Flow is a psychological state in which individuals feel entirely absorbed in their activity (Csikszentmihalyi, 2000). This state is likely to arise when individuals feel they have developed the skills to complete a challenging task.
A series of feelings, sensations, experiences, or conditions, characterize this state (Csikszentmihalyi, 2000& Nakamura & Csikszentmihalyi, 2002). In particular, when individuals experience flow:
Individuals can experience a sense of flow in many contexts (Csikszentmihalyi, 2000). Artists, for example, might experience this state while playing an instrument or performing. Sporting participants might experience this state while performing at the limits of their capacity. At work, the key features of flow are immersion or absorption, enjoyment of the task, and intrinsic motivation or interest (Ghani & Deshpande, 1994).
Flow is assumed to be more prevalent during work than leisure time (Czikszentmihalyi & Lefevre, 1989). That is, during work, the tasks are usually sufficiently challenging to generate flow. According to one study, employees experience flow 44% of the time at work, boredom 20%, and anxiety the remaining 36% (Donner & Czikszentmihalyi, 1992). Indeed, flow is even more elevated in managers (Donner & Czikszentmihalyi, 1992).
Flow is often assumed to have evolved to encourage growth and development. Specifically, when individuals engage in challenging tasks--tasks that facilitate growth and development--the ensuing state of flow is experienced as pleasurable. This pleasure reinforces the inclination of individuals to engage in other challenging tasks in the future (Csikszentmihalyi, 1993).
Many authors had previously described analogous experiences. Maslow (1959), for example, referred to these events as peak experiences, representing moments of happiness, fulfillment, and achievement that are evoked when individuals feel they have realized their potential.
Csikszentmihaly and his coulegues (Csikszentmihaly 2000& Nakamura & Csikszentmihalyi, 2002) identified several factors that foster this state of flow. In particular, individuals are more likely to experience this state when their goals are clear, the feedback they receive is unambiguous, and their skills are congruent with the demands of their activity, called the balance hypothesis (Nakamura & Csikszentmihalyi, 2002). The balance hypothesis has also been clarified: Not only should skills match demands, but both skills and demands should be elevated and challenging (Massimini & Carli, 1988).
Initially, the evidence that such factors promote flow was derived from correlational studies (e.g., Mannell, 1979& Csikszentmihalyi & Rathunde, 1993). Nevertheless, some experimental support has been garnered.
For example, in a study conducted by Keller and Bless (2008), participants attempt the computer game called Tetrus. The difficulty of this task was manipulated. Participants were more likely to manifest the state of flow, such as underestimate the time that had elapsed, when the task was moderately difficult. In other words, as hypothesized, flow was experienced when the skills of individuals roughly matched the demands or challenges of this task.
Ceja and Navarro (2009) argued that many other factors could affect the prevalence of flow. Specifically, factors that increase the likelihood that individuals perceive demands as a challenge rather than as a threat, such as sense of coherence or meaning at work, could augment the incidence of flow.
According to the four channel model of flow, initially proposed by Csikszentmihalyi (1975), individuals tend to experience one of four states: apathy, boredom, anxiety, and flow. The skills of individuals in a particular setting and the level of challenge to which they are exposed determine which state will prevail. In particular, low skill and low challenge promotes apathy& low skill but high challenge promotes anxiety& high skill and low challenge promotes boredom& and high skill and high challenge promotes flow. This flow is characterized by high levels of arousal and focus but not always positive emotions or motivation.
A revised version of the four channel model of flow, called the experience fluctuation model (Lambert, Chapman, & Lurie, 2013 & Massimini & Carli, 1988), challenges the notion that all the manifestations of flow are associated with only high levels of both skill and challenge. In addition to apathy, boredom, anxiety, and flow, this model differentiates four other states--relaxation, worry, arousal, and control--each evoked by moderate levels of either skill or challenge. Moderate skill with low challenge promotes relaxation. Low skill and moderate challenge promotes worry. Moderate skill and high challenge promotes arousal. And high skill coupled with moderate challenge promotes control.
