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Making Flow Happen: The Effects of Being Recovered on Work-Related Flow Between and Within Days

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This article examines variations of work-related flow both between and within days. On the basis of the effort-recovery model (Meijman & Mulder, 1998), we hypothesized that a person's relative day-specific state of being recovered (i.e., feeling refreshed) in the morning is positively related to subsequent day-level flow experiences during work. Taking into account research on circadian rhythms of human functioning, we further hypothesized that flow experiences follow a U-shaped pattern within the working day and that feeling recovered will affect this pattern. One hundred and twenty-one software professionals provided data on recovery at the start of the working day and on flow at 3 occasions during the day, for a period of 5 consecutive working days (resulting in 493 day-level and 1,340 occasion-level data points). Three-level multilevel models showed that relative day-level state of being recovered predicted day-level flow experiences in the hypothesized direction. The data did not support a general curvilinear, U-shaped main effect of flow experiences within the day. However, people in a relatively high state of being recovered in the morning experienced the predicted U-shaped pattern, whereas poorly recovered people experienced a gradual decrease in flow experiences over the course of the working day. This study emphasizes the importance of recovery during nonwork time for flow experiences within the entire working day, thereby extending research on task characteristics with personal resources when examining predictors of flow. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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RESEARCH REPORT
Making Flow Happen: The Effects of Being Recovered on Work-Related
Flow Between and Within Days
Maike E. Debus
University of Zurich Sabine Sonnentag
University of Mannheim
Werner Deutsch
Technical University of Braunschweig Fridtjof W. Nussbeck
Bielefeld University
This article examines variations of work-related flow both between and within days. On the basis of the
effort-recovery model (Meijman & Mulder, 1998), we hypothesized that a person’s relative day-specific
state of being recovered (i.e., feeling refreshed) in the morning is positively related to subsequent
day-level flow experiences during work. Taking into account research on circadian rhythms of human
functioning, we further hypothesized that flow experiences follow a U-shaped pattern within the working
day and that feeling recovered will affect this pattern. One hundred and twenty-one software profes-
sionals provided data on recovery at the start of the working day and on flow at 3 occasions during the
day, for a period of 5 consecutive working days (resulting in 493 day-level and 1,340 occasion-level data
points). Three-level multilevel models showed that relative day-level state of being recovered predicted
day-level flow experiences in the hypothesized direction. The data did not support a general curvilinear,
U-shaped main effect of flow experiences within the day. However, people in a relatively high state of
being recovered in the morning experienced the predicted U-shaped pattern, whereas poorly recovered
people experienced a gradual decrease in flow experiences over the course of the working day. This study
emphasizes the importance of recovery during nonwork time for flow experiences within the entire
working day, thereby extending research on task characteristics with personal resources when examining
predictors of flow.
Keywords: recovery, flow experiences, curvilinear effects, experience sampling study
Flow is an engrossing and enjoyable state of mind that occurs
when people feel optimally challenged and are fully absorbed in
their current activity (e.g., Csikszentmihalyi, 1999). Work-related
flow has been shown to be related to positive, organizationally
relevant outcomes (e.g., self-efficacy; Salanova, Bakker, & Llo-
rens, 2006; job performance under certain conditions, Demerouti,
2006;Eisenberger, Jones, Stinglhamber, Shanock, & Randall,
2005;Kuo & Ho, 2010), and even to cross over between people
(Bakker, 2005). However, as we know from other areas in our
daily lives, good things usually do not last forever. The same in
fact applies here: Flow is considered to be a fragile state and a
short-term peak experience (Bakker, 2005,2008;Csikszentmi-
halyi, 1996). In support of this notion, research has shown that a
large amount of variance in flow accrues from daily and momen-
tary characteristics (Ceja & Navarro, 2011;Fullagar & Kelloway,
2009;Nielsen & Cleal, 2010). Moreover, the literature on circa-
dian rhythms in humans suggests that the state of flow does not
vary completely at random, but may exhibit a curvilinear,
U-shaped pattern within the working day (e.g., Van Dongen &
Dinges, 2000).
To gain a deeper understanding of this within-person variability
in flow, it is imperative to examine predictors that vary within
similar time frames. In doing so, previous studies have focused on
fluctuations in daily or momentary task characteristics and activ-
ities to predict fluctuations in daily or momentary work-related
flow experiences (Fullagar & Kelloway, 2009;Nielsen & Cleal,
2010). However, by definition flow is a demanding and challeng-
ing state (e.g., Csikszentmihalyi, Abuhamdeh, & Nakamura,
2005), suggesting that people need personal, energetic resources in
This article was published Online First February 10, 2014.
Maike E. Debus, Department of Psychology, University of Zurich,
Zurich, Switzerland; Sabine Sonnentag, Department of Psychology, Uni-
versity of Mannheim, Mannheim, Germany; Werner Deutsch, Institute of
Psychology, Technical University of Braunschweig, Braunschweig, Ger-
many; Fridtjof W. Nussbeck, Department of Psychology, Bielefeld Uni-
versity, Bielefeld, Germany.
Werner Deutsch, who contributed to developmental psychology through
his research and teaching, passed away on October 12, 2010. He was
involved in the conceptualization of this study.
Correspondence concerning this article should be addressed to Maike E.
Debus, Arbeits- & Organisationspsychologie, Universität Zürich, Binz-
mühlestrasse 14/12, 8050 Zürich, Switzerland. E-mail: m.debus@
psychologie.uzh.ch
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Applied Psychology © 2014 American Psychological Association
2014, Vol. 99, No. 4, 713–722 0021-9010/14/$12.00 DOI: 10.1037/a0035881
713
order to experience this pleasurable state of mind. For this reason,
the current study expands previous knowledge by investigating a
person’s state of being recovered (i.e., feeling refreshed; Binnew-
ies, Sonnentag, & Mojza, 2009) as a personal resource, and its
relation with daily flow experiences, above and beyond the impact
of task type.
More precisely, we connect the literatures on recovery and flow
to examine whether a person’s relative state of being recovered at
the beginning of a working day relative to other days (i.e.,
between-day variation) will (a) be related to that person’s average
degree of flow experiences that day and (b) affect the time course
of flow experiences within that same day. Hence, this study
focuses on deviations of a person’s day-specific state of being
recovered relative to his or her average; by doing so, we are able
to model within-person fluctuations by simultaneously excluding
all effects that would be due to differences between persons
(Bolger, Davis, & Rafaeli, 2003;Ohly, Sonnentag, Niessen, &
Zapf, 2010).
As such, this study will lead to a better understanding of daily
preconditions enabling flow at work, thereby contributing to the
literature in several ways. First, previous research has analyzed
how a workday as a whole may benefit from being well recovered
(i.e., between-day variations; Binnewies et al., 2009;Sonnentag,
Mojza, Demerouti, & Bakker, 2012)—a principle that the current
research applies to the study of flow. More important, however,
this study extends previous research in that we add a dynamic
perspective by examining potential benefits of recovery that unfold
during the day. By connecting the literature on flow with the
literatures on recovery and biological, circadian processes, we are
able to explain fluctuations in flow within the course of the
working day. Moreover, we examine the state of being recovered
as a day-specific personal resource. In doing so, our study provides
insight for individuals and managers about to what extent flow
experiences can be fostered through personal resources, beyond
focusing on strategies that target work and task design issues.
The State of Being Recovered as a Predictor of
Variations in Flow Between Days
In general, flow has been described as consisting of nine ele-
ments (e.g., Csikszentmihalyi, 1975/2000,1990): (a) a balance
between high skills and high challenges (i.e., skill–challenge bal-
ance); (b) clear goals; (c) clear and immediate feedback; (d)
concentration; (e) a merging of action and awareness, meaning that
the activity becomes almost automatic; (f) a sense of control over
the action; (g) a feeling that the activity is intrinsically rewarding;
(h) the loss of self-consciousness; and (i) the transformation of
time, that is, hours seem to pass by like minutes.
