CAS in Teams

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This page highlights Western-led research on teamwork dynamics, undertaken from a complex adaptive systems lens, drawing connections to the broader literature.

Table of Contents

  1. Introduction: complex systems in team behaviour and dynamics
  2. Dr. Sarah McLean: how do team dynamics influence a learning environment?
  3. Dr. Shannon Sibbald: how do successful teams implement and scale-up knowledge?
  4. Dr. Hayden Woodley: how to leadership and team development emerge in teams?
  5. Dr. Paul Tremblay: what can statistical modelling tell us about team dynamics?
  6. Dr. Jonathan De Souza: how are musical ensembles dynamic, social networks?
  7. Dr. Sayra Cristancho: how do teams adapt to unexpected situations?
  8. Conclusion
  9. Acknowledgments and Document History

 

Introduction

Complex adaptive systems (CAS), as the name implies, are systems made complex by dynamic, often unpredictable patterns of small and large-scale interactions among components within the system (Holden et al., 2005). Importantly, CAS are open systems characterized by uncertainty about how they evolve over time (Ramos-Villagrasa et al., 2017). If you are interested in learning more about the characteristics of CAS, see Introduction to CAS for more details. CAS may seem abstract, but examples abound in all aspects of daily life, some of the most illustrative of which are found in teamwork. From orchestras to surgical teams to ant colonies, understanding teams as CAS enables multidisciplinary inquiry and insight into team functioning (Ramos-Villagrasa et al., 2017).

This CAS Lab project will analyze the diverse contexts and obstacles within which teams operate to provide potential applications that address emerging challenges and examine the applicability of CAS to different fields of study. More specifically, the studies profiled below highlight Western-led complexity-informed research by six faculty members on teams. Additional articles from the broader literature discourse on teams emphasize the diverse opportunities for interdisciplinary collaboration within this area.

References

Ramos-Villagrasa, P. J., Marques-Quinteiro, P., Navarro, J. & Rico, R. Teams as Complex Adaptive Systems: Reviewing 17 Years of Research. Small Group Research 49, (2017)

Holden, L. M. Complex adaptive systems: concept analysis. Journal of Advanced Nursing 52, 651–657 (2005).

Dr. Sarah McLean

sarah-mclean.jpgDr. Sarah McLean is an Assistant Professor in the faculty of Physiology and Pharmacology. Dr. McLean’s PhD work focused on endocytosis and cellular signaling pathways.  Her current research interests centers on applications of team dynamics to learning environments. Specifically, she is interested in the interplay of student and community teams, and how they work together. Dr. McLean has worked on several educational research and experiential learning projects. In fact, she introduced Western’s first community-engaged learning course in the Bachelor of Medical Sciences program – Medical Sciences 4300.

We can consider the student-professor dynamic as an educational team with the common goal of student learning. A recent study assessed the effect of a flipped classroom on undergraduate medical sciences students (McLean, 2020). A flipped classroom repurposes class time to focus on application and discussion of material rather than concepts. Concepts are learned using ‘self-study’ modules outside of class time. They were able to show that the flipped classroom dynamic engaged students in deeper, more active learning. This study also suggested that the flipped classroom approach encouraged independent learning.

Dr. McLean further explored students' views of incorporating a flipped classroom in a medical sciences university course (McLean, 2018). Surveys were designed to evaluate students’ views of the instructor's role in the flipped classroom. These surveys were given to students at three points during the course. They found that students noted changes in the instructor's role over time. Survey results also highlighted students’ value of frequent communication with the instructor. The authors also found that students valued discussion and interaction with peers as the most valuable aspects of the flipped classroom, highlighting the value that student participants placed on interactions and collaborative learning.   

In a recent book chapter, Dr. McLean provides support for educators in developing flipped classroom courses. She introduces helpful frameworks and describes her experiences implementing and designing these courses in higher education. When considering the broader discourse on complexity science and education, Davis and Sumara explore the nature of teaching and the role of the educator with regards to learning using a complexity lens. In another study, McMurty and colleagues conducted a collaborative action research project alongside a team of educators who created and implemented an interprofessional health teams' course for undergraduate students from diverse faculties. These researchers sought to explore student participant responses after course designers adapted the course to incorporate novel collective learning concepts informed by complexity science research. The study was conducted to generate novel insights about interprofessional healthcare team education, through the interaction between collective learning ideas and student teams as the course progressed. They drew upon previous work by Davis and Sumara on conditions for nurturing the emergence of intelligent collectives. These conditions include decentralized interactions and organization, diversity and commonality, and openness and constraints. Their study described valuable changes to course content and found novel insights regarding collective learning, consensus, and trust in interprofessional health team education.

Recent work by McGee & Poojary used a systems-based framework and interview data to understand the perceived relationships between diverse stakeholders at a university in the context of blended learning. Authors drew on the Complex Adaptive Blended Learning Systems (CABLS) framework by Wang and colleagues who describe blended learning, the stakeholders involved, and their interactions as a complex system. Their analysis identified five themes that characterize stakeholders’ perceptions of their relationships in a blended-learning ecosystem. These themes included: 1) feeling that collaborative relationships were necessary in curriculum development, problem solving, student learning, and technology selection, 2) perceiving relationships within CABLS to be complex, 3) perceiving relationships within CABLS to be dynamic, 4) supporting interdependent relationships, and 5) perceiving those relationships were student-centered.

In another study, Hwang and Chen explored the flipped classroom approach from the lens of a collective problem-solving mechanism to examine students’ interactions and learning engagement using an interactive response system (IRS). Collaborative problem-solving, according to Häkkinen and colleagues, is a learning strategy where students internalize and apply the learning outcomes of any content through inter-group discussion and collaboration. The results of their study illustrated that students in the collective problem-solving promotion-based flipped classroom had better performances, collective efficacy, a higher level of knowledge construction and deeper interactions compared to students subject to conventional instructional approaches. In the area of team dynamics, the flipped learning approach enhanced students’ interactions, collaboration and discussion by sharing their comprehension and in-depth thinking with their classmates to accomplish problem-solving.

