Emotion Regulation as a Determinant of RecoveryExperiences a.docx
1. Emotion Regulation as a Determinant of Recovery
Experiences and Well-Being: A Day-Level Study
Eva Maria Schraub, Sarah Turgut, Vera Clavairoly,
and Karlheinz Sonntag
Heidelberg University
This study examined the impact of two emotion regulation
strategies,
reappraisal and expressive suppression, on recovery experiences
and
affective well-being after significant study-related events. In a
sample of
63 undergraduate students who completed a time-contingent
daily diary
over 14 consecutive days (726 diary entries), the assumption
that per-
ceived emotional stress during study-related events would
reduce affec-
tive well-being at bedtime (
� �0.28, p � .001) was supported.
Multilevel analyses further showed that recovery experiences
partially
mediated this negative relationship (
� 0.39, p � .001). As postulated,
reappraisal buffered the adverse effects of emotional stress on
recovery
experiences (
� 0.05, p � .01). Unexpectedly, expressive suppression
had the same buffering effect (
� 0.04, p � .05). We conclude that
an additional, fine-grained focus on context and time would
3. 2013, Vol. 20, No. 4, 309–335 1072-5245/13/$12.00 DOI:
10.1037/a0034483
309
increase the risk of not being able to relax after work (Cropley
& Purvis,
2003; Rau, 2006; Sonnentag & Bayer, 2005), the follow-up
question
remains largely unanswered (for an exception, see, e.g.,
Sonnentag &
Kruel, 2006): Which determinants impede or facilitate recovery
experi-
ences after demanding and stressful days? To ground practical
advice
(e.g., for stress management training design) on empirical
evidence, we
need to identify the processes that influence recovery
experiences after
stressful days.
One process that may explain recovery from job stress is
emotion
regulation. The Job Demands-Resources model (Demerouti,
Bakker,
Nachreiner, & Schaufeli, 2001) predicts that personal resources
moderate
the consequences of job demands (Bakker & Demerouti, 2007;
Xantho-
poulou, Bakker, Demerouti, & Schaufeli, 2007). In line with
this predic-
tion, research shows that individual differences in emotion
regulation
affect the way work-related emotional events relate to
4. individual well-
being and performance. For example, studies by Ciarrochi,
Dean, and
Anderson (2002) and by Giardini and Frese (2006) indicate that
emotion-
ally competent people who actively address emotional job
stressors do
have less adverse health effects later. Raftery and Bizer (2009)
found that
negative feedback enhanced test performance of individuals
who habitu-
ally reinterpreted (i.e., reappraised) situations (“reappraisers”),
but did
not affect test performance of individuals suppressing the
expression of
their feelings (“suppressors”). Schraub, Stegmaier, and Sonntag
(2011)
showed that changes at their workplace did not affect
suppressors’ well-
being. Extending this line of individual differences research, we
examine
the impact of emotion regulation as a determinant of students’
recovery
from emotional stress during study-related events.
Altogether, our aim in this study is to integrate research lines
on work
stress, recovery, and emotion regulation. First, we examine the
role of
recovery experiences in the relationship between emotional
stress during
study-related events and later affective well-being. Second, we
investi-
gate the impact of two different emotion regulation strategies on
the
relationship between emotional stress and recovery experiences.
5. To our
knowledge, the role of emotion regulation has not yet been
analyzed with
regard to the recovery process. Third, we further extend
research on
emotion regulation by analyzing situational emotion regulation
behavior
in a diary design. This design allows for detection of the effects
of
intrapersonal variation in the use of specific emotion regulation
strategies,
while controlling for individual differences in emotion
regulation. To our
knowledge, situational emotion regulation has so far only been
analyzed
in the emotional labor context (i.e., a context where emotions
need to be
regulated as part of one’s job; Hochschild, 1983); for example,
in cus-
tomer service (e.g., Totterdell & Holman, 2003).
310 Schraub, Turgut, Clavairoly, and Sonntag
THEORETICAL BACKGROUND AND HYPOTHESES
DEVELOPMENT
Effects of Emotional Stress on Recovery Experiences and
Subsequent
Affective Well-Being
Emotional stress is characterized by negative emotional
experiences,
such as anger or anxiety (Chang, Johnson, & Yang, 2007).
According to the
6. Person–Environment fit perspective (cf. Edwards, Caplan, &
Harrison,
1998), it indicates a lack of match (i.e., a mis-fit) between
environmental
demands or supplies and a person’s abilities or needs.
Emotional stress can
lead to a variety of negative consequences for individuals’ well-
being,
attitudes, and behaviors (cf. Fisher & Ashkanasy, 2000;
Fredrickson &
Joiner, 2002; Weiss & Cropanzano, 1996). Previous studies
have shown that
events at work that are being perceived as stressful are
negatively related to
affective well-being (Elfering et al., 2005). If an emotional
event at work is
furthermore being perceived as significant, the likelihood
increases that its
effects spill over into leisure time (e.g., Williams & Alliger,
1994). In the
present study we conceptualize the perceived stressfulness of a
study-related
event as “an individual’s response to work-related
environmental stressors”
(Jex, Beehr, & Roberts, 1992, p. 623).
As proposed by Conservation of Resources theory (Hobfoll,
1989),
people strive to protect and accumulate resources to deal with
their environ-
ment. Being a means to regenerate depleted resources (cf.
Sonnentag, Per-
rewé, & Ganster, 2009), recovery experiences should help
people to restore
affective well-being and associated personal resources such as
optimism after
7. periods of emotional stress. People experience recovery by
refraining from
any activities involving effort expenditure so that resources can
be rebuilt
(Hobfoll, 1998; Meijman & Mulder, 1998) or by actively
engaging in
recovery activities which foster new resources (Geurts &
Sonnentag, 2006;
Hobfoll, 1998; Sonnentag, 2001). Sonnentag and Fritz (2007)
identified
distinct recovery experiences that promote recovery. One of
them is psycho-
logical detachment, defined as an “individual’s sense of being
away from the
work situation” (Etzion, Eden, & Lapidot, 1998, p. 579).
Another recovery
experience is relaxation, which is “characterized by decreased
sympathetic
activation and becomes evident in a decrease in heart rate,
muscle tension,
and other indicators of activation” (Sonnentag, Binnewies, &
Mojza, 2008, p.
675). Recent research demonstrates that resource-providing
activities pro-
mote the recovery experiences psychological detachment and
relaxation
which in turn are positively associated with well-being and with
feeling
recovered (Ragsdale, Beehr, Grebner, & Han, 2011).
