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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 (perceived) importance of the activity and by the achievement motive. Furthermore, flow predicted performance in two of the three studies.
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ORIGINAL PAPER
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
Introduction
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.
1
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 (&)
Lehrstuhl fu
¨
r Psychologie, Technische Universita
¨
tMu
¨
nchen,
Lothstr. 17, 80335 Mu
¨
nchen, Germany
e-mail: engeser@wi.tum.de
F. Rheinberg
University of Potsdam, Potsdam, Germany
1
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
section).
123
Motiv Emot
DOI 10.1007/s11031-008-9102-4
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).
2
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,
2
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
implication.
Motiv Emot
123
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
task challenge).
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
¨
ler (2007);
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;
Schu
¨
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,
Low
High
ANXIETY
F
W
O
L
BOREDOM
Challenge
Low
High
Challenge
APATHY
ANXIETY FLOW
High Low
Skill
BOREDOM/
RELAXATION
Low High
Skill
Fig. 1 Original Flow Model
(left-hand side;
Csikszentmihalyi 1975) and
reformulated quadrant Model of
Flow (right-hand side;
Csikszentmihalyi and
Csikszentmihalyi 1988)
Motiv Emot
123
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
performance.
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
perceived importance.
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
in statistics
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.
Method
Participants
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.
Procedure
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
(Engeser
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.
Motiv Emot
123
Measures
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’’.
3
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).
Results
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
3
Hope of success is equivalent to the achievement motive as
measured by Atkinson (1957).
Motiv Emot
123
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.
4
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
flow.
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
12345 6789
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)
4
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.
Motiv Emot
123
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.
Discussion
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
Statisticsab c
-1,5
-1
-0,5
0
0,5
1
Balance
Pac-Man (T1)
SD = -1 SD = 0 SD = 1
SD = -1 SD = 0 SD = 1
Balance
French course (T1)
SD = -1 SD = 0 SD = 1
Balance
Perceived Importance SD = 1
Perceived Importance SD = -1
Fig. 2 Interaction of perceived
importance and balance on Flow
(Studies 1, 2, and 3)
-1,5
-1
-0,5
0
0,5
1
SD = -1 SD = 0 SD = 1
Balance
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)
DR
2
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*
R
2
0.284
Note: N = 246; * p \ 0.05, ** p \ 0.10
Motiv Emot
123
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;
Schu
¨
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
controlled for.
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.
Method
Participants
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.
Procedures
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.
Measures
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
and
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).
Motiv Emot
123
Results
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-
ceived importance.
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.
Discussion
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
statistically).
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
course
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
Motiv Emot
123
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.
Method
Participants
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.
Procedures
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
performance.
Measures
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-
berg
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.
Results
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
found.
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 \
0.01 and
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
Motiv Emot
123
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.
Discussion
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
model.
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,
b =-
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.
General discussion
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
Motiv Emot
123
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
expected.
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
greater depth.
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
investigations.
Motiv Emot
123
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-
tham 2002).
Appendix—Flow Short Scale
not
at all
partly very
much
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.
easy difficult
Compared to all other activities which I partake
in, this one is …
low high
I think that my competence in this area is ...
too
low
just
right
too
high
For me personally, the current demands are ...
Motiv Emot
123
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... It was noted that these characteristics of the LC-NE system in relation to task performance show remarkable resemblance to key characteristics of the psychological flow state 15 . Flow is indicated by full task immersion, optimal performance 8,20,21 , and intermediate arousal 18 . Hence, we hypothesize that, to some extent, flow may be a psychological manifestation of LC-NE activity being in the peak of the engagement mode. ...
... However, the relation between subjective task difficulty and flow (Fig. 1b) followed a quadratic trend and the quadratic model fit significantly better to the data than the linear model (subjective task difficulty: Supplementary Table S2. This later finding is in line with flow theory and the mainstream of flow studies 8,9,39,40 , suggesting that the highest flow experience appears when individuals perceived a match between their skills and task difficulty. ...
... Even though we expected that the 4 assigned difficulty levels would, on average, induce different levels of flow, previous studies suggested that mainly the subjective experience of task difficulty in relation to one's own skill is relevant for flow to occur 8,39 . In other words, what is important is the perceived balance between personal skill and challenges 41 . ...
