Models of Team Development

Sequential development models describe a series of stages where teams accomplish particular goals within each stage and transition to the next. A seminal example of a sequential developmental model that has informed the field of team effectiveness research for over 60 years is Tuckman’s model of forming, storming, norming, performing [Reference Tuckman23], and ultimately adjourning [Reference Tuckman and Jensen19]. This model was formulated by analysis of 26 group development studies that were primarily focused on socioemotional structures and task activities. The Tuckman model focuses on two parallel team processes: the socio-emotional structure within the team and task behaviors exerted by the team. During the storming and norming phases, the social–emotional aspect plays a central role, whereas in the performing, when socio-emotional issues are largely addressed, the task activity is predominant.

Drawing from group dynamics and organizational behavioral research, a sequential four-phased linear developmental model for generic transdisciplinary research has been proposed more recently [Reference Hall, Vogel, Stipelman, Stokols, Morgan and Gehlert24]. This model consists of the following phases: (1) Development, a phase where the group explores the problem space and identifies the disciplines that need to be involved; develops an approach, shares a mission and goals, and establishes trusting relationships. (2) Conceptualization, a phase where the group develops research questions, formulates hypotheses, establishes a conceptual framework, and plans a research design that integrates collaborators' disciplinary perspectives and knowledge domains. (3) Implementation, a phase where the research program is launched, conducted, refined, and the individual roles of team members are clarified as additional new members are engaged and integrated into the team. (4) Translation, a phase where the team applies research findings to advance progress along the discovery–development–delivery continuum to ultimately provide innovative solutions to real-world problems. In this model, outcomes are to provide a bridge to animal or human studies that have broader societal impact. The four phases of the disciplinary model are thought to be primarily sequential, where all phases must be encountered by the team and in a linear manner, although the model allows for recursive steps to occur.

Findings From Observational Studies of TTs in CTSA Environment

Although the linear sequential model of transdisciplinary research is conceptually intuitive, we examined whether this model is applicable to TT development in the CTSA environment (see Methods and Supplementary File 1). We re-examined a mixed methods assessment of temporal features of 10 TTs composed of over 100 members in an academic CTSA environment [Reference Wooten, Calhoun, Bhavnani, Rose, Ameredes and Brasier5,Reference Wooten, Rose, Ostir, Calhoun, Ameredes and Brasier6]. A rubric to observe team evolution was developed, assessing team development in two major dimensions: (1) Capacity and (2) Productivity. The first domain, capacity, is a measure of effective team processes. These processes include the development of a shared vision, efficient meeting management, transformational leadership, and communication. The second dimension, productivity, is an outcome-based measure of how the team transitions across the translational spectrum. Productivity included the development of a mature research plan, production of research output (manuscripts, products), and societal impact of its translational intervention [Reference Wooten, Calhoun, Bhavnani, Rose, Ameredes and Brasier5,Reference Wooten, Rose, Ostir, Calhoun, Ameredes and Brasier6]. Relevant findings included that not all teams matured at the same rate across both dimensions or in the same way. A radial diagram from this study illustrates the point (Fig. 3). One team showed substantial growth in capacity (research generation, research production, external collaboration, and vision, Fig. 3a). In contrast, another team advanced only in development of its research plan (Fig. 3b). Importantly, for all of these TTs, productivity was not uniformly linear, but punctuated by sudden changes in new concepts, product development, and resources/funding. These punctuated, unpredictable/stochastic changes should be incorporated into any model for assessing and supporting TTs in an academic environment (generic disruptive changes are schematically illustrated in Fig. 4).

Figure 3. Stochastic growth of translational teams (TTs). Radar graphs depicting growth of four exemplar teams. Axes represent four research output factors and four team process factors assessed by independent observers in TTs in a CTSA environment. Outcomes are EC, external communication/collaboration; MM, meeting management; TL, transformative leadership; VC, vision and charter; PT, progress in translation; RC, research communication and program growth; RG, research generation; RP, research plan. Data from 2011 are shown in yellow and data from 2013 are shown in purple. Areas of overlap, which represent outcomes that have been maintained or improved in 2013 versus 2011, are shown in magenta. Note that TTs grow in different dimensions and to different extents. Reproduced with permission from [Reference Wooten, Calhoun, Bhavnani, Rose, Ameredes and Brasier5].