As Lambert, Chapman, and Lurie (2013) showed, some of the experiences that are typically ascribed to flow are actually observed when people experience control--that is, high skill but moderate challenge. That is, in this study, 7 to 10 times a day, participants were told to complete some questions about their existing state. In particular, they were asked to indicate the extent to which they perceived the situation as challenging and the degree to which they felt they had developed the skills to meet this challenge. In addition, manifestations of flow, including enjoyment, concentration, happiness and intrinsic motivation were assessed. Enjoyment, happiness, and intrinsic motivation, but not concentration, were actually more likely to be observed when participants experienced control rather than flow.
Some job characteristics can also increase the likelihood that employees experience flow at work. Csikszentmihalyi (2003) showed that clear goals, the provision of feedback, and a sense of control--in addition to the combination of elevated skills and challenging tasks-were related to flow. Similarly, Bakker (2005) showed that autonomy, social support, supervisory coaching, and performance feedback facilitated flow in music teachers and their students.
Many studies have shown that such job characteristics--as defined by Hackman and Oldham (1980), comprising task identity, autonomy, skill variety, job feedback, and task significance--promote work motivation and job satisfaction more generally (e.g., Behson, Eddy & Lorenzet, 2000). Demerouti (2006) showed these core job characteristics were related to experiences of flow as well.
These job characteristics are assumed to enable individuals to achieve work goals as well as facilitate personal growth and curb job demands. As a consequence, individuals can express themselves physically, cognitively, and emotionally during their work (Kahn, 1990 & see work engagement). They can thus immerse themselves in their work rather than worry about other demands and expectations. For example, task identity, in which individuals feel they are granted a definable piece of work, ensures their goals are clear. Hence, employees do not need to question or reconsider the task but can absorb themselves in this endeavor (Csikszentmihalyi, 1997).
Similarly, autonomy ensures that individuals experience a sense of control, which promotes positive affect (Saavedra & Kwun, 2000), curbing the perceived need to reassess the task they have chosen (e.g., Kuhl, 2000). Skill variety might also ensure the job is sufficiently complex to promote a sense of challenge--a key ingredient of flow.
Some tasks or activities are especially likely to induce flow. For example, when employees plan future tasks, solve important problems, or evaluate alternatives, they become more likely to experience flow (Nielsen & Cleal, 2010). In particular, planning instills a sense of control over the environment, evoking flow. Problem solving and evaluation invoke a vast range of skills, also inducing flow.
Rathunde and Csikszentmihaly (2005) maintain that schools that apply the Montessori Method seem to incite experiences of flow opportunities for their students. Indeed, their studies do indicate that flow experiences are indeed more prevalent in Montessori schools relative to traditional schools.
This study sampled the experiences of 290 students, enrolled in either Montessori or traditional middle schools. Students at Montessori schools reported more positive perceptions of their environment, their teachers, and their friends.
The philosophy that underpins the Montessori method remains contentious, because Dr. Montessori seldom wrote about her underlying rationale (Standing, 1998). Nevertheless, some core concepts have emerged over time (Standing, 1998). For example, the method assumes that inner drives or directives from nature guide the normal development of children. Children should thus be granted the autonomy to follow these inherent directives. These directives change as the child proceeds through distinct phases of development: from learning through physical senses until 6 to developing abstract principles, imagination, and social skills between 6 and 12 and so forth. For young children, the role of teachers is to furnish the environment with physical objects that enable students to apply their natural instincts, which facilitates learning. In response to misbehavior, for example, teachers will orient the focus of students to positive activities rather than control the children themselves. When this autonomy is provided, intense concentration will follow, which facilitates independence, spontaneity, discipline, and harmony. Children also learn from each other, and thus mixed age groups are beneficial.
In practice, during the ages of 0 to 6, the materials that are available in the environment can be divided into five divisions: practical life, sensorial, math, language, and culture. Practical materials include devices that enable individuals to lace, tie, or button clothes, pour, scoop, or sort items, and prepare food-all intended to facilitate the natural instinct to develop physical coordination and care for themselves. Sensory materials include wooden blocks and objects, varying in size, color, and shape, to experience the natural order of their physical environment. Mathematical materials include beads and other materials that can be used to explore mathematical principles. Materials are also provided to facilitate writing, reading, geography, and science.
At work, the experience of flow seems to vary considerably across time. The variability seems random. That is, flow seems to rise and decline erratically. Ceja and Navarro (2009), however, showed this variation across time is not entirely random. Instead, some pattern underlies this variation, called a chaotic dynamic.
