MARISA SALANOVA, ARNOLD B. BAKKER and SUSANA LLORENS
FLOW AT WORK: EVIDENCE FOR AN UPWARD SPIRAL
OF PERSONAL AND ORGANIZATIONAL RESOURCES*
ABSTRACT. The present 2-wave study among 258 secondary school teachers
investigates the relationship between personal and organizational resources on
the one hand, and work-related ﬂow on the other hand. On the basis of
s (1988) conservation of resources theory, Bandura’ social cognitive
theory (1997; 2001), and Fredrickson’s (1998) ‘‘broaden-and-build’’ theory of
positive emotions, we formulated two hypotheses: (1) personal resources (i.e.,
self-eﬃcacy beliefs) and organizational resources (including social support
climate and clear goals) facilitate work-related ﬂow (work absorption, work
enjoyment, and intrinsic work motivation); and (2) work-related ﬂow has a
positive inﬂuence on personal and organizational resources. The results of a
series of structural equation modeling analyses oﬀer clear support for both
hypotheses. The theoretical and practical implications of these ﬁndings are
KEY WORDS: ﬂow, organizational resources, personal resources, positive
What is a good work life is a basic topic for workers and organi-
zations nowadays, being of fundamental relevance for psycholog-
ical research as well. Recently, Luthans (2002a, b) has noted the
need for Positive Organizational Behavior (POB) research, de-
ﬁned as the study and application of positively oriented human re-
source strengths and psychological capacities that can be measured,
developed, and eﬀectively managed for performance improvement
in today’s workplace (Luthans, 2003, p. 179). Wright (2003) has
argued that the mission of POB must also include the pursuit of
* This research was supported by a grant from the Bancaixa Foundation
(#11232.01/1) and the Spanish Ministry of Science & Technology (CICYT
Journal of Happiness Studies (2006) 7:1–22 Springer 2006
employee happiness and health as viable goals in themselves. The
present two-wave study among school teachers will focus on the
experience of ﬂow at the workplace. We concur with Wright
(2003) that the pursuit of ﬂow is a viable goal in itself, and will
examine its dependence on and value for the quality of employ-
ees’ working environment. More speciﬁcally, the current study
will examine whether personal and organizational resources facil-
itate ﬂow at work, and whether employees who experience ﬂow
mobilize more resources over time.
Flow at Work
Csikszentmihalyi (1997, p. 29) has described the ﬂow experience
as: ‘‘one that many people have used to describe the sense of
eﬀortless action they feel in moments that stand out as the best
in their lives.’’ In this state, people are intensely involved in an
activity and so nothing else seems to matter. In addition to the
pleasure in the activity and the intrinsic interest to continue
doing it, the total immersion in an activity seems to be a central
aspect of the ﬂow experience (Csikszentmihalyi et al., 1993; Ellis
et al., 1994; Ghani and Deshpande, 1994; Larson and Richards,
1994). On the basis of these previous studies, Bakker (2005)
applied the concept of ﬂow to the work situation, and deﬁned
ﬂow as a short-term peak experience at work that is character-
ized by absorption, work enjoyment and intrinsic work motiva-
tion. Absorption refers to a state of total concentration, whereby
employees are totally immersed in their work. Time ﬂies, and
they forget everything else around them (cf. Csikszentmihalyi,
1990). Employees who enjoy their work and feel happy make a
very positive judgment about the quality of their working life
(cf. Veenhoven, 1984). This enjoyment or happiness is the out-
come of cognitive and aﬀective evaluations of the ﬂow experi-
ence (cf. Diener, 2000; Diener and Diener, 1996). Finally,
intrinsic work motivation refers to the need to perform a certain
work-related activity with the aim of experiencing the inherent
pleasure and satisfaction in the activity (cf. Deci and Ryan,
1985). Intrinsically motivated employees are continuously inter-
ested in the work they are involved in (Harackiewicz and Elliot,
1998). Employees who are motivated by the intrinsic aspects of
MARISA SALANOVA ET AL.
their work tasks want to continue their work; they are fasci-
nated by the tasks they perform (Csikszentmihalyi, 1997).
Do Resources Lead to Flow at Work?
Bakker’s (2005) study among music teachers showed that
organizational resources can be important antecedents of ﬂow
experiences among teachers and their students. He found that
teachers working at schools with high levels of autonomy, social
support, supervisory coaching, and feedback were most likely to
experience ﬂow at work. Demerouti et al., (2001) deﬁned job
resources as those physical, psychological, social, or organiza-
tional aspects of the job that either/or: (1) are functional in
achieving work goals; (2) reduce job demands and the associ-
ated physiological and psychological costs; (3) stimulate per-
sonal growth and development. Examples of job and
organizational resources are social support from colleagues, per-
formance feedback, skill variety, job control, and supervisory
coaching. These resources have motivational potential because
they make employees’ work meaningful, hold them responsible
for work processes and outcomes, and provide them with infor-
mation about the actual results of their work activities (cf.
