Journal of Occupational and Organizational Psychology (2009). 82, 595-615 n d U H c • h,((•' 2009 The British Psychokigicat Society * - ' ^ ^ ™ bociety

595

The

BritishPsychological

www. bpsjournals.co.uk

'Flow* at work: An experience sampling approach

Clive J. Fullagar'* and E. Kevin Kelloway^'Kansas State University. Manhattan, Kansas, USA^Saint Mary's University, Halifax, Canada

One of the core constructs of the positive psychology movement is that of 'foroptimalexperience. The current study investigated the relationship between 'flow', thecore job dimensions, and subjective well-being (SWB). as well as distinguishing betweenthe state and trait components of flow. Experience sampling methodology (ESM) wasused to track 40 architectural students over a 15 week semester while they engaged instudio work. Hierarchical linear modelling (HLM) indicated that 74% of the variancein flow was attributable to situational characteristics compared to dispositional factors.Results also indicated that academic work that was high in skill variety and autonomy wasassociated with flow. Flow was found to be correlated with positive mood. Cross-laggedregression analysis showed that momentary flow was predictive of momentary mood andnot vice versa. The strengths and limitations of using ESM to study subjective workexperiences and well-being are discussed, as well as the implications of the study of flow

or optimal experience for industrial/organizational psychology.,.

A repercussion of Martin Seligman's (1999) call for a 'positive psychology' can bewitnessed in the upswing in organizational research that emphasizes optima!experience, transcendent pertbrmance, excellence, and positive deviance ((Cameron,Dutton, & Quinn, 2003; Luthans, 2001). One concept that would seem to be particularlyappropriate to positive or^ianizationa! scholarship isthat of "flow'. Flow' was a term firstcoined by Csikszentmihal) i (1975) wbo defined it as the holistic sensation tbat peoplefeel when they act witb total involvement (p. 4)'. Flow bas been defined as theexperience of working at full capacity, with intense engagement and effortless action,where personal skills match required challenges (Nakamura & Csikszentniihalyi, 2002).It is regarded as an 'optimal experience' to such an extent tbat the two terms are oftenused interchangeably.

Originally Csikszentmihalyi studied flow among painters, rock climbers, dancers,and musicians. Most research has focused on the experience of flow in voluntaryand pleasurable leisure and sporting activities. However, the experience of flowhas also been frequently reported wbile engaging in work related tasks as opposed toleisure activities (Csikszentmibalyi & LeFevre, 1989; Fave & Massimiiii, 1988). This is not

* Correspondence should be addressed to Dr Clive j. Fullagor, Department of Psychology, Kansas State University, Manhattan,KS 66506, USA (e-mail: fullagar@ksu.edu).

IDOI: 10.1348/096317908X357903

596 Give }. Fullagar and L Kevin Keihway

siir|:)rising given tiiat many of the precursors of flow (such as immediate feedback,comiiiensurate challenges and skills, and clear goals) are more likely to be found in workactivities. Despite the fact that flow experiences have been reported in work, relativelylittle research has studied flow in the work context. The current research is alongitudinal investigation of some of the antecedents and outcomes of flow whileengaging in work activities.

Operationalizations of flowA remarkable consistency has been found in the description of flow across diversesettings (C^sikszentmihalyi, 1975). The dominant characteristic of early models of flowwas the emphasis on the balance between challenges and skills (Csikszentmihalyi, 1975).However, qualitative and quantitative research has identified several key elementsinherent in the experience of flow (Csikszentmihalyi, 1990,1993;Jackson, 1996:Jackson& Marsh, 1996). Specifically flow has been characterized as consisting of ninecomponent states (Csikszentmihalyi, 1990, 1993). The first is a challenge-skill halunceand it refers to the match that must be present between the skills required to peribrnithe task and challenges of the situation. If the challenges in the activity exceed theindividual's skill level then faistration rather than flow occurs. If the activity does notchallenge the individual's skills then the result is boredom. If both challenges and skillsare balanced but do not exceed a specific level of difficulty and complexity then apathy islikely to occur. Flow only occurs when the balance between challenges and skillsexceeds a level that is topical for daily experiences. Second, there is a merging of actionand atvareness. One s involvement in the task is such that behaviour becomes automaticand spontaneous and there is little awareness of the self other than what one is doing.Third, there is a strong sense of what has to be done and clarity of goals. Fourth, theactivity itself provides clear, immediate and unambiguous feedback concerningprogress towards one's goals. Fifth, there is an extremely high degree of involvementwith, focus on, and concentration on the task at hand. Attention and energies areexquisitely focused on the task and there is an absence of distraction. Sixth, there is asense of exercising control without having to try and be in control thai is termed theparadox of control. People in flow report they feel in control but as soon as theirattention shifts to tr> ing to maintain control they lose the sense of flow. Seventh, there isa loss of self-consciousness as all concern for self disappears and the person becomesone with the activity. Eighth, there is a transformation of time that seems to occur inwhich there is loss of time awareness. Finally, the ninth dimension of flow is the autotelicexperience. Csikszentmihalyi (1990) coined the term autotelic experience from the twoGreek words auto, meaning self, and telos meaning goal. Such optimal experiences are anend in themselves and are so enjoyable that they become intrinsically motivating. Flow isdescribed as such an enjoyable experience that one is motivated to return to this state.

Considered together these nine dimensions of flow provide a comprehensivemeasure of optimal experience (Jackson & Ecklund, 2004). In the most recent researchon flow in the workplace flow has been measured as either a global construct(e.g. Demerouti, 2006) or the challenge/skill dimension of flow has been focused on(e.g. Eisenberger. Jones, Stinglhaniber, Slianock, & Randall, 2005). Such unidimensionalapproaches have been found to be psychometrically acceptable üackson & Ecklund,2002; Jackson & Marsh, 1996; Marsh & Jackson, 1999), Indeed, the nine subscaies offlow have been found to be highly correlated and there appears to be a substantialproportion of shared variance (Jackson & Ecklund, 2004).

