The "Inside Researcher" in Team Processes Research: The Good, The Bad, and Lessons Learned

Osnat Wine

Abstract: There is continuous interest in understanding team processes and how to optimize interdisciplinary teamwork. Inquiry from the 'inside' involves researchers as actors immersed in the study setting as complete member researchers performing a dual role. Through this position, they can generate contextually embedded knowledge and contribute to an empirical understanding of collaborative research processes and practices. Some contest that personal involvement and emotional investments in the setting may impact the neutrality required for research. In this presentation, we draw from our reflections and experience from a qualitative case study on team processes and present research and practice benefits and challenges for team science inside researchers. The inside researcher position enabled accessibility and familiarity with the team, meetings, and documents; advanced extensive knowledge of the case and context; and presented opportunities to witness the research project activities, and team processes, and observe how those evolved over time. This enabled in-depth exploration and understanding of the collaborative process. The inside researcher formed good relationships with team members as colleagues and as a researcher, and fostered willingness, openness, and trust of team members. At the same time, learnings about the collaboration barriers and facilitators through reflection and evaluation by the inside researcher provided formative input for the ongoing project operation and planning of strategies to optimize team development.   However, inside researchers face methodological challenges. Beyond the burden of a dual role, being invested in the research project and its success meant that it was potentially more challenging to establish a neutral point of view and ensure all perspectives were accurately accounted for. Moreover, participants /team members may not always explain themselves fully because of their acquaintance with the inside researcher, and the inside researcher may be led by their own assumptions and personal experience as a member of the group. At times it was difficult to separate the researcher's perspectives from those of the participants. We employed a robust process of verification throughout the study design and conduct of data generation, analysis, and reflection, which helped mitigate these challenges. These strategies will be discussed in this presentation. Inside researchers that support evaluation, facilitation, and operation of team science projects like embedded researchers, integration and implementation scientists, and interdisciplinary executive scientists could have implications on the research conducted. They need to consider and acknowledge their role as part of the research design. Despite the costs, inside researchers are well situated to develop intimacy with the available knowledge, pursue empirical research and provide unique insights from lived experience to the science of team science.

Facilitating Multidisciplinary Working Groups to Promote Cross-Center Collaboration

Mara Minion

Abstract: BackgroundIn 2017, the National Cancer Institute (NCI) launched the Cancer Center Cessation Initiative (C3I) as part of the NCI Cancer Moonshot program with the goal of integrating evidence-based tobacco treatment services into clinical care. The C3I Coordinating Center, housed at the University of Wisconsin, adopted an evidence-based, team-science approach to develop a multidisciplinary, collaborative community committed to tobacco cessation. In 2020, the Coordinating Center formed 5 working groups, with the goal of building a community-led consortium focused on defining the future of tobacco cessation in cancer care delivery.As funding for consortia increases across the NIH, working groups provide an opportunity to leverage the power of unique networks to conduct high-impact science. Yet few resources exist that detail best practices for building working groups that accomplish collaborative scientific goals. In this talk, we will detail specific steps we took to engage researchers, clinicians, tobacco treatment specialists, and program managers in building and leveraging the C3I network.ApproachThe Coordinating Center solicited research questions from centers that could be uniquely addressed by a sustained C3I consortium. We categorized the questions into 5 areas and formed the C3I Working Groups around those: (1) diversity, equity, and inclusion; (2) family and social support systems; (3) implementation science; (4) sustainability; and (5) telehealth. We nominated 2-3 co-chairs per group, prioritizing demographic and disciplinary diversity and pairing early-career investigators with established investigators. We coordinate the monthly meetings; track progress towards deliverables; note points of synergy and facilitate cross-group connections; and identify funding and dissemination opportunities for projects. ResultsEngagementAcross the Working Groups, there are 110 participants with 38 individuals engaged with multiple groups. This multidisciplinary consortium consists of 42 C3I program leads, 23 tobacco treatment specialists, 16 clinical collaborators, 13 project managers, 7 non-C3I collaborators. Of the 52 funded centers, 45 are represented in the groups.  DisseminationThe first key deliverable for the C3I Working Groups was a collection of positions papers published in JNCCN in November 2021. Each group contributed a short paper that explains the importance of their topic on tobacco cessation in cancer patients who smoke, notes work currently being done in C3I, and outlines a research agenda. Future DirectionsIn May 2022, the Coordinating Center will host a virtual meeting for the working groups to facilitate cross-C3I projects, a goal that has generated interest but proved logistically difficult. The meeting will include a project proposal competition, in which members propose collaborative projects for feedback from a panel of cessation experts and the potential for subawards from the Coordinating Center.