311Emotion Regulation, Recovery, and Well-Being
Several diary studies highlight the importance of adequate
recovery
8. experiences for well-being (cf. Demerouti, Bakker, Geurts, &
Taris, 2009).
Nevertheless, these studies also indicate that especially when
resources are
spent (e.g., because of high job demands), the risk of
insufficient recovery
after work increases (Cropley & Purvis, 2003; Rau, 2006;
Sonnentag &
Bayer, 2005). One reason for this effect may be prolonged
cognitive engage-
ment (i.e., lack of psychological detachment), which is a likely
reaction to
emotional stress (cf. Geurts & Sonnentag, 2006). Thus, it seems
that recovery
experiences are often impeded at precisely the times when they
are most
needed.
According to stress theories such as the Person–Environment Fit
per-
spective (cf. Edwards, Caplan, & Harrison, 1998) and the
Conservation of
Resources theory (Hobfoll, 1989), we expect that emotional
stress during
significant study-related events negatively impacts people’s
affective well-
being at bedtime, which serves as an indicator of feeling
recovered (Son-
nentag, 2001). As recovery experiences during afterwork hours
restore lost
resources and positively affect peoples’ well-being (Demerouti
et al., 2009),
we further hypothesize that recovery experiences mediate the
negative effects
of emotional stress on affective well-being. Thus, we
formulated the follow-
9. ing hypotheses:
Hypothesis 1: Emotional stress during a significant study-
related event
negatively affects affective well-being at bedtime.
Hypothesis 2: Recovery experiences mediate the negative
relationship
between emotional stress during a significant study-related
event and
affective well-being at bedtime.
Emotion Regulation as a Moderator of the Effects of Emotional
Strain
Emotion regulation refers to people’s efforts to influence the
experience,
intensity, duration, or expression of activated emotions during
emotionally
distressing events (Gross, 1998b). Gross (2001) developed a
process-oriented
model of emotion regulation to classify the strategies people use
to regulate
their emotions, and distinguished between antecedent-focused
regulation and
response-focused regulation. Antecedent-focused regulation
(e.g., cognitive
reappraisal of the situation) comes early in the emotion-
generative process.
Response-focused regulation (e.g., expressive suppression) is
applied when
emotions are already fully experienced and only modifies the
emotional
display, not the experience. Researchers ascribed to and
empirically found
quite diverse effects for these different types of emotion
10. regulation.
312 Schraub, Turgut, Clavairoly, and Sonntag
Expressive suppression, on the one hand, is an ineffective
strategy in
terms of altering emotional experiences. It decreases the
emotional expres-
sion, but not the intensity of the felt emotion (Gross, 2001; cf.
Gross & John,
2003). Because of the cognitive load that the suppression of
emotional
expression imposes, expressive suppression is associated with
impaired
memory and social functioning. Various experimental studies
demonstrated
this effect. Richards and Gross (1999, 2000), for example,
found that study
participants who were asked to suppress emotional expressions
during the
presentation of emotion-eliciting material showed decreased
incidental mem-
ory for information presented during the suppression period and
increased
cardiovascular activation. Schmeichel, Vohs, and Baumeister
(2003) re-
vealed that after a required suppression period, study
participants performed
demanding logical tasks significantly worse than a control
group. Johns,
Inzlicht, and Schmader (2008) revealed a similar effect for
spontaneous
expressive suppression: Study participants who suppressed the
expression of
11. negative emotions performed worse on tests of cognitive ability
afterward.
As a study by Butler and colleagues (2003) shows, restricted
cognitive
capacity because expressive suppression does not only affect
mere cognitive
tasks, but also translates to being less responsive in social
interactions.
Expressive suppression is further associated with negative long-
term out-
comes. People who frequently suppress their emotional
expression appar-
ently experience less positive and more negative emotions and
have lower
cognitive capacity as well as worse cardiovascular functioning
(Richards,
2004; Srivastava, Tamir, McGonigal, John, & Gross, 2009). A
longitudinal
study showed that suppressors had weaker social connections at
the end of
college (English, John, Srivastava, & Gross, 2012). They also
reported more
depressive symptoms, were less satisfied with their lives and
relationships,
and were more pessimistic about their future (Gross & John,
2003).
Reappraisal, on the other hand, is an effective emotion
regulation strat-
egy, because it occurs very early in the emotion generative
process and then
even prevents the development of negative emotions (Gross,
2002). Thus, it
imposes a much smaller cognitive load than expressive
suppression, because
it does not consume cognitive capacity to monitor one’s feelings
12. and behav-
ior later on (Gross, 2002; Richards & Gross, 2000).
Accordingly, reappraisal
does not negatively influence cognitive performance. For
example, it did not
decrease memory when people were asked to reappraise the
situation while
viewing emotional slides (Richards & Gross, 2000). In contrast
to expressive
suppression, reappraisal does not impair interpersonal
communication (But-
ler et al., 2003). It is associated with positive long-term
outcomes on health,
memory, and social relationships (English et al., 2012; Gross &
John, 2003;
Richards & Gross, 2000; Srivastava et al., 2009).
Reviewing the emotion regulation literature, we found that with
few
exceptions (e.g., Tschan, Rochat, & Zapf, 2005), most empirical
studies
313Emotion Regulation, Recovery, and Well-Being
published so far were either experimental (e.g., Gross, 1998a),
analyzed
individual differences (e.g., Ciarrochi et al., 2002; Giardini &
Frese, 2006;
Raftery & Bizer, 2009; Schraub et al., 2011), or focused on
emotional labor
(e.g., Totterdell & Holman, 2003). However, in environments
where display
rules are weaker and more informal than they are in the service
context (cf.
13. Bono & Vey, 2005), people may determine for themselves when
and how to
regulate their emotions. Moreover, theories on interpersonal
effects of emo-
tion regulation (Côté, 2005; Van Kleef, 2009) and the
independence of
emotion regulation styles suggest that people may apply
different and some-
times concurrent emotion regulation strategies depending on the
context. To
both complement and extend prior studies, we therefore chose to
examine
situational regulation efforts instead of individual differences in
this diary
study. We adapted an emotion regulation questionnaire to
situational regu-
lation to gain insight into short-term consequences of specific
emotion
regulation behavior.