Flow at work 597

Ffow: State or trait

Theodore Mischel (1969) has stated that 'clarification of key concepts must be part ofthe busiticss of theory construction in psychology' (p. 264). An important pieceof this clarification process is distinguishing whether a construct is a trait or a state(Alien & Potkay. I98I). A trait construct is a behaviour that individuals perform regularly- it is a reliably permanent internal disposition, A state construct, on the other hand,is more ephemeral and is a behaviour that is performed occasionally in response tosituational contingencies (Allport & Odbtrt, 193(>)-

There is some debate as to whether flow is a state or a trait. Flow as a state is definedas a psychological state of mind that is transitory, existing at a given moment in time andat a partictilar level of intensity, and experienced while performing a specific activity.However, Csikszcntmihaiyi (1990) has also referred to the 'autotelic personality" in thatcertain individuals may have psychological characteristics that make them more proneto the experience of flow regardless of the situation. Allen and Potkay (1981) havepointed out that the distinction between tniit and state constructs is frequently andarbitrarily made by varying the instructions on scales that contain the same items. Wbena state construct is being assessed participants are asked to rate their state of minti'today' or 'now'. When traits are measured, subjects are required to rate their state ofmind 'in general". This arbitrary distinction can be found in operationalizations of flow(Jackson & Ecklund, 2004). Dispositional assessments of tbe fiow experience (e.g. tbedispositional fiow scale, Jackson & Ecklund, 2004) measure the general tendency toexperience flow during an activity usually selected by the participant. Respondents arerequired to recall and rate the frequency' with which he/she experience tbe variouscharacteristics of flow G^ckson & Ecklund, 2004), The premise bebind this approach isthat individuals who report higher frequencies of flow characteristics have a greaterpredisposition to experience How, State measures of flow (e,g. the flow state scale (FSS),Jackson & Ecklund, 2004) are usually administered immediately following an activity',and assess the level of flow characteristics during the activity. By surveying participantsclose to the conclusion of an activity it is assumed thai a more accurate assessment ofthe flow experience is assessed, one that is less susceptible to recall bias. As Allen andPotkay (19K1) note, changing the instructions presumes that there is a change in therespondent's orientation from state to trait. Using such minor changes in the wording ofsurvey instructions to differentiate between state and trait constructs is an arbitrary andtenuous distinction.

Tbere are other, more valid ways of distinguishing between state and trait constrticts,Zuckerman (1983) has suggested that state and trait variables behave in ver)' differentways. First, trait variables should be expected to exhibit low variation from situation tosituation. That is they should have high test-retest reliabilit)' and shotild n<H changemuch with transient changes in situational conditions. State variables, on the otherhand, should vary considerably as situational contingencies and characteristics change(i.e. they should have low te.st-retest reliability). Second, trait variables sbould correlatemore bighly with other trait measures and state measures sbould be more stronglyasstKiated witb situational. or moment-to-nioment variables. To date no research hasbeen undertaken that sifts out the trait versus state components of fiow. This is animportant distinction, not only from the perspective of conceptual clarification, but alsoin terms of the practical and theoretical implications of the study of flow.

As we point out above, recent operationalizations of flow (Jackson &. Ecklund, 2002,2004) have defined it as both a state and a trait construct. Flow is described as apsychological state inflluenced by state-based factors with individuals varj'ing in their

598 Clive j. Fullagar and L Kevin Kellaway

propensity to regularly experience flow Oackson, Kitniecik, Ford, & Marsh, 1998).Most research that has investigated both the trait and state aspects of flow hasproposed that the relationship between situational characteristics and state-based flowis tnoderated by dispositional eletnents. For example, Kimiecik and Stein (1992) havesuggested that ibr athletes tbe relationship between experience of flow and engagingin sporting activities is contingent upon whether tbe athletes have a task- or egt>involved goal orientation. More recently, two studies that have investigated flow atwork found tbat tbe relationship between flow (challenge-skill balance) and mood, taskinterest and performance was moderated by need ibr achievement ( Eisenberger et al.,2005), and that tbe relationship between flow and in-role and extra-role performancewas dependent on conscientiousness (Demerouti, 2006).

Although personality characteristics stich as achievement motivation, aestheticism,and inquisitiveness have been associated with flow (Csikszentmibalyi, Ratbunde, &Wlialen, 1993: Jackson, 1984) no research bas been conducted tbat sifts out tbe traitversus state components of optimal experience. The first aim of the current study wasto track flow over ati extended period of time in order to assess the extent to whichit varied from task to task and across individuals. If flow is a dispositional construct,tben one would expect variation between tbe individual's predisposition to experienceflow, and little moment-to-moment variance. On the otber hand, if tbere is a large state-related component in flow, tben one should expect to find considerable variation inmomentary flow.

Hypothesis /: Ratings of flow will exhibit both within-individual variance (state) and between-individual (trait) variance with the fornner accounting for most of the variance in ratings.

Task characteristics and fiowlhere has been a considerable amount of research that has investigated the linksbetween specific aspects of a person s job and his/her well-being (see Warr, 1999, for astimmary). One framework that has been particularly influential in tinderstanding thisrelationship is the job characteristics model (Hackman & Oldham, 1980; Oldham, 1996).Turner, Barling, and Zacharatos (2002) have argued the utility of tbis model in under-standing the concept of "healthy' work and in promoting psychological and physicalwell-being.

The JCM outlines five core job cbaracteristics that influence workers' attitudes andbehaviour (Hackman & Oldbam. 1975, 1980). Skill variety refers to tbe degree to wbichtbe job requires different activities and skills to carry out the task. Task identity is theextent to wbich the job requires completion of a whole and identifiable piece of work.Task significance indicates the degree to which the job bas a meaningful impact onotber people. Autonomy refers to the extent to which the worker has independentdiscretion in determining the schedule and process of work. Finally, feedback is tbeextent to which the worker is provided with information concerning bow well be/she Isperforming. Tbese live core job characteristics are proposed to impact on criticalpsychological states tbat then influence affective and motivational outcomes.