Bakker et al., 2003; Hackman and Oldham, 1980). Although we
could not ﬁnd other studies relating organizational resources to
work-related ﬂow, several recent studies have provided addi-
tional evidence for the motivational potential of resources. In
their study among human service professionals (including con-
sultants, nurses, and teachers), Bakker et al. (2004) have shown
that resources foster work engagement, which, in turn, is pre-
dictive of (colleagues’ ratings of) organizational citizenship
behavior. Similarly, Bakker et al. (2003) have shown that pro-
duction workers’ organizational resources foster organizational
commitment, which, in turn, causes reduced absence frequency.
Furthermore, in a series of studies in several occupational set-
tings, Salanova and her colleagues have shown that organiza-
tional resources can be important predictors of work
engagement, which, in turn, is predictive of important organiza-
tional outcomes including service climate (Salanova et al., in
press), and group performance (Salanova et al., 2003). Finally,
Houkes (2002) included several job resources in her longitudinal
RESOURCES AND FLOW AT WORK 3
research among bank employees and teachers, and found evi-
dence for a causal relationship between the ‘‘motivating poten-
tial score’’ (an additive index, including skill variety, task
identity, task signiﬁcance, autonomy, and job feedback) and
intrinsic work motivation. By contrast, a lack of organizational
resources has a detrimental eﬀect on workers’ motivation and
performance (e.g., Wong et al., 1998), since it precludes actual
goal accomplishment, and undermines employees’ learning
opportunities (e.g., Kelly, 1992).
This latter reasoning is consistent with Conservation of
Resources (COR) theory (Hobfoll, 1989, 1998, 2002). Accord-
ingly, people seek to obtain, retain, and protect resources and
stress occurs when resources are threatened with loss or lost, or
when individuals fail to gain resources after substantive
resource investment. Thus, COR-theory also places the acquisi-
tion and facilitation of resources as a central motivational con-
struct. It is assumed that individuals seek to acquire and
maintain resources, including objects (e.g., a home, clothes,
food), personal characteristics (e.g., self-esteem), conditions
(e.g., being married or living with someone who provides social
support), and energies (e.g., time, money, and knowledge).
However, the two principal types of resources that have been
examined within the COR framework are personal and psycho-
social resources (Hobfoll et al., 2003). The present study will fo-
cus speciﬁcally on self-eﬃcacy (i.e., a personal resource), in
addition to organizational resources.
Personal resources are aspects of the self that are generally
linked to resilience. The best studied of these pertain to individ-
uals’ sense of their competence to successfully control and
impact their environment (Hobfoll et al., 2003). Self-eﬃcacy has
shown its power as a buﬀer in stress processes, and it has also
been related to better health, better self-development and great-
er social integration (Bandura, 1997, 1999, 2001). In addition,
there is considerable evidence regarding the positive eﬀects of
self-eﬃcacy on performance and well-being in diﬀerent domains
such as the workplace, school, and sports (Bandura, 1999,
2001). For example, research in the domain of work shows that
high levels of eﬃcacy beliefs have a positive impact on employ-
ee well-being (Grau et al., 2001) and work engagement
MARISA SALANOVA ET AL.
(Salanova et al., 2003), and can buﬀer the negative impact of
job demands on burnout (Salanova et al., 2000; 2002). Accord-
ing to Bandura (1997, 2001), eﬃcacy beliefs contribute to moti-
vation in several ways. Eﬃcacy beliefs inﬂuence (a) the
challenges people pursue, (b) the eﬀort they expend, and (c)
their perseverance in the face of obstacles.
So far, self-efﬁcacy (as a personal resource) can be seen as
one of the resources people want to protect (a very important
one, which has received considerable research attention). The
organizational resources we included are yet other examples of
resources people are motivated to protect. In other words: all
resources we studied are part of the resource reservoir people
possess. This also means that COR theory can be seen as the
overall framework we used for our study.
Therefore, we hypothesize that, in addition to organizational
resources, self-efﬁcacy beliefs may also enhance the experience
of ﬂow at work:
Hypothesis 1: Organizational resources (e.g., social support
and clear goals) and personal resources (i.e., self-efﬁcacy beliefs)
have a positive inﬂuence on the experience of work-related ﬂow
(i.e., absorption, work enjoyment and intrinsic work motiva-
tion) (causation hypothesis)
Does Flow at Work Lead to an Accumulation of Organizational
and Personal Resources?