Helping Cross-disciplinary Teams to Solve Problems: The Role of Task Demonstrability

Gwen Wittenbaum

Abstract: Cross-disciplinary teams are employed to solve the world's most pressing problems under the assumption that solutions to these problems necessitate integrated efforts involving multiple disciplines (Eigenbrode, et al., 2007). This cross-disciplinary collaboration is best fostered by understanding team dynamics (Fiore, 2008).Successful employment of cross-disciplinary teams to solve pressing problems is facilitated with an understanding of the conditions under which the team task is solvable. Kerr (2017) argued that "the group's task is the most neglected moderator in group research" (p. 681). Nearly everything that groups do depends on the task demands, and according to Kerr, small group research pays too little attention to the moderating role of a group's task. In a similar vein, a recent review of the science of team science did not highlight features of the team task as an important moderator of science team processes and outcomes (Hall, et al., 2018). If cross-disciplinary teams of scientists are to solve pressing problems, such problems must be solvable, a concept known as task demonstrability (Laughlin, 1980). Task demonstrability originally was conceived along a continuum from intellective tasks with objectively true answers to judgmental tasks whose solutions are a matter of preference or judgment. Tasks are demonstrable by groups when: a) there is group consensus on a verbal or mathematical system, b) sufficient information for a solution within the system, c) group member knowledge of the system to recognize a correct solution when advanced by another member, and d) sufficient ability, motivation, and time for the correct group member to demonstrate it to incorrect members.Bonner, et al. (2021) modified the task demonstrability model for application to organizational teams working on complex problems, recognizing the original model's limitations based on task features and heterogeneity of member expertise. Bonner et al. wrote, "Organizational teams are often tasked with solving challenging problems in which success is ambiguous and subject to interpretation. Thus, for tasks that either do not possess correct answers or for which correct answers could not be demonstrated if they existed, the success of a problem-solving group must be evaluated subjectively by team members or other stakeholders" (p. 5). Using Bonner et al.'s logic, "contemplating whether a task is impossible is an essential consideration‚ however, if the task is possible, then the relevant question becomes whether the members of a given team have the necessary knowledge, skills, and abilities to solve it" (p. 6).The key to effective cross-disciplinary science teams is recognizing and fostering task demonstrability by employing the task demonstrability scale (Bonner, et al., 2021) and understanding interventions that can enhance a team's task demonstrability, such as team member belief that the task is demonstrable (e.g., Stasser & Stewart, 1992).

Grand Challenges and Emergent Modes of Convergence Science

Alexander Petersen

Abstract: Convergence science is an intrepid form of interdisciplinarity designed for strategically addressing grand scientific challenges, and defined by the US National Research Council as "the coming together of insights and approaches from originally distinct fields" [NRC,2014]. Convergence aims to address grand scientific challenges. In the last decade, the convergence paradigm has been championed by funding agencies the world over, becoming a model for designing flagship research programs. Nevertheless, there is still no established framework for measuring and evaluating convergence. To address this gap, we propose a novel framework for measuring convergence according to two fundamental dimensions of re- search outcomes - social and conceptual. The social dimension codifies the disciplinary pedigree of authors in scientific publications, connoting core expertise associated with institutionalized education. The conceptual dimension codifies the knowledge, methods, and equipment, which may together exceed the researchers' core expertise. The latter quantifies a partial mismatch between the allocated social resources and the scientific needs of convergence products - a key aspect that eludes other approaches. To operationalize the social and conceptual classification of publications, we use the Classification of Instructional Programs (CIP) and the Medical Subject Headings (MeSH) ontologies, respectively, comprised of thousands of entities each. As a way of demonstrating the qualities of the proposed measurement framework, we applied it in the study of the human brain research ecosystem - a nexus of convergent activities.