Although the majority of research has supported tbe validity of tbe JCM (Fried &Ferris. 1987; Taber SL Taylor, 1990), most studies have not tested tbe full, tbree-stage model (Behson, Eddy, & Lorenzet, 2000). Specifically, tbe mediating effects of tbecritical psycbological states have been neglected, witb the preponderance of researchassessing the direct impact of the core job characteristics on a varietj' of outcomes

Row at work 599

(Behson et al., 2000; Johns, Xie, & Fang, 1992). Recently there has been some meta-analjtical evidence to suggest that the critical psychological states in the J("M do explainsome additional variance in the outcome measures (Behson et al.. 2000). One consistentconclusion regarding research on the JCM is that more research attention needs to takeinto account p,sychological .states in the development of an understanding ofthe impactof work on employees Oohns et al., 1992; Renn & Vandenberg, 1995).

The aim of the present research was not to validate the JCM but to use it as anomological fratnework to establish the validit)' of flow as a critical psychological statethat mediates the relationsliip between certaiti core job characteristics atid well-beingamong individuals who are engaged in work activities. Recently, Demerouti (2006)found that motivating job characteristics are indeed positively related to How at work.However, Demerouti did not ascertain the differential effects of each ofthe five corejob dimensions on flow. Following Fried and Ferris' (1987) guidelines she calculated asingle, unweighted additive index that assessed the motivating potential score of thejob. We believe that curtain core job characteristics have stronger conceptual links toflow and that others will be weakly related or unrelated to optimal experience.

For example, the utilization of a variety of skills to meet the challenges of work hasconsistently been found to be associated with job satisfaction and motivation (Ciavin &Axelrod, 1977; O Brien. 1983). Csikszentmihalyi's flow theory also holds that an optimalbalance between challenges and skills inherent in a task is crucial for the intrinsicallymotivating and satisfying subjective state that he termed flow (Csikszentmihalyi, 1990).Those tasks that require the utilization of many skills antl abilities will be the mostintrinsically rewarding and conducive of flow (('sikszentmihaiyi & Rathimde, 1992).C;onseqiiently, one would expect a strong association between the core job charac-teristic of skill variety and the experience of flow.

Not only is it important that the task be challenging to facilitate flow, but there mustalso be clear goals so that the performer can remain fully engaged in the task. Feedbackbecomes the mechanism that enables individuals to clarifj- their progress towardsthese goals (Jackson & Bcklund, 2004). Csikszentmihalyi (1997) states that immediatefeedback is a necessary component to experience flow. Feedback is also an importantcore dimension outlined by the job characteristics model. However, unlike the flowexperience where feedback is intrinsic to the task itself. Hackman and Oldham (1975)emphasize feedback from extrinsic sources such as supervisors, co-workers and clients.Nonetheless, one would expect the two forms of feedback to be related and thatfeedback from outside sources should be related to the overall experience of flow.

Autonomy and a sense of control also have been argued to be central elements ofboth flow (Csikszentmihalyi, 1997) and the core job dimensions (Hackman & Oldham,1975). There is also some empirical evidence indicating that autonomy is an antecedentof flow experiences (Bakker. 2005, 2008). More recently a tneta-analysis of locus ofcontrol at work (Ng, Sorensen. & Eby. 2006) found internal locus of control to besignificantly associated with positive task characteristics and motivation. Consequentlyon would expect high levels of autonomy to be positively correlated witb flow.

However, task significance and task identity refer to broader perspectives of workconcerning whether a job has a substantial impact on the lives of other people andthe extent to which the job entails an entire product. Flow has been associated withindividual engagement in specific, isolated, activities that do not necessarily have animpact on any one else beyond the individual performing the task or consist of ameaningful whole. Consequently one would not expect these job characteristics to beassociated with optimal experience.

600 Clive j. Fullagar and E. Kevin Kelloway

Hypothesis 2: The experience of flow will be positively related to (a) skill variety, (b) feedback,and (c) autonomy.

F/ow and well-beingOne of the repercussions of positive psychology' has been an upswing in interest inwell-being. A distinction has been made between hedonic and eudaimonic well-heing(Kyan & Deci, 2001; Waterman, 1993). Hedonic approaches are pleasure-based anddefine well-being in terms of tbe presence of pleasure and tbe absence of pain. Well-beingfrequently is assessed by measuring momentary positive aJfecl or mood (e.g. Diener, Sub,Lucas, & Smitb, 1999; Kabneman, Diener. & Schwarz, 1999). Eudaimonic approaches,on tbe other hand, emphasize optimal functioning and personal expressiveness(Waterman, 1993). For example, Ryff has defined psychological well-being from aeudaimonic perspective and as consisting of six dimensions: autonomy; personalgrowth; seli-acceptance; life purpose; mastery; and positive relatedness (Ryff. 1989,1995; Ryff & Keyes, 1995). Flow could also be regarded as a momentary form ofeudaimonic well-being. It consists of many of the same aspects of psychological well-being; a sense of control and mastery over the task, a clarity of purpose, a beliefthat one possesses the skills to perform well, engagement in and relatedness to the task,and a sense of enjoyment (Csikszentmihalyi & Csikszentmihalyi, 1988). There is evidenceto suggest that well-being is best conceptualized as a multidimensional construct thatincludes botb hedonic and eudaimonic aspects (King & Napa, 1998; Mcíiregor & Little,1998; Ryan & Deci, 2001). However, emotional positivity is regarded as beingindependent of eudaimonic well-being. Indeed, positive affect has been suggested to bean outcome of eudaimonic processes. Tbere is some longitudinal research tbat suggeststhat positive affectivity is an outcome of both eudaimonic functioning (Reis, Sbeldon,Gable, Roscoe, & Ryan, 2000; Ryff & Singer, 1998; Sheldon, Ryan, & Reis, 1996) and flow(Han, 1988; Hull, 1991; Massimini & Carli, 1988) further strengthening the convergentvalidity argument tbat eudaimonic well-being and flow are conceptually similar.