Besides the fact that flow is a state of deep enjoyment and
total immersion in a task, the literature suggests that it is also a
state that requires initial energy or resource expenditure (Csik-
szentmihalyi, 1996;Csikszentmihalyi et al., 2005;Nakamura &
Csikszentmihalyi, 2002). On the basis of interviews with sur-
geons, chess players, and rock climbers, Csikszentmihalyi
(1996, p. 110) concluded that flow did not occur during unde-
manding activities but when being engaged in “difficult activ-
ities that stretched the person’s capacity and involved an ele-
ment of novelty and discovery.” During flow, people are
successfully applying above-average skills to meet the demands
of above-average tasks. In other words, the extent to which a
person experiences flow on a certain day depends not only on
characteristics of the task or the activity he or she is engaging
in (e.g., Fullagar & Kelloway, 2009;Nielsen & Cleal, 2010),
but also on the extent of personal, energetic resources that the
person is able to invest on that day.
In the current study, we use the effort-recovery model (ERM;
Meijman & Mulder, 1998), a resource-oriented approach, as an
overall framework when examining work-related flow. The ERM
theorizes that people need to invest resources in order to fulfill
their work demands. Investing these resources, however, leads to
load reactions and resource depletion (see also Hobfoll, 1989).
During the subsequent recovery process (as should happen during
leisure time), demands are reduced and invested resources are
replenished and restored. Related to this, Binnewies et al. (2009)
argued that a successful recovery process results in the subjective
state of being recovered, that is, a state of feeling refreshed and full
of energy. This state of being recovered in the morning of a
specific working day is a consequence of the previous rest period,
indicating that energetic and affective resources have been replen-
ished. At the same time, it is the initial state that makes people well
equipped for the following working period (Sonnentag & Zijlstra,
2006).
Empirical research has already supported the assumption that
a person’s relative day-specific state of being recovered in the
morning predicts performance-related behaviors on the same
day, such as task performance, personal initiative, and organi-
zational citizenship behavior (Binnewies et al., 2009). Further-
more, a recent study (Sonnentag et al., 2012) has also demon-
strated a link with daily work engagement as a state that
captures a person’s subjective perception of his or her work. As
such, these findings make clear that the extent of being well
recovered not only contributes to behavior-related indicators,
but is also reflected in the way a person experiences the
workday; that is, how he or she is “living through” it (Weiss &
Rupp, 2011, p. 87). Hence, in an attempt to extend previous
findings in the context of subjective experiences at work, we
propose that when a person comes to work in a more well-
recovered state than on average, he or she will be more mentally
and physically refreshed and will experience a relatively high
level of energy. This state of recovery and high energy will
enable the person to invest his or her resources into demanding
tasks of above-average difficulty and to become fully immersed
and concentrated on these tasks. On these days, as a conse-
quence, the individual will be able to experience the highly
pleasant state of flow.
Generally, we argue that the postulated effect of being re-
covered will emerge above and beyond the effects of activity
type. As mentioned previously, type of activity has been shown
to be a crucial predictor of flow experiences (e.g., Nielsen &
Cleal, 2010). However, we argue that task type is not the only
route to flow, but that personal resources, especially being
recovered, matter as well. In sum, our first hypothesis states:
Hypothesis 1: A person’s relative day-level state of being
recovered is positively related to his or her day-level flow
experiences (i.e., the person’s average flow levels on that
same day), over and above the effects of activity type.
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714 DEBUS, SONNENTAG, DEUTSCH, AND NUSSBECK
The State of Being Recovered as a Predictor of
Within-Day Fluctuations of Flow
As mentioned in the introduction, flow has also been shown to
exhibit substantial variation within days (Ceja & Navarro, 2011).
Research on circadian rhythms in humans (e.g., Van Dongen &
Dinges, 2000) suggests that over different time points (i.e., occa-
sions) within the working day, this variation does not occur com-
pletely at random, but may exhibit a systematic, U-shaped pattern.
Specifically, it has been shown that several measures of alertness
and cognitive functioning temporarily decline around noontime
and then rise again (e.g., Hildebrandt, Rohmert, & Rutenfranz,
1974;Monk, 2005;Rodríguez-Sánchez, Schaufeli, Salanova, Ci-
fre, & Sonnenschein, 2011), which is mirrored by body tempera-
ture, hormone levels, and other physiological processes (e.g.,
Monk, 2005;Van Dongen & Dinges, 2000). This particular
U-shaped pattern in cognitive and physiological functioning is
commonly known as the “postlunch dip” (e.g., Blake, 1967;
Colquhoun, 1971;Monk, 2005) and is mostly an effect of the
circadian clock, a tiny cluster of about 10,000 nerve cells located
in the suprachiasmatic nucleus in the hypothalamus (e.g., Klein,
Moore, & Reppert, 1991). As flow is a state that goes along with
alertness and optimal cognitive functioning (e.g., Csikszentmi-
halyi, 1990,1996), it is reasonable to assume that flow may follow
a curvilinear pattern within the working day. Again, we propose
that this pattern will emerge above and beyond the impact of task
type. Hence, our second hypothesis states:
Hypothesis 2: A person’s flow levels exhibit a curvilinear (i.e.,
U-shaped) pattern over different occasions within the working
day, over and above the effects of activity type.
Nonetheless, research has shown that this curvilinear pattern is
not as robust and universal as one might think (e.g., Lack &
Lushington, 1996;Van Dongen & Dinges, 2000). In fact, it ap-
pears that the postlunch dip does not necessarily occur in all
individuals or in all cognitive and performance measures likewise
(e.g., Carrier & Monk, 2000;Lack & Lushington, 1996;Smith &
Maben, 1993;Van Dongen & Dinges, 2000). Moreover, studies
have shown that certain factors can change the shape of the curve
(e.g., Smith & Maben, 1993;Van Dongen & Dinges, 2000). As an
example, Hildebrandt et al. (1974) showed that the extent of train
conductors’ early afternoon peak in error frequency was dependent
on their accumulated tiredness.
In line with these findings, researchers have suggested that in
addition to the aforementioned circadian process, which affects
human functioning by driving wakefulness and alertness, there is
a parallel homeostatic process. This parallel process impairs hu-
man functioning by driving a person’s need for sleep and recovery
(e.g., Borbély, 1982;Edgar, Dement, & Fuller, 1993;Van Dongen
& Dinges, 2000). Beginning with a person’s awakening, the ho-
meostatic process gradually increases over the day. Hence, this
reasoning suggests that stimuli and personal states relating to
resource availability and fatigue may heighten one process relative
to the other (cf. Van Dongen & Dinges, 2000).
On the basis of the above arguments, a person’s recovery level
appears to be an important boundary condition for the pattern of
cognitive functioning and subjective states of mind within the
working day. Hence, besides the effects on day-level flow pro-
posed above, being better recovered in the morning should predict
the specific time course of flow experiences within the working
day. In particular, we propose that being poorly recovered consti-
tutes an additional burden for the organism during the entire
working day (e.g., Van Dongen & Dinges, 2000). Hence, when
people are poorly recovered, the homeostatic process should more
strongly interfere with the curvilinear pattern of flow experiences
(i.e., the circadian process). As a consequence, the curvilinear
pattern should be weaker, that is, emerge less markedly among
those people. In extreme cases, a person’s functioning might
become dominated by the homeostatic process. Again, we propose
that the hypothesized interaction effect emerges beyond the impact
of task type. Thus, our third hypothesis states:
Hypothesis 3: A person’s relative day-level state of being
recovered predicts the pattern of flow experiences over the
course of the working day: The U-shaped pattern of flow
experiences should be stronger on days with relatively higher
recovery levels compared to days with relatively lower recov-
ery levels, over and above the effects of task type.