To sum up, the flipped learning approach shapes a different form of team dynamics as collaboration within groupwork is the focus of a flipped classroom context. The flipped learning approach provides teachers and researchers with creative and effective curricula that engage students with class discussions and knowledge construction along with other inter-group learning skills.

Table of Contents

References

Davis, B., & Sumara, D. (2007). Complexity science and education: Reconceptualizing the teacher's role in learning. Interchange, 38(1), 53-67.  

McLean, S. (2020). Designing and Delivering Flipped Courses: From Instructor and Student Perceptions of Basic Medical Sciences. In Active Learning in College Science (pp. 551-566). Springer, Cham.  

McLean, S., & Attardi, S. M. (2018). Sage or guide? Student perceptions of the role of the instructor in a flipped classroom. Active Learning in Higher Education, 1469787418793725.  

McLean, S., Attardi, S. M., Faden, L., & Goldszmidt, M. (2016). Flipped classrooms and student learning: not just surface gains. Advances in physiology education.  

McGee, E., & Poojary, P. (2020). Exploring Blended Learning Relationships in Higher Education Using a Systems-Based Framework. Turkish Online Journal of Distance Education, 21(4), 1-13.  

McMurtry, A. (2010). Complexity, collective learning and the education of interprofessional health teams: Insights from a university-level course. Journal of Interprofessional Care, 24(3), 220-229.

Hwang, G. J., & Chen, P. Y. (2019). Effects of a collective problem-solving promotion-based flipped classroom on students’ learning performances and interactive patterns. Interactive Learning Environments, 1-16.

Häkkinen, P., Järvelä, S., Mäkitalo-Siegl, K., Ahonen, A., Näykki, P., & Valtonen, T. (2017). Preparing teacher-students for twenty-first-century learning practices: a framework for enhancing collaborative problem-solving and strategic learning skills. Teachers and Teaching, 23(1), 25–41.

Dr. Shannon Sibbald

sibbald_s_160x180.jpgDr. Shannon Sibbald is an Associate Professor with appointments in Family Medicine, the School of Health Studies, and the Schulich Interfaculty Program in Public Health. Her research interests include interdisciplinary health systems, interprofessional health care teams, implementation science, and knowledge translation. Overall, her research investigates how successful teams implement and scale-up knowledge. Her research on teams ultimately supports the delivery of high-quality health care to patients in an integrated way. She mainly uses qualitative research methods, including multiple-case comparison, mixed methods, and content and discourse analysis. Dr. Sibbald also studies innovative pedagogy for interprofessional teams, with an overarching aim to support the development of high-performing teams.

Dr. Sibbald and colleagues explored team-based learning (TBL) and the complexities of employing it in public health. TBL puts the responsibility of learning on students to mimic real-world public health practice. The researchers suggest a peer-evaluation approach to measure TBL to encourage equal contributions and discourage social loafing. Overall, their method decreased major sources of dissatisfaction in teams and supported the development of cohesive, high-functioning teams.

TBL has been widely studied in efforts to improve educational outcomes. For instance, Thompson and colleagues explored relationships among variables associated with team performance outcomes, including team cohesion, size, and gender. They administered the National Board of Medical Examiners (NBME) Psychiatry Subject test to third-year medical students, first to individuals and then to teams, to assess team performance variables. The authors found that teams on later medical school rotations scored higher on a team test. Additionally, individual NBME test scores, along with team cohesion, positively correlated significantly with team test scores. Another study by Buhse and colleagues described the use of TBL in interprofessional classrooms to teach chronic illness concepts. The researchers argued that classroom TBL experiences provided students opportunities to enhance their problem-solving skills in reference to real-world problems, extend their knowledge of other professional roles, and develop collaboration skills as members of an interprofessional team.

In another study, Dr. Sibbald and colleagues used social network analysis (SNA) to understand how evidence-based knowledge flowed through multidisciplinary healthcare teams within primary care. The authors were able to show that obtaining research knowledge was perceived to be a shared responsibility. However, applying the knowledge to patient care was seen as the team leader’s responsibility. Key players in knowledge uptake and sharing were residents, senior physicians, and nurse practitioners. Interestingly, quantitative data pointed to senior doctors as being primary knowledge sources, while qualitative interviews suggested residents were a primary source. The authors acknowledged no consistent or formal process for the team to acquire, share, and apply knowledge collectively. Nevertheless, they suggest interventions to create a more streamlined system of knowledge uptake in these teams. Suggestions included formalizing modes of communication, improving knowledge-sharing activities, and increasing the active use of allied health professionals.

Recent work by Dr. Sibbald and colleagues applied a complexity lens to develop a healthcare ecosystem map characterizing the Middlesex London Ontario Health Team (MLOHT). They adopted a systems approach to create a visual representation of coordinated care systems for patients with chronic obstructive pulmonary disease (COPD) and heart failure (HF). This approach allowed the researchers to promote an understanding of how care systems work in caring for these patients and identified gaps in care that should be prioritized. Applying a complex systems approach to coordinated care systems helped paint a ‘real-world’ picture of care that can inform future decision-making processes by MLOHT. In another recent collaboration with Dr. Kothari, from the School of Health Studies, Dr. Sibbald commented on some of the implications of using complexity concepts to conceptualize and inform knowledge translation.

Knowledge flow through professional teams has been widely studied in attempts to understand and refine knowledge exchange practices. These efforts are especially true in the healthcare field, where knowledge transmission and application influence patient outcomes. A study by Kneck and colleagues explored knowledge flow within an integrated healthcare and social care organization through a case study on an older patient with complex care needs. This study found that information flow between partners in care was obstructed because of double documentation, complementary information channels, and information loss. Additionally, though patients were expected to play an active role in transmitting information, they were considerably excluded from information flow by their healthcare team. A paper by Al-Salamah and colleagues provides a potential solution to the issue of knowledge obstruction. They suggest using a Virtual Organizations (VO) system wherein the care teams will be treated as a type of VO, and the patient and their care will be tracked dynamically. This approach would create a unified patient record in which patient information from multiple sources may be accessed. They suggest that this model allows for dynamic team communication, coordination, and collaboration through the tracking of care teams and their members in an automated care process. In theory, this can help facilitate a patient-centric, team-oriented environment.