Figure 4. Disruptive events in translational team (TT) maturation. Inherent in the evolutionary learning model, team outcomes (publications, grants, intellectual property, interventions) do not accumulate in a linear process, but are marked by disruptive events serving as “critical moments” or “transition points.” These external events are shown by vertical arrows. Shown are two such external transition points associated with transition from formation to knowledge generation, and the transition from knowledge generation to transition. Transition points promote adaptive evolution of the TT, stimulating new collective knowledge and, in some cases, causing transition to the next phase of evolution.

Limitations of Sequential Linear Models

Although the four-phase sequential model of transdisciplinary research provides a useful framework for enhancing efficiency in generic transdisciplinary research teams, the empiric finding that team maturation is stochastic makes problematic the application of sequential models that assume development is the result of a predictable, fixed order of phases [Reference Chidambaram and Bostrom15]. This limitation obscures the ability to measure the effectiveness of interventions on improving the performance of TTs in the CTSA environment.

Another important limitation of the four-phase sequential model is that the phases of team development are not clearly separable in real-world TTs. For example, hypothesis generation, a characteristic that distinguishes conceptualization from the development phase, in reality, occurs very early in TT development. In our observations of TTs in academic environment, TTs develop their membership and research hypothesis concurrently, not sequentially. Additionally, CTSA-type TTs conduct multiple research projects simultaneously, which are at different stages of maturation, making the application of a single sequential model difficult (See Vignette, Box 1).

Additionally, the four-phase sequential model is insufficiently developed for inclusion of Dissemination Science for high-performance TTs in the CTSA environment. Although the model applies the term “translation” to application of research generically for societal benefit, the explicit inclusion of dissemination and implementation (D&I) science is omitted. D&I is the process of applying the most effective strategies to successfully disseminate, implement, and sustain evidence-based/effective practices in real-world settings [Reference Leppin, Mahoney and Stevens25,Reference Mehta, Mahoney and Leppin26]. To be effective, D&I is a systematic approach that teams must take for effective utilization of translational interventions. This approach includes (1) stakeholder involvement early in the process of translational intervention design (stakeholders include patients, funders, advocacy groups, and purveyor organizations); and (2) application of the principles of Design for Dissemination and Sustainability (DDS) to enhance the fit between a health program, policy, or practice and the context in which a translation is intended to be adopted. Without this fit, beneficial translations are unsustainable in the health care environment and abandoned. We contend that D&I is sufficiently distinct in its approach to merit explicit designation in relevant developmental models of high-performance TTs in the CTSA environment.

Most importantly, the four-phase sequential model focuses on stage-relevant activities but does not describe how team-emergent competencies arise. These specific competencies have a robust evidence base associated with high-performance TTs in the CTSA environment ([Reference Wooten, Calhoun, Bhavnani, Rose, Ameredes and Brasier5] and reviewed in [Reference Brasier, Rolland and Burnside14]). Without understanding when team-emergent competencies are needed or arise, team training-focused interventions may not be directed when they could have maximal impact.

Viewing Team Development Through an Evolutionary Learning Perspective

Team development refers to progressing the state of maturation of the team knowledge and product across the translational spectrum (Fig. 4). Knowledge generation and translational advances are nonlinear; their course is punctuated by unanticipated research findings, outside discoveries, and/or changes in extramural funding priorities. These disruptive events serve as a “critical moment” or “transition point” in punctuated equilibrium theory [Reference Gersick27] that triggers a team learning episode [Reference Edmondson28] (Fig. 4). Defined earlier, a team learning episode refers to a shift in the teams SMM or its TMS, resulting in a process improvement within the team. Effective TTs are continuously adapting to new challenges through this process. Studies of the core processes and emergent states underlying team learning [Reference Rico, Gibson, Sanchez-Manzanares and Clark29–Reference Wiedow and Konradt32] have found a strong positive, causal, relationship between team learning/adaptation and team performance [Reference Maynard, Kennedy and Sommer33]. This finding suggests that focusing on promoting team learning behavior will impact TTs. Of specific relevance to the translational product developed by TTs, team learning has a strong positive effect on the innovativeness and speed to market of the new products [Reference Sarin and McDermott34].

