studies has been
made on a solely descriptive basis. To more substantially
support the claim that the activity moderates the relation-
ship between balance and ﬂow, we compared all three
studies in one analysis.
Meta-analysis: A direct comparison of the three studies
To realize the direct comparison between all three studies
in one analysis, two effect-coded variables for study were
used as predictors along with the interaction between bal-
ance and squared balance. If the interaction between
balance and the effect-coded variable reaches signiﬁcance,
the linear relationship of balance with ﬂow will differ
between the studies. If the interaction with the squared
balance is signiﬁcant, the quadratic relationship between
balance and ﬂow will differ between the studies. Before
computing the interaction, balance was z-standardized
within each study and the ﬁrst measures of the second and
third study were included (we excluded the second measure
to ensure independence). The linear and quadratic rela-
tionship of balance was signiﬁcant, b =-0.34,
t(365) =-6.26, p \ 0.00 and b =-0.41, t(364) =-
6.96, p \ 0.00. The ﬁrst effect-coded variable representing
the statistics course as compared to the entire sample was
not signiﬁcant, b = 0.01, t(363) = 0.11, p = 0.91. The
interaction with balance was marginally signiﬁcant,
0.10, t(362) =-1.77, p = 0.78, and was signiﬁcant for the
interaction with squared balance, b = 0.20, t(361) = 2.93,
p \ 0.01. The implication is that for the statistics course (in
comparison to the whole sample), the linear relation
between balance and ﬂow was marginally stronger and the
quadratic relationship was signiﬁcantly weaker. The sec-
ond effect-coded variable—representing Pac-Man as
compared to the entire sample—was signiﬁcant, b = 0.17,
t(360) = 3.01, p \ 0.01. This means that ﬂow was higher
for playing Pac-Man. The interaction with balance was not
signiﬁcant, b = 0.02, t(359) = 0.52, p = 0.61, but the
interaction with squared balance was signiﬁcant, b =
-0.14, t(358) =-0.34, p = 0.02. This indicates that for
Pac-Man, the linear relationship did not differ, but the
quadratic relationship was stronger. Thus, the difference
relationship between balance and ﬂow for the three activ-
ities can be considered reliable. In this respect, our ﬁrst
hypothesis, in which we reasoned that the importance of
the activity moderates the effect of perceived balance on
ﬂow, is therefore supported beyond descriptive analysis.
In all three studies, we measured ﬂow in all its components
and empirically examined how the balance of difﬁculty and
skill inﬂuences ﬂow. We hypothesized that the inﬂuence of
balance on ﬂow will be moderated by the perceived
importance of an activity and the achievement motive.
Both hypotheses were empirically supported, as well as the
hypothesized inﬂuence of ﬂow on performance.
In the highly important activity of learning statistics,
ﬂow was still high when the demand was low. For the less
important activity of playing the computer game Pac-Man,
ﬂow was highest when balance was present and low when
the demand was too low or too high. Learning French was
located in between statistics and Pac-Man. There was a
moderate linear and quadratic relationship between balance
and ﬂow (in statistics, the linear relationship was pre-
dominant, and in Pac-Man the quadratic relationship was
predominant). This was precisely the result that we had
The activities compared here also differ in various fur-
ther characteristics other than importance. Therefore,
possible alternative explanations could account for the
moderating role. Nevertheless, we see importance as the
crucial aspect because the moderating role of perceived
importance showed analogous results. However, the results
should be replicated in experimental settings in which
everything but importance is kept equal. This would give
our reasoning an even more solid empirical base.
It should also be pointed out that the perceived impor-
tance was measured including items assessing the worries
about mistakes and failure. Therefore, the importance of an
activity itself might only be a moderator when worries are
aroused due to the perceived importance. With our
importance measure, we therefore captured possible threat
of important activities. Experimental studies could best
address this problem by separately varying both aspects in
order to shed more light on this important issue.
Other studies (e.g. Ellis et al. 1994; Moneta and
Csikszentmihalyi 1996, 1999; Pﬁster 2002) found a weak
but reliable interaction between challenge and skill, but we
did not ﬁnd this in our ﬁrst study. Besides the fact that
these studies did not measure ﬂow in its components, the
high importance of learning statistics could explain the
different results in our study. According to our hypotheses,
the interaction of difﬁculty and skill would be expected for
Pac-Man, but here we measured only the perceived balance
(and not difﬁculty and skill separately). Due to the strong
quadratic relationship of balance for Pac-Man, one could
assume that difﬁculty and skill interact. Based on this
assumption, the mixture of various degrees of importance
in ESM studies would result in a weak interaction effect of
challenge/difﬁculty and skill. To shed more light on this,
future research should address the importance of the
activity in ESM studies.
The fact that most other studies measured perceived
challenge, while we measured perceived difﬁculty in our
ﬁrst study, might also explain why we did not ﬁnd a reli-
able effect of the interaction of difﬁculty and skill on ﬂow
(it seems conceptually clearer to use difﬁculty as it seems
to be less confounded with skill). But taking into account
that Pﬁster (2002) found no empirical evidence that asking
about challenge and/or difﬁculty affects ﬂow differently,
this alternative explanation is rather unlikely. Nevertheless,
a clariﬁcation with respect to challenge and difﬁculty in
future research seems necessary, mainly when ﬂow
research still relies heavily on the balance issue.