Studies on knowledge flow have been used to inform other professional teams as well. Scropfer and colleagues used social network analysis to examine knowledge transfer regarding sustainability among construction teams. They found that network densities among construction workers were low, meaning few direct ties among the network. This finding suggests large amounts of implicit knowledge were distributed through few strong ties within the network. They also found that preferred knowledge sources were colleagues or peers, suggesting that trust-based relationships may contribute to these strong ties. In their paper, Sorenson and colleagues argue that the value of connectedness to a knowledge source significantly depends on the nature of the knowledge being exchanged. They hypothesized that complex knowledge would have difficulty spreading regardless of the source, whereas moderately complex knowledge would spread more easily through stronger social ties. Their findings support the hypothesis that social proximity promotes the transfer of moderately complex knowledge.

In a study on knowledge flow in interdisciplinary teams, Haythornwaite applied social network analysis to understand what kind of knowledge forms the basis of coworkers’ relationships and how this is accomplished. Members of these interdisciplinary teams were asked what they learned from their closest coworkers and what they taught them in return. The author showed that the exchange of factual knowledge is only one important way of enabling knowledge flow. Other ways include learning processes, information about methods, research, learning about technology, brainstorming, socialization, networking, and administrative work. This paper provides a case for the complexity of knowledge flow and acquisition among interdisciplinary teams.  

Dr. Sibbalds’ work regarding knowledge flow in multidisciplinary health care teams has provided insights into how we approach education and knowledge exchange in healthcare. Drawing on research from other professions helps us understand various aspects of the learning process, such as how different types of knowledge spread in interdisciplinary teams and learning mechanisms that take place. Approaching knowledge exchange in teams from a complexity lens can provide a more holistic view on how different professional teams engage in this process and help us learn how to facilitate learning in interdisciplinary teams.

Table of Contents

References

Sibbald, S. L., John-Baptiste, A. & Speechley, M. Navigating the Complexities of Evaluating Team-Based Learning in the Graduate Classroom. Pedagogy in Health Promotion 5, 254–260 (2019).

Sibbald, S. L., Wathen, C. N., Kothari, A. & Day, A. M. B. Knowledge flow and exchange in interdisciplinary primary health care teams (PHCTs): an exploratory study. J Med Libr Assoc 101, 128–137 (2013).*

Hussey, A. J. et al. Confronting complexity and supporting transformation through health systems mapping: a case study. BMC Health Serv Res 21, 1146 (2021).

Kothari, A., & Sibbald, S. L.  Using Complexity to Simplify Knowledge Translation: Comment on" Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation". Int. J. Health Policy Manag 7, 563 (2018).

Buhse, M. & Della Ratta, C. Enhancing Interprofessional Education With Team-Based Learning. Nurse Educator 42, 240–244 (2017).

Thompson, B. M. et al. Team cohesiveness, team size and team performance in team-based learning teams. Medical Education 49, 379–385 (2015).

Kneck, Å., Flink, M., Frykholm, O., Kirsebom, M. & Ekstedt, M. The Information Flow in a Healthcare Organisation with Integrated Units. International Journal of Integrated Care 19, 20 (2019).

Al-Salamah, H., Skilton, A., Gray, A., Allam, O. & Morry, D. An Innovative Approach to Providing Dynamic Support for Distributed Healthcare Teams. 11.

Schröpfer, V. L. M., Tah, J. & Kurul, E. Mapping the knowledge flow in sustainable construction project teams using social network analysis. Engineering, Construction and Architectural Management 24, 229–259 (2017).

Sorenson, O., Rivkin, J. W. & Fleming, L. Complexity, networks and knowledge flow. Research Policy 35, 994–1017 (2006).

Haythornthwaite, C. Knowledge Flow in Interdisciplinary Teams. in Proceedings of the 38th Annual Hawaii International Conference on System Sciences 254a–254a (2005). doi:10.1109/HICSS.2005.372.

Dr. Hayden Woodley

hayden-woodley.jpgDr. Hayden Woodley is an Assistant Professor of Organizational Behaviour at Ivey Business School. His research interests include emergence in teams, leadership, and team development in the context of human resource management, which are the focus of many of his studies profiled below. His current research focuses on understanding the process of emergence and what it means in the context of team-level phenomena.

Dr. Woodley collaborated on a study looking at different team conflict types and their implications for team performance (O’Neill et al., 2018). The team conflict types of interest in this study were task conflict (TC), relationship conflict (RC), and process conflict (PC). Previous research on team conflict argued that task conflict positively influenced team performance while relationship and process conflict were harmful. This research employed a team-centric approach, to investigate teams with varying degrees of TC, RC, and PC, as well as provide insight into how these variables interact to influence team success. Using Latent Profile Analysis (LPA), the authors identified four conflict profiles and found that team performance can be predicted based on these profiles. When RC and PC are low, TC was shown to be beneficial to team performance. However, as RC and PC increased, teams became increasingly dysfunctional, and performance decreased. The findings of this study support some use of team conflict profiles to inform teamwork dynamics research and highlight their value in developing workplace intervention strategies.

Previously, Dr. Woodley collaborated on another study investigating the efficiency of a training program centred on the effective use of conflict in student teams (O’Neill et al., 2017). This training program equipped students with techniques to use conflict constructively. The researchers showed that groups that received training demonstrated improved team functioning and reduced probability of unwanted conflict. Interestingly, the strongest effect was observed in teams that participated in additional ‘booster’ sessions focused on reflection. Taken together, these studies provide insight into how conflict, and conflict profiles, can be effectively managed to improve team performance.