Article
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Psychological flow is a state of full task immersion. The present study was conducted to test the hypothesis that psychological flow is positively related to activity of the phasic locus coeruleus-norepinephrine (LC-NE) system, which supports decisions on whether to engage in or disengage from the current activity. Subjective flow was assessed among 36 participants who engaged in a gamified version of the n-back task with various difficulty levels (0, 1, 2, and 3 back). During the tasks, continuous pupil diameter and EEG were recorded. We found that psychological flow and two presumed indicators of the phasic LC-NE activity (pupil dilation and EEG P300 amplitude) fit inverted U-shapes with increasing subjective task difficulty. Moreover, a positive linear relationship between psychological flow and pupil dilation (not with P300) was found. In conclusion, this study indicates the involvement of the LC-NE system in the peak experience of flow.
... An increase in positive valence is reported to correlate negatively with theta power in frontal areas, and asymmetry in power in the lower alpha bands correlates with self-reported valence [24]. Focal positions in the International 10-20 system appear to be Fpz for arousal and F3 and F4 for valence [25]. ...
... To further discriminate Flow from other states, we propose to complement the classical affective computing arousalvalence modality by assessing also the perceived level of challenge of the task and personal skills. Among the dimensions characterizing Flow, the challenge-skill balance is considered the more meaningful [18,23,24,25]; as in the quadrant described in Massimini et al. (1988) and Delle Fave et al. (2011), flow represents the high skills and high challenge state, in opposition to anxiety, boredom, and apathy states [18,19]. Since the levels of arousal, valence, challenge, and skill are subjective assessments, we intend to integrate, as objective dimension, the level of attention of the subject to the task. ...
... SAM consists of 5 stylized mannequins creating a 5-point Likert scale, representing a growing level of valence and a growing level of arousal; a more detailed valuation can be reached by adding points between the Manikins as in Mehrabian et al. (1974) [28] creating a 9-point Likert scale [29]. Challenge and skill levels of the subjects will be evaluated via three further 9-point Likert scales as in Engeser et al. (2008): "Compared to all other activities which I partake in, this one is…" (easy/difficult), "I think that my competence in this area is... (low/high), "For me personally, the current demands are..." (too low/just right/too high) [25]. The attention on the task will be assessed via the analysis of the eye-gaze movements [30][31][32], using eye-tracking. ...
... According to Worood, even if the level of task challenge was high, but the task was rewarding, she might experience flow, thereby shifting the focus from her skills to the perceived importance of the task. Similar findings appeared in Engeser and Rheinberg's (2008) study, in which they only found partial support for the substantial effect of the challenge-skills balance in determining flow, so they concluded that the effect of the interaction between skills and challenge also depends on other factors such as "the (perceived) importance of the activity and the individual achievement motive" (p. 168). ...
... It corresponds to the experience during which individuals are fully involved in the present moment (Nakamura & Csikszentmihalyi, 2002), a state of optimal experience and maximal concentration, when people act at the peak of their capacity (Csikszentmihalyi, 2008;Csikszentmihalyi & LeFevre, 1989;Šimleša et al., 2018). The literature suggests that there is a positive relationship between flow and performance in learning settings (Engeser et al., 2005;Schüler, 2007, as cited in Schüler & Brunner, 2009), artistic and scientific creativity (Perry, 1999;Sawyer, 1992, as cited in Schüler & Brunner, 2009, and that flow can predict academic performance (Engeser & Rheinberg, 2008). Flow may also be positively related to creativity (MacDonald et al., 2006;Zubair & Kamal, 2015;Cseh et al., 2015). ...
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During the first lockdown due to Covid-19 pandemic, project-based learning (PBL) had to be implemented remotely. To gain knowledge on this learning context, we monitored motivation (in Self-Determination Theory framework), flow, social identification and self-rated performance during a 10-day project conducted with 281 engineering students. Final grades were also collected as objective performance indicators. Results show that intrinsic and identified motivation, flow, self-rated performance and group identification increased throughout the project, which suggests that remote PBL stimulated students' needs for autonomy and competence without hindering their need for relatedness. Furthermore, the analysis of significant predictors of teams' and individuals' performance draw avenues for improving PBL and stimulating intrinsic and identified motivation, for the subject and for the project, at the right time along the program.