Concerning the effects of emotion regulation, we argue that the
strategy
of reappraising the situation might buffer negative effects of
emotional stress
on recovery experiences because it is applied early in the
emotion-generative
process and changes peoples’ interpretations of the respective
situation
(Gross, 2001). Reappraisal changes the emotional experience to
being more
positive, making subsequent regulation efforts unnecessary.
Thus, resources
are not bound for further self-control. We hypothesize that:
Hypothesis 3: Reappraisal buffers the negative impact of
emotional
14. stress on recovery experiences.
In contrast, researchers relate expressive suppression to mainly
negative
outcomes because it does not change the experience of
emotional stress, but
only suppresses its display, thereby consuming cognitive
resources that
otherwise would be available for other tasks (Raftery & Bizer,
2009).
Because of this heightened cognitive load, we expect this
regulation strategy
to interfere with recovery experiences and hypothesize that:
Hypothesis 4: Expressive suppression enhances the negative
impact of
emotional stress on recovery experiences.
To summarize, we expect recovery experiences to be an
explanatory
mechanism for a negative relationship between emotional stress
during
study-related events and affective well-being at bedtime.
Moreover, we deem
the use of reappraisal and expressive suppression to
differentially affect the
relationship between emotional stress and recovery experiences.
The frame-
work that integrates the research questions is depicted in Figure
1.
314 Schraub, Turgut, Clavairoly, and Sonntag
METHOD
15. Contextual Factors
This diary study was conducted with undergraduate students of
a German
university who belonged to the first year of students in a
reorganized
curriculum (i.e., bachelor/master programs replacing a diploma
curriculum).
All students had received excellent grades in their final high
school exams.
Facing so far unstandardized processes, first-year students often
reported
insecurity regarding curriculum requirements and examination
procedures.
Because they knew that they would eventually have to apply and
compete for
a place in a master’s program to obtain an academic degree that
would
qualify them for the jobs they desired, they also experienced
some pressure
concerning their future careers. In addition, growing
international competi-
tion and the implementation of tuition in Germany contributed
to make
studying a full-time job with a high stress level (Cooke,
Bewick, Barkham,
Bradley, & Audin, 2006; Obergfell & Schmidt, 2010).
Apart from classes at university, students need to self-determine
their
time and resources for their studies. According to the literature,
recovery
becomes even more difficult in an unregulated work–life
situation (Ahrent-
zen, 1990; Cropley, Dijk, & Stanley, 2006; Sonnentag & Kruel,
16. 2006).
Although a student’s life may differ from an employee’s life
concerning the
amount of work hours, it is important to note that their average
level of stress
and strain may not necessarily differ from an employee’s level.
Recent
studies show that students experience high levels of stressors
and strains
(e.g., Obergfell & Schmidt, 2010) and as Ragsdale and
colleagues (2011) put
it, “stress processes with a weekly cycle occur with students as
well” (p. 154).
In the final questionnaire of the diary, several participants
reported that they
had experienced the diary as a great opportunity to reflect on
their study-
related and private experiences and to become aware of certain
behavior
tendencies.
Recovery
experiences
Affective well-
being
Emotional stress
Expressive suppression Reappraisal
Figure 1. The proposed conceptual scheme.
315Emotion Regulation, Recovery, and Well-Being
17. Sample and Procedure
In return for research participation credits required by their
schedule, 67
full-time undergraduate students of a German university
volunteered to
participate in the study. All of them completed a paper-and-
pencil question-
naire containing questions about demographics and personal
traits. They then
received a structured paper-based diary within which they had
to answer a
1-page questionnaire each night before going to bed on 14
consecutive days.
The research assistants reminded participants of this task each
night via short
messages on their cell phones. They were assured of anonymous
data
treatment, and that their cell phone numbers could not be
assigned to their
data. The research assistants also pointed out that they could be
contacted in
case of any questions or issues. We matched questionnaires by
an individual,
self-generated code.
Out of the 67 diaries that had been distributed, 65 were
returned; this
equals a return rate of 97%. Because two participants had to be
excluded
because of being on holiday while participating in the study, the
final sample
consisted of 63 participants (51 women and 12 men) with an
average age of
21 years (SD � 2.9 years). All were full-time students, working
18. on study
assignments for between 3 and 12 hr per day, with an average
working time
of 4.8 hr per day (SD � 2.1).
Measures
The focus study variables emotional stress, recovery
experiences, emo-
tion regulation, and affective well-being at bedtime were
assessed in the
diary, whereas control variables were assessed in the general
questionnaire.
We instructed participants to refer to their studies when asked
for work-
related experiences.
Emotional Stress
Analogous to the procedure used by Gable, Reis, Impett, and
Asher
(2004), we asked participants to recapture their most significant
study-related
event of the respective day and to briefly describe it. To assess
emotional
stress during this event, the questionnaire then provided nine
items from a
translated and adapted version of Fisher’s (2000) job emotion
scale (Cole,
Bruch, & Vogel, 2006). The participants had to rate their
experience of
emotions such as “frustrated,” “disappointed,” and “worried” in
relation to
the study-related event on a 5-point Likert scale ranging from 1
� “not at all”
19. 316 Schraub, Turgut, Clavairoly, and Sonntag
to 5 � “very much.” Because this rating reflects a specific and
situation based
evaluation of the perceived emotions during the study-related
event in
question, the emotional stress values reflect how stressful the
event was
perceived. Cronbach’s alpha indicated a reliability of � � .89.
Recovery Experiences
We assessed two specific recovery experiences in the evening,
psycho-
logical detachment and relaxation, with items from Sonnentag
and colleagues
(2008) recovery experience questionnaire in its German version.
In total, six
items asked to what extent the participants detached from their
studies and
relaxed. Example items are, “Tonight, I was able to forget about
university
work” (psychological detachment from work) and, “Tonight, I
was doing
things during which I was able to relax” (relaxation).
Participants could rate
the items on a scale ranging from 1 � “not at all” to 5 � “very
much.” To
examine the factor structure prior to aggregating the items of
this scale, we
conducted an exploratory factor analysis. Without rotation, all
items con-
verged on one factor with an eigenvalue greater than one. This
factor
20. accounted for 72.8% of the variance; all item loadings exceeded
.82. Cron-
bach’s alpha for the composite scale was � � .93.
