Sacha N. Duff, Katherine Del Giudice, Matthew Johnston, Jesse Flint
Design Interactive, Inc., Orlando, FL
Bonnie Kudrick

Transportation Safety Administration, Office of Security Capabilities, Washington D.C.

This paper presents a novel approach to diagnosing and measuring teamwork in complex sociotechnical systems. First, the underlying theoretical constructs that have inspired the development and use of a multi-level model to study team phenomena from a general systems perspective are presented. Next, in an attempt to theoretically ground the construct, “flow state” will be presented as an isomorphic variable in a multi-level model, meaning it is represented similarly at the system, team, and individual level. Approaching processes embedded in organizations from this perspective allows diagnosis of the systemic influences that contribute most to the variance in performance, identification of pervasive latent systemic failures, and the development of a tailored taxonomy of behavioral teamwork dimensions, which can then be translated into metrics to measure teamwork within any observable complex process.


The use of teams is prevalent in organizations spanning every industry. Organizations are becoming increasingly reliant on teams to manage the complexity of modern work (Rosen et al., 2011). Teams can be highly beneficial in meeting the performance goals of an organization, as they represent a larger resource pool from which to draw than does a lone individual. “Teams are used when errors lead to severe consequences; when the task complexity exceeds the capacity of an individual; when the task environment is ill-defined, ambiguous, and stressful; when multiple and quick decisions are needed; and when the lives of others depend on the collective insight of individual members “(Salas et al, 2008). However, teams inhere complex dynamics that require delicate balance in order to elicit and harness the positive potential, while preventing the pitfalls. As Hackman et al. (1998) puts it, “teams are somewhat akin to audio amplifiers: Whatever passes through the device—be it signal or noise—comes out louder.”
Teams in organizations are embedded in complex sociotechnical systems that influence their behavior. This hierarchical nesting and coupling necessitates the use of multiple levels – individual, team, and the higher-level context – in efforts to understand and investigate team phenomena (Kozlowski & Klein, 2000). A systems perspective views any given outcome to be the result of a confluence of factors stemming from many sources at different levels of a complex system. Human factors and system design are interrelated such that the creation and analysis of sociotechnical systems are considered from the standpoint of the effect on the human, with the goal of optimizing human performance to ultimately improve overall system outcomes.

Teamwork behavior is a multi-faceted concept that has been difficult to conceptualize (Rosseau et al., 2006). Most of what is studied in and about organizations are phenomena that are intrinsically mixed-level, rarely strictly micro or macro in character (Rosseau, 1985). In the past 20 years, research in the area of job and organizational design has strongly encouraged researchers to examine the different levels of analysis, such as the individual, the team, and the organization (Hackman, 2000; Hitt et al. 2007; Carayon, 2009). This view is in line with general systems theory principles, which recognize that influences at levels above and below have an effect on the focal unit of analysis. Without this consideration, it is easy to erroneously attribute outcomes at one level of the system to process influences stemming from another.

Many studies interested in teamwork dimensions, even in intact teams, are studied in a laboratory context with a predefined task designed to elicit behavior related to the dimension of interest. In a recent study by Heyne, Pavlas & Salas (2011) aimed at understanding the effects of flow state on team process and outcomes, the researchers created a planning task with two levels of complexity, and distributed pertinent information across team members so that collaboration and sharing were necessary. They were then able to measure and correlate (among other things), perceived sharedness as a correlational attribute of team flow (Heyne, Pavlas & Salas, 2011). Measuring the teamwork dimensions that exist in “real world” work teams requires a different approach. In this paper we propose the use of the construct of flow to develop quantifiable metrics to examine performance at the organizational, team, and individual levels of sociotechnical system processes, and address the challenge of identifying and measuring teamwork within work teams in situ.