Method
Sample and Procedure
The study was conducted with programmers, software engi-
neers, and web designers. We first approached software companies
by phone and informed them about the study. In total, 219 people
expressed interest in participating, but only 121 completed the
study with a sufficiently high response rate (specific criteria for
data inclusion are reported below).
Data were collected by one general survey before, and by four
surveys on each day during the study week (i.e., Monday to
Friday). We administered all surveys on the Internet by sending
participants personalized e-mail links to the questionnaires. Par-
ticipants received the link to the general survey immediately after
their registration; we sent links to the four surveys for each day
(i.e., one prework questionnaire and three questionnaires during
work) during a 5-day interval (Monday to Friday). This resulted in
20 daily surveys. We used experience sampling methodology
(ESM) to assess flow with the three questionnaires during work.
ESM tries to sample randomly from a person’s everyday experi-
ences (e.g., Csikszentmihalyi & Rathunde, 1993;Hormuth, 1986)
and is often used in studies assessing flow experiences (e.g.,
Csikszentmihalyi & Figurski, 1982). To be able to map measure-
ment occasions with participants’ individual circadian rhythms, we
asked participants when they usually start and finish work (in the
general survey). Accordingly, we sent ESM signals with links to
the daily questionnaires at three distinct time points during a
typical workday, approximating individual circadian rhythms. De-
pending on the time participants started work, the signal for
Occasion 1 was sent during the morning hours (between 9:06 and
11:44 a.m.), for Occasion 2 around noontime (between 11:18 a.m.
and 2:35 p.m.), and for Occasion 3 in the afternoon (between 1:15
and 5:53 p.m.). Generally, we sent signals to the ESM question-
naires at least 55 min after the previous one (but not more than 5
hours later).
Before analyzing the data, we deleted all questionnaires that
were answered less than 30 min after the previous one and ques-
tionnaires that had been answered before the signal (indicating that
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715
RECOVERY AND FLOW AT WORK
participants were catching up a previously missed questionnaire).
Furthermore, we only included participants in the final analysis
who provided complete data sets from at least 3 days and had only
missed one of the three questionnaires during work. Among the
121 persons included in the final analyses, 18.2% were female.
Participants’ age ranged from 21 years to 63 years (M35.5,
SD 9.7); mean professional experience was 10.1 years (SD
8.9). Furthermore, 61.2% of participants held a university degree,
10.7% had completed 2- to 3-year professional training and ob-
tained an additional professional degree, 18.2% had completed 2-
to 3-year professional training, and 9.9% did not hold any profes-
sional degree. On average, participants worked 5.3 days (SD
0.6) a week and completed 8.7 hr (SD 10.1) of overtime work
per week. Based on the timestamps that were collected in tandem
with the surveys, we calculated average answering delays to the
three questionnaires during work that measured flow, which were
9 min 53 s (SD 8 min 11 s) for the first, 10 min 29 s (SD 7
min 45 s) for the second, and 10 min 15 s (SD 8 min 5 s) for the
third questionnaire.
Measures
For each participant, we collected data at three levels of analy-
sis, namely the level of the person (Level 3; i.e., variables that vary
between persons), the day (Level 2, i.e., variables that vary be-
tween days), and the occasion (Level 1, i.e., variables that vary
within days). We assessed occasion-level variables (i.e., flow
experiences and type of activity) with the three questionnaires
during work each day. Day-level state of being recovered was
assessed with the daily prework questionnaire, and all person-level
variables (i.e., general level of flow experiences and all person-
level control variables) were assessed with the general survey.
Occasion-level measures (i.e., Level 1). Occasion-level flow
was assessed with nine items from the Flow Short Scale by
Rheinberg, Vollmeyer, and Engeser (2003; for the English version,
see Rheinberg, 2008; for studies published in English using this
measure, see Engeser & Rheinberg, 2008;Schüler & Brunner,
2009) to be answered on a 7-point Likert scale, ranging from 1
not true at all to7very true. Participants were instructed to
report their flow experience at the present moment. Sample items
were “At the moment, I feel just the right amount of challenge”
and “At the moment, I am totally absorbed in what I am doing.”
Cronbach’s alpha ranged from .85 to .95 over all measurements.
Occasion number was coded as 1, 2, or 3 for every day and
every participant, depending on when he or she received the
respective signal.
Day-level measures (i.e., Level 2). State of being recovered
in the morning was assessed with the four-item scale by Sonnentag
and Kruel (2006). On a 5-point Likert scale from 1 not true at
all to5very true, the scale measures the degree to which a
person feels recovered in the morning. A sample item was “This
morning, I feel well rested.” Cronbach’s alpha ranged from .91 to
.94 over the 5 days.
Measures in the general survey (i.e., Level 3). General level
of flow was assessed with the same nine items used for the
day-level surveys. Participants were instructed to indicate how
much the items reflect their experience of flow at work in general.
Cronbach’s alpha was .89.
Control Variables
To rule out alternative interpretations, we assessed a number of
control variables. To control for type of activity at the occasion
level, we asked participants to write down their current activity
when receiving the ESM signals. We then coded three activity
categories, namely programming activities (programming, fixing
bugs, etc.), administrative activities (bookkeeping, writing in-
voices, etc.), and personal activities (taking a break, chatting with
a colleague, etc.). Activities were then dummy coded, taking the
category “programming activities” as the reference category (both
dummies, d1 and d2, were coded as 0). “Administrative activities”
were coded d1 1 and d2 0, and “personal activities” were
coded d1 0 and d2 1.
At the person level, we controlled for person-level flow
experiences because they may impact the respective day-level
flow score (cf. Petrou, Demerouti, Peeters, Schaufeli, & Het-
land, 2012). On the basis of previous research concerning
facilitators and inhibitors of flow (e.g., Demerouti, 2006;Tri-
emer, 2001), we also controlled for general time pressure (Sem-
mer, 1984) and job control (Zapf, 1993), yielding Cronbach’s
alphas of .86 and .80, respectively. Finally, we also controlled
for age, days of work per week, start of work, end of work, and
overtime work (each measured with one item) at the person
level and for the respective day of the week at the day level
(e.g., Daniels & Harris, 2005).
Analytical Strategy
Due to the nested data structure (Level 1: occasions; Level 2:
days; Level 3: persons), we applied multilevel modeling using
the HLM 6.0 software (Raudenbush, Bryk, Cheong, Congdon,
& du Toit, 2009). In line with methodological recommendations
regarding diary studies, we centered person-level variables at
the grand mean and day-level variables at the respective person
mean (in order to remove all variance that is due to between-
person differences; Ohly et al., 2010;Petrou et al. 2012). We
recoded the occasion numbers prior to entering them into the
regression equation (Occasion 1 0, Occasion 2 1, Occasion
32, and 0, 1, and 4 for the quadratic trend), suggesting that
the intercept denotes the starting level in flow at the first
measurement occasion (e.g., Hox, 2010). To test the proposed
main effect and interaction hypotheses, we followed the rec-
ommendations by Aiken and West (1991) and compared a set of
seven nested models by means of a likelihood ratio statistic.