In a recent study, McLarnon & Woodley (2021) discuss the emergence of collective efficacy in virtual teams. Collective efficacy refers to a team’s shared confidence in its ability to reach its goals and is a positive predictor of team effectiveness. Longitudinal data was collected over three time intervals from virtual teams participating in a 10-week business simulation activity to assess the dynamic nature of collective efficacy through the use of multilevel latent growth and consensus emergence modelling. Though previous discourse suggested that collective efficacy takes time to develop, this paper found that collective efficacy scores at the beginning of a project predicted subsequent team performance. The findings provide an argument for the importance of team-building early on. The study also found that collective efficacy scores decreased over time – teams began confident in their projects, but this shared confidence decreased as the project progressed. The extent to which this decrease occurs appears to be a significant predictor of team performance. On the other hand, Dr. Woodley and colleagues showed that smaller decreases in collective efficacy scores over the course of a project were associated with greater performance. Dr. Woodley is currently expanding on the dynamic nature of this construct by examining how a team's collective efficacy changes over time.

In other disciplines, recent studies have also highlighted the value of collective efficacy in the context of teamwork. For instance, collective efficacy research in education has emphasized the construct’s importance in supporting student success. For instance, a recent study looked at the impact of self vs. collective efficacy on small-group team performance among early adolescents (Khong, Liem, Klassen, 2017). Individuals were randomly assigned to small groups and given three puzzle or math-based tasks. The results revealed that collective efficacy was a stronger predictor of group performance than self-efficacy. Contrary to previous research findings, when self-efficacy was higher in individuals, organization performance suffered as a whole. Teachers often use small group learning to encourage collaborative learning, so research into how to best apply this technique is beneficial for teachers and students alike.

Collective efficacy research has also provided insight into healthcare team dynamics. A recent study investigated the impact of simulation training on self and collective efficacy and its contribution to patient outcomes (Egenberg, 2017). The authors showed that simulation training significantly increased self-efficacy and collective efficacy levels in an interprofessional care team of obstetricians, midwives, and auxiliary nurses, improving patient outcomes by substantially reducing severe postpartum hemorrhage. Furthermore, these improved patient outcomes correlated with improved collective efficacy in the care teams. Their research provides a case for training programs to enhance collective efficacy in health teams, where effective collaboration directly impacts patient health.

Furthermore, another study by Barling and colleagues (2018) looked at destructive leadership behaviours that impact surgical team performance. This study investigated how destructive leadership qualities, namely passivity, abusive supervision, and over-controlling leadership, influenced the surgical team's collective efficacy and psychological safety. The results showed that abusive supervision and over-controlling leadership were associated with lower collective efficacy and levels of psychological safety among the care team.

Overall, these studies provide unique perspectives into strategies for improving collective efficacy and team performance. Insights regarding collective efficacy and team performance can be widely applied across disciplines, including business management, academia, education, and healthcare.

Table of Contents

References

O'Neill, T. A., McLarnon, M. J., Hoffart, G. C., Woodley, H. J., & Allen, N. J. The structure and function of team conflict state profiles. Journal of Management, 44(2), 811-836 (2018). 

Woodley, H. J., McLarnon, M. J., & O’Neill, T. A. The emergence of group potency and its implications for team effectiveness. Frontiers in psychology, 10, 992 (2019).

McLarnon, M. J. W., & Woodley, H. J. R. Collective efficacy in virtual teams: Emergence, trajectory, and effectiveness implications. Canadian Journal of Behavioural Science, 53(2), 187–199 (2021).

O'Neill, T. A.; Hoffart, G. C.; McLarnon, M. M. J. W.; Woodley, H. J.; Eggermont, M.; Rosehart, W.; Brennan, R. "Constructive controversy and reflexivity training promotes effective conflict profiles and team functioning in student learning teams", Academy of Management Learning and Education, 2: 257 - 276 (2017).     

Khong, J. Z. N., Liem, G. A. D. & Klassen, R. M. Task performance in small group settings: the role of group members’ self-efficacy and collective efficacy and group’s characteristics. Educational Psychology, 37, 1082–1105 (2017). 

Egenberg, S., Øian, P., Eggebø, T. M., Arsenovic, M. G. & Bru, L. E. Changes in self-efficacy, collective efficacy and patient outcome following interprofessional simulation training on postpartum haemorrhage. Journal of Clinical Nursing, 26, 3174–3187 (2017).

 Goddard, R. D., Skrla, L. & Salloum, S. J. The Role of Collective Efficacy in Closing Student Achievement Gaps: A Mixed Methods Study of School Leadership for Excellence and Equity. Journal of Education for Students Placed at Risk, 22, 220–236 (2017).

Barling, J., Akers, A. & Beiko, D. The impact of positive and negative intraoperative surgeons’ leadership behaviors on surgical team performance. The American Journal of Surgery, 215, 14–18 (2018).

Dr. Paul Tremblay

DPaul-F-Tremblay.jpgr. Paul Tremblay is an assistant professor in the Department of Psychology. His interests and connections to complex adaptive systems (CAS) lie in how quantitative research methods used in behavioral and health sciences might inform research investigating concepts such as emergence and change over time. Many students and colleagues collaborate within course projects, thesis research, or other programs of research, investigate topics that overlap with CAS in some way. Two of these areas have included work team dynamics and studies of how people change or remain the same over time in some dimensions of psychology such as personality and mental health characteristics. In relation to work team research, he regularly teaches a graduate course in multi-level modelling (MLM). MLM is a procedure well suited to investigate variables that influence teams and team members’ job performance. This may include members’ personality traits, values, goals, and team characteristics such as team cohesiveness or conflict. MLM allows researchers to see how different factors affect an outcome at different levels. This can be applied to team dynamics research to compare factors at the individual and team level. This review will look at how MLM has been used by students of Dr. Tremblay, and more broadly in team dynamics research.

MLM has been broadly applied to teams research and could provide a framework for future work. MLM can help overcome limitations of traditional research by expanding the scope of analysis. It can also help us understand micro, meso, and macro interactions between teammates (Ribeiro et al).  MLM provides a more realistic picture compared to other statistical analysis methods (Greenland). This allows a 'big picture' understanding of networks of complex interactions.