Is it also possible that ﬂow experiences lead to more organiza-
tional and personal resources? Some recent studies indeed found
evidence for reversed causal relationships between organizational
resources and employee psychological well-being. In her study
among bank employees and teachers, Houkes (2002) found evi-
dence for a reversed causal relationship between the motivating
potential score and intrinsic work motivation. Furthermore,
Wong et al. (1998) reported that time 1 job satisfaction pre-
dicted several organizational resources assessed at time 2
(2 years later), including autonomy, task identity, skill variety,
task signiﬁcance and feedback. In a similar vein, in a cohort
study among a heterogeneous sample of Dutch employees,
De Lange (2003) found a reversed causal relationship between
job control and job satisfaction.
RESOURCES AND FLOW AT WORK 5
Taken together, these ﬁndings suggest that work motivation
and job satisfaction can both be outcomes as well as predictors
of organizational resources, such that enhanced well-being
results in more favorable organizational resources over time.
This is consistent with Hobfoll’s (1989, 2002) claim that because
resources are valued either in their own right or because they
enable the acquisition or preservation of other valued resources,
people are motivated to create resources. Once resources are
obtained, people are motivated to protect them. In addition, the
hypothesis that ﬂow at work may lead to more personal and
organizational resources over time is consistent with Fredrick-
son’s (1998, 2002) ‘‘broaden-and-build’’ theory of positive
emotions. Accordingly, positive emotions broaden people’s
momentary thought-action repertories and build their enduring
personal resources. Research has indeed shown that positive
emotions such as joy, happiness, and interest have long-term
adaptive beneﬁts because broadening builds enduring personal
resources including physical, intellectual, social and psychologi-
cal resources (Fredrickson and Branigan, 2005; Fredrickson,
1998, 2001). Moreover, research with the broaden-and-build the-
ory showed that momentary experiences of positive emotions
can build enduring psychological resources and trigger upward
spirals toward emotional well-being. Thus, positive emotions not
only make people feel good at the moment, but also feel good in
the future (Fredrickson and Joiner, in press). The current study
will focus on the building eﬀect (i.e., the second hypothesis in
the theory). Accordingly, positive experiences (such as ﬂow at
work) build people’s enduring personal resources. Indirect evi-
dence consistent with this building hypothesis can be drawn
from correlational and experimental studies that link positive
states, traits and behaviors with physical, intellectual and social
resources (Boulton and Smith, 1992; Bryan, Mathur & Sullivan,
1996; Caro, 1988; Hazen and Durrett, 1982).
Furthermore, research on Social Cognitive Theory (Bandura,
1997, 1999, 2001) shows that experiencing positive emotions
increases self-eﬃcacy as a personal resource. When people judge
their capabilities, they rely partly on bodily information con-
veyed by emotional states. According to Bandura, emotions are
especially relevant when it comes to stress and health: negative
MARISA SALANOVA ET AL.
mood diminished perceived eﬃcacy, whereas positive mood
enhances it. As is a tradition in psychological research, most
studies investigated the impact of negative emotions (i.e., anxi-
ety) on self-eﬃcacy (Bandura, 1997). Studies relating positive
states (including ﬂow) to self-eﬃcacy are sparse, but there is evi-
dence showing that positive states inﬂuence self-eﬃcacy, Salano-
va et al. (2002) showed that positive states at work such as
enthusiasm, satisfaction and comfort inﬂuenced self-eﬃcacy
through work engagement in diﬀerent occupational groups. Re-
sults indicated that the more positive experiences, the more
engagement, which, in turn, predicted future self-eﬃcacy.
On the basis of these theories and ﬁndings, we formulated
our second hypothesis. The two hypotheses are graphically
depicted in Figure 1.
Hypothesis 2: The experience of work-related ﬂow (i.e.,
absorption, work enjoyment and intrinsic work motivation) has
a positive inﬂuence on organizational and personal resources
(reversed causation hypothesis).
Participants and Procedure
A follow-up study with two waves was carried out among
Spanish secondary school teachers. At the beginning of the
Figure 1. The upward spiral model of resources and ﬂow at work.
RESOURCES AND FLOW AT WORK 7
academic year, a letter was sent to 50 secondary schools
explaining the goal of the research. Self report questionnaires
including scales to measure the main variables of the current
study plus other scales related to psychological well-being were
distributed among 600 secondary teachers from these schools
and were sent back through surface mail to the university. In
total, 484 respondents from 34 schools returned the question-
naire (81% response rate). Eight months later, at the end of the
academic year, identical questionnaires were distributed among
the same schools. After deletion of missing cases, it turned out
that 258 teachers (57% women, 43% men) from 24 schools had
completed both questionnaires, and their scores could be used
in the longitudinal analyses. Thus, 57% of the teachers who
participated at T1 also participated at T2. The mean age of the
sample was 40 years (SD = 7.01).