Hedonic well-being has been primarily operationalized as subjective well-being(SWB) or bappiness (Diener & Lucas, 1999; Kabneman. 1999; Ryan & Deci. 2001).An important component of SWB is the presence of positive mood. The otbercomponent is life satisfaction. In the current study we focused on assessing momentarypositive affectivity rather tban life satisfaction as the latter bas been found to berelatively stable across time and to only slightly fluctuate around an individual set-point(Fujita & Diener, 2005). Flow experiences have been ibund to be associated with suchpositive moods and emotions as excitement, joy, ecstasy, happiness, and pride (Bloch,2002) and SWB (Br>ce & Hawortb, 2002; Clarke & Haworth, 1994; Fave & Massimini,1988; Seongyeul, 1988). LeFevre (1988) found tbat the more time people spent in (lowthe more positive affect tbey experienced. Tbe little empirical researcb tbat has beenundertaken on flow in a work context suggests tbat people who enjoy tbeir jobs (jftenreport experiences of flow while at work (Hryce & Haworth, 2002; c;sikszentmihalyi,1990; Csikszentmihalyi & LeFevre, 1989; Haworth & MiU. 1992). More recently flow(speciflcally bigh challenge skill balance) bas been ibund to be associated witb greaterpositive mood and bigher performance, particularly among bigb need for achievers(Eisenberger et al., 2005).

However, much (îf the research tbat has investigated tbe relationsbip betweenflow and mood is confounded by a methodology that makes for a tautt)logous rationalefor the relationsbip between flow and well-being. Csikszentmibalyi ( 1975) initially asked

Flow at work 601

subjects to describe highly enjoyable experiences or activities in order to definethe characteristics of flow. Also there has been some research to suggest that therelationship between flow and well-being may not be as strong as originally proposed.Clarke and Haworth (1994) found that college students perceived work where skillsexceeded challenges to be more enjoyable than flow activities where skills matchedchallenges. Csikszentmihalyi (1997) has commented on the paradoxical nature of flowin that although individuals report positive experiences with flow at worit, they may notnecessarily feel happy at work. The current research surveyed a wide variety of workactivities (not just enjoyable ones) in order to determine the relationship between flowand hedonic well-being.

Finally, although flow has been associated with positive mood, the causal directionof this relationship has yet to be established, AH of the research on flow at workand míMid has been cross-sectional in nature. In contrast, in the current study we adopta longitudinal design in order to sift out the lagged effects of momentary flow onmomentarj- mood and vice versa. Consistent with our view of flow as a criticalpsychological state, we, we hypothesize that;

Hypothesis 3: The experience of flow will be positively related to positive mood.[ • .

The current studyThus, the current study is an examination of the relationships between flow, taskcharacteristics and hedonic well-being. We propose that flow is primarily a state-likevariable (111 ), that flow is related to task characteristics (H2), and that the experience offlow is associated with hedonic well-being or mood (H3). To examine these hypotheseswe use an experience sampling methodology (ESM) in a sample of architecture studentsengaged in studio work.

Architectural students were chosen as the focus for our study for two reasons. First,architectural students spend long hours working on creative projects in studio thatwould tend to indicate that they are engaged in tasks that have a high motivatingpotential and that are conducive to flow. Second. 5th year students engage in studioprojects that are representative of the work that they would do as professional archi-tects, so the ecological validity of the work tasks is high. In our study, all participantswere enrolled in a Sth-year studio in architectural design.

Studio sessions focus on the synthesis of basic social, functional, teclinical, andaesthetic factors in design and students are required to work independently on projectsthat visually represent design ideas. Being the final studio in the architecturalprogramme, students engage in work higlily similar to the activities of their profession.Architectural studio consists of two components; 3 weekly, 4 hour formal sessions,where students collectively meet with their professor to discuss their projects; and morefrequent and inf(ïrmal independent sessions where students work on their own on theirprojects. These latter sessions usually take place at night and continue into the earlyhours of the morning. Our data collection focused on these sessions in whichindividuals worked freely on their own architecture projects.

MethodologyExperience samp/ing methodologyThe current study utilized an HSM. ESM Is a method of data collection where participantsare assessed at repeated moments over the course of time while functioning in their

602 Clive }. Fuilagar and £ Ksvin Kelioway

natural settings. Data were collected by handheld personal digital assistants (PDAs).Participants were required to carry the PDAs for a specific sampling period and torecord their behaviours, attitudes, and experiences. The PDAs were configured using aeustomized software called experience sampling program (ESP; Barrett & FeldmanBarrett, 2000).

Participants and procedureParticipants in the study consisted of 40, 5th-year architectural students from the schoolof architecture at a large American university in the Midwest of the United States. At thebeginning of the semester tour studio classes were solicited ibr 40 volunteers.Participation in the study was completely voluntary and individuals who fully partici-pated received an honorarium ($50) in return for their participation. The researchprotocol was reviewed and approved by the university Institutional Review Board.The data were collected across 15 weeks of the Fall semester of 2004. During an initialsession participants were asked to provide the researcher with three days and timeperiods during the coming week when they were most likely to be workingindependently on their projects in studio.' Each student was then given a PDA that waspre-programmed to signal participants at two random moments during the threestipulated weekly studio periods. Students were instructed to complete the work taskthat they were involved in before completing a short survey on the PDA. Once tbe initialsignal had beeped, the alarm repeated itself every 5 minutes for 15 minutes until thesurvey bad been completed. The average time between the initial signai and theparticipant s response was 4.7 minutes. Bu-ticipants were informed that they shouldonly respond when beeped during a work-related activity and that they should respondonly once during any single studio session. This process was repeated t)n a weekly basisuntil 25 trials had been completed (giving 1,000 data points in total). Tbe average timebetween measurement periods was 2.3 days.

Experienced-sampied measuresEacb PDA was programmed with an experience sampling form (ESF) that was designedto elicit a wide range of information on tbe participant s activities, mood, perceptions ofoptimal performanee, and psychological state of flow. For the researcb process to be theleast disruptive, it was important that each trial should not take longer than twominutes. Each PDA was programmed to beep participants at two random times duringevery block of diarized studio time, with the restriction that no two signals would be lesstban 60 minutes apart.