Variables in Multi-level Models

Multi-level models may incorporate several types of variables in an attempt to represent the phenomena of interest as accurately as possible and thereby making its representations subject to empirical test (Rousseau, 1985). Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting – 2014 573
Copyright 2014 Human Factors and Ergonomics Society. DOI 10.1177/1541931214581121
Specification of a testable multi-level model requires at once a level of detail that allows operationalization of the model and a level of abstractness that captures those aspects of the phenomena that generalize across levels (Miller, 1978). Cross-level theories are models specifying relationships among variables at different levels. Composition theory is a special class of cross-level theory. Theories of composition specify the functional relations producing variables at different levels that are presumed similar along some dimensions. Isomorphism exists when the same functional relationship can be used to represent constructs at more than one level, and is a type of variable that implies that constructs mean the same thing across levels (Rousseau, 1985). Each level might analyze different data; use multiple approaches, techniques, and visual representations; and provide different insights. The combination of insights from all levels will be considerably larger than their sum (Borner, 2010).

Figure 1. Multi-level model of flow in a sociotechnical system. An illustration of a multi-level model adapted from the Work System Model (WSM) (Carayon & Smith, 2000) that represents how the impact of the work system factors at each level of the organization influences overall system effectiveness. This model describes the flow construct and the main measures associated with each.

“Flow” as an Isomorphic Variable

The concept of “flow” in the psychological literature is defined as an optimal state of being. “Flow” is the term used to describe the “holistic sensation that people feel when they act with total involvement” (Csikszentmihalyi, 1988). An individual in flow is completely engaged in a task, experiencing concentration and enjoyment. Researchers in a variety of disciplines have found the concept of an optimal state of experience theoretically useful and have applied it to the study of a diverse set of activities from rock climbing and ocean cruising to meditation and ordinary work (Csikszentmihalyi & Csikszentmihalyi, 1992). A key characteristic that the flow model shares with other contemporary theories is interactionism (Magnusson & Stattin, 1998). Interactionists see behavior as a function of both person and situation, with the nature of the combined effect broadly conceived. This behavior is viewed as a combined result of contextual and individual difference effects (Kozlowski et al., 2000). Rather than focusing on the person, abstracted from the context (i.e. traits, personality types, stable dispositions), flow research has emphasized the dynamic system composed of person and environment interactions (Nakamura and Csikszentmihalyi, 2002). This characteristic of the flow construct aligns with general systems theory, lending support to the notion that it is a suitably abstract yet applicable construct to define as an isomorphic variable across all levels.

In the next section we will introduce the psychological construct of “flow”, first described by Csikszentmihalyi (1988). Flow will serve as an isomorphic construct to form the basis of a multi-level model designed to measure the quality and effectiveness of teamwork, and specify the relationship between individual performance, teamwork and system level organizational processes and outcomes. This approach asserts that flow at the individual level is similar at the team level, but also the system level. At the system level, flow is represented as the system’s “optimal state of function”, and indicated by the smooth progression of tasks toward organizational goals. An effective team’s collective task is the maintenance of both the “system flow”, which can be thought of as workflow within the focal process, and each individual member’s ability to enter into flow, which will be referred to as flow potential. The proficiency of the team, expressed through various dimensions of observable teamwork behaviors, enables the individual members to be able to perform their individual tasks optimally, which in turn translates to better performance at the system level, as indicated by system level outcomes.

“Flow disruptions” have been utilized as a means of diagnosing latent systemic failures in complex sociotechnical systems such as aviation and healthcare, and have been found to be correlated with errors during several types of surgical procedures (Wiegman et al, 2007, Parker, 2010). Across studies, communication and coordination, or “teamwork” related flow disruptions consistently prevail (Wiegman et al, 2007, Parker, 2010). Weigman et al (2007) discovered that of all disruptions recorded during cardiovascular surgery, “teamwork/communication was the only factor that contributed significantly to the overall model”. A standardized data collection tool for collecting flow disruptions was developed and validated for use in the operating room (Parker et al, 2010), and later transferred onto a tablet-PC based version and adapted for the trauma care process (Blocker et al., 2010). A version of this tool with an additional page provided for the entry of data related to the compensatory strategies enacted by the team in terms of behavioral teamwork dimensions, is envisioned for use in data collection to validate the model presented here.