Team learning episodes are discernable periods where teams become aware of problems or are subjected to disruptive events. Team learning processes involve team members seeking new information, gathering feedback, and collectively interpreting this information for use in subsequent performance cycles [Reference Edmondson28]. Through this activity, TT members evaluate the outcome, examine past performance, and develop plans for next steps, resulting in collective learning and adaptation [Reference Edmondson28]. Importantly, each step in the phases of team development can be viewed as a learning or performance episode [Reference Marks, Mathieu and Zaccaro35].

In contrast to the linear development models discussed above, an “unfolding” evolutionary learning model has been proposed [Reference Wiese and Burke20]. The unfolding evolutionary learning model emanated from a comprehensive literature survey that proposed how teams learn, what they learn, and when they do it. This model extended the well-known Gersick “punctuated equilibrium model” [Reference Gersick27] in two important ways: (1) Evolutionary learning accounts for the fact that teams are engaged in multiple projects simultaneously, often at different stages of maturity; and (2) Evolutionary learning incorporates our empiric findings that TTs are subjected to multiple transition points throughout their maturation arising from external disruptors leading to learning cycles (schematically shown in Fig. 4), rather than a single transition point arising after a period of inertia, as proposed by Gersick.

This unfolding evolutionary model is an appropriate development model of TTs because this model: (1) incorporates phases of team development consistent with empiric observations of TTs [Reference Wooten, Calhoun, Bhavnani, Rose, Ameredes and Brasier5]; (2) accounts for multiple, stochastic transition points that promote team learning and adaptation (see Vignettes in Boxes 1 and 2); (3) embraces the empiric findings that TTs are engaged in multiple projects simultaneously (see Box 1; and [Reference Calhoun, Wooten, Bhavnani, Anderson, Freeman and Brasier3]); and (4) enables understanding of what knowledge skills and attitudes (KSAs) are needed at each phase of team development associated with high-performance TTs. Consequently, we maintain that the unfolding evolutionary learning model provides a relevant foundation for formulating a TT development model. For simplicity, we will subsequently refer to this model as an “evolutionary learning model.”

A Model of TT Development Based on Team Learning

Based on the relevancy of the “unfolding” learning and limitations of sequential developmental models, we propose an adapted three-phase evolutionary learning model for TT maturation. Each phase conducts activities focused on the major TT goal from team formation (Formation), to conducting research (Knowledge Generation) to implementation in health care/community (Translation). Transitions to the next phase are dependent on collective team learning, sometimes initiated by “disruptive events” (Fig. 4). The collective learning response to these disruptive events gives rise to team-emergent competencies used by the team throughout its subsequent maturation; these KSAs are linked to performance as reviewed earlier [Reference Brasier, Rolland and Burnside14] (Fig. 5). We will first describe the primary activities within each phase, followed by the team learning and competencies that arise from them.

Figure 5. Evolutionary learning model for translational team (TT) maturation. Shown is a schematic model of the phases of TT development with external transition points. The team formation phase conducts activities focusing on enhancing team membership, establishing the basis of inter-team trust and developing a shared mental model. These are fostered by transformational leadership behaviors. As the team develops its hypothesis and acquires resources, it transitions into a knowledge generation phase. KSAs developed by the team in its formation phase support and are refined by activities in the knowledge generation phase. During knowledge generation, the team conducts activities in hypothesis testing, evaluation, and refinement. As the evidence base of effectiveness of the translation is established, the team transitions into the translation phase. KSAs (e.g., transactive memory systems, psychological safety and shared leadership) learned during the knowledge generation phase support activities in translation, including engaging new stakeholders, and purveyors. During translation, complex “Collaborative problem solving” leads to transdisciplinarity.

In the first phase, “Formation,” initially leadership assembles and leads a group in a series of activities leading to a team approach to address a translational problem. Initially, leadership is responsible for engaging and integrating scientists from different disciplines, developing members roles, applying meeting management processes, and role modeling to build a trusting environment (Fig. 5). In this process, group co-creation arises. Through group interactions (meetings and retreats), group members form communication networks and develop a shared understanding of the team’s purpose and goals, co-creation of norms of interactions, and hypothesis development (Fig. 5).