Unless otherwise noted, we entered all variables as fixed effects
into the regression equation. We chose this approach in order to
(a) keep the models as parsimonious as possible (e.g., Hox,
2010) and (b) avoid estimation problems specifically at Level 2
because there are only three measurement occasions at Level 1
for every day (for an analogous case in dyadic data analysis, see
Kenny, Kashy, & Cook, 2006). The literature suggests time
point variables to be entered along with the control variables in
order to establish a baseline for calculating any further effect
sizes (Bliese & Ployhart, 2002;Hox, 2010). Thus, our analysis
first provides a test for Hypothesis 2, and then for Hypothesis
1, followed by the test for Hypothesis 3.
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716 DEBUS, SONNENTAG, DEUTSCH, AND NUSSBECK
Results
Descriptive Statistics
Means, standard deviations, and zero-order correlations are dis-
played in Tables 1 and 2. We started by examining the variance
parts on each level of analysis by inspecting the null model,
including only the intercept (Bliese, 2000;LeBreton & Senter,
2008). For occasion-level flow experiences, person-level variance
(Level 3) accounted for 44.5%, day-level variance (Level 2) ac-
counted for 8.3%, and occasion-level variance (Level 1) accounted
for 47.2% of the total variance.
Hypotheses Testing
Table 3 presents the results of the multilevel regression analysis
used to test our hypotheses. The table also includes McFadden’s R
2
(McFadden, 1974), a conventional effect size measure for maxi-
mum likelihood procedures. In Models 1 and 2, we entered the
respective control variables, with general level of flow experiences
being a significant predictor (t9.98, p.001) in Model 1, and
both task-related dummy variables emerging as significant predic-
tors of flow experiences in Model 2 (t
d1
⫽⫺4.12, t
d2
⫽⫺3.41,
ps.001). The finding suggests that flow occurs more often in
programming activities than in administrative and personal activ-
ities. Model 3 included occasion number (to control for a potential
linear trend; Aiken & West, 1991), which emerged as a significant
predictor (t⫽⫺2.76, p.01). Due to the later test of the
cross-level interaction with day-level state of being recovered, we
entered linear occasion number with a random slope. The chi-
square test of residuals performed by the HLM 6.0 software
indicated that the respective random slope varied significantly
between days,
2
(492) 592.34, p.01. Contrary to Hypothesis
2, quadratic occasion number did not yield a significant effect (t
1.46, ns) in Model 4. Hence, flow experiences did not exhibit a
general curvilinear pattern within the working day. Again, due to
the later cross-level interaction with day-level state of being re-
covered, we entered quadratic occasion with a random slope. This
random slope was not significant,
2
(408) 447.88, p.09,
based on the traditional 5% significance level.
1
In line with Hy-
pothesis 1, day-level state of being recovered significantly pre-
dicted day-level flow experiences in Model 5 (t3.70, p.001).
In Model 6, the Linear Occasion Day-Level State of Being
Recovered interaction (which served as a further control term;
Aiken & West, 1991) was not significant (t0.89, ns).
Because interaction effects can occur without a significant main
effect of the respective predictors (Cohen, Cohen, West, & Aiken,
2003; see also Goltz & Smith, 2010), we proceeded with testing
the Quadratic Occasion Day-Level State of Being Recovered
interaction in Model 7 (for the same approach, see Baer & Old-
ham, 2006). Moreover, cross-level interactions may occur also in
cases where no random slopes can be found (see Snijders &
Bosker, 2012; see also Bliese & Jex, 2002;Ilies, Scott, & Judge,
2006;La Huis & Ferguson, 2009). Thus, we tested the interaction
between quadratic occasion and day-level state of being recovered,
which proved to be statistically significant (t2.26, p.05).
2
An inspection of the interaction plot (see Figure 1) revealed that
the shape of the curve concerning high and low relative state of
being recovered looked somewhat different from what we had
hypothesized, however. In the case of a high relative state of being
recovered, participants experienced the hypothesized U-shaped
pattern with a dip in flow experiences around noontime and a
subsequent uplift. However, persons with a low relative state of
being recovered experienced no Ushape at all, but reported a
relatively steady level of flow until the second measurement oc-
casion (though not reaching the flow levels of the relatively
well-recovered participants) and then a decline. Thus, Hypothesis
3 was not supported.
3
Discussion
In line with the ERM (Meijman & Mulder, 1998), this
experience-sampling study showed that the more a person felt
recovered in the morning of a specific day (relative to his or her
average of feeling recovered), the more flow he or she experienced
on average during work that day. Although the data did not support
a curvilinear main effect of flow experiences within the working
day (above the effect of task type), we could show that a person’s
within-day course of flow experiences was affected by his or her
relative state of being recovered. More specifically, when being
well recovered, people experienced the predicted U-shaped pattern
of flow experiences across the working day, while we found a
different shape for relatively poorly recovered people (i.e., they
started low, stayed low around noontime, and experienced a de-
crease in flow in the afternoon).
As mentioned before, the shape of the flow patterns looked
somewhat different from our initial predictions, as the U-shaped
pattern only emerged among well-recovered persons. In fact, this
finding might be interpreted on the basis of the two-process model
(e.g., Van Dongen & Dinges, 2000) that we introduced earlier. Put
differently, when people are well recovered, their flow experiences
follow a curvilinear pattern, reflecting the previously mentioned
circadian process. However, when being poorly recovered, it ap-
1
Readers might be interested in whether the linear occasion term and the
quadratic occasion term also vary between persons. For this reason, we
added random slopes to the respective predictors in Model 3 (for the linear
occasion term) and in Model 4 (for the quadratic occasion term). Both
slopes did not vary significantly between persons:
2
(120) 133.99, ns,
for the linear occasion term and
2
(120) 128.85, ns, for the quadratic
occasion term. For this reason and to keep the models as parsimonious as
possible, we again removed these random slopes from Models 3 and 4 and
all further models of our analysis.
2
Due to the restricted number of Level 1 units (three measures per day),
only a restricted number of Level 2 random effects can be estimated
(Kenny et al., 2006). Thus, we excluded the Level 2 random intercept from
Model 4 onward. However, to control for spurious effects, we also ran all
models with a random Level 2 intercept, but without a random slope of
occasion number. The analyses yielded the same results; that is, the
Quadratic Occasion Day-Level State of Being Recovered term was a
significant predictor of occasion-level flow experiences (t2.46, p
.05).
3
Following the suggestion of one reviewer, we tested an alternative set
of models in which we entered linear occasion and quadratic occasion (in
Models 2 and 3, respectively) before entering the dummy-coded activity
types (in Model 4). In these models, both linear occasion (t⫽⫺2.90, p
.01) and quadratic occasion (t1.96, p.05) were significant. In line
with Hypothesis 2, this finding suggests a curvilinear pattern of flow over
the course of the working day. After entering the two dummy variables,
however, quadratic occasion did not yield a significant effect any longer.
Thus, the specific U-shaped pattern of flow seems to be accounted for by
activity type.
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717
RECOVERY AND FLOW AT WORK
pears that the parallel homeostatic process dominates over the
circadian process, thereby leading to a more or less gradual de-
crease of flow experiences during the day. Hence, one may argue
that a certain level of recovery is essential for the “natural”
curvilinear pattern to emerge at all (for a similar argument on
workload and fatigue, see Grech, Neal, Yeo, Humphreys, & Smith,
2009).
In sum, our study is one of the first (cf. Demerouti, Bakker,
Sonnentag, & Fullagar, 2012) to connect the literature on flow at
work with the literatures on recovery and circadian processes. As
such, our study extends previous findings in several ways. First, as
mentioned above, we investigated a personal resource at the day-
level instead of focusing on situational aspects, as has been done
previously (Fullagar & Kelloway, 2009;Nielsen & Cleal, 2010).