Specifically, in a study conducted by Ceja and Navarro (2009), participants were granted access to a personal digital assistant or PDA. Six times a day, over three weeks, a signal was emitted. Participants answered questions that assessed their level of flow. Two measures of flow were extracted: One measure was derived from the extent to which the activity they were completing at that time was challenging multiplied by the degree to which participants felt they have acquired the skills to complete this task. The second measure was derived from the extent to which the participants perceived this activity as enjoyable, interesting, and absorbing--the three cardinal features of flow. This sampling of flow at many times at various contexts is called the experience sampling method.
The authors then constructed several figures to examine flow over time, examining each participant separately. One of the figures merely represented flow on the Y axis and time on the X axis, revealing a seemingly erratic pattern. The second figure was called a visual recurrence analysis, which is designed to ascertain whether the pattern is random or chaotic--that is, not actually random. In approximately 80% of the participants, the pattern was chaotic. That is, roughly, some changes across time were common than other changes, which diverges from a random pattern.
Chaotic or nonlinear patterns emerge in several conditions. Specifically, when several distinct systems mutually interact with one another, these patterns arise. For example, in this instance, flow represents the manifestation of behavior, subjective experience, thinking, and physiological responses, all influencing with one another (Ceja & Navarro, 2009). Specifically, the subjective experience of flow might encourage behaviors that align with interests, which amplifies the flow, and so forth.
Many studies indicate that individual characteristics may influence the likelihood of flow (e.g., Haworth, Jarman, & Lee, 1997& Keller & Blomann 2008). Keller and Blomann (2008), for example, showed that individuals who feel their success and satisfaction depends on events they cannot control, and thus do not report an internal locus of control, are not as likely to experience flow. That is, their attention is often oriented towards other people and events, instead of their own private needs, compromising flow.
Teng (2011) showed that temperament and character more broadly may influence the likelihood or level of flow. In this study, participants completed a measure that assesses four facets of temperament: harm avoidance, novelty seeking, reward dependence, and persistence (Cloninger, Svrakic, & Przybeck, 1993). Temperament represent styles of processing information that are primarily heritable. In addition, participants completed a measure that assesses three facets of character: self directedness, cooperation, and self transcendence (Cloninger, Svrakic, & Przybeck, 1993). Finally, participants completed a measure of flow, comprising three items.
Novelty seeking was positively associated with flow. That is, individuals who like to explore unusual and novel activities and objects tend to be report higher levels of flow. Furthermore, persistence was also associated with flow. Taken together, these findings might indicate that people who seek novelty and persist despite obstacles are more likely to embrace challenging experiences. This inclination to embrace challenge enhances the skills of individuals as well. This combination of challenge and skill is regarded as the cardinal source of flow.
Self transcendence, in which individuals feel connected to nature, the universe, and spiritual values, was also associated with flow. Conceivably, because of their self transcendence, people are not as likely to feel threatened when they fail. They will, therefore, embrace challenges, increasing the likelihood of flow.
According to Baumann and Scheffer (2010), the primary characteristic that determines whether individuals are likely to experience flow is called an achievement flow motive. To clarify, motives are derived from the various actions that individuals have learnt to apply in specific contexts (cf., extension memory in the context of personality systems interaction theory). Often, across a variety of contexts, a particular subset of behaviors, such as intimate conversations, tends to be reinforced. Individuals thus feel inclined to enact these behaviors in many settings, manifested as motives.
These motives tend to differ across individuals. Some individuals, for example, often strive to improve intimacy. Other individuals feel motivated to attract attention, to avert rejection, to seek familiar people, to gain status, to influence other people, or to gain independence. Hence, many of the motives of individuals relate to affiliation or power.
In addition to affiliation or power, many of the motives that guide individuals revolve around achievement instead. Individuals, for example, can be motivated to fulfill standards of excellence, cope with failure, avoid disappointment, or experience a sense of flow.
Thus, the flow motive is only one of many different motivations that might be invoked. According to Baumann and Scheffer (2010), the flow motive comprises two distinct, but interrelated, facets: the motivation to seek challenges and the motivation to master or resolve these difficulties.