The activities conducted by teams in Formation are fundamental to developing the competency domains of “Affect,” a team-emergent KSA of empathy, consideration, and rapport for other members [Reference McAllister9], forming a “Communication” system, a KSA that integrates knowledge and information into collective team work and task work, and advancing practice of “Management” referring to organizing, planning, and executing components of a TT research program. Illustrated in Table 1, with specific competencies, these KSAs are specially relevant and associated with high-performance TTs [Reference Brasier, Rolland and Burnside14].

Transition from the Formation phase is provided by an external trigger of funding or resource acquisition, leading to collective group learning (see Vignette in Box 1; Figs 4 and 5). Upon acquisition of resources, the team reflects on its hypotheses and goals, refines these to align with available resources and realigns with its experimental plan, completing a learning cycle. The TT then transitions to the next phase building—and applying in more sophisticated ways—the “Affect,” “Communication,” and “Management” competency domains. These KSAs are incorporated into the shared behavior of the team, supporting activities in the next phase.

In the second phase, “Knowledge Generation” (Fig. 5), team members engage in conducting research projects, sometimes multiple, involving interdependent task work. In the process of coordinating complex taskwork, team members develop an understanding of how research will be completed in the TT and, through interpersonal interactions, share individual knowledge with their teammates. During the process of conducting research, research findings result in hypothesis refinement and adaptation. New members are brought onboard.

Activities within the Knowledge Generation phase are supported by the team-emergent KSA domains developed earlier (Table 1). For example, inter-team “Communication” skills enable team members to have a deepened understanding of “who” on the team has “what” expertise, enabling the formation of a transactive memory system (TMS), itself linked to accomplishment [Reference Li and Huang36] and ultimately high-performance teams [Reference Brasier, Rolland and Burnside14]. As data and its interpretation are generated by the task work conducted in the Knowledge Generation phase, team members challenge, test, and explore assumptions. These activities are supported by advancing the specific “Affect” competencies of trust and cohesion (Table 1, Fig. 5).

Multiple team learning cycles may arise in the Knowledge Generation phase, depending on the complexity of the translational project being conducted. External triggers for new rounds of team learning are driven by unexpected experimental results, outside findings, or changes in funding (see Vignette in Box 2; Figs. 4, 5). These triggers produce introspection, goal realignment, or refinement of the hypothesis or translational product. These disruptive events are addressed by leadership behaviors in “sense-making,” where leadership provides a mental image of where the team is and where they are going to create an action plan in the face of uncertainty [Reference Ashmos and Nathan37]. Consequently, KSAs developed from learning cycles in the Knowledge Generation phase include specific competencies of psychological safety, knowledge sharing, sense-making, and developing a TMS [Reference Brasier, Rolland and Burnside14]. These team-emergent competencies are used, adapted, and further refined during the Translation phase.

In the third phase, “Translation,” TTs incorporate new stakeholder members, establish networks with health systems, providers and purveyor organizations, and respond to their input, expanding cognitive diversity. Application of DDS principles refines the translational product promoting adaptations and perspective-seeking may trigger additional transition points of learning (see Vignette in Box 2).

Team-emergent KSAs arising within Translation include “Collaborative Problem Solving,” a team-emergent KSA characteristic of high-performance TTs within psychologically safe environments and enhanced by cognitive diversity [Reference Brasier, Rolland and Burnside14]. “Collaborative Problem Solving” involves integration of diverse intellectual practices, methods and biases into a common interpretation of observed phenomena [Reference Graesser, Fiore, Greiff, Andrews-Todd, Foltz and Hesse38], resulting in transdisciplinarity [Reference Lotrecchiano and Misra39]. Substantial changes in perspective or the translational project may trigger re-iteration of the Knowledge Generation phase, illustrating the nonlinearity of TT development [Reference Wooten, Calhoun, Bhavnani, Rose, Ameredes and Brasier5].

Origins and Development of Team-emergent KSAs During TT Maturation

A recent scoping review of the SciTS evidence base identified 15 team-emergent competencies whose expert application leads to enhanced team performance (listed in Table 1) [Reference Brasier, Rolland and Burnside14]. In this analysis, observable behaviors of the KSA were described along a continuum from “novice” to “expert.” It is axiomatic that each of these competencies develops as a result of team interactions and undergoes maturation and refinement before an “expert” level of proficiency is reached. In the following section, we consider antecedents of the competency and interdependence with competencies in other domains. Finally, we place these within the phases of the evolutionary learning model leading to a model for how the competencies mature as the TT develops.