Moreover, we applied the perspective of how a workday as a
whole may benefit from recovery (cf. Binnewies et al., 2009)tothe
context of flow. Even more important, we clearly extended this
literature by demonstrating how being recovered can affect the
time course of flow as a subjective, work-related perception over
the course of the day. Although the respective effects sizes were
relatively small in size (which might in part be due to the fact that
we applied a rather conservative testing strategy including several
control variables), theoretically, our findings suggest that the cir-
cadian and homeostatic processes, and the effect of recovery on
them, not only are relevant for flow, but might generalize to a more
broader range of on-the-job experiences—and ultimately also per-
formance outcomes.
There are two findings relating to the linear and curvilinear
effect of occasion that merit further consideration. First, although
the linear effect of occasion merely served as a control term in our
analysis (Aiken & West, 1991), the data revealed that its slope
significantly varied between days, suggesting that the strength of
this decline is affected by higher level variables. Although day-
level recovery failed to reach significance as a moderating vari-
able, future research might examine other variables (e.g., sleep
indicators) that particularly impact this decline. Second, our data
revealed that the slopes of both the linear and quadratic occasion
numbers did not vary between persons. Put differently, it appears
that the overall pattern of flow is relatively uniform across indi-
viduals.
Limitations, Directions for Future Research, and
Practical Implications
Our study has some limitations. First, as we only used self-
report measures, we cannot rule out that common method variance
(Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) has inflated the
relationships. However, self-report measures are most likely the
best way to catch subjective states of feelings that were investi-
gated in this study. Furthermore, we obtained our focal constructs
at different times (i.e., in the morning and at three occasions during
work) and in a “right now” frame of reference, which is more
robust to avoiding common method bias than concurrent, retro-
spective measurements (see Bolger et al., 2003).
Second, our sample of software professionals was highly spe-
cific, which might limit the generalizability of our findings. We
chose this particular sample because the experience of flow is very
well documented among programmers and software engineers
Table 1
Means, Standard Deviations, and Zero-Order Correlations of All Person- and Day-Level Variables
Variable MSD 12345678910
1. Day-level state of being recovered 3.67 0.93 .40
ⴱⴱ
2. Day-level flow experiences 5.14 0.91 .50
ⴱⴱ
3. General level of flow experiences 4.97 0.96 .40
ⴱⴱ
.69
ⴱⴱ
4. Time pressure 2.85 0.93 .03 .15 .16 —
5. Job control 4.16 0.62 .06 .26
ⴱⴱ
.34
ⴱⴱ
.23
6. Age 35.45 9.73 .05 .05 .19
.21
.36
ⴱⴱ
7. Start of work
a
1.36 0.48 .06 .11 .09 .15 .12 .04
8. End of work
b
1.60 0.49 .11 .03 .18
.32
ⴱⴱ
.37
ⴱⴱ
.21
.24
ⴱⴱ
9. Days of work per week 5.31 0.64 .04 .01 .12 .29
ⴱⴱ
.41
ⴱⴱ
.21
.11 .34
ⴱⴱ
10. Overtime work 8.67 10.09 .06 .09 .12 .49
ⴱⴱ
.11 .12 .04 .33
ⴱⴱ
.32
ⴱⴱ
Note. Correlations below the diagonal are person-level correlations (N121), with day-level measures being aggregated to the person level. Correlations
above the diagonal are day-level correlations (N493), with occasion-level flow (N1,340) being aggregated to the day level.
a
1before 9 a.m.; 2 between 9 a.m. and 12 noon.
b
1between 3 and 6 p.m.; 2 after 6 p.m.
p.05.
ⴱⴱ
p.01 (two-tailed).
Table 2
Means, Standard Deviations, and Zero-Order Correlations of All Occasion-Level Variables
Variable MSD 123 45
1. Occasion number
2. Quadratic occasion number (i.e., occasion
2
).99
ⴱⴱ
3. Occasion-level flow 5.14 1.08 .06
.06
4. Occasion-level activities Dummy 1 0.64 0.48 .04 .04 .11
ⴱⴱ
5. Occasion-level activities Dummy 2 0.09 0.28 .01 .03 .05 .41
ⴱⴱ
p.05.
ⴱⴱ
p.01 (two-tailed).
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
718 DEBUS, SONNENTAG, DEUTSCH, AND NUSSBECK
(e.g., Roque, 2011; see also Chen, 2006). Related to this, however,
it might have been helpful to use a more fine-grained measure of
task type in the current study (instead of only differentiating
between three categories). Hence, it might be the case that results
differ for different samples and more fine-grained operationaliza-
tions of task type.
Third, although we demonstrated systematic patterns of flow
experiences over the course of the working day, our occasion-
related study design is a first but rough attempt to model the
proposed within-day processes. For this reason, future research
might benefit from sampling more occasions within the day and/or
sampling richer person-specific information. Apart from a person’s
start of work (which we took into account), circadian processes
may also be affected by several additional variables, such as hours
since waking up (e.g., Van Dongen & Dinges, 2000), food intake
(e.g., Karnani et al., 2011), and activities that were pursued before
coming to work. Hence, future research might model circadian
(work-related) processes more accurately by taking these variables
into account.
For future research dealing with recovery, flow, and other subjec-
tive and behavioral processes at work, it appears promising to take a
greater “magnifying lens” as has been done previously. Generally,
models that focus on the day level (i.e., diary studies; e.g., Mehl &
Conner, 2012) are currently experiencing a strong upsurge. However,
it might be helpful to examine whether certain states also fluctuate
within even shorter time frames. Besides the fact that the lowest level
of analysis also includes error variance (e.g., Bryk & Raudenbush,
1992), our data showed that the majority of variance in flow actually
accrued from the occasion level. Thus, ignoring such lower levels
might imply that researchers are only tapping the tip of the iceberg for
some psychological processes.
Table 3
Multilevel Models Predicting Occasion-Level Flow
Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Level 3 predictors (t)
Age 1.38 1.19 1.16 1.10 1.09 1.08 1.07
Time pressure 1.08 1.07 1.12 1.18 1.18 1.18 1.17
Job control 1.62 1.54 1.53 1.43 1.41 1.41 1.40
Days of work per week 1.30 1.38 1.33 1.21 1.22 1.21 1.22
Overtime work 0.61 0.44 0.43 0.50 0.52 0.52 0.52
Start of work 0.09 0.13 0.08 0.07 0.06 0.07 0.07
End of work 1.68 1.49 1.52 1.49 1.46 1.47 1.45
General level of flow experiences 9.98
ⴱⴱⴱ
9.82
ⴱⴱⴱ
9.84
ⴱⴱⴱ
9.86
ⴱⴱⴱ
9.89
ⴱⴱⴱ
9.89
ⴱⴱⴱ
9.91
ⴱⴱⴱ
Level 2 predictors (t)
Day of the week 1.00 0.99 0.69 0.55 0.53 0.55 0.56
Day-level state of being recovered 3.70
ⴱⴱⴱ
1.98
2.72
ⴱⴱ
Level 1 predictors (t)
Dummy 1 (activity type) 4.12
ⴱⴱⴱ
3.99
ⴱⴱⴱ
3.85
ⴱⴱⴱ
3.75
ⴱⴱⴱ
3.74
ⴱⴱⴱ
3.74
ⴱⴱⴱ
Dummy 2 (activity type) 3.41
ⴱⴱⴱ
3.41
ⴱⴱⴱ
2.88
ⴱⴱ
2.80
ⴱⴱ
2.82
ⴱⴱ
2.84
ⴱⴱ
Occasion 2.76
ⴱⴱ
2.18
2.24
2.24
2.23
Occasion
2
1.46 1.52 1.53 1.53
Cross-level interactions (t)
Occasion Day-Level State of
Being Recovered 0.89 1.90
Occasion
2
Day-Level State of
Being Recovered 2.26
2log-likelihood 3,379.20 3,359.53 3,347.04 3,354.72 3,341.22 3,340.44 3,335.35
Differential 2log 89.29
ⴱⴱⴱ
19.67
ⴱⴱⴱ
12.50
ⴱⴱ
7.68
ⴱⴱ
13.50
ⴱⴱⴱ
0.78 5.09
df 9231111
McFadden’s pseudo-R
2
.025 .031 .035 .033 .037 .037 .038
McFadden’s pseudo-R
2
.006 .004 .000 .004 .000 .001
Note. Data denote tvalues. Occasion linear occasion; Occasion
2
quadratic occasion.
p.05.