In a series of studies, Baumann and Scheffer (2010) administered the operant motive test to assess and characterize this flow motivation. Specifically, a series of schematic illustrations was presented. One illustration, for example, depicted a person perhaps climbing a hill. Another illustration portrayed two people near a series of rectangular items, positioned on a table. Participants were asked questions, like "What is important for the person in this situation", "How does the person feel and why", as well as "How does this story end".
If individuals experience a flow motivation, their needs to seek and master difficulties are likely to be activated. These needs should then shape their responses to these questions. The participants might, for example, contend that perhaps the protagonist is feeling invigorated and enthralled as they master this task. Thus, allusions to positive feelings of curiosity, interest, excitement, concentration, absorption, challenge, variety, or stimulation while learning align to this need to seek and master difficulties, representing a flow motivation.
In the first study, participants completed this test twice, two years apart. The researchers found that achievement flow motivation was stable over time: Flow motivation at the two times generated a correlation than exceeded .7. Furthermore, flow motivation was inversely associated with pressure to achieve--another motive that can be gauged by this test.
In the second study, Baumann and Scheffer (2010) whether this achievement flow motive is compromised by tangible incentives. That is, sometimes, individuals are motivated by a sense of fascination, challenge, and interest, called intrinsic motivation. In contrast, on other occasions, individuals are motivated by tangible incentives, such as money or status. In this state, individuals monitor the needs of other people, rather than undertake the tasks they inherently prefer, compromising flow.
According to this account, intrinsic motivation, and not tangible incentives, should guide the choices of individuals who experience flow motive should be governed. These individuals should be aware of their own preferences. They should not, therefore, confuse their own preferences with the constraints that are imposed by anyone else--a confusion that is sometimes called self infiltration.
Baumann and Scheffer (2010) indeed showed that self infiltration is inversely associated with this achievement flow motive. To assess self infiltration, participants were instructed to decide which of 48 clerical tasks they would prefer to complete. Next, the experimenter indicated some other tasks the participants need to complete. Later, participants were asked to recall which tasks they chose, representing a measure of self infiltration. If participants demonstrated an achievement flow motive, they could more readily remember which tasks they chose. They were more cognizant of their own preferences.
The third study examined whether or not the flow motivation is associated with efficiency. That is, this motivation seems to coincide with awareness or access to personal needs and preferences. This access enables individuals to identify the actions that optimize success in various contexts, represented in a cognitive system called extension memory. This motivation, therefore, should be associated with efficient or effective performance.
To assess this possibility, participants of a career development program completed the operant motives test. They also sought feedback about their work performance from three other individuals, such as managers or customers. Several of the questions related to efficiency, such as the capacity to be decisive, to manage resources effectively, and to satisfy customers. Achievement flow motivation was indeed associated with work efficiency. The final study confirmed this motivation is indeed correlated with momentary experiences of flow as well as behaviors that manifest the need to seek and master challenges.
Taken together, these findings imply that individuals who experience flow often form challenging, but plausible, goals and intentions (cf., personality systems interaction theory). These goals and intentions are plausible because they are, at least partly, derived from preferences that evolved from past experiences. The implementation of these plausible goals corresponds to the experience of flow.
Many authors highlight the importance of flow. Novak (1996), for example, maintains that flow is perhaps the key to wellbeing and engagement at work.
Many studies show that flow is associated with various indices of performance. In sporting competitions, flow is correlated with success (Jackson, Kimiecik, Ford, & Marsh, 1998). At school, flow is also related to progress though the school curriculum (Csikszentmihalyi, Rathunde, & Whalen, 1993).
Several factors, such as personality, can moderate the effect of flow on outcomes. Demerouti (2006), for example, revealed that flow is especially likely to enhance performance in conscientious employees.
Specifically, as Demerouti (2006) highlights, flow will improve performance only when employees are immersed in productive activities. That is, as Nakamura and Csikszentmihalyi (2002) concede, individuals can seek flow in activities that diverge from the interests of the employer, the society, or even themselves. Employees who are especially conscientious are more likely to dedicate their efforts to tasks and activities that align with the needs of their organization. Hence, flow in these conscientious individuals should increase performance on tasks that are integral to the organization and, therefore, should be rated favorably.