In the studies mentioned above, a flow disruption was defined as any issues in teamwork, technology/instruments, training, or the environment that result in deviation from the natural progression of an operation, thereby potentially compromising safety (Wiegman, 2007). Without articulating it specifically, this definition indicates that disruptions can stem from any combination of components of the system, thereby representative of a system level phenomenon, while the outcome of interest, surgical errors, is measured at an individual level, thereby only assessing the effect that the system on the individual performance of the surgeon. Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting – 2014 574
Furthermore, teamwork related disruptions were the most significant in relation to errors, suggesting that teamwork is an important mediating factor to surgical performance, but deeper analysis was limited to the flow disruption event description, and therefore unable to provide a more holistic view of the teamwork behaviors and processes referenced. Explicating the levels of analysis inherent to the metrics used prevents errors of misspecification.

Extrapolation of the “Flow” Concept Across Levels

Individual. According to Csikszentmihalyi (1990), the flow experience is a condition ‘‘in which people are so involved in an activity that nothing else seems to matter at the time.’’ Flow experiences are suggested to be intrinsically rewarding because they allow one to become fully involved in a task and stretch his or her skills and abilities to the limit (Csikszentmihalyi & Rathunde, 1993). In a “flow” state an individual is: Challenged, focused, clear about their goals, provided immediate feedback, totally immersed, in control of their actions, absent of any self-consciousness or the lack of self-confidence that might come the activity, and experiencing time pass differently (Csikszentmihalyi, 1990). Schaffer (2013) proposed 7 flow conditions: (1) knowing what to do (2) knowing how to do it (3) knowing how well you are doing (4) knowing where to go (if navigation is involved) (5) high perceived challenges (6) high perceived skills (7) freedom from distractions.

On an individual level, flow makes us the best we are able to be, producing our finest work (Nokia, 2013). From a high-level of human performance, the potential to enter into a flow state depends on the ability of an individual to maintain their physiological and psychological resources within an acceptable range, and be met with opportunities to willfully apply those resources toward the pursuit of some end. The degree to which this occurs translates into an individual “state”, which impacts the subjective and objective quality of the individuals’ experience and performance (See Figure 2).

Figure 2. Experience Fluctuation Model (Csikszentmihalyi, 1997)

It is most often an employment setting that provides the opportunity for circumstances conducive to adult individuals to enter a flow state. Csikszentmihalyi and LeFevre’s (1989) findings highlight jobs as a major source of flow for adults more so than leisure. Many features and facets of a job affect the necessity to expend valuable physiological and psychological resources, while others, like teamwork, provide a means of preserving the resources of each individual by affording the ability to dynamically shift workload between members.

Team. The team level represents the interaction between members within the context of the other system components. When a team is in flow, it is innovative, harmonious and productive. Being part of it improves the performance of each member. Communication is purposeful and clear. Friction is seen as an opportunity, not a personal threat. The balance is just right, and everything flows. (Nokia, 2013). Ghani et al. (1991) propose that “optimal flow” based on a cognitive theory of human motivation, provides a useful measure of individuals’ experiences as they participate in group work. Team flow is most closely paralleled in the literature by the concept of team cohesion, which includes implicit and explicit coordination. Other constructs such as group identity seem to be antecedents to flow in a review of group communication research but may lack the dynamic components we associate with individual flow: concentration and enjoyment while absorbed in a task (Ghani, 1991). It is reasonable to expect that a team in flow will exhibit high levels of behaviors that indicate team cohesion. The individual contribution to team flow includes the maintenance of the individual’s own potential to enter flow state, measured along dimensions of individual performance, but also to perform proactive teamwork processes that may contribute to a team members’ ability to enter a flow state.