Teams vary in size, and MLM can be applied to clusters as small as two individuals (dyads) and as large as entire countries. In a project headed by Dr. Lynne Zarbatany who studies bullying and peer cliques, the clusters consisted of children (8-14y/o) in peer cliques of 3-8. Tremblay collaborated on the statistical modeling procedures in this project using MLM. They found victimization was mitigated over the school year by greater centrality and friendship within the cliques.

In addition to applications of individuals within cliques or teams, it is also possible to study larger groups. Many large national surveys, such as the World Values Survey, provide data on large samples of people within many countries. With data from individual respondents, it is possible to investigate – for example – how a person’s life satisfaction is influenced by both individual characteristics and nation level variables. As an example, one student in Dr. Tremblay’s MLM course investigated the potential influences of income and religiosity on life satisfaction at the individual and country level (Plouffe & Tremblay, 2017). At the individual level, income and religiosity were positive predictors of life satisfaction. However, at the country level, an aggregate index of nation level income had no significant influence on life satisfaction. The aggregate index of nation religiosity was a negative predictor. An important point in this example is that religiosity of a person does not necessarily overlap with aggregate religiosity level of the nation.

Although Dr. Tremblay has not yet published works on complexity in team dynamics itself, he is interested in adapting quantitative methods such as MLM and structural equation modeling (SEM) using longitudinal or intensive repeated measures designs to investigate more dynamic components of behavior. Many of these methods have focused on simpler linear equations, but progress will require collaboration with researchers who are more well versed in nonlinear dynamics and network analysis. A sample of the research literature of studies that have used MLM reveals some interesting team examples.

For instance, Bell and colleagues applied MLM to research Formula One racing teams. MLM allowed them to determine how much individual drivers and teams matter. It also considered the extent the team's legacy affected success. The measure of success was based on points scored in each race. The points were nested within the context of a driver, a team-year, and a team. They found that the team and team-year have a more significant impact than individual drivers on success. Team-effects were more important for success than driver-effects, and this importance increased over time. This means that the legacy a team holds over time contributes to their team performance.

MLM research has also provided insight into American college football games. Wang et al. were able to show that 'home-field advantage' is not just a superstition. They were able to show that home-field advantage roughly equates to a 6-point advantage for home teams and a 3-point disadvantage for away teams. This advantage was shown to vary in strength between different football conferences as well. 

In other sports research, Thomas and colleagues used MLM to create an argument for team building. They investigated how the degree to which a team identifies with each other impacts performance. On a team level, they found the degree that teammates socially identify with each other predicts performance. Individual levels of social identification did not impact overall team performance. This research supports the impact of group-level identification on group-level outcomes, such as team performance.

MLM has also been applied in the context of healthcare management. Bogeart et al looked at the relationship between hospital environments and nurse wellbeing. Positive ratings of environmental factors were associated with reduced negative outcomes among nurses. They found reduced burnout, better job outcomes, and better quality of care. Further research by Bogeart looked at nurse wellbeing in psychiatric hospitals. They found two factors that predicted nurse turnover and quality of care. These factors included depersonalization and nurse-physician interactions.  A study by Leinweber et al used MLM to further study nurse burnout. On an individual level, high work-family conflict put nurses at risk of emotional exhaustion. On a department level, good leadership and nursing support reduced this risk. These studies provide vital insight which can inform healthcare management and practitioner support.  

Beyond sports and health sciences, MLM is well entrenched in business team management research (Aguinis). Hitt and colleagues recommend applying MLM to existing models of management research. They argue that typical management research uses a single level of analysis, painting an incomplete picture. Management problems are mainly due to multilevel interactions and research models should reflect this. Penger et al used MLM to look at the impact of authentic leadership on work teams. The purpose was to look at interactions between authentic leadership and job satisfaction and work engagement in individuals. They found a positive relationship between authentic leadership, employee satisfaction, and work engagement. Further, they found perceived supervisor support determined this relationship at an individual level. This research suggests that it is useful and valuable for supervisors to display authenticity to their employees. A different study by Simsek et al explored the impact of top management team characteristics on organizational outcomes. This study specifically looked at the influence of behavioral integration. Behavioral integration refers to the degree to which a group engages in mutual and collective interactions. The study used MLM to see how CEO-, team-, and firm-level determinants shape these interactions. At the CEO-level, they found that collectivism and tenure impact behavioral integration. This suggests that the power and experience of CEOs allows them to influence team processes. At the team level, diversity in goal preferences and education level impact behavioral integration. At the firm level, behavioral integration was positively associated with firm performance. Conversely, firm size negatively predicted performance, and firm age was unrelated. 

These are just a few of many examples of areas of research where MLM has been applied to gain novel insights into team dynamics. Looking at the impact of different factors through different lenses of analysis helps us to better understand and predict team outcomes. Dr. Tremblay’s work with MLM has contributed to further development of the model and its application.

Table of Contents

References

Zarbatany, L., Tremblay, P. F., Ellis, W. E., Chen, X., Kinal, M., & Boyko, L. (2017). The peer clique experiences of victimized children. Merril Palmer Quarter, 63, 485-513. Doi:10.13110/merrpalmquar1982.63.4.0485

Plouffe, R. A., & Tremblay, P. F. (2017). The effect of income on life satisfaction: Does religiosity play a role? Personality and Individual Differences, 109, 67-71.

Bell, A., Smith, J., Sabel, C. E. & Jones, K. Formula for success: Multilevel modelling of Formula One Driver and Constructor performance, 1950–2014. Journal of Quantitative Analysis in Sports 12, 99–112 (2016). 

Wang, W., Johnston, R. & Jones, K. Home Advantage in American College Football Games: A Multilevel Modelling Approach. Journal of Quantitative Analysis in Sports 7, (2011). 

Ribeiro, J. et al. The Role of Hypernetworks as a Multilevel Methodology for Modelling and Understanding Dynamics of Team Sports Performance. Sports Med 49, 1337–1344 (2019). 

Thomas, W. E. et al. Team-level identification predicts perceived and actual team performance: Longitudinal multilevel analyses with sports teams. British Journal of Social Psychology 58, 473–492 (2019). 