The ﬁnding that the achievement motive moderates the
relationship between balance and ﬂow was part of the ﬁrst
study. Analogous to the risk-taking model of Atkinson
(1957) and Brunstein and Heckhausen (2008), both aspects
of the achievement motives moderate the effect of balance
on ﬂow. Individuals high in the implicit achievement
motive of hope of success experience more ﬂow when the
demand is perceived as just right (e.g. during a task of
medium challenge). Individuals high in explicit fear of
failure experience less ﬂow in this regard. The fact that
other personal variables also moderate the relationship
between balance and ﬂow was shown by Keller and Bless
(2008) for the action versus state orientation.
Flow while preparing for a statistics exam or learning
French is associated with performance at the end of the
semester, even when controlling for ability. For the com-
puter game Pac-Man, the relationship was less strong and
only marginally signiﬁcant. This can easily be explained
because ﬂow should foster performance due to it is a highly
functional state (e.g. high concentration); in addition, ﬂow
can be expected to foster performance due to its rewarding
nature. Thus, if more ﬂow is experienced, further engage-
ment in an activity should be more frequent, which should
foster performance. For Pac-Man, the long-term effect of
more frequent engagement could not be accounted for, and
this might be the reason why the relationship is weaker
here. Future research should consider the functional and
rewarding aspects when studying ﬂow and performance. It
might even be possible to sequentially separate ﬂow and
performance in order to study the causal relationship in
Examining ﬂow research in the light of our results,
the following conclusions can be drawn: (1) The ﬂow
state, as conceptualized by qualitative interviews by
Csikszentmihalyi (1975) and measured by the Flow Short
Scale, predicts performance. (2) The strong reliance on
the skill-challenge balance needs to be questioned. The
effect of balance depends at least on the (perceived)
importance of the activity and the individual achieve-
ment motive. The aspect of ‘‘autotelic personality’’ has
long been discussed as a moderator (Csikszentmihalyi
1975) and the achievement motive might be one part of
this personality type. The fact that variables other than
the importance of an activity—and not the person him/
herself—determine ﬂow has recently been demonstrated
for goals (Rheinberg et al. 2007; see also Abuhamdeh
et al. 2005). (3) Future research should probably not only
(operationally) deﬁne ﬂow with only one component (the
skill-challenge balance) and instead measure ﬂow in its
multidimensionality. Most ideal would be to measure
ﬂow ‘‘online’’ via unobtrusive physiologically based
indicators or with some reliable and observable aspects
of behavior or expressions. Such measures are not yet
available and should form the subject of future
Flow research has begun to provide an understanding of
the reasons for intrinsic motivation. Experiencing ﬂow is
one reason for engaging in activities even without any
(obvious) external rewards. The present research also
applies the ﬂow concept to activities that are not considered
to be solely intrinsically motivated, which has been the
case from the very beginnings of ﬂow research. By
studying ﬂow in daily experience (see experience sampling
method in the introduction), it was expected that ﬂow could
potentially be experienced in any activity (e.g. depending
on the challenge and skill ratio). Csikszentmihalyi and
LeFevre (1989) even found more ﬂow in activities at work
(see also Rheinberg, et al. 2007). When studying motiva-
tion for different (daily) activities, it is also clear that
motivation can rarely be understood as completely intrin-
sically or extrinsically motivated.
When we study ﬂow, we are also studying the absence
of ﬂow (e.g. low levels of ﬂow). For example, we found
lower mean levels of ﬂow in the highly important
activity of learning statistics compared to playing a
computer game. On average, individuals would therefore
be less inclined to learn statistics. Or to put it another
way, they are less intrinsically motivated in this respect.
This ﬁnding is also in accordance with the contemporary
conception of intrinsic motivation: High instrumentality
tasks or ego-threatening conditions will hinder intrinsic
motivation (e.g. Deci and Ryan 2000; Elliot and Hara-
kiewicz 1996). On the other hand, external demand or
ego-threatening conditions may even foster ﬂow if the
personal skill is high compared to the task difﬁculty.
This has parallels in to the ﬁnding that fear can lead to
higher performance for easy tasks (e.g. Mueller 1992),
and in the goal-setting theory, the strongest effects of
external standards on performance were found for easy
tasks (e.g. when skill exceeds difﬁculty; Locke and La-
Appendix—Flow Short Scale
I feel just the right amount of challenge.
My thoughts/activities run fluidly and smoothly.
I don’t notice time passing.
I have no difficulty concentrating.
My mind is completely clear.
I am totally absorbed in what I am doing.
The right thoughts/movements occur of their own accord.
I know what I have to do each step of the way.
I feel that I have everything under control.
I am completely lost in thought.
Compared to all other activities which I partake
in, this one is …
I think that my competence in this area is ...
For me personally, the current demands are ...
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