Emotion Regulation
For the assessment of the participants’ emotion regulation, we
adapted
four reappraisal and two expressive suppression items from the
German
version (Abler & Kessler, 2009) of Gross and John’s (2003)
emotion regu-
lation questionnaire to situational emotion regulation. This tool
measures
cognitive reappraisal and expressive suppression as two
uncorrelated styles
of intrapersonal emotion regulation and demonstrates adequate
psychometric
properties in terms of validity and reliability (Gross & John,
2003). We asked
the participants to indicate to what extent they reappraised the
situation (e.g.,
“I controlled my emotions by changing the way I think about
the situation I
was in”) and suppressed the expression of their feelings (e.g., “I
kept my
emotions to myself”) during the study-related event they had
described
beforehand. Participants answered via a 7-point Likert scale
ranging from
1 � “not at all” to 7 � “very much.” To assure that reappraisal
and
expressive suppression formed two separate factors, we
submitted all emo-
tion regulation items to a principal components analysis with
oblique rota-
21. tion. Corroborating the measures’ discriminant validity, two
factors emerged
with eigenvalues greater than 1, accounting for 78.0% of the
variance. The
317Emotion Regulation, Recovery, and Well-Being
items’ primary loadings on their appropriate factors were
greater than .82;
cross-loadings were lower than .26. The internal consistency
was � � .89 for
reappraisal (Cronbach’s Alpha) and r � .80 for expressive
suppression
(Spearman’s correlation coefficient).
Affective Well-Being
We assessed affective well-being at bedtime with six items of
the
well-being scale by Warr, Butcher, and Robertson (2004) that
we translated
into German using the back-translation procedure (Brislin,
1970). Partici-
pants rated their general nonevent related well-being with these
items (e.g.,
“At the moment, I feel happy”) on a 5-point Likert scale
ranging from 1 �
“not at all” to 5 � “very much.” Because of the employed diary
design this
measure reflects a situational evaluation of the affective well-
being. Cron-
bach’s alpha for this scale was � � .83.
Controls
22. To ensure that day-level affective well-being could actually be
explained
by the day-level predictors, we controlled for the
sociodemographic data age
and gender (assessed with one item each), as well as for
dispositional
affectivity. Positive and negative affectivity significantly
influence a person’s
recovery, affective well-being, and performance (Connolly &
Viswesvaran,
2000; Lyubomirsky, King, & Diener, 2005; Marco & Suls, 1993;
Watson &
Clark, 1984). We measured dispositional affectivity using
Krohne, Egloff,
Kohlmann, and Tausch’s (1996) validated German version of
the Positive
and Negative Affect Schedule (PANAS; Watson, Clark, &
Tellegen, 1988).
Participants rated the extent to which they generally experience
10 positive
feelings (e.g., “I generally feel proud”) and 10 negative feelings
(e.g., “I
generally feel upset”) on a 5-point Likert scale ranging from 1
� “not at all”
to 5 � “very much.” Cronbach’s alpha was � � .83 for positive
affectivity
and � � .89 for negative affectivity.
Data Analyses
With the diary design of this study, we collected repeated
measurement
data. The two-level study consisted of day-level data (Level 1)
and person-
level data (Level 2), with days being nested in persons. For this
23. kind of study,
it is necessary to use the multilevel random coefficient
modeling method
318 Schraub, Turgut, Clavairoly, and Sonntag
(MRCM; also called hierarchical linear modeling, HLM) to
accurately fit the
hierarchical data structure (e.g., Netzlek, Schröder-Abé, &
Schütz, 2006;
Raudenbush & Bryk, 2002). This method offers the advantage
of working
with different levels of analysis simultaneously, such that
interrelations on
different levels are statistically independent of each other
(Netzlek et al.,
2006). In the analyses, each data level is treated as a formally
independent
submodel. Use of the MRCM for analyzing multilevel data are
important for
several reasons: First, the MRCM offers the advantage that any
bias in
standard errors and statistical tests because of the
interdependence of the data
are adjusted for (Krull & MacKinnon, 2001; Snijders & Bosker,
1993).
Second, MRCM takes the hierarchical data structure into
account and thus
avoids aggregation of the day-level variables which would
result in a loss of
power of the statistical tests because of the reduced number of
higher level
units. Thus, if the variability in the data was neglected,
misinterpretation of
24. the results would likely occur (Hox, 2010).
We used HLM 6.0 (Raudenbush, Bryk, Cheong, Congdon, & Du
Toit,
2004) for our analyses. We centered the person-level control
variables
positive and negative affectivity at the grand mean and all day-
level predic-
tors at the respective person mean.
RESULTS
Descriptive Results
Participants reported 726 study-related events altogether (M �
11.5;
SD � 2.3). Table 1 depicts the means, SDs, and correlations of
all study
variables. The correlation of r � .43 between reappraisal and
expressive
suppression on the day-level indicates that these two strategies
were often
applied in conjunction.
Hypotheses Testing
To test our hypotheses, we first calculated null models (Model
0) that
included the intercept as the only predictor. For data evaluation
this step is
necessary, as it verifies whether sufficient variance exists in the
criterion
variables on the day-level as well as on the person-level to be
explained by
the respective predictors. For each hypothesis, we then added
the relevant
25. control variables in a second model (Model 1) and afterward
conducted
analyses with the predictors (Models 2 and 3). The models
integrate every
participant by a regression line including an intercept and a
slope. For each
319Emotion Regulation, Recovery, and Well-Being
T
ab
le
1.
M
ea
ns
,
SD
s,
an
d
In
te
rc
or
re
40. m
al
e.
*
p
�
.0
5.
*
*
p
�
.0
1.
320 Schraub, Turgut, Clavairoly, and Sonntag
multilevel model, model fit indices (deviances) indicate the
model fit for the
data. Differences of the deviances of two subsequent models
follow a
chi-square distribution and indicate if the additional predictors
explain a
significant additional amount of variance.
As Model 0 in Table 2 shows, the variance on both levels was
indeed
41. sufficient for both recovery experiences and affective well-
being. Further-
more, both reappraisal and expressive suppression showed high
levels of
intrapersonal variance, indicating that it made sense to study
their effects on
a daily basis.
Next, we entered the control variables gender, age, and negative
as well
as positive affectivity (Level 2) as predictors in Model 1. In
Model 2, we
additionally entered emotional stress (Level 1). Finally, in
Model 3, we
included recovery experiences (Level 1). We tested each model
for improved
fit over the previous model by calculating differences in the
deviances (� �2
log likelihood) and submitting them to a chi-square test. Table 3
depicts the
results.