Bogaert, P. V., Clarke, S., Roelant, E., Meulemans, H. & Heyning, P. V. de. Impacts of unit-level nurse practice environment and burnout on nurse-reported outcomes: a multilevel modelling approach. Journal of Clinical Nursing 19, 1664–1674 (2010). 

Van Bogaert, P. et al. Impacts of unit-level nurse practice environment, workload and burnout on nurse-reported outcomes in psychiatric hospitals: A multilevel modelling approach. International Journal of Nursing Studies 50, 357–365 (2013). 

Leineweber, C. et al. Nurses’ Practice Environment and Work-Family Conflict in Relation to Burn Out: A Multilevel Modelling Approach. PLOS ONE 9, e96991 (2014). 

Greenland, S. Principles of multilevel modelling. International Journal of Epidemiology 29, 158–167 (2000) 

Simsek, Z., Veiga, J. F., Lubatkin, M. H. & Dino, R. N. Modeling the Multilevel Determinants of Top Management Team Behavioral Integration. AMJ 48, 69–84 (2005) 

The Etiology of the Multilevel Paradigm in Management Research - John E. Mathieu, Gilad Chen, 2011. https://journals.sagepub.com/doi/abs/10.1177/0149206310364663?casa_token=s_zKXCytIOkAAAAA:YdoIFgokBl0xZtEnfICBNICmZ9Mn_bCrTn0-_QNNje-aHiVILWdXeTHVCiEkGUcREWq_2YfldVUL

Hitt, M. A., Beamish, P. W., Jackson, S. E. & Mathieu, J. E. Building Theoretical and Empirical Bridges Across Levels: Multilevel Research in Management. AMJ 50, 1385–1399 (2007). 

Penger, S. & Černe, M. Authentic leadership, employees’ job satisfaction, and work engagement: a hierarchical linear modelling approach. Economic research - Ekonomska istraživanja 27, 508–526 (2014).

Dr. Jonathan De Souza

Jonathan-De-Souza.jpgDr. Jonathan De Souza is an Associate Professor of Music Theory at Western. He uses a complex blend of music theory, cognitive science, and philosophy to conduct his research. Previous works by Dr. De Souza have employed a complexity lens to look at musical networks. In Orchestra Machines: Old and New (2018), Dr. De Souza contrasts historical and contemporary musical networks. He combines technical and historical study of instruments with philosophical perspectives to investigate technical aspects of music and their connections with humans. He conceptualizes various orchestras, orchestral machines, and contemporary music projects as networks, and looks at information flow through these systems.  

His research interests have recently broadened to include team dynamics, as they apply to musical ensembles, when he gained interest in network analysis. Dr. De Souza’s current research involves conceptualizing musical ensembles as dynamic, fast-changing social networks. He is interested in large musical ensembles and the group dynamics that allow musicians to coordinate in real time. Dr. De Souza has a strong interest in music and technology, which further inspires him to investigate the techniques, systems, and/or tools that are used to support large-scale coordination in musical ensembles. On a smaller scale, the leading-following behaviors of members in musical ensembles are of interest to Dr. De Souza. 

Dr. De Souza’s current research is informed by social network analysis (SNA). SNA offers a wide variety of tools to analyze complex connected systems (Carley, K.M). Though Dr. De Souza has not yet published work using SNA, its application can be seen through examples in literature. For instance, a study by Muller and colleagues used SNA to examine synchronization of choir singing. They looked at cardiac, respiratory, vocal, and gestural activity among individual singers in the choir context. Results of this study showed respiratory and cardiac activity sync within members of the choir while singing. Respiratory and cardiac synchronization were also coupled to vocalizing patterns and hand movements of the conductor. Overall, they were able to show that the choir functions as a ‘superorganism’ of choir members in synchrony, and this system creates boundaries for individual singers. 

A more recent study by Muller and Lindenberger constructed networks of interactions between brains and guitars in a duet. This study provided insight into the interpersonal and instrument-brain interactions by using network analysis on EEG and instrument sound recordings. They were able to show that during an improvisation guitar duet, synchronization was dynamic, oscillatory, and characterized by specific peaks. This ultimately shows synchronization patterns between guitars and brains are temporal and are comprised of signals from different instruments and brains in a duet. 

McAndrew and Everett applied SNA to look at classical British composers. While composers are typically considered ‘soloists’, this study hypothesized that their work depends significantly on interaction and collaboration. They suggested this is because of knowledge flow, reduced risks of developing new knowledge, and that different positions in a network are associated with different levels of innovation. This research supported the idea that musical composition is a collective invention, and prompts further studies to further refine this theory.

Broadly speaking, SNA has been used to study dynamic networks across a wide variety of disciplines. In one example, Fransen and colleagues used SNA to provide insight into interpersonal networks of athletes and coaches. They assessed shared leadership between coaches and athletes, studying four leadership types: task, motivational, social, and external leadership. They found no difference in task and external leadership roles between coaches and athletes. However, athletes were perceived as better motivational and social leaders compared to coaches. This research provided insight into team psychology and leadership structure in sports teams.

In another example, researchers utilized SNA to gain insight into healthcare teams (Cott et al). This research used SNA to describe the structure of multidisciplinary long-term care teams as they work in real-time. Analysis revealed that the teams could be broken down into two sub teams – a multiprofessional team that involved decision-making, and a nursing team that is more mechanistic and task based. This supports a clear hierarchy where decisions are made by non-nursing health professionals, which are carried out in practice by nurses.

SNA, as utilized by Dr. De Souza and other researchers, can be broadly applied to inform CAS research. The studies discussed highlight how SNA informs research into synchronization and coordination in teams, and show its application in De Souza’s current projects.

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References

Carley, K. M. DYNAMIC NETWORK ANALYSIS. 165.

Müller, V., Delius, J. A. M. & Lindenberger, U. Complex networks emerging during choir singing. Annals of the New York Academy of Sciences 1431, 85–101 (2018).

Müller, V. & Lindenberger, U. Dynamic Orchestration of Brains and Instruments During Free Guitar Improvisation. Front Integr Neurosci 13, 50 (2019).