Row was measured using nine items from tbe FSS-2 (Jackson & Ekiund, 2004;Jackson & Marsh, 1996). Although this scale was designed to assess flow experienceswithin a particular physical activity, it was deemed appropriate for tbe current research.Like sports activities (the tiriginal focus for measurement), studio work occurred duringa clearly defined time period witb clearly defined activities comprising the 'event'. Thescale was theoretically grounded in Csikszentmihalyi's (1990) nine-dimensionaloperationalization of flow. It would have been too intnisive and disruptive to includeall 36 items of FSS-2 scale for each of the 25 trials. Moreover, althougli designed to

' These times did not include the regular formal meeting times of studio which occurred on Mondays, Wednesdays, and Fridaysfrom 1:00 p.m. to 5:00 p.m.

flow at work 603

measure nine dimensions of flow, it is common to take an 'overall' flow score as the sumof the dimensional scores for use in fiirther analysis (see e.g. Jackson & Marsh, 1996).Consequently, nine items were chosen on tbe basis of their loadings on each of the ninedimensions derived in the original construct validation study (Jackson & Eklund, 2004).The flow questions were preceded by tbe momentary statement, 'Please answer tbefollowing questions in relation to your experience in the work activity' that you wereengaged in when you were beeped'. Ratings were obtained by tapping with thestylus one of the following options; strongly disagree, disagree, neither agree nordisagree, agree, strongly agree which were scored 1-5 accordingly. Each item had to beresponded to lor tbe trial to progress. The order in wbich the flow items appeared in ihesurvey was randomized across situations in order to reduce response set. A total flowscore was computed tbat consisted of the sum of tbe scores on the nine flow items. Theinternal consistency coefficient of the flow scale (computed on witliin-individual meanitem ratings) was satisfactory (a = .83).

job characteristics model

Fifteen items from tbe job diagtiostic survey were used to assess participants' perceptionsof tbe task characteristics associated witb studio work (Hackman & Oldbam, 1975).Tlie job diagnostic survey is thet)reticaliy grounded in tbe job characteristics modeland assesses each of the five core task dimensions (skill variety, task identity, tasksignificance, autonomy, and feedback from the job) witb six items. Items were rewordedto be appropriate for studio work (e.g. How much were you left on your own to do yourown work?'; "How repetitious was the work in .studio?) The measure was administered atthe end of the semester. Internal consistency was satisfactory for all five core dimensions(skill variety. .71; task identity, .85; task significance, .60; autonomy, .83; and feedbackfrom tlie job, .81).

Hedonic well-being

Mood was measured witb 10 positive emotions (alert, bappy, cheerfiil, strong, active,sociable, involved, excited, clear, and relaxed) and 10 negative emotions (drowsy, sad,irritable, weak, passive, lonely, detaehed, bored, confused, and tense) tbat werearranged as bipolar items (e.g. alert-drowsy, bappy-sad). These emotions were selectedas they have been used to conceptually represent the major forms of pleasant andunpleasant emotion (Csikszentmihalyi & Csikszentmihalyi, 1988; Diener, Smith, &Fujita. 1995). A shorter list was selected as more appropriate fbr experience samplinf;(Scollon, Diener. Oishi, &. Biswas-Diener. 2005). Tbe mood items were formatted as'slider' questions, where respondents bad to move a slider towards the bipolar adjectivethat best described their mood at the time that tbey were beeped. Tbe slider questionswere scored on a scale of 1-10, with higher scores being indicative of more positivemood. Again the order of tbe mood items was randomly varied for eacb trial. Theinternal reliability of the mood scale was found to be satisfactory (Cronbach'sa — .96).

IAnalysesTo investigate tbe effect of job characteristics on flow and fiow on hedonic well-being,HLM was used (Bryk & Raudenbush, 1992). The HLM modelling approach is anextension of multiple regression that allows investigation of tbe relationships betweenvariables manifested at two levels of analysis. In longitudinal analyses there are a series

604 Clive ]. Fulhgar and £ Kevin Kelhway

Table I. Means (M), standard deviations (SD), and intercorrelations across individuals for ali study

variables (N = 40)

1. Flow2. Mood3. Si<ili variety4, Tasi< identity5. Tasl< significance6. Autonomy7. Feedback

M

32.315.913.824. i 23.753.253.16

SD

3.061.250.580.400.700.710.71

2

073«*

3

0.64**0.60**

4

0.260.41**0.21

5

0.58**0.69 *0.72**0.14

6

0.40*0.43**0.170.33*0.19

7

0.46**0.53**0.30*0.57**0.30*0.45**

*p < .05 (one-tailed); **fî < .01 (one-taiied).

of repeated measures for each individual that constitute the lowest level of variables andthe individual level becomes the second level. Thus, in the current research, to examinewithin-individual relationships between flow and mood, at the first-level, mood wasregressed on to flow for each of the 40 participants in the study. At the second level theparameters estimated at level 1 (intercepts and slopes) were regressed on level 2variables - in this case the job characteristics. HLM 5 was used (Bryk, Raudenbush, &Congdon, 2()()()) to test the hierarchical models.

One of the assumptions of regression analysis is that residual effects are indepen-dent. Obviously time-series data violate this assumption of residual independence atlevel 1 (Hofmann, Griffin. & Gavin, 2000). Cinisequently level 1 models were developedthat accounted for the residual autocorrelation in the data. This was accomplished bycontrolling for lagged flow and lagged mood in the level 1 equations. 1-igged flow andmood were centred at the mean for each individual thus removing tbe between-individual variance in this control variable (Bryk & Raudenbusch, 1992), In the currentstudy, we hypothesize and test a lagged hypothesis (i.e. flow predicts mood). To test thiswe estimated a series of cross-lagged regressions.

Results !

Hypothesis t: Between- and within-individuaf variation 'Means, standard deviations, and inter-correlations across individuals for all studyvariables are presented in Table 1. Before proceeding with the tests of the hypotheses,systematic within- and between-individual variance in the ratings of flow and moodwere investigated. These statistical assumptions were tested using two null models forboth flow and mood (see Table 2). The results indicated that there was substantialwithin- and between-individual variance for both flow and mood (Flow: a' — 22.81,Too = 8.19; Mood: CT^ — 2.22, TIH) = 1.45). Altbough HLM does not provide asignificance test for within-individual variance (a* ), it does provide a chi-squared testthat indicated that the between-individual variance was significant for both flow andmood (Flow: T,„, ^ 8.19, x^(39) = 388.95,/i < .01; Mood: Too = 1-45, x^(39) = 673.83,p < .01). The intra-class correlation for the flow measure was .26.^ indicating that

+ TOO)).