Csíkszentmihályi (1989) characterized group flow with five Cs: (1) clarity – knowing what’s expected of you (2) centering – knowing that your teammates are interested in what you’re doing (3) choice – knowing that you have options (4) commitment – a sense of trust in your team that lets you feel non-self-conscious (5) challenge – increasingly complex challenges to tackle. A series of experiments by Walker (2010) support the notion that social or group flow experiences are even more enjoyable than solitary experiences of flow, and found to be especially true in teams with high levels of interdependence. There are two types of team level flow, one is co-active flow during which you are working on your own task in the company of others, and the other is interactive flow, which is collaborating on a task with others (Walker, 2010). Flow is infectious and spreads quickly within teams – in highly interdependent situations, people may act as agents of flow for one another – in other words, highly cohesive teams in which there is agreement on goals, procedures, roles, and modes of communication, social flow is more likely to occur (Walker, 2010). Social flow should be easily seen in highly cohesive teams in which there is agreement on goals, procedures, roles, and patterns of interpersonal relations and the competency of team members is uniformly high (Hackman et al., 2000). However, a team may also need a significant team-level challenge for its members to experience social flow (Sawyer, 2007).

System. The system level is the result of all the system components working together simultaneously to enact
Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting – 2014 575
the system level goals of the organization. We refer to phenomena or outcomes as being at the “system level” if they are inextricable from one another, or unable to be attributed to any one component of the system, and are therefore derived from multiple components of systemic influence. Systemic factors are many, including the immediate environment, the resources available, personnel hierarchy structures, reward systems, technological consistency and compatibility, task design and workflow demands. Examples of system level outcomes are patient outcomes in an ICU unit or passengers processed at an airport checkpoint. The methodology driven by the proposed model incorporates the consideration of system level performance outcomes relative to individual performance and teamwork processes, while enabling diagnosis of the system via additional measures related to the flow construct as described in the next section. Specific metrics and means of capturing the main construct will depend upon operationalizing the constructs to the domain of application as well as the data collection methods available.

Measures of Flow State at Each Organizational Level

Individual. The rate of flow experience and the potential for reaching a flow state is influenced by the individual’s ability to maintain high levels of physiological and psychological resources, matched with sufficient task elicited challenge and the perceived skill to meet such. At the individual level, flow state can be measured behaviorally, affectively, cognitively, or some combination thereof. When the team is the focal level of analysis, flow state at an individual level can be operationalized as individual task performance. This can be extrapolated as a reflection of the overall function of the team, since the team is mutually working to preserve the individual potential of all team members to enter a flow state. Depending upon the goal of the analysis, these measures can be supplemented with affective impressions of flow related qualities, such as perceived skill and challenge, by querying the individual via experience sampling during or after their task, through the use of pagers or surveys (Csikszentmihalyi & Rathunde, 1993;). Another means of assessing flow state at the individual level includes the utilization of sensors that monitor physiological responses, paired with algorithms to infer cognitive states relative to flow, such as engagement, boredom, stress or fatigue.

Team. At the team level, we will be utilizing a variable to assess team function termed “compensatory strategies”. Compensatory strategies represent the team’s response to “flow disruptions”, which occur at the system level, and represent instances of sub-optimal system performance. The compensatory strategy enacted in response to the disruption closes the loop on the incident and provides valuable insight into the proficiency of the team in terms of strategies and behaviors exhibited toward the rectification or mitigation of a disruption. After a flow disruption event, the envisioned data collection system provides an opportunity for the observer to enter information surrounding the strategies enacted by the team to mitigate, rectify, or moderate the effect of the event. The information is complementary to the flow disruption event, which enables the proficiency of the response to be attributed to the team. The compensatory strategy information includes a description of the team’s actions in response to the event, the roles involved, the degree of mitigation of the event (relative to event severity), the phase of the process in which it occurred, and the behavioral dimensions of teamwork enacted. An integrated framework of behavioral teamwork dimensions developed by Rousseau, Aube & Savoie (2006) will be incorporated into the data collection tool as a generic model from which to begin the process of tailoring the taxonomy to the process of interest, and facilitate the identification of the corresponding behavioral indicators of each of the teamwork dimensions.