McAndrew, S. & Everett, M. Music as Collective Invention: A Social Network Analysis of Composers. Cultural Sociology 9, 56–80 (2015).

Fransen, K. et al. Who takes the lead? Social network analysis as a pioneering tool to investigate shared leadership within sports teams. Social Networks 43, 28–38 (2015).   

Cott, C. “We decide, you carry it out”: A social network analysis of multidisciplinary long-term care teams. Social Science & Medicine 45, 1411–1421 (1997).   

Dr. Sayra Cristancho

Sayra-Cristancho.jpgDr. Cristancho is an Associate Professor in the Department of Surgery, a faculty member at the Institute for Earth and Space Exploration, and a scientist at Western University's Centre for Education Research & Innovation (CERI). Her current research focuses on how action teams in high-stakes situations adapt to unexpected situations. Examples of such teams include military teams, business teams, emergency response teams and musical ensembles. Her main research interests related to teams include collective adaptation, teamwork, and swarm intelligence (SI).

Swarm intelligence refers to self-organized organisms' complex collective behaviours. Collective self-healing, a feature of swarm intelligence, is enabled when team members demonstrate the ability to swap tasks. Interviewing members of action teams have allowed Dr. Cristancho and colleagues to describe three main approaches that allow teams to become self-healing: 1) doing what is necessary to reach the team's goal, 2) allowing for redistribution of leadership and 3) recognizing individual limitations and demonstrating the ability to ask for help.

SI has been used to understand human teams and their interactions in some industries. Swarm Intelligence (SI), while nascent in healthcare, offers a unique perspective to conceptualize relationships among medical teams. Lessons from SI can help medical teams adapt through distributed leadership and interchangeable collaboration. Dr. Cristancho's work invites systems researchers to focus on the interactions among individuals, interconnections between them, and their overall environment instead of viewing each independently.

In a recent article, Dr. Cristancho introduces ways to improve and conceptualize collective adaptive behaviours among healthcare professionals. Such collective behaviours are central to swarm intelligence. They exist in nature among fish and bird colonies, as well as social insects. Dr. Cristancho's article examines SI's key features from examples of collective behaviours in nature. She further explores how these features can inform novel understandings of human team dynamics and offer alternative solutions to research on adaptation in medical teams. Dr. Cristancho's recent work focuses on two principles of swarm intelligence to contribute to the understanding of teamwork in healthcare: (1) collective self-healing and (2) trace-based communication. These principles will be discussed further below.

Collective self-healing

Collective self-healing describes a swarm's ability to cope with failure and is highly reliant on interchangeability, i.e. the ability to exchange roles or tasks usually performed by another team member in response to unforeseen changes in the environment.

In a recent commentary, Cristancho and Taylor advocate for the use of an aggregate complexity orientation to explore interactions among team members using analogies from sociobiology. They describe complexity in social insects and use ants as an example of teams capable of collective self-healing. Ant colonies perform tasks as part of a collective where every interaction informs team function. This commentary's purpose is to inspire novel ways of thinking about enabling self-healing in healthcare teams, addressing fragmented care, and examining a systems approach to problem-solving roles and responsibilities. Her articles draw on examples from nature to provide insightful analogies to prepare medical teams in a constantly changing work environment.

Humans and animals are not the only subjects of interest when it comes to collective behaviours. Interestingly, self-healing has been explored in the context of robot teams. Liu and colleagues showed that incorrect information shared by faulty robots leads to swarm disconnection and incorrect heading directions. They argue that a centralized human operator should provide a 'trust' signal that corrects for faulty behaviour to keep the swarm on track. Using this system, the 'trust' signal was effective in correcting faulty behaviours through self-healing.

Dr. Cristancho's work also expands on another characteristic of self-healing teams: asking for help. One collaboration sought to understand how interprofessional (IP) surgical team members engage in helping behaviours. This research examined the relationship between work context and clinician willingness to help others and provides insights into the conditions and intrinsic factors that influence clinician's decisions to offer help.

Trace-based communication

Trace-based communication (TBC) is a type of nonverbal communication that supports collective adaptation. It relies on objects to modify work environments and prompts team members to adopt a particular behaviour. Recent interviews by Cristancho and Field reveal insights into how interdisciplinary clinicians conceptualized trace-based communication in a clinical setting. Healthcare team members admitted that leaving 'traces' was an implicit practice intended to prevent errors and promote efficacy. Dr. Cristancho's research highlights that TBC may improve a team's situational awareness and complement their response to unexpected challenges. This idea is underexplored in healthcare, given the tendency to primarily associate non-verbal communication with body language and gestures. Dr. Cristancho's team is currently working on understanding TBC among surgeons and its implications in high-stakes situations where communication might be hindered. This kind of innovative research is advancing our current understanding of non-verbal communication in healthcare settings.

Other innovations in non-verbal communication have looked at teaching the use of non-verbal skills to future healthcare teams through cross-disciplinary learning (Aung & Hariharan, 2021 and Van & Wijnen-Meijer, 2018). For example, a recent pilot initiative by Hall and colleagues explored medical students' perceptions of features of nonverbal communication after attending a chamber musician practice. In a collaboration between Harvard Medical School and George Washington University, Pearl and Greenberg explore similar musical education contributions to Interprofessional Healthcare Teams (IPHCT) training.

In earlier qualitative research, Dr. Cristancho employed visual methods from systems engineering to explore surgical team challenges. In a 2015 study, Cristancho and colleagues integrated the 'rich pictures' method from systems engineering to describe how medical teams deal with complex situations. Her research team used drawings to foster 'big picture thinking' and facilitate the description of experiences during interviews. Rich pictures can describe how people experience reality given different objects, ideas, feelings, beliefs, etc. The aesthetic feature of rich pictures can complement other research methods to illustrate complex situations from different perspectives and help overcome language limitations to express the nonvisible aspects of human experience. Dr. Cristancho and colleagues also employed this method to better understand the non-procedural aspects of surgical challenges. Dr. Cristancho and her team also combined rich

pictures and semi-structured interviews to explore the ICU and neurology clinician perceptions to describe how they navigate complexity in clinical practice. As this work uncovered, engaging in patient advocacy constituted a key strategy that clinicians use to cope with complexity.