Flow at work 605

Table 2. Parameter estimates and variance components of null models tested {N = 999)

Model equations -yoo "¥io ""^ Too

Null model 1^

Row^ = ßoi + U 32.30 - 22.81 8.19**

Mood,j = ßoj + r^¡ 5.90 - 2.22 1.45**

ßoj ~ "Yoo "t" "Oi

"ßoj is the average level of f low/mood for individual j; -yoo is the grand mean of f low/mood scores;

a^ — var{r,j) the within-individual variance in flov^i/mood (computed as the average squared distance

f rom individual momentary scores and the individual's mean score); and TOO — v^riUoj) the betv/een-

individual variance in f low/mood (the variance of the 40 average f lov//mood scores).

*p < .05; **p < .01.

between-individual variance accounts for 26% of the total variance in flow. This wouldsuggest that 74% of the variance is attributable to witbin-individual variation. For mt>od,40% of the total variance was accounted for by between-individual variance, indicatingthat 60% of the variance was due to witbin-individual variation in momentary' mood.Tbese results suggest that How varies considerably from situation to situation :md tbat itbehaves predominantly like a state construct.

We also tested whether individuals' flow and mood nitings were randomlydistributed or serially dependent (Hies & Judge, 2002). Comparing the level I variancecomponents of the lagged regression model with that of tbe null models indicated thattbe lagged measures explained fbur and less tban one percent of the within-individualvariance for How and mood respectively.^ Lagged flow was found to be a significantpredictor of momentarj- flow (Flow: -YK, =^ 0.20, p < .05), but mood was seriallyindependent of its lagged effect (Mood: ^n, = 0.08, p > .05). All subsequent modelswhere flow was the outcome variable were controlled for tbe lagged effect.**

Hypothesis 2: Job characteristics and flowHypotbesis 2 suggested that the individual variance in flow would be associated witbtbe job characteristics outlined by Hackman and Oldbam's model (1975). Based ontbeoretical arguments, the ñve job characteristics were bierarchically entered into themodel as ibilows: skill variety; feedback; autonomy; task identity; and task significance.In order to test tbis hypothesis, two models were calculated: a random-coefficientregression model and an intercepts-as-outcomes model (see Table 3). Tests wereundertaken to ascertain the proportion of variimce in tbe intercept terms tbat wasexplained by the five job characteristics at level 2 (see Table 3). Tbe t tests for thefixed effects in tbe intercepts-as-outcomes model assessed the significance ofthe relationships between tbe five job characteristics and momentary' flow, wbilecontniJling for the lagged effects of flow. Tbese tests indicated that botb skill variety(7i, = 0.05, t(M) = 2.64, p < .05) and autonomy (7,, ^ 0.03, i(34) = 2.12, p < .05)were significant predictors of between-group variimce in flow after controlling for

'Percent = in^(nullmodel) - (T^OaggedeffectsmodelWtr'inulimodel).•* Following Hies and judge (2002) these results arc not presented in the main models as they ore not of substantiveinleresi fiere.

606 Clive } . Fullagar and £ Kevin Kelloway

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serial dependence effects. Neither feedback, task significance nor task identity weresignificant predictors.

To determine tbe magnitude of the relationship between the two significant jobcharacteristics and flow, we calculated separate intercepts-as-outcomes models. Usingthe residual variance in tbe intercepts, we found that skill variety' accounted for 1 S% oftbe between-individual variance in flow (TQÜ ^ 23.91, X"C39) ^ 58.67, p < .05), andautonomy explained 8% of the variance (TOO = 25.72, x^(39) = 59.45, p < .05).^

Hypothesis 3: Flow and moodIt was predicted that momentary flow would be related to momentary positive moodboth across and witbin individuals. The results of the hierarchical linear model thatwas tested are presented in Table 4. Tlie pooled slope parameter was significantlydifferent from zero (7io ^ 0.19, i(39) ^ 16.04, p < .01). The direction of the pooledslope of the regression of flow on to mood (710) indicates that higher levels of floware associated with more positive mood. The magnitude of this relationship wascalculated by comparing the within-individual variance in mood from tbe first nullmodel with the within-individual variance after controlling for flow. ' Flow accountedfor i6% of tbe within-individual variance in mood. Similarly, comparing the level 2variance components of random regression model and the null model indicated thatflow explained 30% of the between-individual variance in mood. "' In sum, resultstended to support tbe bypothesis that momentary flow was assoeiated withmomentary positive mood.

We also tested the causal direction of the relationship between flow and mood usinglagged measures of mood and flow, respectively. Results suggested tbat lagged fitiw waspredictive of mood (7,0 = 0.11, i(39) = 2.28, p < .05), and that lagged mood was notassociated with later now (7,0 = 0.10, i(39) = 0.74, p > .05).

DiscussionThe current study tried to understand tbe nature of flow at work by longitudinallytracking the flow state in a sample of architecture students working on studio projects.Specifically, we want to sift out the state and trait components of flow, to ascertainwbich work-related characteristics were associated with flow, and to determine therelationship between momentary flow and positive mood. Our results suggested thatbotb within-individual and between-individual variance comprised flow with tbeformer accounting for 74% of the total variance. Thus, flow bas both state and traitcomponents with the former predominating. Second, our data suggest that skill varietyand autonomy are the two job characteristics that make unique independentcontributions to the predictions of flow. The remaining three job characterizitics

*R^ = (Toolrandomregression) - Too(Jntercepts-os-outcomes))/T(jo(ran</omregression).R for level I model — (tr (one-way ANOVA model) - (/(random regression model))I(r(one-way ANOVA model).R for level 2 model — (-(jofone-way ANOVA model) - Tgolrandom regression model))lToo(one-way ANOVA model).