The analysis showed that Model 1 improved significantly over
Model 0
(� �2 log likelihood � 25.16, df � 7, p � .001). The control
variables
positive and negative affectivity were significant predictors in
this model. As
proposed in Hypothesis 1, the intensity of emotional stress
during a signif-
icant study-related event negatively affects affective well-being
in the late
evening. To test this hypothesis, we compared the model fit of
Model 1 to the
one of Model 2 in which the variable emotional stress was
entered. As Model
42. 2 showed an improved model fit (� �2 log likelihood � 137.35,
df � 8,
p � .001), emotional stress contributed significantly to the
prediction of
affective well-being, and did so beyond the effects of negative
and positive
affectivity. Thus, data supported Hypothesis 1. The intensity of
emotional
stress during a significant study-related event had a negative
impact on
affective well-being at bedtime.
In Hypothesis 2, we postulated that recovery experiences
mediate the
negative relationship between emotional stress and affective
well-being.
Comparing the model fit between Model 2 and Model 3, the
difference
Table 2. Hierarchical Linear Modeling Estimates of Null
Models
Dependent variable �00 �2
00
% of total variance that
is within-person
Affective well-being 3.32 0.46 .15*** 75.41
Recovery experiences 2.78 0.56 .11*** 83.58
Reappraisal 2.37 1.92 .38*** 83.48
Expressive suppression 2.70 2.74 .35*** 88.67
Note. N � 726 occasions, N � 63 participants. �00 � pooled
intercept; �2 � within-person
variance;
43. 00 � between-person variance. % of total variance that is
within-person was
computed with the formula �2/(�2 �
00).
*** p � .001.
321Emotion Regulation, Recovery, and Well-Being
T
ab
le
3.
M
ul
til
ev
el
E
st
im
at
es
fo
r
M
od
63. �
.0
1.
*
*
*
p
�
.0
01
.
322 Schraub, Turgut, Clavairoly, and Sonntag
between the deviances was again significant (� �2 log
likelihood � 176.03,
df � 9, p � .001). This indicates that recovery experiences
contributed
significantly to the prediction of affective well-being beyond
the previous
variables.
According to Baron and Kenny (1986), we tested the data for a
mediation
effect in a stepwise procedure (results can be found in Table 3
and 4): First,
the analyses showed that a significant relationship between the
independent
variable, emotional stress, and the mediator, recovery
64. experiences existed
(
� �0.22, p � .001). Second, we tested whether the relationship
between
the mediator, recovery experiences, and the dependent variable,
affective
well-being at bedtime, was significant. The analyses revealed a
significant
relationship between recovery experiences and affective well-
being (
�
0.39, p � .001). Third, we tested the relationship between the
independent
variable, emotional stress, and the dependent variable, affective
well-being,
which proved to be significant (
� �0.28, p � .001). The effect of
emotional stress on affective well-being decreased (from
� �0.28 to
�
�0.19, p � .001) but remained significant after the mediator
was included in
the analyses. Because this speaks against full mediation, we
tested for a
partial mediation effect conducting the Sobel Test (Sobel, 1982)
in its
adjusted form for multilevel data (Krull & MacKinnon, 2001).
In support of
Hypothesis 2, the test revealed that the mediating effect for
recovery expe-
riences was significant (z � �.57, p � .001). Thus, recovery
experiences
partially mediated the negative relationship between emotional
stress and
affective well-being.
65. Hypotheses 3 and 4 postulated different moderating effects of
reappraisal
and expressive suppression on the negative impact of emotional
stress on
recovery experiences. We supposed reappraisal to be buffering
(Hypothesis
3) and expressive suppression to be enhancing (Hypothesis 4)
the negative
impact of emotional stress on recovery experiences. Again, we
computed
models of multilevel estimates in order to test the prediction of
recovery
experiences. Table 4 depicts the results. As before, Model 1
contained the
control variables gender, age, and negative as well as positive
affectivity
(Level 2) as predictors. The difference of the likelihood ratio
between Model
0 and Model 1 was significant (� �2 log likelihood � 25.16, df
� 7, p �
.001). In a next step, we entered emotional stress, reappraisal
and expressive
suppression as predictors in Model 2, which was then compared
with Model
1. Model 2 showed a significantly improved model fit (� �2
log likelihood �
146.91, df � 10, p � .01). While emotional stress was
negatively related to
recovery experiences (
� �0.22, p � .01), reappraisal and expressive
suppression did not prove to be significant predictors of
recovery experi-
ences. To test the moderation hypotheses (Hypotheses 3 and 4),
we include
the interactions between emotional stress and reappraisal and
expressive
66. suppression, respectively, in Model 3. Compared with Model 2,
Model 3
323Emotion Regulation, Recovery, and Well-Being
T
ab
le
4.
M
ul
til
ev
el
E
st
im
at
es
fo
r
M
od
el
s
90. 324 Schraub, Turgut, Clavairoly, and Sonntag
showed a significantly smaller likelihood ratio (� �2 log
likelihood � 9.12,
df � 12, p � .01). The interactions of emotional stress during a
study-related
event with reappraisal (
� 0.05, p � .01) and expressive suppression (
�
0.04, p � .05) showed significant influences on recovery
experiences in the
evening. Figure 2 displays these interaction effects.
An inspection of the simple slopes revealed that as expected in
Hypoth-
esis 3, reappraisal buffered the negative impact of emotional
stress on
recovery experiences. However, in contrast to Hypothesis 4,
expressive
suppression did not enhance the negative impact of emotional
stress on
recovery experiences, but had a buffering impact as well. We
tested the
simple slopes of both interaction effects using an online HLM
calculator
(Preacher, Curran, & Bauer, 2006). We selected 1 SD above the
mean (high
reappraisal and expressive suppression, respectively) and 1 SD
below the
mean (low reappraisal and expressive suppression, respectively)
as condi-
tional values of the emotion regulation strategies. For low
reappraisal and
91. expressive suppression, the negative simple slopes relating
emotional stress
to recovery experiences were significant (z � �1.98 for
reappraisal and z �
�1.96 for expressive suppression, p � .05). For high reappraisal
and ex-
pressive suppression, the simple slopes were also negative, but
nonsignificant
(z � �1.83, p � .078 for reappraisal and z � �1.85, p � .066
for expressive
suppression). Thus, only on days when reappraisal and/or
expressive sup-
pression were low, emotional stress was significantly,
negatively related to
recovery experiences.