System. If the “flow” of a system represents functioning in its optimal state, a disruption to the “flow” indicates a sub-optimal instance of performance at the system level. Wiegmann et al. (2007) defined surgical flow disruptions as any issues in teamwork, technology/instruments, training, or the environment that result in deviation from the natural progression of an operation, thereby potentially compromising safety. There is clear evidence that there is a significant relationship between surgical performance and disruptions in the flow of surgery (Wiegmann et al., 2007; ElBardissi et al., 2008). The same line of reasoning suggests that disruptions to the primary task in any context can be detrimental to the overall quality of outcomes. Identification and classification of the sources of disruption to an organizational process can assist with diagnosis and the development of tailored interventions. In addition to the classification of the event type, information about the specific event are recorded, including a description of the event, the roles involved, the phase during the process in which the disruption occurred, and the severity of the impact of the event on the progression of the primary task are recorded.

If we use the metaphor of the human body as the epitome of a complex system, we can consider flow disruptions to be like symptoms of an underlying illness. When there is a malfunctioning internal organ in the body, we cannot view the body part directly to identify the illness; we can however, observe the symptoms that a person is experiencing as a result of the body attempting to continue to function with a faulty part. The symptoms, when viewed holistically, can lead to the identification of the underperforming part, and diagnosis of the problem. Similarly, it is difficult to identify latent failures within a system, until an adverse event occurs and they are identified retrospectively as a contributing factor. Identifying disruptions to the “flow” or optimal state of a process provides a means of identifying latent failures prospectively, before they have a chance to play a role in an adverse event. The qualitative information associated with the collection of flow disruption data can be analyzed to reveal information about the specific features of the disruption event, which can provide valuable insight for creating tailored interventions or measurement techniques to address or monitor any recurrent or severely impactful systemic failure.


Using any predefined framework to measure teamwork within an organization is to limit the results of the potential discovery to the dimensions imposed by the framework. By utilizing the modified flow disruption data collection methodology, it is possible to empirically identify the dimensions of teamwork that are being enacted within each task phase of the overall process. In an organization, this process will allow the identification of different dimensions of teamwork that may be enacted in different teams. This not only allows comparison between teams in the types of behaviors exhibited, but also empowers identification of alternative or exemplary teamwork dimensions that may constitute innovative approaches that could be shared across teams if shown to significantly improve performance. The identification of flow disruptions and compensatory strategies provides an empirically grounded method for populating a teamwork behavior framework that is specific to the focal process. Organization of variables into a multi-level model allows the incorporation of performance data at both the system and individual levels, without excessive risk of misspecification of levels. This methodology can be adapted to analyze the organizational influences affecting teamwork in any context.

This paper describes a multi-level sociotechnical model that utilizes the psychological construct of “flow” as an isomorphic variable. This model can be translated into a methodology capable of empirically identifying the specific instances of sub-optimal system state, and the exact teamwork processes being enacted in response to those instances, while simultaneously considering the dynamic effect that the systemic events have on the individual. The insight provided by the analysis of the flow disruption, compensatory strategies, and flow state data will allow an organization to diagnose major systemic influences, identify teamwork process constructs and the corresponding behavioral indicators, and incorporate individual performance and experience, in order to fully understand the influence of each on the effectiveness of the overall system. This model provides a theoretical basis for explicating the phenomena of teamwork as an emergent property of individual actions in response to organizationally imposed conditions, including constraints and affordances. A multi-level systems-based methodology to team performance measurement can be flexibly applied to many types of teams in many organizations. Theoretical constructs organized into testable multi-level models provide a means of operationalizing the systems perspective for use in diagnosing and improving organizational processes.