To assess this possibility, individuals completed a measure that assessed flow at work, specifically absorption, enjoyment, and intrinsic work motivation. Colleagues then rated the extent to which these participants performed well in their prescribed role, with questions like "(This person) achieves the objectives of the job" as well as engaged in discretionary behavior to enhance the organization, with items like "Takes initiative to orient new employees to the department even though not part of his/her job description". Finally, conscientiousness was assessed& participants specified the extent to which they were reliable, disorganized, and so forth. Flow was related to both measures of performance, but only when conscientiousness was elevated.
Dietrich (2004) developed a model to characterize the neurocognitive mechanisms that might underpin flow. In short, according to Dietrich (2004), flow may coincide with diminished activation of the prefrontal cortex and structures in the medial temporal lobe.
Specifically, explicit cognitive processes--processes that demand deliberate control, effort, and awareness--are primarily associated with the prefrontal cortex and medial temporal lobe. In contrast, explicit cognitive processes, which are effortless, automatic, and efficient, but relatively inflexible, are governed by the basal ganglia. When prefrontal activation diminishes, analytical and meta-conscious processes are suppressed temporarily, enabling the experience of flow.
To clarify, the basal ganglia underpins the acquisition of practiced routines. That is, the basal ganglia might recognize patterns. Specifically, this region might recognize patterns in the physical movements of the individual as well as sights, sounds, smells, words, emotions, and other stimuli. The basal ganglia then store and utilize these patterns of behavior, forming automatic routines or habits, potentially initiated during flow.
The basal ganglia can partly detect patterns from as few as three repetitions. That is, three repetitions are sufficient to increase the association between specific stimuli or behaviors--even without the intervention of conscious awareness.
To illustrate, in one study, participants had to press a sequence of buttons on a keyboard in response to specific stimuli on a screen. For some of the participants, the same sequence was repeated each time. For other participants, the sequence was not repeated. Because the sequence was long, none of the participants were consciously aware of the pattern. Nevertheless, participants pressed the buttons more rapidly if the sequence had been presented earlier, presumably underpinned by the basal ganglia (see Rauch, Savage, Brown, Curran, Alpert, Kendrick, et al., 1995).
Interestingly, in this study, some of the participants did decipher the pattern consciously. They could either describe the pattern in words or demonstrate the pattern with their fingers. These individuals were especially rapid in pressing the buttons later. Somehow, conscious awareness of the pattern facilitated the response--a facilitation that was arguably underpinned by processing in the prefrontal cortex.
Studies use a variety of measures to assess this experience. For example, Keller and Bless (2008) used a self report scale that assesses involvement and enjoyment as well as measures that evaluate feelings of control and perception of time. In contrast, Jackson and Eklund (2002) developed a more comprehensive measure--a measure that assesses the nine key facets of flow and comprises 36 items.
Nielsen and Cleal (2010) devised a short measure of flow, particularly applicable if participants need to answer these questions repeatedly over a day or week. The items included "To which degree did you use your skills", "To which degree did you feel challenged by the activity", and "Did you feel able to concentrate". Cronbach's alpha was .85, and confirmatory factor analysis established the items pertain to one factor, RMSEA = .055.
Flow comprises nine facets (e.g., Csikszentmihalyi, 2000& Nakamura & Csikszentmihalyi, 2002). These nine facets of flow can be distinguished. Indeed, even the antecedents and consequences of flow vary across the nine facets. For example, when individuals engage in exercise, increasing their effort from 50% to 100% their maximum level tends to increase both concentration and, to a lesser extent, the sense of challenge. The same increase in effort tends to curb the clarity of goals, however (Connonlly & Tennenbaum, 2010).
Individuals tend to experience a sense of flow, and thus persist with goals, when a pursuit is challenging but achievable. Scott and Nowlis (2013) uncovered an intriguing technique that can be used to achieve this sense of challenge but feasibility. In particular, rather than pursue a specific target, such as to lose 3 kg in weight, people should pursue a range, such as to lose between 2 and 4 kg in weight. The lower level seems feasible, increasing the likelihood that participants will feel their goal can, to some extent, be achieved. The higher level, however, is more demanding, instilling a sense of challenge.
To illustrate, in one study, conducted by Scott and Nowlis (2013), the participants were attendees of a weight loss program over three weeks. During the first session, participants specified the number of pounds they would like to lose over this time. They were instructed to specify either a single number, such as 2 pounds, or a range, such as between 1 and 3 pounds. Finally, after the three weeks, participants were asked whether they would like to continue the program. If participants specified a range, instead of a single number, they were more inclined to continue with the program in the future. That is, they were more likely to reengage with this goal, even after controlling weight loss, although this difference was only marginal.