^R^ = (^a^(control model) - o^i^random regression modelW(^{one-way ANOVA model) - (165.15 - 112.77)/165.15 = 0.32.' R ^ = (Too(random regression model) - TQo(controlmodel))lToo(one-way ANOVA model) = (71.79 - 62.36)1128.77^0.07.

608 Clive J. Fullagar and E. Kevin Keltoway

Table 4. Parameter estimates and variance components testing relationship between f low and mood

{N = 999)

Model equations -yoo Tio f^^ Too T K

Ran do m-coefficient regression model t 'LI:Mood„ = ßo, + ß,^(Flow,^) + r,jL2: ßoj = 7oo + ^o,L2:ß|^ = ^,o + U„

-0.27 0.19* 1.41 2.95 0.00

"•yoo is the mean of the intercepts across groups: 7io is the mean of the slopes across groups; (T^{ri,) isthe level I residual variance; TOO(UO,) is the variance in intercepts; and 7| i (U|,) is the variance in slopes.*p< .01.

(feedback, task significance, and task identity) did not contribute to the prediction.Finally, our results showed that the experience of flow preceded changes in mood.

Howard Ciardner (2002) has pointed out that one of the reasons that psychologistshave neglected to study the actual experience of work has been the lack of anappropriate methodology to scientifically study internal feelings. In this context, it isappropriate to note that our results emerge from the use of a rigorous methodolog)' thatallows the testing of longitudinal relationships and addresses the known problems ofcross-sectional survey data (e.g. Scolion, Kim-Prieto, & Diener. 2003; Smyth & Stone,2003). We believe that the use of the ESM methodology substantially advances the studyof How and offers considerable advantage in other areas of organizational research. ESMhas been used to assess flow experiences in daily life among elementar)' and secondaryschool sttidents (Csikszentmihalyi & Hunter, 2003), students (Carli. Delle Fave, &Massimini, 1988; Csikszentmihalyi & Urson, 1984), family members (Larson & Richards,1994), psychiatric patients (Massimitii, Csikszentmihalyi, & Carli, 1987), and nursinghome residents (Voelkl, 1990). The product of such a methodology- is a den.se andsystematic description of the external conditions and subjective experience of flow indifferent situational contexts and at different moments in time. Despite the iaet that ESMallows for the study of the interactions between person and situational variables withouttbe limitations of traditional research designs (Hormuth, 1986; Hies & Judge, 2002),relatively little use has been made of ESM in organizational psycbological research.

Tlie current study had three main findings. First, we bave argued that it is importantto distinguish between the state and trait components of flow. Tbe results of the presentresearch suggest that flow, or optimal experience, is predominantly a situational stateof mind rather tban a trait or disposition. We found tbat approximately tbree quarters oftbe variance in flow was attributable to situation-io-situation fluctuations. Dispositionalvariables or traits tend to be stable across time and place (Zuckerman, 1983).Consequently such variables are less amenable to manipulation. State variables on theother hand are dynamic and cbange across time and situation. State variables are capableof being manipulated and are likely the result of an interaction between personaldispositions and tbe environment. Previous research on flow (e.g. Demerouti, 2006;Eisenberger et ai, 2005) has tended to blur the state-trait distinction. Flow has beentreated as a state variable, in tbat situational variables have been correlated with it,but measured as a tniit construct, in tbat participants' flow experiences have beenassessed across a broad range of activities. Furthermore, both of these studies were

Fiow at work 609

cross-sectional in nature making it impossible to sift out between-individual variationfrom within-individual variance.

Our study allowed this separation :md resulted in the finding that most of thevariance in fiow was within-individual giving rise to the possibility that the experienceof flow is governed by characteristics ofthe task or situation. In the particular context ofthe workplace, our results suggests that tasks can be designed to enhance the likelihoodof experiencing flow. Just what aspects of task design are important is informed by oursecond finding.

Tiie second finding of the current research was that certain work or taskcharacteristics are associated with optimal experience. Earlier research by Demerouti(2006) has found that motivating job characteristics are correlated with the experienceof flow at work. However, Demerouti did not specifically identify which job charact-eristics were associated with flow. Our finditigs indicate that flow was associatedwith studio work that was perceived as requiring different skills and that allowedthe student creative choice and discretion in their work. That these two jobcharacteristics should be correlated with flow may be due to the nature of the workthat we studied. Architectural work is cognitive in nature, with students having to uselogic and reasoning to identifv' the strengths and weaknesses of alternative solutions orapproaches to problems. Architecture studio requires students to engage in complexproblem solving where they have to independently identify problems and reviewrelated information to develop and evaluate options and implement unique solutions.The architectural student is more isolated, less monitored, and engaged in activities thatare more cognitively complex and creative in nature than many other kinds of work.Even though a different state of mind was investigated in the current research, theresults lend support to Hackman and Oldham's (1975) contetition that core jobcharacteristics are important in generating critical psychological states (such as flow).

Thus, organizations wishing to enhance employees' experience of. or likelihoodof experiencing flow, are well-advised to design ta.sks that incorporate these elementsof skill variety and autonomy. Our findings suggest that engaging in tasks that requirecomplex skills, resolving challenging problems and expressing creativity leads to theexperience of flow.

Finally, the current findings also indicated a significant and reliable relationshipbetween flow and pt)sitive mood. Students who experienced higher levels of flow alsoreported being more alert, happy, involved, excited, and so forth. This is consistent withprevious research that has found fiow to be associated with hedonic well-being, orpositive mood (Eisenberger fi rt/., 2005; Nakamura & Csikszentmihalyi. 2002). Howeverthe rationale for this relationsliip has been criticized for its circularity. Research on flowhas tended to focus on self-reports of single moments of well-being. The current studyattempted to overcome this methodological shortcoming by randomly sur\'eying abroad range of work activities, not just optimal ones, over several weeks. I.eFevre ( 1988)found that motivation, activation, and satisfaction were higher in flow experienced atwork, hut that affect was not. In fact mildly negative mood was reported by engineersand managers who had experienced flow. Positive affect was more likely to be related tofiow in leisure activities. The difference between these and the current findings mayagain be in the nature of work that was studied, ln architectural studio work many ofthetasks that are associated with high levels of flow are the same as those experienced inleisure activities (e.g. drawing atid model making).