ⴱⴱ
p.01.
ⴱⴱⴱ
p.001.
4
5
6
7
noisaccO hgiHnoisaccO woL
Flow
Low sta te of b eing recovered
High state of being recovered
Occasion 1 Occasi on 2 Occasion 3
Flow experiences
Figure 1. Interaction of quadratic occasion number by day-level state of
being recovered on occasion-level flow experiences.
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719
RECOVERY AND FLOW AT WORK
Finally, future research might also examine in more detail the
exact mechanism and relevant boundary conditions of the
recovery–flow link. Although we have shown that recovery is
related to flow, the translation of available resources into flow
experiences might happen via intermediary variables, such as
effort or resource allocation (e.g., Pierro, Kruglanski, & Higgins,
2006), and may be enhanced or buffered by factors such as
intrinsic motivation originating from task characteristics. Hence,
future studies might delve more deeply into the mechanisms un-
derlying the relationship between recovery and flow.
4
Practically speaking, our study highlights that to experience
flow it is important to recover well during nonwork time. Research
suggests that leisure time experiences such as mental detachment
and relaxation help people to recover (e.g., Sonnentag, Binnewies,
& Mojza, 2008). Hence, managers should encourage their employ-
ees to disengage from work when at home and to find some way
of relaxing and mentally detaching from work. Moreover, on
low-recovery days, people might be well advised to engage in rest
periods to “reload” their resources (Trougakos, Hideg, Cheng, &
Beal, 2013). By deliberately engaging in breaks, employees might
thus be able to prevent a gradual decrease in flow experiences.
In sum, we showed that day-level resources in the form of
feeling recovered affect not only a person’s average level of flow
that day, but also the particular course of flow experiences within
that same day. In doing so, our study highlights the necessity of
examining variation in flow within the working day and the
importance of moving from task characteristics to personal re-
sources when studying flow at work.
4
We thank an anonymous reviewer for pointing out these ideas.
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Received May 31, 2012
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722 DEBUS, SONNENTAG, DEUTSCH, AND NUSSBECK
... Several authors have also investigated the relation between context, described for instance in terms of time of the day and activity, and perceived engagement and flow [7], [19], [43]. Debus et al. [19], show that during programming activities flow occurs more often than during personal or administrative activities. ...
... Several authors have also investigated the relation between context, described for instance in terms of time of the day and activity, and perceived engagement and flow [7], [19], [43]. Debus et al. [19], show that during programming activities flow occurs more often than during personal or administrative activities. Nielsen et al. [7] demonstrate that planning, problem solving, and evaluation activities are significant predictors of flow, whereas brainstorming activities are not [7]. ...
... We tested the association between each type of context and perceived flow using a linear mixed effects model. We conducted three tests, using the flow score as dependent variable, the context type as independent variable, and the subject as random effect to take in consideration the correlation among samples due to the repeated measurements, as in [19]. We encoded the context variables using the "dummy coding" procedure, which assigns zeros and ones depending on the group membership as in [19]. ...
Conference Paper
Flow is a positive affective state occurring when individuals are fully immersed into an activity. Being in flow during work activities can lead to higher performance and productivity. Despite the importance of flow at work, few approaches have been proposed for its automatic recognition using sensor data and most existing studies are conducted in laboratory settings with simulated work activities. In this paper, we investigate the use of physiological data, collected using wrist-worn devices, combined with context information, obtained through self-reports, to automatically distinguish between low and high levels of flow. We investigate the role of the context for flow perceptions and in its automatic recognition. Further, we compare the performance of several sensor fusion strategies based on shallow and deep learning. To evaluate our approach we use a data set of 390 activities collected during actual work days. Our results show that using raw blood volume pulse, electrodermal activity and the type of activity as input to a sensor-based late fusion approach, implemented using convolutional neural networks, allows to reach a balanced accuracy of 70.93%.
... Flow is always investigated during a certain activity in a certain context, and their variety in the identified studies is large: (a) work-or study-related activities such as work, learning (Peterson and Miller, 2004;Rathunde and Csikszentmihalyi, 2005;Wright et al., 2007;Ceja and Navarro, 2011;Stephanou, 2011;Demerouti et al., 2012;Ryu and Parsons, 2012;Debus et al., 2014;Escartin Solanelles et al., 2014;Hernandez et al., 2014), and teaching (Coleman, 2014), (b) leisure (Rodríguez-Sánchez et al., 2011b), (c) professional dancing (Hefferon and Ollis, 2006;Panebianco-Warrens, 2014), (d) music festivals (Jonson et al., 2015), (e) creative activities such as designing clothes (Min et al., 2015) and visiting arts courses or making art (Reynolds and Prior, 2006;Bass, 2007;Jones, 2013;van der Hoorn, 2015), (f) gaming (e.g., Ivory and Magee, 2009;Thin et al., 2011;Bodzin, 2013, 2016) and several online activities (e.g., Guo and Poole, 2009;Faiola et al., 2013;Hsu et al., 2013;Meyer and Jones, 2013;Wang et al., 2015), (g) research activities (Hudock, 2015;Zha et al., 2015) and information technology use (Pilke, 2004), (h) sports (e.g., Koehn and Morris, 2014;Deol and Singh, 2016;training vs. competition;Swann et al., 2012Swann et al., , 2015a, (i) translation activities (Mirlohi et al., 2011), (j) psychological rehabilitation activities (e.g., Bassi et al., 2012;Nissen-Lie et al., 2015), (k) extreme contexts such as rituals (Lee, 2013) and extreme weather during climbing (Bassi and Delle Fave, 2010) and even (l) firstaid activities, whereby professionals experienced more flow than volunteers ). This large list shows ...
... Having a clear goal (Shin, 2006;Guo and Poole, 2009;van Schaik et al., 2012) and a clear role (Steele and Fullagar, 2009;Panadero et al., 2014) as well as having control (Shernoff et al., 2003) or autonomy (Bakker, 2005) are positively associated with flow. Furthermore, it was found that being prepared (Swann et al., 2012) and being recovered in the morning is positively associated with flow at work during the day (Debus et al., 2014). Smith et al. (2012) found that organizational safety climate is associated with flow. ...
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Flow is a gratifying state of deep involvement and absorption that individuals report when facing a challenging activity and they perceive adequate abilities to cope with it ( EFRN, 2014 ). The flow concept was introduced by Csikszentmihalyi in 1975, and interest in flow research is growing. However, to our best knowledge, no scoping review exists that takes a systematic look at studies on flow which were published between the years 2000 and 2016. Overall, 252 studies have been included in this review. Our review (1) provides a framework to cluster flow research, (2) gives a systematic overview about existing studies and their findings, and (3) provides an overview about implications for future research. The provided framework consists of three levels of flow research. In the first “Individual” level are the categories for personality, motivation, physiology, emotion, cognition, and behavior. The second “Contextual” level contains the categories for contextual and interindividual factors and the third “Cultural” level contains cultural factors that relate to flow. Using our framework, we systematically present the findings for each category. While flow research has made progress in understanding flow, in the future, more experimental and longitudinal studies are needed to gain deeper insights into the causal structure of flow and its antecedents and consequences.