Taken together, the data supported Hypotheses 1–3. Emotional
stress had
a negative relationship with affective well-being and recovery
experiences
partially mediated this relationship. That is, the negative impact
of emotional
stress on recovery experiences was weaker when the person
reappraised the
situation. In contrast to our expectations in Hypothesis 4,
expressive sup-
Figure 2. The moderating effects of reappraisal and expressive
suppression on the relationship
between emotional stress and recovery experiences.
325Emotion Regulation, Recovery, and Well-Being
pression had the same effect as reappraisal; it also buffered the
92. negative
impact of emotional stress on recovery experiences.
DISCUSSION
The present study integrates different lines of research from
work stress,
emotion regulation, and recovery, which to our knowledge have
not been
considered in combination before. The study shows how the
situational
application of emotion regulation can improve recovery
experiences and
subsequent affective well-being after stressful study-related
events. It further
extends the literature by taking an intrapersonal approach on
these topics,
leading to results that complement the conclusions drawn from
individual
differences research.
The aim of the present study was threefold: Our first goal was
to examine
the role of recovery experiences in the relationship between
emotional stress
and affective well-being. Analyses showed a negative impact of
emotional
stress during a significant study-related event on affective well-
being at
bedtime. Recovery experiences partially mediated this negative
relationship.
Our second goal was to test whether the application of emotion
regulation
strategies as a response to emotional stress impacts recovery
experiences
after stressful events at university or concerning the studies.
93. The use of
reappraisal to regulate one’s emotions buffered the negative
impact of
emotional stress on recovery experiences, as did the use of
expressive
suppression. Both the mediation and moderation results even
hold when
controlling for trait positive and negative affectivity,
strengthening the va-
lidity and significance of this study. Our third goal was to shed
light on the
effects of situational (instead of habitual) emotion regulation by
implement-
ing a diary design.
In summary, the study extends previous research in the stress
domain in
several ways. With regard to existing research on recovery
experiences, our
results improve the understanding of the link between job/study
demands and
health outcomes. Mediating effects of recovery experiences
have recently
been supported in a longitudinal study (Kinnunen, Feldt,
Siltaloppi, &
Sonnentag, 2011); however, our study is among the first to
support these
results on the day-level. Because there is only little research
concerning
predictors of recovery experiences, our findings further expand
current
knowledge by revealing emotional stress to be an inhibitor of
recovery
experiences (e.g., Cropley & Purvis, 2003; Sonnentag & Bayer,
2005).
Furthermore, with its focus on situational emotion regulation,
94. our study
extends recovery research by pointing out a determinant that has
similar
beneficial effects as for example job control (cf. Cropley et al.,
2006); both
326 Schraub, Turgut, Clavairoly, and Sonntag
reappraisal and expressive suppression apparently help in
detaching and
relaxing from emotional stress.
These results complement research on individual differences in
emotion
regulation and on experientially manipulated emotion
regulation, which
highlight reappraisal as a healthy form of emotion regulation
(e.g., John &
Gross, 2004; Mauss, Cook, Cheng, & Gross, 2007). They imply
that, in the
notion of the Job Demands-Resources Model (Demerouti et al.,
2001),
emotion regulation strategies can be considered a personal
resource that
moderates the consequences of emotional job demands. In
accordance to the
theoretical assumption that reappraisal prevents the experience
of emotional
stress to grow bigger by reframing the situation (Gross & John,
2003), our
results show that reappraisal apparently makes further self-
control after the
emotionally stressful event less necessary, so that resources are
freed for
95. promoting recovery experiences. Unexpectedly, we found that
expressive
suppression, which is considered a rather unhealthy way of
emotion regula-
tion when applied habitually (John & Gross, 2004; Srivastava et
al., 2009),
also buffered negative effects of emotional stress. Although we
argued for a
resource-consuming negative effect of expressive suppression,
this contra-
dictory finding is in line with results of some other studies,
giving rise to the
question of whether expressive suppression should generally be
considered a
detrimental strategy (e.g., Befahr & Cronin, 2010; Cole, Walter,
& Bruch,
2008; Schraub et al., 2011). In the present study, the definition
and measure-
ment of expressive suppression as situational emotion
regulation may explain
the unexpected positive effect of expressive suppression. This
means that we
accounted for inter- and intrapersonal variation and focused on
emotion
regulation in a specific situation rather than a habitual
regulation strategy.
Suppressing one’s emotional expression during the experience
of increased
emotional stress, in this case, turned out to be a useful
mechanism to cope
with the stress. We concur with Befahr and Cronin (2010) in
assuming that
interpersonal effects might play a significant role in explaining
beneficial
effects of expressive suppression. In the present study, the
situational sup-
96. pression of expressions of emotional stress may have evoked
more positive
responses from interaction partners. These positive responses
may have, in
turn, distracted from the negative experience. This would be in
line with
Sonnentag and Fritz’ (2007) argumentation that the expression
of negative
emotions can undermine recovery experiences by inducing
continuing cog-
nitive and emotional engagement with the adverse experience.
Our study results underline the importance of considering
intrapersonal
variation in emotion regulation; more than 80% of the variance
in emotion
regulation was attributable to intrapersonal variance. Thus,
contextual or state
antecedents seem to be stronger predictors of momentary
emotion regulation
than individual differences are. As discussed earlier, such a
state focus may
327Emotion Regulation, Recovery, and Well-Being
lead to different outcomes than a habitual focus, and it may
only be the
chronic use of expressive suppression that has detrimental
effects.
We consider the diary design of the present study to be its
particular
strength. Reducing probability for retrospective biases (Alliger
& Williams,
97. 1993), the diary method more adequately captures emotional
experiences and
well-being than do assessments at only one or two points of
time, because
emotions and well-being change in short intervals. Furthermore,
effects of
intrapersonal variance in emotion regulation can only be
detected by repeated
time- or event-contingent measurement, as used in this study.
Indeed, the
diary design and the respective adaptation of Gross and John’s
(2003)
emotion regulation questionnaire made it possible to reveal
short-term effects
of expressive suppression that diverge from those typically
found in cross-
sectional studies, or in longitudinal studies merely using
individual difference
measures. In addition, the high intrapersonal variance in
affective well-being
(about 75%) implies that by analyzing day-level antecedents of
affective
well-being, we gained information that gets lost in studies that
conceptualize
affective states as between-subjects variables (Netzlek et al.,
2006).
Limitations and Implications for Future Research
Clearly, the sample of this study limits the generalizability of
its results.