A second study was conducted in a laboratory. M&Ms were placed in front of participants& their goal was to consume as few M&Ms as possible. Next, participants specified the maximum number they would eat, such as 5, or a range, such as 3 to 7. After 25 minutes, they completed questions that assess feelings of accomplishment and the likely they would reengage with this goal in the future. The range was more likely to evoke feelings of reengagement, even after controlling the number of M&Ms consumed. This relationship was mediated by feelings of accomplishment.
Subsequent studies replicated and extended these findings. For example, these studies showed that achievement needs be dependent on skill instead of luck. If dependent on luck, the pursuit of these goals does not evoke a sense of challenge or accomplishment.
Asakawa, K. (2004). Flow experience and autotelic personality in Japanese college students: How do they experience challenges in daily life? Journal of Happiness Studies, 5, 123-154.
Bakker, A. B. (2005). Flow among music teachers and their students: The crossover of peak experiences. Journal of Vocational Behavior, 66, 26-44.
Bakker, A. B. (2008). The work-related flow inventory: Construction and initial validation of the WOLF. Journal of Vocational Behavior, 72, 400-414.
Basom, M. R., & Frase, L. (2004). Creating optimal work environments: Exploring teacher flow experiences. Mentoring and Tutoring, 12, 241-258.
Baumann, N., & Scheffer, D. (2010). Seeking flow in the achievement domain: The achievement flow motive behind flow experience. Motivation and Emotion, 34.
Behson, S. J., Eddy, E. R., & Lorenzet, S. J. (2000). The importance of the critical psychological states in the job characteristics model: A meta-analytic and structural equations modeling examination. Current Research in Social Psychology, 5, 170-189.
Ceja, L., & Navarro, J. (2009). Dynamics of flow: A nonlinear perspective. Journal of Happiness Studies, 10, 665-684.
Cloninger, C. R., Svrakic, D. H., & Przybeck, T. R. (1993). A psychobiological model of temperament and character. Archives of General Psychiatry, 50, 975-990.
Connonlly, C. T., & Tennenbaum, G. (2010). Exertion-attention-flow linkage under different workloads. Journal of Applied Social Psychology, 40, 1123-1145.
Conti, R. (2001). Time flies: Investigating the connection between intrinsic motivation and the experience of time. Journal of Personality, 69, 1-26.
Csikszentmihalyi, M. (1975). Beyond boredom and anxiety: San Franciso, CA: Josey-Bass.
Csikszentmihalyi, M. (1985). Emergent motivation and the evolution of the self. Advances in Motivation and Achievement, 4, 93-119.
Csikszentmihalyi, M. (1988). The flow experience and its significance for human psychology. In M. Csikszentmihalyi & I. Csikszentmihalyi (Eds.), Optimal experience. Psychological studies of flow in consciousness (pp. 15-35). New York: Cambridge University Press.
Csikszentmihalyi, M. (1990). Flow. New York: Harper & Row.
Csikszentmihalyi, M. (1993). Flow: The evolving self. New York: HarperCollins.
Csikszentmihalyi, M. (1996). Creativity. New York: HarperCollins.
Csikszentmihalyi, M. (1997). Finding flow. New York: Basic.
Csikszentmihalyi, M. (2000). Beyond boredom and anxiety. Experiencing flow in work and play. San Francisco: Jossey-Bass. (Original work published 1975).
Csikszentmihalyi, M. (2003). Good business, leadership, flow, and the making of meaning. USA: Penguin Books.
Csikszentmihalyi, M., & Csikszentmihalyi, I. (Eds.). (1988). Optimal experience. Cambridge: Cambridge University Press.
Csikszentmihalyi, M., & LeFevre, J. (1989). Optimal experience in work and leisure. Journal of Personality and Social Psychology, 56, 815-822.
Csikszentmihalyi, M., & Nakamura, J. (1989). The dynamics of intrinsic motivation: A study of adolescents. In R. Ames & C. Ames (Eds.), Research on motivation in education (pp. 45-71). New York: Academic Press.
Csikszentmihalyi, M., & Nakamura, J. (1999). Emerging goals and the self-regulation of behavior. Advances in social cognition, 12, 107-118.