In order to test whether the drop-outs differed from the
panel group, we compared the T1 background variables of both
groups (i.e., age, gender, type of school (private versus public),
teaching experience, and organizational tenure). Results from
ANOVAs and v
analyses showed that there were no signiﬁcant
diﬀerences between the groups regarding background variables.
We therefore concluded that the panel group does not diﬀer
from the drop-outs in terms of background variables.
Organizational Resources were measured with the short version
of the FOCUS Organizational Culture Questionnaire
´lez-Roma et al., 1995), including twelve items. Four re-
sources were assessed with three items each, namely: (1) social
support orientation or the extent to which there are kindly and
supportive relationships among organizational members (e.g.,
‘‘How many people with personal problems are helped?’’), inno-
vation orientation or the extent to which there is openness to
new ideas and projects (e.g., ‘‘How many people are trying out
new ways of working?’’), rules orientation or the extent to
which organizational members’ behavior is regulated by formal
norms and rules (e.g., ‘‘How often are jobs performed accord-
ing to deﬁned procedures?’’), and goals orientation or the extent
to which activities and behaviors are oriented towards the
MARISA SALANOVA ET AL.
attainment of previously established objectives (e.g., ‘‘How of-
ten does management specify the targets to be attained?’’). The
answer categories ranged from 1(‘‘never/nobody’’) to 6 (‘‘al-
ways/most of the people’’). Cronbach’s alpha of each scale is
shown in Table I.
Personal Resources was measured as eﬃcacy beliefs. It was
measured with Schwarzer’s (1999) 10-item generalized self-
eﬃcacy scale. However, consistent with social cognitive theory,
the scale was slightly adapted for use in speciﬁc domain (i.e.,
the workplace, which is schools in our study). One example
item is: ‘‘I will be able to solve diﬃcult problems in my work, if
I invest the necessary eﬀort.’’ The anchors ranged from 1
(‘‘never’’) to 6 (‘‘always’’). Self-eﬃcacy was included in the
structural equation model as a latent variable, indicated by two
reliable halves. Cronbach’s alpha of the scales as measured at
T1 and T2 is shown in Table I.
Flow at work was assessed with the WOrk-reLated Flow scale
(WOLF; Bakker, 2001). The WOLF includes thirteen items
measuring absorption (4 items), work enjoyment (4 items), and
intrinsic work motivation (5 items). Examples are: ‘‘When I am
working, I forget everything else around me’’ (absorption),
‘‘When I am working very intensely, I feel happy’’ (work enjoy-
ment), and ‘‘I get my motivation from the work itself, and not
from the rewards for it’’ (intrinsic work motivation). The partic-
ipants were asked to indicate how often they had each of the
experiences during the preceding week (0 = never, 6 = every
The data were analyzed with structural equation modeling tech-
niques using the AMOS software package (Arbuckle, 1997).
Each of the model components was included as a latent factor
in the model, and was operationalized by the subscales intro-
duces above as observed, indicator variables. Speciﬁcally, orga-
nizational resources was indicated by four variables (social
support orientation, innovation orientation, rules orientation
and goals orientation), ﬂow at work was indicated by the three
ﬂow scales of the WOLF (absorption, work enjoyment, and
RESOURCES AND FLOW AT WORK 9
Means, standard deviations (SD), Cronbach’s Alpha (on the diagonal), and correlations for the study variables, N= 258
Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Time 1 Variables
1. Support Orientation 3.71 1.07 0.86
3.47 1.20 0.65*** 0.87
3. Rules Orientation 3.93 1.09 0.29*** 0.38*** 0.68
4. Goal Orientation 3.50 1.21 0.51*** 0.63*** 0.49*** 0.82
5. Absorption 3.57 1.03 0.05 0.09 0.05 0.19** 0.78
6. Work Enjoyment 4.44 1.08 0.18** 0.19** 0.09 0.24*** 0.54*** 0.90
7. Intrinsic Work
3.91 1.25 0.21** 0.20** 0.14* 0.32*** 55*** 0.83*** 0.89
4.02 0.94 0.19** 0.19** 0.10 0.23*** 0.24*** 0.48*** 0.43** 0.88
3.84 0.96 0.20** 0.19** 0.13* 0.22*** 0.28*** 0.45***. 0.41** 0.70** 0.90
Time 2 Variables
4.22 1.17 0.09 0.06 0.12* 0.18** 0.15* 0.21** 0.28*** 0.25*** 0.26*** 0.86