Findings from examining undergraduate students who reported
4.8 work
hours per day on average cannot be directly applied to
employees in a work
setting; demographic characteristics like age, family
98. responsibilities and
education might be important moderators of the consequences of
significant
emotional events on well-being. However, Chang and
colleagues (2007),
who compared employee and student samples with regard to the
relationship
between the experience of emotional stress and organizational
citizenship
behaviors in a meta-analysis, found a stronger effect for the
employee
sample. Taking these authors’ finding into account, our findings
could be
even more pronounced in an employee sample. Although we
consider our
results relevant for the current generation of university
students, we recom-
mend their replication in another context and with a
demographically more
diverse sample.
A methodological issue that may be improved in future research
is data
collection. A time-contingent assessment with higher frequency
(e.g., three
times per day) or an event-contingent assessment would allow
the capture of
events, emotions and behavior even closer to their occurrence
and with
higher internal validity. However, our repeated-measurement
design allowed
for control of between-person differences in the focus study
variables and
thus represents a more adequate assessment for the dynamic
constructs we
focused on than a cross-sectional assessment would have been.
99. 328 Schraub, Turgut, Clavairoly, and Sonntag
For a possible explanation of the moderation effects, especially
for the
unexpected effect of expressive suppression in the present
study, we draw on
interactional theories of emotion regulation (Côté, 2005). These
suggest that
interaction partners’ reactions determine one’s well-being,
meaning that (a)
one’s (positive) emotional expression is mirrored in (b)
(positive) reactions
by the interaction partner, which again (c) influences (enhances)
one’s
well-being. Research showing that the suppression of verbal and
nonverbal
expressions of emotional stress at the workplace buffers
negative effects on
recovery and performance (Brown, Westbrook, & Challagalla,
2005; Cole et
al., 2008; Sanz-Vergel, Demerouti, Moreno-Jiménez, & Mayo,
2010) sup-
ports this line of reasoning. It may well be that expressive
suppression has
positive effects in high-power-distance situations (e.g., between
student and
professor), but that it has negative effects in equal-power
relationships (e.g.,
between student and significant other). Because we did not
assess reactions
by interaction partners in this study, we recommend follow-up
research to
shed light on this possible explanation of beneficial short-term
100. effects of
expressive suppression.
A related variable that determines the effects of expressive
suppression
are cultural values. In experimental settings, Butler, Lee, and
Gross (2007,
2009) demonstrated that cultural context moderated the
consequences of
expressive suppression on both social and physiological
measures during
face-to-face interactions between American university students.
Matsumoto,
Yoo, and Nakagawa (2008) further showed that cultural values
determine the
use and the consequences of certain emotion regulation
strategies. We can
thus hypothesize that cultural orientations in Germany or, more
specifically,
in the academic setting, may have influenced our findings. As
Schwartz
(1999) demonstrated, Germans value intellectual autonomy
higher than af-
fective autonomy, which means that the pursuit of one’s own
intellectual
ideas is more important than the pursuit of affectively positive
experiences.
Hofstede (2001) found that Germans (compared with other
cultures) have a
medium level of uncertainty avoidance, which means they don’t
feel very
comfortable with ambiguity, thus taking measures to reduce it
and to control
the future. A hypothesis drawn from this could be that the
combination of
rather low Affective Autonomy and rather high uncertainty
101. avoidance might
make emotion regulation via expressive suppression more
probable and
accepted. The assessment of cultural context ranging from
participants’
values on the individual level to organizational and societal
culture (cf. Chao,
2000) would help to clarify this issue and extend the study’s
external validity.
An additional variable that also relates to cultural values and
that should
be addressed in further studies is work significance. If work is
highly
significant for a person’s self-concept, negative work-related
emotional ex-
periences might provide more threat to this person’s self and,
consequently,
have stronger negative effects.
329Emotion Regulation, Recovery, and Well-Being
As shown in this and quite some other studies, recovery
experiences are
an important resource for affective well-being. Guided by
Conservation of
Resources theory (Hobfoll, 1989), a next step of research could
be to
investigate what factors help people not only to engage in
recovery experi-
ences, but to preserve their positive effects.
Practical Implications
102. The importance of emotion regulation and of daily recovery
experiences
in maintaining people’s well-being has been supported in this
study. Because
high levels of psychological stress and strain have been reported
for the
current student generation (Obergfell & Schmidt, 2010), we
encourage uni-
versities to expand training and coaching programs, for example
by integrat-
ing a preventive module on healthy studying techniques in
introductory
courses. In such stress management trainings (for examples, see
Roger &
Hudson, 1995; Walach et al., 2007), the topics of emotion
regulation and
recovery experiences should be addressed (cf. Hahn et al.,
2011). According
to our findings, participants should be informed about the
potential harm of
emotional stress for their well-being, learn how to regulate
negative emo-
tional experiences, as well as become aware of the importance
of actively
seeking recovery experiences. Altogether, students would
thereby learn to
reflect on their work-life-balance. Doing so in an early stage
might not only
prevent cases of psychologically exhausted students, but might
also exert
beneficial effects on people’s stress management in their later
careers.
CONCLUSION
As this study demonstrates, recovery experiences depend on the
103. way
people deal with experiences of emotional stress. On days on
which partic-
ipants either reappraised the situation or suppressed their
emotional expres-
sions more than usual, they reported greater recovery
experiences. In line
with previous research, we conclude that reappraisal can be
recommended as
a healthy strategy to regulate one’s emotions. In addition, the
suppression of
emotional expressions may, applied in the situation evoking
emotional stress,
be helpful in overcoming experiences of emotional stress. We
conclude that
by learning how to use emotion regulation during emotionally
stressful work
situations, people can foster subsequent recovery experiences
that restore
their resources and consequently improve their affective well-
being at bed-
time.
330 Schraub, Turgut, Clavairoly, and Sonntag
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Received January 30, 2011
Revision received August 7, 2013
Accepted August 9, 2013 �
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335Emotion Regulation, Recovery, and Well-Being
Lab-3: Cyber Threat Analysis
In Lab-3, you will do some cyber threat analysis by browsing
several websites and services maintained by either security
companies, volunteers, or hackers. Nothing will harm your
119. computer as long as you don't push the limits by clicking on the
links and ignoring the browser's security warnings.