Affect refers to the development of empathy, affiliation, and rapport between members on the basis of shared regard for the other members of the TT [Reference Lotrecchiano, DiazGranados and Sprecher4,Reference McAllister9]. Within this domain, three specific complementary competencies linked to high performance are “trust,” the confidence that team members have in the abilities of their colleagues to do reproducible work, share results, and discuss their interpretations, “cohesion” the strength and extent of interpersonal connection between team members, and “psychological safety,” a shared belief that the team environment is safe for risk-taking, formulating opposing ideas, or challenging team assumptions [Reference Edmondson28].

Current evidence supports that maturation of these three specific components of “Affect” is distinct and depends on different member interactions and contexts. Specifically, during Formation, the team leader works with potential team members to establish norms for interactions, communication, and data sharing that are safe for exchange, hypothesis development, and discussing alternative interpretations. In this phase, trust is initially displayed as trust in leadership or select team members (Table 2). However, it is intra-team trust, not just that of trust in selected members, that is most highly linked to team performance [Reference De Jong, Dirks and Gillespie40]. Intra-team trust develops after trusting norms have been established by leadership behaviors. In addition, studies have shown that cohesion and satisfaction are antecedents to intra-team trust [Reference DeOrtentiis, Summers, Ammeter, Douglas and Ferris41]. We therefore propose that intra-team trust evolves within interactions and task-work conducted during Knowledge Generation and Translation (Table 2).

Table 2. Maturation of competencies across translational team

For each team-emergent competency domain, specific competencies are tabulated with examples of their proficiency for the three phases of evolutionary team learning. Abbreviations: Comm, communication; Mgmt, management; Collab, collaboration.

Like the maturation of intra-team trust, development of psychological safety is a group-level construct [Reference Edmondson28], dependent on multiple team and interpersonal antecedents. A meta-analytic review of ∼ 5,000 groups examining the origins of psychological safety and their effectiveness established that leadership interactions and work environment were more important than individual personality characteristics, such as emotional stability and openness to experience [Reference Frazier, Fainshmidt, Klinger, Pezeshkan and Vracheva42]. Others have proposed that psychological safety arises from trusting interpersonal relationships developed with other team members, organizational norms, as well as with the team leadership [Reference Edmondson and Lei43,Reference Kahn44]. Psychological safety is, therefore, based on group-level interpersonal relationships, particularly with the leader, a supportive work environment, and organizational norms. We argue that the appearance of this complex and robust KSA would emerge in actively interacting teams, after team Formation (Table 2).

Communication is a team-emergent competency domain that refers to the ability to integrate knowledge and expertise in team member interactions and in task work. Specific competencies within “Communication” include “knowledge sharing,” a behavior where team members provide other members with technical information, know-how and skills relevant to advancing the team’s translational product, and transactive memory system (TMS), a group-level understanding of knowledge of “who” on the team has “what” expertise. In a manner similar to the maturation of specific competencies within team “Affect,” competencies within “Communication” arise from interdependent taskwork activities conducted during the Knowledge Generation phase of team development. Information sharing has been most intensively studied in the organizational literature as this behavior provides competitive advantage for knowledge-based or technological organizations. This work has found that trust, an organization’s learning orientation, and positive interactions/cohesion with other team members, including reciprocity, are all enabling factors for effective knowledge sharing. Knowledge sharing is highly influenced by team leadership behaviors, particularly transformational leadership [Reference Liu and Li45,Reference Liu and Phillips46]. Knowledge sharing is foundational for more advanced forms of information sharing, knowledge management systems, where a team’s collective knowledge is shared within the larger organization or field [Reference Earl47]. Hence, maturation of knowledge sharing advances as the team’s cohesion and trust develop from information sharing, advancing to knowledge sharing, and then to knowledge management systems (Table 2).

A TMS refers to group-level knowledge, where team members know “who” on the team has “what” expertise [Reference Wiese and Burke20], developed through reciprocal exchange and joint effort between individual team members on collaborative activities [Reference Li and Huang36]. Establishing a TMS involves both information processing as well as group identification. Using a social cognition framework, Liao proposed that the quality of information sharing by multidisciplinary team members with their own professional view/identification plus an established sense of group identity are both antecedents for a TMS [Reference Liao, Jimmieson, O’Brien and Restubog48]. Hence, team “Affect” enables a TMS. A TMS is important as work shows that sharing group-level knowledge saves time in taskwork, particularly in dynamic environments, leading to goal accomplishment [Reference Li and Huang36] and high performance [Reference Brasier, Rolland and Burnside14].