... The concept of flow has since been used as the overarching concept of optimal (fully immersed) experiences in play, games, learning and work (see e.g. Paras, 2005;Whalen and Csikszentmihalyi, 1991;Engeser and Rheinberg, 2008;Thomson and Jaque, 2016;Warren and Donaldson, 2017;Chan and Ahern, 1999). ...
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Since its introduction almost 50 years ago, the concept of ‘flow’ has been descriptive of optimal experiences, also in relation to play. However, the explorative nature of play leads to some discrepancies between flow and the optimal experience of play. In this paper the differences between flow and play are explored, leading to proposing the state of ‘wonder’ (directed at exploration) as an alternative to ‘flow’. From this perspective, the study further explores how we may design toys that enable meaningful experiences of play, identifying opportunities in designing for toys as the enablers of immersed experiences of wonder.
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Chapter
What constitutes enjoyment of life? Optimal Experience offers a comprehensive survey of theoretical and empirical investigations of the 'flow' experience, a desirable or optimal state of consciousness that enhances a person's psychic state. The authors show the diverse contexts and circumstances in which flow is reported in different cultures, and describe its positive emotional impacts. They reflect on ways in which the ability to experience flow affects work satisfaction, academic success, and the overall quality of life
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What constitutes enjoyment of life? Optimal Experience offers a comprehensive survey of theoretical and empirical investigations of the 'flow' experience, a desirable or optimal state of consciousness that enhances a person's psychic state. The authors show the diverse contexts and circumstances in which flow is reported in different cultures, and describe its positive emotional impacts. They reflect on ways in which the ability to experience flow affects work satisfaction, academic success, and the overall quality of life
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Achievement is undoubtedly the most thoroughly studied motive. It was first identified in Henry A. Murray's list of “psychogenic” needs as “n(eed) Achievement,” and described in the following terms: To accomplish something difficult. To master, manipulate or organize physical objects, human beings, or ideas. To do this as rapidly and as independently as possible. To overcome obstacles and attain a high standard. To excel one's self. To rival and surpass others. To increase self-regard by the successful exercise of talent (Murray, 1938, p. 164). Murray can also be considered a pioneer of achievement-motivation research in another respect, namely, as the author of the Thematic Apperception Test (TAT). McClelland, Atkinson, Clark, and Lowell (1953) later developed this instrument into one of the best known and most frequently used procedures for measuring people's underlying motives. In their ground-breaking monograph The Achievement Motive, McClelland and associates (1953) defined achievement motivation as follows: DEFINITION A behavior can be considered achievement motivated when it involves “competition with a standard of excellence.” This definition allows a myriad of activities to be considered achievement motivated, the crucial point being a concern with doing those activities well, better than others, or best of all. The striving for excellence implies quality standards against which performance can be evaluated: people may compare their current performance with their own previous performance (“to excel oneself”), for instance, or with that of others (“to rival or surpass others”), as Murray had already specified (see above).
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Introduction DEFINITION Motivation can be defined as the “activating orientation of current life pursuits toward a positively evaluated goal state”. (Rheinberg, 2004a, p. 17) The purpose of a definition of this kind is to describe the essential qualities of a term as succinctly as possible. Finer points have to be considered separately. In the present case, at least two points need further elaboration: The “positively evaluated goal state” may be to avoid or prevent undesired events. The qualities of avoidance motivation may differ from those of approach motivation (Chapters 4–9). The second point is rather more complicated, and is the focus of the present chapter. When, as here, the definition of motivation focuses on a goal state, there is a risk of premature conclusions being drawn about where the incentives motivating behavior are located. It is easy to assume that the goal state has incentive value, and that the pursuit of the goal-directed activity is purely instrumental to bringing about that goal state, i.e., that the appeal of an activity resides solely in its intended outcomes. This is the approach taken by scholars such as Heckhausen (1977b) and Vroom (1964). Unfortunately, this rather rash conclusion sometimes holds and sometimes does not. It is beyond question that people often engage in activities simply because they want to achieve or modify a particular goal state.