To ensure %100 security, you can consider using the Firefox
browser inside your Kali VM instead of using the browser at
your computer. If you proceed with your computer, then it is
recommended to update your browser if it is out of
date.Section-1: Analysis of zone-h.org
Zone-h.org is used and most probably operated by hackers to
share the websites that they defaced. They don't provide any
details on how they hacked the website; instead, they share the
URL of the defaced website and a mirror for the defaced
webpage.
If you are planning to use your computer instead of Kali VM, it
is strongly suggested to open a new incognito/InPrivate/Private
browser window for the following steps:
1) Enter the website:
www.zone-h.org
2) Click on the Archive menu on the top menu. You will see the
result screen similar to below:
There is a lot of information on defaced websites on this page,
including the original URL and the hacked version of the
website (on the mirror link at the rightmost column). Hacked
versions of the websites give some clues on the motivations of
the hackers; you can see political reasons, have some fun, or a
basis to make cyberspace secure.
The legends M and R provide more insight on the defacement.
M means mass defacement. If you click one of the M letters,
you can see the defacements initiated from a specific IP
address. Mass defacements are usually succeeded by the help of
scripts. Hackers prepare the scanning and exploitation scripts,
scan thousands of websites for a particular vulnerability, and
exploit the ones that have the specific vulnerability.
3) Click on one of the M letters you spotted, and see the
websites defaced from the same IP address. You can see the IP
120. address in the address bar.
Note: You can perform a whois query to see the detailed
information about the IP address you found, including contact
information and geographical location.
4) To see a redefacement, you can click one of the R letters you
spotted.
Below is an example screenshot of a redefacement, myschool.ng
website has been defaced twice in two years.
5) You can click the ENABLE FILTERS link at the top and
search for the websites with gov extension. You can see the
result of this query below.
Section-2: Pastebin.com
A pastebin site hosts the text-based data such as source codes,
code snippets, and anything worth sharing. Pastebin.com is the
oldest pastebin site. Pastebin.com had been hosting the pastes of
the hacktivist group, Anonymous. After pastebin.com started
monitoring the site for illegally pasted data, Anonymous began
to a new service:
https://anonpaste.org. This pastebin site is used for
hacktivist purposes. Anybody can paste text here and -so-called-
securely sent. You cannot search among pasted content.
There are many small and restricted pastebin sites on the dark
web. A specific hacker group may share things like exploit
codes, malicious payloads internally. They also use the pastebin
services to share the information they stole like passwords,
credit card numbers, etc.
You can see the public pastes in the pastebin website. Google
121. indexes public pastes. You can perform the following searches
on Google and check whether there are pastes in pastebin.com.
Please review the search sites to get an idea of what kind of
information is being shared among hackers in the pastebin.
· Exploit code site:pastebin.com
· Shellcode site:pastebin.com
· Malware code site:pastebin.com
· Keylogger code site:pastebin.comSection-3: Interactive Threat
Maps
There are many websites and services that provide threat
intelligence data. Some of them provide information for free;
most of them offer paid subscriptions.
These are two services from Cisco and SANS Institute,
respectively.
https://talosintelligence.com/reputation_center/: Shows the
malicious hosts spreading malware and sending spam e-mail on
the world map. You can check the reputation of the IP
addresses and domain names on this serves as well.
https://isc.sans.edu/threatmap.html: Shows the density of the
different threat feed per country.
SANS Institute provides a FightBack service on this address:
https://isc.sans.edu/fightback.html. They forward the
strong cases to the ISPs after analyzing the logs and other
evidence provided by the Internet user.
Last but not least, the following blog page provides the top 10
cyber-attack maps; it is worth reviewing as it gives the
screenshots and a fair amount of information.
https://securitytrails.com/blog/cyber-attack-mapsSection-4:
Fighting with Spam and Malware
Thousands of phishing websites try to trick people into
122. believing that they are on the official website so that they try to
steal sensitive information like passwords, credit card numbers,
SSNs. If you come up with such a website, you can submit it to
Phishtank.org. Phishtank database has been used by reputation
engines and virus scanners, such as virustotal.com. Therefore
you help to secure cyberspace. The website of PhishTank is
https://phishtank.org.
URLhaus does a similar thing for the websites that spread virus.
The website of URLhaus is
https://urlhaus.abuse.ch.
You can review both web services. For example, enter the
PhishTank website and see the recent submissions similar to
below:
You can click on the ID numbers to see the phishing
websites.Section-5: Checking URLs
Below services are just two examples by which you can check
websites:
https://www.virustotal.com: Check the website if it spreads
malware, or it is a phishing website. Currently, VirusTotal
makes the controls of the submitted URLs using ~80 different
antivirus services.
https://sitecheck.sucuri.net: Check the website for malware and
blacklisting.
You can choose some websites from PhishTank and URLhaus
and scan them using VirusTotal and Sucuri’s SiteCheck.Weekly
Learning and Reflection
In two to three paragraphs (i.e., sentences, not bullet lists) using
APA style citations if needed, summarize, and interact with the
content covered in this lab. Summarize what you did as an
123. attacker, what kind of vulnerabilities did you exploit, what
might have prevented these attacks. Mention the attackers and
all of the targets in your summary. You can provide topologies,
sketches, graphics if you want. In particular, highlight what
surprised, enlightened, or otherwise engaged you. You should
think and write critically, not just about what was presented but
also what you have learned through the session. You can ask
questions for the things you're confused about. Questions asked
here will be summarized and answered anonymously in the next
class.
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![usefully
enhance our knowledge of the effects of emotion regulation on
stress
responses.
Keywords: diary study, emotion regulation, well-being,
recovery
After stressful events, people need time to recover to restore
their
resources (Meijman & Mulder, 1998). Accordingly, recent
evidence
shows that recovery experiences are positively related to
different mea-
sures of psychological well-being (e.g., Geurts & Sonnentag,
2006; Hahn,
Binnewies, Sonnentag, & Mojza, 2011; Sonnentag, 2003;
Sonnentag &
Fritz, 2007). However, while studies indicate that high work
demands
Eva Maria Schraub, Sarah Turgut, and Karlheinz Sonntag,
Department of Industrial and
Organizational Psychology, Psychological Institute, Heidelberg
University, Germany; Vera
Clavairoly, Department of Psychological Aging Research,
Psychological Institute, Heidelberg
University, Germany.
Correspondence concerning this article should be addressed to
Eva Maria Schraub, Work
and Organizational Psychology, Heidelberg University,
Hauptstrasse 47-51, 69117 Heidelberg,
Germany. E-mail: [email protected]
International Journal of Stress Management © 2013 American
Psychological Association](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)