3.32 1.24 0.52*** 0.62*** 30*** 58*** 0.19** 0.23*** 0.26*** 0.21*** 0.24*** 0.15* 0.91
12. Rules Orientation 3.71 1.14 0.30*** 0.29*** 0.54*** 0.46*** 0.12* 0.14* 0.19** 0.13* 0.09 0.08 0.43*** 0.67
13. Goal Orientation 3.37 1.24 0.43*** 0.49*** 0.41*** 0.69*** 0.23*** 0.21*** 0.30*** 0.29*** 0.26*** 0.21** 0.74*** 0.54*** 0.88
14. Absorption 3.50 1.01 0.05 0.05 0.07 0.17** 0.72*** 0.45*** 0.45*** 0.14* 0.19** 0.16** 0.22*** 0.13* 0.24*** 0.82
15. Work Enjoyment 4.41 1.01 0.21** 0.25*** 0.12* 0.30*** 0.44*** 0.72*** 0.68*** 0.39*** 0.43*** 0.29*** 0.34*** 0.26*** 0.35*** 0.47*** 0.89
16. Intrinsic Work
3.81 1.19 0.16** 0.21** 0.16* 0.29*** 0.44*** 0.64*** 0.75*** 0.35*** 0.35*
* 0.29*** 0.34*** 0.24*** 0.35*** 0.52*** 0.79*** 0.87
4.08 0.83 0.16** 0.16** 0.09 0.24*** 0.31*** 0.43*** 0.41*** 0.58*** 0.27*"* 0.29*** 0.33*** 0.19** 0.38*** 0.31*** 0.35*** 0.45*** 0.89
3.92 0.89 0.18** 0.21** 0.11 0.21** 0.21** 0.37*** 0.36*** 0.54*** 0.61*** 0.28*** 0.32*** 0.14* 0.35** 0.22*** 0.53*** 0.43*** 0.84*** 0.93
Note. All correlations are signiﬁcant at the ***p< 0.001 level, **p< 0.01, *p< 0.05.
MARISA SALANOVA ET AL.
intrinsic work motivation), and self eﬃcacy was indicated by
two reliable halves of Schwarzer’s modiﬁed scale.
A number of competing structural equation models were
ﬁtted to the data in several steps. First, a model without cross-
lagged structural paths but with temporal stabilities and syn-
chronous correlations (Model 1) was speciﬁed. The temporal
stabilities were speciﬁed as correlations between the constructs
for each possible pair of measurement waves. This model esti-
mates therefore the total stability coeﬃcient between measure-
ment waves 1 and 2, without decomposing the variance into
constituent paths (direct and indirect eﬀects) (see Pitts et al.,
1996). Second, this stability model was compared with three
more complex models that were nearest in likelihood to the
hypothesized structural model: (a) Model 2: a model that is
identical to Model 1 but also includes cross-lagged structural
paths from Time 1(T1) organizational resources and T1 per-
sonal resources to T2 work-related ﬂow. This model is the cau-
sality model; (b) Model 3: a model that is identical to Model 1
but also includes cross-lagged structural paths from T1 ﬂow to
T2 organizational resources and T2 personal resources. This is
the reversed causation model; (c) Model 4: a model that includes
reciprocal relationships between organizational resources, per-
sonal resources, and work-related ﬂow. This model includes all
paths of Model 2 and 3, and is called the reciprocal model.
For all models, the measurement errors of the same indica-
tors (i.e., subscales) collected at different time points were
allowed to covary over time (e.g., a covariance is speciﬁed be-
tween the measurement error of work enjoyment as measured at
T1 and the measurement error of this scale as measured at T2).
While in cross-sectional data measurement errors should gener-
ally not covary, in longitudinal measurement models the errors
of measurement corresponding to the same indicator should co-
vary over time. According to Pitts et al. (1996), this speciﬁca-
tion of covariance between errors of measurement accounts for
the systematic (method) variance associated with each speciﬁc
The various nested models were compared by means of the
diﬀerence test (Jo
¨reskog and So
¨rbom, 1986). Besides the chi-
square statistic, the analysis assessed the goodness-of-ﬁt index
RESOURCES AND FLOW AT WORK 11
(GFI) and the root mean square error of approximation
(RMSEA), Furthermore, AMOS provides several ﬁt indices that
reﬂect the discrepancy between the hypothesized model and the
baseline, Null model. In the present analyses, the comparative
ﬁt index (CFI) and the non-normed ﬁt index (NNFI) are uti-
lized. Marsh, Balla and Han (1996) recommended the latter two
indices, because they are less dependent on sample-size than the
statistic and the GFI. In general, models with ﬁt indi-
ces > 0.90 and RMSEA < 0.08 indicate a good ﬁt (Hoyle,
1995). Preliminary analyses showed that the demographics
(included as covariates) were not systematically related to the
model variables, and did not modify the results of the model
testing. Therefore, to facilitate model estimation, the
demographics were excluded from all further analyses.