... For example, in a study by Bidee et al. (2017), the authors found that the proportion of withinperson (compared to between-person) variance ranged from 43% to 54% across different motivational variables (i.e., need satisfaction of autonomy, competence and relatedness, and intrinsic motivation). Other studies focusing on similar motivational constructs, such as work engagement, vigour, flow, and self-efficacy, have found similar results (e.g., Casper et al., 2017;Debus et al., 2014;van Woerkom et al., 2016). ...
... Having a wide variety of occupations in Study 2 sample was important mainly for one reason: the majority of studies sampled in Study 1 came from homogeneous occupations (e.g., Almeida et al., 2016;Clinton et al., 2017;Debus et al., 2014) and from employees who often worked for the same company and/or department (e.g., Huang et al., 2015;Ilies et al., 2007;Rudolph et al., 2016). This sample homogeneity in the studies analysed in Study 1 may be reflective of a threat to the ecological validity of the results obtained. ...
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Within-person analysis of data from longitudinal designs has become popular in the field. However, important characteristics of the design can influence that variability. In this paper, we examine how the number of measurement points obtained per participant influences in the within-person variance in work motivation. Using two sources of evidence (a systematic review and an empirical study) we show how the number of assessments substantially influences the amount of within-person variance reaching values of 52%-54% of total variance. We found that a minimum of 25-30 measurement points per participant is required to be rigorous.
... According to flow's attribute description, flow is most likely to occur when the challenge of a situation is balanced with a person's ability to cope with this challenge [20]. Analogously, in work situations, employees experience work-related flow when their work needs are matched with their skills [25]. Work-related flow is a peak experience generated by individuals at work, characterized by clear goals, focus, and matching of skills with challenges [24]. ...
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Objective: Patient safety is a worldwide problem and a focus of academic research. Human factors and ergonomics (HFE) is an approach to improving healthcare work systems and processes. From the perspective of the cognitive ergonomics of HFE, the aim of this study is to improve the flow level, communication skills, and safety attitudes of surgeons through focused-attention meditation (FAM) training, thus helping to reduce adverse clinical events. Methods: In total, 140 surgeons were recruited from three hospitals in China and randomly divided into two groups (FAM group and control group). The FAM group received 8 weeks of FAM training, while the control group was on the waiting list and did not receive any interventions. Three scales (WOLF, LCSAS, and SAQ-C) were used to measure the data of three variables (flow, communication skills, and safety attitude), respectively, at two times, before and after the intervention (pre-test and post-test). The incidence of adverse events during the intervention was also collected for both groups. Results: The ANOVA results showed that all three variables had a significant main effect of time and significant interactions between time and group. The independent-sample T-test results showed that the incidence of adverse events during the intervention was significantly lower in the FAM group than in the control group. Conclusions: The intervention of FAM could significantly improve surgeons’ flow levels, communication skills, and safety attitudes, potentially helping to reduce adverse clinical events.
... In general, performance costs are found in the critical relative to the control group, and this effect is influenced by numerous factors. High calorie diet or high carbohydrate diet are the most important determinants of the effect, and even though there is a great heterogeneity with respect to the particular tasks and performance measures, empirical findings seem relatively robust (Bes et al., 2009;Reyner et al., 2012;Debus et al., 2014). Nevertheless, a detailed comparison of results across studies remains difficult because of the large differences in the use of tasks and performance metrics, as most of them hardly meet current psychometric standards (cf. ...
Article
Full-text available
In this work, we evaluate the status of both theory and empirical evidence in the field of experimental rest-break research based on a framework that combines mental-chronometry and psychometric-measurement theory. To this end, we (1) provide a taxonomy of rest breaks according to which empirical studies can be classified (e.g., by differentiating between long, short, and micro-rest breaks based on context and temporal properties). Then, we (2) evaluate the theorising in both the basic and applied fields of research and explain how popular concepts (e.g., ego depletion model, opportunity cost theory, attention restoration theory, action readiness, etc.) relate to each other in contemporary theoretical debates. Here, we highlight differences between all these models in the light of two symbolic categories, termed the resource-based and satiation-based model, including aspects related to the dynamics and the control (strategic or non-strategic) mechanisms at work. Based on a critical assessment of existing methodological and theoretical approaches, we finally (3) provide a set of guidelines for both theory building and future empirical approaches to the experimental study of rest breaks. We conclude that a psychometrically advanced and theoretically focused research of rest and recovery has the potential to finally provide a sound scientific basis to eventually mitigate the adverse effects of ever increasing task demands on performance and well-being in a multitasking world at work and leisure.
... In this context, we would like to emphasize the role of recovery: it was shown that a dynamic balance between demands and skills, including regular phases of rest, enhances the likelihood of experiencing flow [64]. This concurs with the findings that individuals who were well recovered in the morning experienced flow more often during the day than individuals who had not recovered [65]. At the same time, studies show that individuals who do not detach from work have a higher risk of developing burnout symptoms (for an overview, see Sonnentag & Fritz [66]). ...
Article
Full-text available
Background: In today's performance-oriented society, burnout symptoms, defined as consequences of chronic work stress, are an increasing problem. To counteract this development, the important aims are (1) to find protective and modifiable factors that reduce the risk of developing and harboring burnout symptoms and (2) to understand the underlying mechanisms. A phenomenon potentially furthering both aims is flow experience. Based on the earlier literature, we developed a psycho-physiological "Flow-Burnout-Model", which postulates positive or negative associations between flow and burnout symptoms, depending on the prevailing situational and personal conditions. Methods: To test our Flow-Burnout-Model, we conducted a systematic literature search encompassing flow and burnout symptoms. Eighteen empirical studies met the inclusion criteria and were analyzed. Results: The findings of the systematic review as a whole suggest a negative association between flow and burnout symptoms, both cross-sectional and longitudinal. According to the findings from longitudinal studies, flow can be interpreted as a protective factor against burnout symptoms, and burnout symptoms can be interpreted as a factor inhibiting flow. In our conclusion, we maintain the assumption of a bidirectional association between flow and burnout symptoms in the Flow-Burnout-Model but modify the initially suggested positive and negative associations between flow and burnout symptoms towards a predominantly negative relationship. Discussion: Mindful of the heterogeneous findings of earlier studies, the resulting comprehensive Flow-Burnout-Model will lay the foundations for future hypothesis-based research. This includes physiological mechanisms explaining the relationship between flow and burnout symptoms, and likewise, the conditions of their longitudinal association.
... The experience sampling method was employed to capture shortterm natural state variations in ESB and emotion to find an accurate causal relationship (Debus, Sonnentag, Deutsch, & Nussbeck, 2014). To ensure an adequate representation of relevant individuals, we chose 200 tourists from a tourism destination where environmental sustainability policies are implemented by government officials, and the majority of tourists were familiar with this type of behavior. ...
Article
Full-text available
Environmentally sustainable behavior influences tourists purchasing decisions on whether to engage in sustainable consumption. Based on resource conservation theory and an actor-centric perspective, the current study investigates how and when engaging in environmentally sustainable behavior directly and indirectly affects emotional exhaustion and unneeded behavior. Emotional exhaustion mediates the relationship between environmentally sustainable behavior and unneeded behavior, with moderating effects of environmental concern and perceived environmental knowledge. Using the experience sampling method, data are collected in a10-day questionnaire from 151 tourists in Shanghai. Results from multi-level structural equation modeling show negative impacts of tourist environmentally sustainable behavior on unneeded behavior via emotional exhaustion. The nexus between environmentally sustainable behavior and emotional exhaustion is stronger at low environmental concern and weaker at perceived environmental knowledge. This conclusion may enrich conservation theory and show practical values for tourism policy makers, producers and marketers.