Developing a Theory of Deception for Team Science

Jacinta Tran

Abstract: Organizations adopt a variety of cross-disciplinary teams that influence organizational structure and various organizational processes. The ethical climate of a group embedded in an organization largely depends upon culture and socialization. This ethical climate affects how members will behave and apply their skills to enhance group performance (de Graaf & Levy, 2011). While scholars have explored ethics from an organizational perspective, limited research goes into the depths of ethics within cross-disciplinary teams. Disruption in the morality of teams can draw a negative impact on member contributions. Deception refers to actions to mislead another person intentionally, knowingly, and purposely (Levine, 2014) and is one of the less-studied ethical problems in team science. Understanding how deception occurs within team-based collaborations can provide directions to maximize group productivity. This research aims to develop theoretical propositions to guide team science research and theory development. This research synthesized scholarship on deception and lies, theories of self-interest, the impact of ethics, organizational functioning, and leadership. Research suggests that individuals with a higher sense of themselves and communication skills can engage in strategic ways to manage their behavior, information, and image while suppressing leakage of deception. Thus, deception in groups can occur if members reflect charismatic characteristics but lack integrity. Additionally, organizational reward systems focus on individualistic rewards. Dominant promotion-oriented incentives can lead members to take action toward personal gain, even at the expense of their group (Sutter, 2019; Zaal et al., 2015). Voluntary members join teams based on the benefits they can obtain along with how they can contribute to group dynamics. In contrast, those who involuntarily join small groups can make decisions solely within their roles or based on what authority asks them to do. The suggested actions can lead to group failure or ineffective communication among group members. Based on current research, our first proposition argues that deception in small groups arises because of the make-up of members who represent morally corrupt personalities. Second, when individualistic motives override group goals, deception persists. Third, deception in a team involves when members omit information because of self-interest. Lastly, involuntary versus voluntary membership affects the levels of deception that group members initiate. Ethics in small groups presents a salient topic when considering the interdependent nature of teams. Recognizing the significance of ethics in small groups allows for transformative leadership where members transcend their self-interest toward group goals (Jackson, 1993). A theory of deception for team science will provide a lens for understanding how ethics occurs in cross-disciplinary teams to promote effective teamwork and collaboration.

Exploring Equifinality Through Modeling Transdisciplinary Integration in Convergence Research

Lisa Gajary

Abstract: RESEARCH PURPOSEEquifinality is the concept that "a system can reach the same final state, from different initial conditions and by a variety of different paths" (Katz & Kahn, 1978).  Although discussions of equifinality are of keen interest to the study of how innovation systems function, there is a persistent lack of empirical evidence for the demonstration of equifinality in the production of successful innovation (e.g., Cirillo, Martinelli, Nuvolari, & Tranchero, 2019).  At the same time, evaluating the success of innovation systems is thorny because innovation system processes possess protracted timeframes, rarely produce well-defined outcomes, and are specified by programs that lack adequate counterfactuals.  Consequently, the complexity at work in successful innovation systems that could produce equifinality may function to obfuscate the detection of equifinality.  The larger purpose of this work is to elaborate on how equifinality could operate within a complex innovation system so that we can gain a better understanding of how to measure it.  The more specific purpose of this work is to elaborate how equifinality may operate within Convergence Research.  DESCRIPTION OF RESEARCH METHODSConvergence Research refers to transdisciplinary integrative efforts aimed at producing ground-breaking scientific innovations that remedy vexing societal problems (Roco, 2016).  Using computational simulation methods (hybridizing agent-based and system dynamics modeling), this study aims to formalize a theory of transdisciplinary integration in Convergence Research and to explore its implications for equifinality through simulation experiments (Davis, Eisenhardt, and Bingham, 2007; Gajary, 2020).  Beginning with the Input-Process-Output (IPO) model (O'Rourke, Crowley, and Gonnerman, 2016), our work develops a computer simulation model representation of the antecedents and inputs to integration, the processes and practices in integration, and the outputs related to integration.  Next, we demonstrate the implementation of the hybrid simulation model architecture with our change argument representations (i.e., different initial states of integration antecedents resulting in the same final state of integration outputs).  Finally, using this simulation model and its outputs, we discuss the implications and insights from the model, producing a preliminary theory for how equifinality may factor into a fuller understanding of transdisciplinary integration in Convergence Research.  STATEMENT FOR ADVANCING FIELDThis research advances the SciTS field by extending our understanding of transdisciplinary integration in Convergence Research by using computational simulation methods to explore how to conceptualize the potential possibilities of equifinality.