In another study, Cristancho and colleagues discussed the issue of problem-solving in clinical practice. The article advocates for shifting the conversation from problem-solving to problem definition at the situational level while considering multiple rather than individual perspectives. Dr. Cristancho emphasizes focusing on problem definition using a Soft System Engineering (SSE) lens to generate new educational practices. SSE helps define stakeholders' orientations to the problem, the interactions between different perspectives and their impact on medical situations.

Dr. Cristancho also explored the development of a systems mindset by focusing on different dimensions (e.g. procedural, organizational, personal, social, etc.). In Systems Engineering, 'resilience,' which usually refers to the capacity to endure disturbances, is an essential factor of self-organizing behaviours in complex situations. It provides three main principles when researching complexity in clinical practice. The first principle promotes the necessity of flexibility as each perspective redefines the problem. The second principle focuses on interrelationships between parts shaping behavioural patterns. The third principle employs a 'multiple perspectives' mindset and its components. Resilience can promote a systems physician mindset, i.e., a mindset capable of learning and adapting during unprecedented conditions.

This systems mindset, according to Bojeun, can be incorporated into several leadership and management strategies, which include: (1) tailoring leadership style based on the needs and feedback of team members; (2) reassigning resources across multiple projects to support team members in accomplishing the project goals; and (3) developing communication skills and creating formal and informal channels, such as socialization events, among team-members to promote multiple perspectives. Healthcare managers might benefit from understanding their team members from multiple perspectives, including their skills, experience and drivers that motivate them to enact their responsibilities.

The highlighted research by Dr. Christancho and others allows us to have a stronger understanding of how collective adaptation works in the real world, and what factors facilitate adaptation. Exploring collective self-healing and trace-based communication as factors that contribute to collective adaptation helps us gain insight into the verbal and non-verbal features of teamwork. This research is important in the context of health teams, but can be broadly applied to leadership, management, and even robotics.

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References

Cristancho, S. 3 Ways to Help Your Team Recover from Disruption. Harvard Business Review. (2021).

Cristancho, S. M. On collective self‐healing and traces: How can swarm intelligence help us think differently about team adaptation? Medical Education 55, 4, 441-447 (2021).

O'Bryan, L., Beier, M. & Salas, E. How Approaches to Animal Swarm Intelligence Can Improve the Study of Collective Intelligence in Human Teams. Journal of Intelligence 8, 9 (2020).

Cristancho, S. M. & Taylor, T. The agility of ants: Lessons for grappling with complexity in health care teamwork. Medical Education 53, 855–857 (2019).

Gordon, D. M. Collective behavior in ants and engineered networks. International Congress of Entomology (2016).

McDonald, D. B. The dynamics of animal social networks: analytical, conceptual, and theoretical advances. Behavioral Ecology 25, (2014).

Liu, R. et al. Trust-Aware Behavior Reflection for Robot Swarm Self-Healing. 9 (2019).

Kennedy, E., Lingard, L., Watling, C. J., Alejandro, R. H., Leigh, J. P., & Cristancho, S. M. Understanding helping behaviors in an interprofessional surgical team: How do members engage?. The American Journal of Surgery 2, 372-378 (2020).

Cristancho, S., & Field, E. Qualitative investigation of trace-based communication: how are traces conceptualized in healthcare teamwork?. BMJ open 101, (2020).

Aung, Y. Y., & Hariharan, R. T. In response: Parallels between medicine and jazz in medical education. Medical Teacher 4, 486-487 (2021).

Van Ark, A. E. & Wijnen-Meijer, M. "Doctor jazz": Lessons that medical professionals can learn from jazz musicians. Medical Teacher 41, 201–206 (2018).

Hall, L. M., Buechler, C., Marusca, G., Brennan, S., & Levine, D. L. Utilizing Chamber Music to Teach Non-Verbal Communication to Medical Students: A Pilot Initiative. Cureus, 13, (2021).

Pearl, P. L., & Greenberg, L. How the jazz medium can inform interprofessional health care teams in improving patient care. Medical Teacher 42, 12, 1337-1342 (2020).

Cristancho, S. Eye opener: exploring complexity using rich pictures. Perspectives Medical Education 4, 138–141 (2015).

Cristancho, S. M., Bidinosti, M. S. J., Lingard, L. A., Novick, R. J., Ott, M. C., & Forbes, T. L. What's behind the scenes? Exploring the unspoken dimensions of complex and challenging surgical situations. Academic Medicine, 89(11), 1540 (2014).

LaDonna, K. A., Field, E., Watling, C., Lingard, L., Haddara, W., & Cristancho, S. M. Navigating complexity in team‐based clinical settings. Medical education 11, 1125-1137 (2018).

Cristancho, S., Lingard, L. & Regehr, G. From problem solving to problem definition: Scrutinizing the complex nature of clinical practice. Perspectives on Medical Education 6, 54–57 (2017).

Cristancho, S. Lessons on resilience: Learning to manage complexity. Perspectives Medical Education 5, 133–135 (2016).

Boujeun, Mark C. "Chapter 13: High-Performing Teams (HPTs)". Program Management Leadership Creating Successful Team Dynamics. CRC Press-Taylor and Francis Group, 141-162 (2014). 855–857 (2019).

Conclusion

This knowledge exchange project highlighted work on team dynamics and complexity by CAS Lab faculty members at Western University as it relates to external research. For further information, and to facilitate connections and collaborations, please contact: caslab@uwo.ca Are you a faculty member working on team dynamics and want to share your research with us? If so, we invite you to become a member.

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Acknowledgments and Document History

This page was originally posted on December 14, 2021. It was written by Kyra Keer, Laura Mejia Torres, Laila Zaitoun as a deliverable for the CEL component of SGPS 9105L. Header photo by Javier Allegue Barros on Unsplash