The finding that flow is associated with positive mood has significant theoretical andpractical implicatiotis. Prederickson (1998, 2001) has argued tliat positive emotions

610 Clive J. Fullagar and £ Kevin Kelhway

broaden individuai resources and tbougbt-action repertories. There is growing evidencethat tbe psychological well-being of workers is predictive of employee performance, jobsatisfaction, and turnover (Cropanzano &Wrigbt, 2001; Staw & Barsade, 1993; Wright &Bonett, 2007; Wright & Staw, 1999). We have argued that flow is a form of eudaimonicwell-being. Our findings suggest tbat flow may be a critical psycbological state that isassociated with positive mood, a core component of psycbological well-being.Furtbermore, we established that the majtirity of variance in flow is determined bysituational characteristics ratberthan individual dispositional factors. This has importantpractical implications for the design of work. Providing work tbat is bigh in autonomyand skill variety is more likeiy to induce flow whicb is likely to have beneficialconsequences for the organization. Flow has been found to be positively associated withoptimal ftmctioning. specifically with botb in-role and extra-role performance(Demerouti, 2006; Eisenherger et al., 2005). Eisenberger et ai. (2005) bave showntbat tbe relationship between flow and employee performance is partially mediated bypositive mood. Earlier research has indicated that the relationship between positivemood and performance is particularly pertinent to tasks ihat require spontaneity andcreativity (Eisenberger & Rhoades, 2001; George & Brief, 1992). Specifically, positivemood has been .shown to promote creative thinking. The longitudinal nature of ourresearch contributes to the literature tbat bas established a relationship betweenoptimal experiences at work and positive mood by suggesting tbat this association isconsistently reliable across time and situations. Clearly flow is an important positivepsycbological variable tbat has been found to be associated with flourishing and one tbatcan be influenced by tbe design of work.

Potential limitations of the researchAlthough the ESM used in tbe current study offers considerable advantage to tbe study offlow and other aspects of organizational behaviour, our application of the methodsuggests several potential limitations to be addressed by future researcb. First,participants in tbe research were architecture students engaged in work-like but notwork tasks suggesting the need to replicate our findings in the context of paidemployment. The experience of employment is more tban perfonning a set of tasks{Kelloway, Gallajíbcr, & Barling, 2005) and the extent to whicb our findings generalize tothe context of paid employment remains an empirical question.

Second, it couid be argued that tbere was not much variation in the nature of thework that we investigated, that is architectural studio work. Cxmsequently any within-subject variance could be attributable to dispositional variations rather than a change inthe work situatiijn. However our sample was drawn from three different studio sessions.Students were responsible for designing unique solutions to architectural problems, Tbenature of the work varied considerably and included a variety of tasks such as reading,drawing, computer programming, model building, writing, computer rendering anddata analysis. Nonetheless, the variance in task characteristics was relatively small.However, this would indicate tbat the strength of relationships between tbe jobcbaracteristics and flow were underestimated in our sample.

Third, in this study we focus on a single 'narrow-band:' indicator of well-being(i.e. mood). In doing so, we parallel earlier studies which have also focused on tberelation between flow and mood (e.g. Eisenberger et ai, 2005) Broader conceptuali-zations of well-being would suggest going beyond measures of affect to includemeasures sucb as aspiration or self-efficacy (e.g. Warr, 1987) and future research would

Flow at work 611

be well advised to consider broadening the domain of inquiry to more fully understandthe relationship between flow and well-being.

Finally, we used a short composite measure of flow in our research. This wasprimarily dictated by the ESM that we used, that necessitates the use of short scales thatare minimally disruptive of the experience being assessed. There seems to be confusionas to an appropriate psychometric form with which to assess flow. There is a growingbody of theory to suggest that flow is a multidimensional construct (Jackson & Marsh,1996; Marsh & Jackson. 1999), yet these dimensions are highly correlated. Furthermore,composite operationalizations of flow fail to distinguish between the preconditionsor antecedents of flow (e.g. challenge-skill balance, clear goals) and the componentsof optimal experience (e.g. enjoyment of. and concentration on the activity).Future research needs to move away from uni-dimensional conceptualizations of flow,and to distinguish between the antecedents and qualities of the construct.

ConclusionRecently there has been an interest in the application of positive psychology tounderstanding organizational behaviour (Cameron et al., 2003; Luthans, 2001, 2002;Turner et al.. 2002). Flow is a construct that is at the centre oí the ptjsitivc psychologymovement (Seligman & Csikszentmihalyi, 2000) and provides researchers with avaluable conceptual framework with which to study the nature and process of w<ïrkexperiences. Luthans has termed the application of positive psychology to workplaceissues positive organizational behaviour (POB; Luthans, 2002). He defines POB as thestudy and application of positively oriented human resource strengths andpsychological capacities that can be measured, developed, and effectively managedfor performance improvement' (Luthans, 2002, p. 58). The focus of POB is on strengthsand virtues that are measurable and contribute to better performance. Luthans (2002)outlines several criteria associated with constructs if they are to make a contribution toPOB. Specifically they should be positive, measurable, capable of being developed, andassociated with optimal performance. Our research has indicated that flow cicarh' fulfilsthese criteria. Furthermore, in order for a POB construct to be developmental it has tobe state-like in nature. We have sbown that fiow is predominantly a state construct. Thisis concordant with C^sikszentmihalyi's (1990) definition of flow as engagement in aspecific activity. He defined flow as the state in which people are so intensely involvedin an activity that nothing else seems to matter; the experience itself is so enjoyable thatpeople will do it even at great cost, for the sheer sake of doing it' (Csikszcntmihalyi,1990, pp. 3-4). Luthans (2002) has identified five variables that fulfil the criteria formaking a contribution to POB: confidence/self-efficacy; hope; optimism; SWB/happi-ness; and emotional intelligence. The current research demonstrates that flow also hasthe potential to be a meaningful positive psychological construct that mediates therelationship between certain core characteristics of work and well-being.

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