Borner, K., Contractor, N., Falk-Krzesinski, H. J., Fiore, S. M., Hall, K. L., Keyton, J., … Uzzi, B. (2010). A Multi-Level Systems Perspective for the Science of Team Science. Science Translational Medicine, 2(49), 49cm24–49cm24.
Carayon, P. (2009). The Balance Theory and the Work System Model … Twenty Years Later. International Journal of Human-Computer Interaction, 25(5), 313–327.
Carayon, P., & Smith, M. J. (2000). Work organization and ergonomics. Applied Ergonomics, 31(6), 649–662.
Csikszentmihalyi, M. (1988). Motivation and creativity: Toward a synthesis of structural and energistic approaches to cognition. New Ideas in Psychology, 6(2), 159–176.
Csikszentmihalyi, M., Finding Flow, 1997.
Csikszentmihalyi, M., & Csikszentmihalyi, I. S. (1992). Optimal Experience: Psychological Studies of Flow in Consciousness. Cambridge University Press.
Csikszentmihalyi, M., & LeFevre, J. (1989). Optimal experience in work and leisure. Journal of Personality and Social Psychology, 56(5), 815–822.
Csikszentmihalyi, M (1990) Flow: The psychology of optimal experience. Harper & Row.
Csikszentmihalyi, M., & Rathunde, K. (1993). The measurement of flow in everyday life: Toward a theory of emergent motivation. In Nebraska Symposium on Motivation, 1992: Developmental perspectives on motivation (pp. 57–97). Lincoln, NE, US: University of Nebraska Press.
ElBardissi, A.W., Wiegmann, D.A., Henrickson, S., Wadhera, R., Sundt, T.S. (2008). Identifying methods to improve heart surgery: an operative approach and strategy for implementation on an organizational level. European Journal of Cardio-thoracic Surgery, 34, 1027-1033.
Ghani, J. A., Supnick, R., & Rooney, P. (1991, January). The Experience Of Flow In Computer-Mediated And In Face-To-Face Groups. In ICIS (Vol. 91, pp. 229-237)
Hackman, J. R. (1998). Why teams don’t work. Social Psychological Applications to Social Issues, 4, 245–267.
Hackman, J. R., Wageman, R., Ruddy, T. M., & Ray, C. R. (2000). Team effectiveness in theory and practice. Industrial and organizational psychology: Theory and practice, 109-129.
Heyne, K., Pavlas, D., & Salas, E. (2011). An Investigation on the Effects of Flow State on Team Process and Outcomes. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55(1), 475–479.
Hitt, M. A., Beamish, P. W., Jackson, S. E., & Mathieu, J. E. (2007). Building theoretical and empirical bridges across levels: Multilevel research in management. Academy of Management Journal, 50(6), 1385–1399.
Kozlowski, S. W., & Bell, B. S. (2003). Work groups and teams in organizations. Handbook of psychology.
Kozlowski, S. W., & Klein, K. J. (2000). A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes.
Kozlowski, S. W., & Salas, E. (1997). A multilevel organizational systems approach for the implementation and transfer of training. Improving training effectiveness in work organizations, 247, 287.
Magnusson, D., & Stattin, H. (1998). Person-context interaction theories. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology: Volume 1: Theoretical models of human development (5th ed.) (pp. 685–759). Hoboken, NJ, US: John Wiley & Sons Inc.
Miller, J. G. (1978). Living systems.
Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow. Handbook of positive psychology, 89-105.
Nokia. (2013). Teams That Flow ebook – Nokia #SmarterEveryday. Technology. Retrieved from
Rosen, M. A., Bedwell, W. L., Wildman, J. L., Fritzsche, B. A., Salas, E., & Burke, C. S. (2011). Managing adaptive performance in teams: Guiding principles and behavioral markers for measurement. Human Resource Management Review, 21(2), 107–122.
Rousseau, D. M. (1985). Issues of level in organizational research: Multi-level and cross-level perspectives. Research in organizational behavior, 7(1), 1-37.
Rousseau, V. (2006). Teamwork Behaviors: A Review and an Integration of Frameworks. Small Group Research, 37(5), 540–570.
Salas, E., Goodwin, G. F., & Burke, C. S. (2008). Team Effectiveness In Complex Organizations: Cross-Disciplinary Perspectives and Approaches. Taylor & Francis.
Sawyer, K. (2008). Group genius: The creative power of collaboration. BasicBooks.
Schaffer, Owen (2013), Crafting Fun User Experiences: A Method to Facilitate Flow, Human Factors International.
Walker, C. J. (2010). Experiencing flow: Is doing it together better than doing it alone? The Journal of Positive Psychology, 5(1), 3–11.
Wiegmann, D. A., ElBardissi, A. W., Dearani, J. A., Daly, R. C., & Sundt III, T. M. (2007). Disruptions in surgical flow and their relationship to surgical errors: An exploratory investigation. Surgery, 142(5), 658–665.