Prior to the model testing, the means, standard deviations,
Cronbach’s alpha coefﬁcients and bivariate correlations (includ-
ing test-retest correlations) were computed (see Table I). As can
be seen from the table, all variables had an alpha coeﬃcient
higher than 0.70 (with the exception of rules orientation which
had an alpha coeﬃcient of 0.68 and 0.67 at T1 and T2), and
test–retest reliabilities of at least 0.54. The highest test–
retest reliabilities resulted for self-eﬃcacy followed by innova-
tion orientation. This means that self-eﬃcacy and innovation
orientation are relatively stable over time.
Table II displays the overall ﬁt indices of the competing models.
In general, all models indicate a good ﬁt since all ﬁt indices are
equal to or higher than 0.90, the RMSEA is smaller than 0.08,
and the ratio between the v
statistic and the number of degrees
of freedom is relatively low. We will ﬁrst concentrate on the
The causality model (M2) proved to be superior to the stability
model (Ml), Delta v
(2) = 35.29, p< 0.001. This suggests that
MARISA SALANOVA ET AL.
the inclusion of cross-lagged paths from organizational resources
and personal resources to ﬂow is substantial. Additionally, the
reversed causality model (M3) ﬁtted signiﬁcantly better to the
data than the stability model, Delta v
(2) = 33.49, p< 0.001.
This indicates that the model with the cross-lagged path from T1
ﬂow to T2 organizational resources and T2 personal resources
showed a better ﬁt to the data than the model including only tem-
poral stabilities and synchronous correlations (M1).
diﬀerence test regarding the stability model vis-a
the reciprocal model (M4; see Table II) revealed that the addi-
tion of reciprocal eﬀects signiﬁcantly improved the stability
model, Delta v
(4) = 103.10, p< 0.001. Moreover, the model
with the cross-lagged reciprocal relationships among the vari-
ables (M4) resulted in a signiﬁcantly better ﬁt to the data than
the causality model (M2) and the reversed causality model
(M3). The results of the v
diﬀerence tests for both comparisons
(M2 versus M4 and M3 versus M4) are Delta v
(2) = 67.81,
p< 0.001, and Delta v
(2) = 69.61, p< 0.001, respectively.
This means that the theoretical model including cross-lagged
reciprocal relationships between organizational resources, per-
sonal resources, and work-related ﬂow ﬁts best to the data.
We will now discuss the speciﬁc structural relationships that
resulted from these models. First of all, it is important to note
that all manifest variables loaded signiﬁcantly on the intended
Goodness-of-ﬁt indices for the alternative resources – ﬂow models, N= 258
df pGFI RMSEA NNFI CFI
M1. Stability Model 250.88 117 0.001 0.90 0.07 0.95 96
M2. Causality Model
(JR, PR ﬁFlow)
215.59 115 0.001 0.91 0.06 0.96 0.97
M3. Reversed Causality Model
(Flow ﬁJR, PR)
217.39 115 0.001 0.92 0.06 0.96 0.97
M4. Reciprocal Model 147.78 113 0.02 0.94 0.04 0.99 0.99
Null Model 3776.16 153 0.001 0.26 0.30 – –
= chi-square; df = degrees of freedom; GFI = goodness-of-ﬁt
index; RMSEA = root mean square error of approximation; NNFI = Non-
nonmed ﬁt index; CFI = comparative ﬁt index. JR = Job Resources,
PR = Personal Resources.
RESOURCES AND FLOW AT WORK 13
latent factors. Inspection of the output revealed that all indica-
tors of organizational resources had loadings on the intended
latent factor higher than 0.50, both at T1 and T2. Furthermore,
a both waves of measurement, the loadings of absorption, work
enjoyment and intrinsic work motivation on the ﬂow factor
were higher than 0.67, whereas the loadings of the two self-
eﬃcacy indicators were higher than 0.92. Second, the autocorre-
lations between the two waves were 0.71 for job resources, 0.60
for ﬂow, and 0.47 for self-eﬃcacy.