Csikszentmihalyi, M., & Rathunde, K. (1993). The measurement of flow in everyday life: Toward a theory of emergent motivation. In J. J. Jacobs (Ed.), Nebraska Symposium on Motivation: Vol. 40. Developmental perspectives on motivation (pp. 57-97). Lincoln: University of Nebraska Press.
Csikszentmihalyi, M., Rathunde, K., & Whalen, S. (1993). Talented teenagers. Cambridge, UK: Cambridge University Press.
Custodero, L. (1998). Observing flow in young people's music learning. General Music Today, 12, 21-27.
Delle Fave, A., & Massimini, F. (1988). Modernization and the changing contexts of flow in work and leisure. In M. Csikszentmihalyi & I. Csikszentmihalyi (Eds.), Optimal experience (pp. 193-213). Cambridge: Cambridge University Press.
Demerouti, E. (2006). Job characteristics, flow, and performance: the moderating role of conscientiousness. Journal of Occupational Health Psychology, 11, 266-280.
Dietrich, A. (2004). Neurocognitive mechanisms underlying the experience of flow. Consciousness and Cognition, 13, 746-761.
Donner, J., & Csikszentmihalyi, M. (1992). Transforming stress into flow. Executive Excellence, 9, 16-17.
Emerson, H. (1998). Flow and occupation: A review of the literature. Canadian Journal of Occupational Therapy, 65, 37-43.
Eisenberger, R., Jones, J. R., Stinglhamber, F., Shanock, L., & Randall, A. T. (2005). Flow experiences at work: For high need achievers alone? Journal of Organizational Behavior, 26, 755-775.
Getzels, J. W., & Csikszentmihalyi, M. (1976). The creative vision. New York: Wiley.
Ghani, D. A., & Deshpande, S. P. (1994). Task characteristics and the appearance of Optimal flow in human computer interaction. Journal of Psychology, 128, 381-391.
Graham, J. M. (2008). Self-expansion and flow in couples' momentary experiences: An experience sampling study. Journal of Personality and Social Psychology, 95, 679-694.
Hackman, J. R., & Oldham, G. R. (1980). Work redesign. Reading, MA: Addison-Wesley.
Haworth, J. T., Jarman, M., & Lee, S. (1997). Positive psychological states in the daily life of a sample of working women. Journal of Applied Social Psychology, 27, 345-370.
Jackson, S. A. (1995). Factors influencing the occurrence of flow sate in elite athletes. Journal of Applied Sport Psychology, 7, 138-166.
Jackson, S. A., & Csikszentmihalyi, M. (1999). Flow in sports. Champaign, IL: Human Kinetics.
Jackson, S. A., & Eklund, R. C. (2002). Assessing flow in physical activity: The Flow State Scale-2 (FSS-2) and Dispositional Flow Scale-2 (DFS-2). Journal of Sport and Exercise Psychology, 24, 133-150.
Jackson, S. A., & Eklund, R. C. (2004). The flow scales manual. Morgantown, WV: Fitness Information Technology.
Jackson, S. A., Kimiecik, J. C., Ford, S. K., & Marsh, H. W. (1998). Psychological correlates of flow in sport. Journal of Sport and Exercise Psychology, 20, 358-378.
Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The flow state scale, Journal of Sport and Exercise Psychology, 18, 17-35.
Jackson, S. A., Martin, A. J., & Eklund, R. C. (2008). Long and short measures of flow: Examining construct validity of the FSS-2, DFS-2, and new brief counterparts. Journal of Sport and Exercise Psychology, 30, 561-587.
Keller, J., & Bless, H. (2008). Flow and regulatory compatibility: an experimental approach to the flow model of intrinsic motivation. Personality and Social Psychology Bulletin, 34, 196-209.
Keller, J., & Blomann, F. (2008). Locus of control and the flow experience: An experimental analysis. European Journal of Personality, 22, 589-607.
Kimiecik, J. C., & Harris, A. T. (1996). What is enjoyment? A conceptual/definitional analysis with implications for sport and exercise psychology. Journal of Sport and Exercise Psychology, 18, 247-263.
Kuhl, J. (2000). A functional-design approach to motivation and volition: The dynamics of personality systems interactions. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Self-regulation: Directions and challenges for future research (pp. 111-169). New York: Academic Press.