... As such, we were interested in the coefficient of the interaction term between the Level 1 and the two Level 2 variables. We chose a fixed-effects approach considering our theoretical focus on within-individual processes and to keep our model as parsimonious as possible (Debus et al., 2014;Hox et al., 2017). Autocorrelation was considered, and the homogeneity of variance among subjects was examined using a variation of Levene's test (Palmeri, 2016). ...
Article
Research suggests that algorithms—based on artificial intelligence or linear regression models—make better predictions than humans in a wide range of domains. Several studies have examined the degree to which people use algorithms. However, these studies have been mostly cross‐sectional and thus have failed to address the dynamic nature of algorithm use. In the present paper, we examined algorithm use with a novel longitudinal approach outside the lab. Specifically, we conducted two ecological momentary assessment studies in which 401 participants made financial predictions for 18 days in two tasks. Relying on the judge‐advisor system framework, we examined how time interacted with advice source (human vs. algorithm) and advisor accuracy to predict advice taking. Our results showed that when the advice was inaccurate, people tended to use algorithm advice less than human advice across the period studied. Inaccurate algorithms were penalized logarithmically; the effect was initially strong but tended to fade over time. This suggests that first impressions are crucial and produce significant changes in advice taking at the beginning of the interaction, which later tends to stabilize as days go by. Therefore, inaccurate algorithms are more likely to accrue a negative reputation than inaccurate humans, even when having the same level of performance.
... Interestingly, research has also demonstrated that flow experience is a highly unstable "optimal experience" in all walks of human activities (Csikszentmihalyi, 1975;Mao et al., 2016), as the individuals keep stretching their skills in coping with fluctuated challenges from the outside world on their way of personal growth. Flow is varied greatly at the within-person level, indicating that it changes at an individual's different stages of life span (i.e., from pre-school to college years) and with its dynamic nature being stressed (Fullagar and Kelloway, 2009;Debus et al., 2014). In this regard, Ceja and Navarro (2009, 2011 have found that the flow state within human beings tends to follow a disordered pattern, that is, flow experience shows a constant fluctuation during each day and does not go steady over time. ...
Article
Full-text available
The present study investigated a conceptual model by testing university students’ flow experience and subjective well-being via considering their underlying mechanisms of academic self-efficacy and self-esteem. A total of 1109 Chinese university students completed a questionnaire containing scales of Subjective Well-being, Flow, Academic Self-efficacy and Self-esteem. Results yielded from the structural equation modelling analysis indicated a significant and positive association between flow experience and subjective well-being, and such an association was sequentially mediated by academic self-efficacy and self-esteem. Findings also provided empirical evidence for the proposed model highlighting the significant role of flow experience at the higher educational context in predicting Chinese university students’ subjective well-being, and how such a relation can be supported by suggested mediating roles academic self-efficacy and self-esteem played.
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Entrepreneurs work in an uncertain, novel, and high-stakes environment. This environment can lead to disagreements and conflicts over how to develop, grow, and run a business venture, thus triggering destructive social interactions. This research sheds light on the role of destructive interpersonal relationships by examining daily perceived social undermining from work partners and how and when this perceived undermining affects entrepreneurs' work engagement. Building on a resource-based self-regulation perspective, we develop a theoretical model of the self-regulation impairment process whereby an entrepreneur's perceived social undermining disrupts sleep quality at night, which dampens work engagement the next day. We further theorize trait resilience as a self-regulation capacity that buffers this impairment process. We test the model in a study based on daily surveys over 10 workdays from 77 entrepreneurs. The results largely support our hypotheses and further indicate that trait resilience is more crucial for less experienced entrepreneurs. Our study contributes to research on how entrepreneurs' interpersonal relationships—particularly destructive ones—affect entrepreneurial well-being.
Chapter
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Abstract: The performance of an activity can have positive incentives per se and individuals may engage in an activity purely for the enjoyment of it. The engagement due to the enjoyment of an activity is often called intrinsic motivation. Beside this understanding of intrinsic motivation other conceptions are presented (self-determination, experience of competence, interest and involvement, mean-end-correspondence, learning-goal orientation). In doing so, the problem became evident, that the term intrinsic motivation refers to different, even conflicting conceptions. With the “Extended Cognitive Model of Motivation” different aspects of motivation are theoretically integrated. Instead of using the term intrinsic motivation, we use the term activity-related motivation. Qualitative and quantitative ways to measure activity-related incentives are outlined. Finally we present an intensively studied activity-related incentive, i.e. the experience of flow.
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In recent years, researchers in work and organizational psychology have increasingly become interested in short-term processes and everyday experiences of working individuals. Diaries provide the necessary means to examine these processes. Although diary studies have become more popular in recent years, researchers not familiar with this method still find it difficult to get access to the required knowledge. In this paper, we provide an introduction to this method of data collection. Using two diary study examples, we discuss methodological issues researchers face when planning a diary study, examine recent methodological developments, and give practical recommendations. Topics covered include different types of diary studies, the research questions to be examined, compliance and the issue of missing data, sample size, and issues of analyses.
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The use of interrater reliability (IRR) and interrater agreement (IRA) indices has increased dramatically during the past 20 years. This popularity is, at least in part, because of the increased role of multilevel modeling techniques (e.g., hierarchical linear modeling and multilevel structural equation modeling) in organizational research. IRR and IRA indices are often used to justify aggregating lower-level data used in composition models. The purpose of the current article is to expose researchers to the various issues surrounding the use of IRR and IRA indices often used in conjunction with multilevel models. To achieve this goal, the authors adopt a question-and-answer format and provide a tutorial in the appendices illustrating how these indices may be computed using the SPSS software.
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Yule (1903) and Simpson (1951) described a statistical paradox that occurs when data is aggregated. In such situations, aggregated data may reveal a trend that directly contrasts those of sub-groups trends. In fact, the aggregate data trends may even be opposite in direction of sub-group trends. To reveal Yule-Simpson's paradox (YSP)-type occurrences, researchers must simultaneously consider the effect of an intervention at specific levels and on the overall model to ensure datasets are accurately analyzed and research findings are appropriately interpreted. The primary objectives of this manuscript are to: (1) examine the history of YSP; (2) describe necessary and sufficient causes for YSP occurrences; (3) provide examples of YSP in research and explain YSP's relationship to multi-level modeling including Hierarchical Linear Modeling (HLM); and (4) discuss YSP's implications for researchers.
Chapter
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.
Article
Objectives: This research aimed to shed light on the relationship between flow experience and performance in sports using a marathon race as an example. We hypothesized that flow influences the marathon race performance by an indirect rewarding effect.We assumed that the positive quality of flow experience rewards the pre-race running activity and thereby enhances training behavior which again leads to high race performance. A methodological issue of the this was to compare the retrospective with the experience-sampling measure of flow. Design: Three studies with marathon runners (Ns ¼ 109, 112, 65 for Studies 1, 2, and 3, respectively) were conducted. Method: They measured flow experience four times during a marathon race either retrospectively (Studies 1 and 2) or using an experience-sampling method during the race (Study 3). Additionally race performance and future running motivation (Studies 1, 2, and 3), pre-race training behavior (Studies 2 and 3) and flow experience in training (Study 3) were measured. Results: The results confirmed the hypothesis showing that flow during a marathon race is related to future running motivation, but is not directly linked to race performance. Instead, race performance was predicted by pre-race training behavior (Studies 2 and 3) which again was fostered by flow during the training (Study 3). The descriptive flow courses of the retrospective and the experience-sampling flow measures were comparable but also showed important differences. Conclusions: We critically discuss the practical implications of the rewarding effect of flow on performance and the advantages of the retrospective and experience-sampling measure of flow.
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Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.