Hypothesis 1 asserted that job and personal resources would
have lagged positive effects on work-related ﬂow. The model
that includes these causal relationships, the reciprocal model
(M4), resulted in signiﬁcant lagged, and positive effects of TI
job resources on T2 ﬂow (b= 0.26, t= 4.07, p< 0.001), as
well as a positive eﬀect of T1 personal resources on T2 ﬂow
(b= 0.41, t= 6.20, p< 0.001). These ﬁndings clearly support
our ﬁrst hypothesis.
Hypothesis 2 stated that T1 ﬂow would have a lagged posi-
tive eﬀect on T2 organizational resources and on T2 personal
resources. The ﬁnal reciprocal model also resulted in signiﬁcant
reversed causal structural relationships. Speciﬁcally, the rela-
tionships were as follows: T1 ﬂow – T2 organizational resources
b= 0.33 (t= 4.72, p< 0.001), T1 ﬂow T2 personal resources
b= 0.47 (t= 7.17, p< 0.001). These ﬁndings clearly support
our second hypothesis. Thus, the results from Model 4 (includ-
ing the reciprocal relationships) showed that both causal and
reversed causal relationships were simultaneously active. The
signiﬁcant paths of the reciprocal model are graphically dis-
played in Figure 2. The hypothesized predictors at TI explained
11% of the variance in T2 resources, 28% of the variance in T2
ﬂow, and 22% of the variance in T2 self-eﬃcacy.
In the current study, the phenomenon of ﬂow at work was
investigated among teachers. The main research questions were
whether ﬂow is facilitated over time by the presence of personal
and organizational resources, and whether ﬂow has a positive
MARISA SALANOVA ET AL.
inﬂuence on the availability of resources over time. In this study,
ﬂow was conceptualized as a short-term peak experience at work
that is characterized by absorption, work enjoyment and intrin-
sic work motivation. Absorption refers to a state of total con-
centration, whereby employees are totally immersed in their
work. Time ﬂies, and they forget everything else around them.
Enjoyment is a positive emotion of feeling happy and endorsing
a very positive judgment about the quality of the working life.
Finally, intrinsic work motivation refers to the need to perform
a certain work-related activity with the aim of experiencing the
inherent pleasure and satisfaction in the activity.
On the basis of a brief literature review, it was predicted that
ﬂow develops over time when personal and organizational
resources are sufﬁciently available, because these positive char-
acteristics foster ﬂow experiences. In addition, we hypothesized
a reversed causal relationship between resources and work-
related ﬂow, i.e., that ﬂow would predict future resources. The
results of SEM-analyses -- using a cross-lagged panel design
Figure 2. Standardized solution (maximum likelihood estimates) of the up-
ward spiral model of resources and ﬂow at work, N= 258.
RESOURCES AND FLOW AT WORK 15
(N= 258 secondary school teachers) supported both hypothe-
ses. More speciﬁcally, the results showed that the theoretical
model including cross-lagged reciprocal relationships between
resources and work-related ﬂow ﬁts best to the empirical data.
This means that both causal and reversed causal relationships
were simultaneously active in the reciprocal relationship be-
tween (organizational and personal) resources and work-related
Organizational resources -- a combination of different types
of organizational climate orientation indicators such as social
support (e.g., people help each other mutually, there are a
good relationship between the co-workers), innovation (e.g.,
teachers can give suggestions to improve the quality of work,
and they can put forward new ideas to improve the work), rules
(e.g., the work that the teacher must do is plenty of norms
and the decisions about the work process is made by the
supervisors and goals, e.g., objectives are clearly deﬁned) --
had a positive inﬂuence on the occurrence of ﬂow among
teachers over the time. Simultaneously, personal resources --
operationalized as strong beliefs about one’s own competence
at work -- also fostered these ﬂow experiences over time, So
far, our ﬁrst hypothesis was conﬁrmed. These ﬁndings are con-
sistent with previous research on the motivational power of re-
sources at the work place (Bakker et al., 2003, 2004;
Demerouti et al., 2002; Hackman and Oldham, 1980; Houkes,
2002; Salanova et al., 2003, 2004). Furthermore, the current
study expands research on COR theory, showing that people
seek to obtain, retain and protect resources in order to avoid
stress and be psychologically healthy, including positive experi-
ences (i.e., ﬂow). COR theory evidences that the acquisition
and facilitation of resources is a central motivational con-
struct. In addition, this study also agrees with predictions
from Social Cognitive Theory, which assumes that self-eﬃcacy
facilitates well-being (Bandura, 1997, 1999, 2003). In this
sense, we found that self-eﬃcacy is a powerful personal
resource to build future positive experiences such as being
immersed in one’s work, to enjoy working, and to feel intrinsi-
cally motivated at work. Feeling competent in the present,
seems to predict being in ﬂow in the future.