Lambert, J., Chapman, J., & Lurie, D. (2013). Challenges to the four-channel model of flow: Primary assumption of flow support the moderate challenging control channel. Journal of Positive Psychology, 8, 395-403.
Mannell, R.C. (1979). A conceptual and experimental basis for research in the psychology of leisure. Society and Leisure, 2, 179-196.
Maslow, A. H. (1959). Cognition of being in the peak experiences. Journal of Genetic Psychology, 94, 43-66.
Massimini, F., & Carli, M. (1988). The systematic assessment of flow in daily experience. In M. Csikszentmihalyi & I. S. Csikszentmihalyi (Eds.), Optimal experience. Psychological studies of flow in consciousness (pp. 266-287). New York: Cambridge University Press.
Moneta, G. (2004). The flow model of intrinsic motivation in Chinese: Cultural and personal moderators. Journal of Happiness Studies, 5, 181-217.
Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89-105). Oxford, UK: Oxford University Press.
Nielsen, K., & Cleal, B. (2010). Predicting flow at work: Investigating the activities and job characteristics that predict flow states at work. Journal of Occupational Health Psychology, 15, 180-190.
Novak, M. (1996). Business as a calling. New York: The Free Press.
Perry, S. K. (1999). Writing in flow. Cincinnati, OH: Writer's Digest Books.
Quinn, R. W. (2005). Flow in knowledge work: High performance experience in the design of national security technology. Administrative Science Quarterly, 50, 610-641.
Rathunde, K. (1997). Parent-adolescent interaction and optimal experience. Journal of Youth and Adolescence, 26, 669-689.
Rathunde, K., & Csikszentmihalyi, M. (2005). The social context of middle school: Teachers, friends, and activities in Montessori and traditional school environments. Elementary School Journal, 106, 59-79.
Rauch, S. L., Savage, C. R., Brown, H. D., Curran, T., Alpert, N. M., Kendrick, A. et al. (1995). A PET investigation of implicit and explicit sequence learning. Human Brain Mapping, 3, 271-286.
Saavedra, R., & Kwun, S. K. (2000). Affective states in job characteristics theory. Journal of Organizational Behavior, 21, 131-146.
Salanova, M., Bakker, A. B., & Llorens, S. (2006). Flow at work: Evidence for an upward spiral of personal and organizational resources. Journal of Happiness Studies, 7, 1-22.
Schmidt, J. A., Shernoff, D. J., & Csikszentmihalyi, M. (2007). Individual and situational factors related to the experience of flow in adolescence: A multilevel approach. In A. D. Ong & M. v. Dulmen (Eds.), The handbook of methods in positive psychology (pp. 542-558). Oxford, England: Oxford University Press.
Scoresby, J., & Shelton, B. E. (2007). Visual perspectives within educational computer games: Effects on presence and flow within virtual learning environments. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL.
Scott, M. L., & Nowlis, S. M. (2013). The effect of goal specificity on consumer goal reengagement. Journal of Consumer Research, 40, 444-459. doi: 10.1086/670766
Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18, 158-176.
Standing, E. M. (1998). Maria Montessori: Her life and work. Plume.
Stein, G. L., Kimiecik, J. C., Daniels, J., & Jackson, S. A. (1995). Psychological antecedents of flow in recreational sport. Personality and Social Psychology Bulletin, 21, 125-135.
Teng, C. (2011). Who are likely to experience flow? Impact of temperament and character on flow. Personality and Individual Differences, 50, 863-868. doi:10.1016/j.paid.2011.01.012
Voelkl, J. E., & Ellis, G. D. (1998). Measuring flow experiences in daily life: An examination of the items used to measure challenge and skill. Journal of Leisure Research, 30, 380-389.
Wells, A. (1988). Self-esteem and optimal experience. In M. Csikszentmihalyi & I. Csikszentmihalyi (Eds.), Optimal experience (pp. 327-341). Cambridge: Cambridge University Press.
Wesson, K., & Boniwell, I. (2007). Flow theory: Its applications to coaching. International Coaching Psychology Review, 2, 33-43.
Wong, M., & Csikszentmihalyi, M. (1991). Motivation and academic achievement: The effects of personality traits and the quality of experience. Journal of Personality, 59, 539-574
Last Update: 5/24/2016