MARISA SALANOVA ET AL.
The second hypothesis was that the experience of ﬂow
(absorption, work enjoyment and intrinsic work motivation) has
a positive inﬂuence on organizational and personal resources.
Here and based on the literature, we assumed a reversed causal
effect of work-related ﬂow on organizational and personal
resources. The results conﬁrmed our hypothesis. More speciﬁ-
cally, experiences of ﬂow at work at the present inﬂuenced the
gain of organizational and personal resources in the future.
These ﬁndings are consistent with previous research on COR
theory (Hobfoll, 1988), broaden-and-build theory (Fredrickson,
1998) and social cognitive theory (Bandura, 1997). For example,
as resources are valuable in their own right, people in one way
or another are motivated to create or increase their resources
(Hobfoll, 1989, 2002), and positive experiences are powerful
mechanism to increase and build organizational and personal
(i.e., self-eﬃcacy) resources over time (Bandura, 1997; Fredrick-
son, 2001). In our study, we found that ﬂow as a positive expe-
rience at work inﬂuenced the organizational and personal
resources among secondary school teachers over time. Further-
more, we found that ﬂow predicted self-eﬃcacy slightly more
strongly than self-eﬃcacy predicted ﬂow. These results ﬁt with
what Hobfoll et al. (2003) found for sense of mastery as well.
Taken together, and perhaps even more interesting, our
results showed that there exist reciprocal relationships between
resources and ﬂow. These results are supporting in a way the
predicted upward spiral in which positive emotions are building
resources that in turn inﬂuence positive emotions (Fredrickson,
2002). Findings of the current study showed that such positive
work-related experiences as ﬂow build organizational and per-
sonal resources, and that sometimes these positive experiences
are reciprocally inﬂuenced by these resources.
Study Limitations and Future Research
A strong point of this study is its longitudinal character. Thus,
the current ﬁndings can be framed in terms of cause and effect
relationships because main variables are measured at different
points of time, and we used several theories to formulate our
hypotheses (Taris, 2000). However, we the results still need to be
interpreted with some caution because of the non-experimental
RESOURCES AND FLOW AT WORK 17
nature of this study. Another strong point is that the model ex-
plained an acceptable part of the variance in the dependent vari-
ables (11% of the variance in T2 resources, 28% of the variance
in T2 ﬂow, and 22% of the variance in T2 self-eﬃcacy). How-
ever, a limitation of this study is that only teachers’ self-reports
were used in the examination of relationships between resources
and ﬂow. Here the problem of common method variance may
have played a role. Because the study was conducted at 24 dif-
ferent schools, we could examine to what extent the ﬂow experi-
ence per school was a function of the resources available at that
school. Additional analyses showed that the scores on the ﬂow
dimensions absorption, work enjoyment and intrinsic work
motivation were highest at those schools where many resources
were available. These ﬁndings indicate that the subjectively re-
ported resources were anchored in the objective working situa-
tion. Results thus oﬀer speciﬁc starting-points for interventions
aimed at mobilizing organizational resources and the promotion
of ﬂow experiences at schools.
Finally, this study is limited to the context of secondary
school teachers. Since the main hypotheses were conﬁrmed
regarding reciprocal relationships between ﬂow and resources, it
would be interesting and relevant to examine this phenomenon
in other occupational ﬁelds. In addition, it would be important
to test the upward spiral model of these relationships using a
cross-lagged panel design with more waves, in order to test the
long time effects of these reciprocal relationships over time.
It can be concluded that it is important for teachers to have
sufﬁcient resources available in their work. There are ‘‘good
ingredients’’ of organizational culture such as social support,
innovation, rules and goals orientation are examples of such
organizational resources. When these resources are present in
the organizational environment teachers are more likely to get
engrossed in their work, to enjoy their work activities, and to
be intrinsically motivated. They seem to form the basic ingredi-
ents of a ‘‘Good Working Life.’’ As Nakamura and
Csikszentmihalyi (2002) noted a good life is one that is charac-
terized by complete absorption in what one does (p. 89). When
MARISA SALANOVA ET AL.
work (re)design takes into account these organizational
resources, there may be a good start, Job (re-)design seems to
be a good organizational strategy to implement in schools in or-
der to better teaching activities performance. On the basis of
this job design, it is possible to optimize the working environ-
ment. On the other hand, feeling competent at work is other
important (personal) resource to optimize at work. Our study
showed once more the positive consequences of self-eﬃcacy: the
power of believing you can...
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Address for Correspondence:
Department of Psychology
Jaume I University
Campus Riu Sec, s/n 12017
MARISA SALANOVA ET AL.