Management refers to the activities conducted in the organization, planning, and executing components of a TT project. Specific competencies include establishing a cognitively diverse membership and shared visioning promoted by project management practices. TTs in an academic setting are composed of a dynamic membership that engages with a strategic nucleus of the PI and core scientific expertise [Reference Calhoun, Wooten, Bhavnani, Anderson, Freeman and Brasier3,Reference Brasier, Rolland and Burnside14]. Engaging this membership and defining their roles and responsibilities are key activities starting in the Formation phase and continuing throughout the Knowledge Generation and Translational phases of the evolutionary learning model (Fig. 4). Specific to the evolutionary learning model, TTs in the Translation phase incorporate the principles and approaches of D&I Science. As a result, new stakeholders, caregivers, patients/patient advocacy groups, and community members become team members. The processes of how these members are engaged and brought on board mature with knowledge sharing and the convergence of a shared, organized understanding of the team’s SMM [Reference Klimoski and Mohammed49]. SMMs work cooperatively with TMS and are important for team effectiveness, especially when teams are faced with complex, dynamic problems engaging in complex interdependent tasks [Reference Mathieu, Heffner, Goodwin, Salas and Cannon-Bowers50].

Although the group develops shared goals and understanding of the hypothesis during team Formation, a fully developed SMM will emerge later as the team conducts inter-dependent task work, as the TT undergoes learning cycles and refines its goals [Reference Klimoski and Mohammed49]. The interdependent research activity, research, hypothesis testing, and refinement activities conducted during the Knowledge Generation phase produce convergence of the SMM (Table 2).

Collaborative problem-solving is a state where the cognitive and social skills of the team are combined to interpret research findings, resulting in a cohesive mental representation of the problem space, resulting in shared interpretations [Reference Graesser, Fiore, Greiff, Andrews-Todd, Foltz and Hesse38]. During the Translation phase, new stakeholders, caregivers, patients, patient advocacy groups, and community members can provide differences in interpretation, perspective, or information processing styles within a team known as cognitive diversity [Reference Hong and Page51–Reference Paletz and Schunn53]. Cognitive diversity influences an emergent collaborative problem-solving property coined “collective intelligence” [Reference Riedl, Kim, Gupta, Malone and Woolley54], highly predictive of a team’s ability to perform on a variety of knowledge tasks[Reference Riedl, Kim, Gupta, Malone and Woolley54]. Collective intelligence transcends traditional scientific boundaries to jointly define a problem, conduct problem-solving activity, and draw in perspectives from diverse team members resulting in the emergence of a “transdisciplinary” research program [Reference Lotrecchiano and Misra39,Reference Stokols55]. Similarly, transdisciplinarity arises as translational boundaries are broken down with high-level interactions amongst the team members.

Leaderships provide the cognitive, motivational, affective, and management processes to help the team thrive in a complex and dynamic environment [Reference Zaccaro, Rittman and Marks13]. Team leaders provide essential support throughout the lifecycle of a TT, including establishing membership, defining roles, setting expectations, resolving conflict, and goal setting. Substantial scholarship has shown how sources of leadership and impactful activities undergo adaptation [Reference Morgeson, DeRue and Karam56]. During Formation, leadership is primarily contributed by a PI inspiring group vision, seeking diverse perspectives, and modeling inclusive behaviors emblematic of transformational leadership skills [Reference Bass and Riggio57]. Underscoring its importance, our empiric studies have shown that high-performance CTSA-type TTs are distinguished by transformational leadership skills, sometimes shared between the PI and the early career trainee [Reference Wooten, Calhoun, Bhavnani, Rose, Ameredes and Brasier5]. Other important leadership activities include providing feedback to members and promoting an environment of psychological safety. TTs with leaders that provide “goal-setting” outperform teams without such leaders [Reference Einstein and Humphreys58,Reference Knight, Durham and Locke59]. “Sense-making” is another leadership activity that enables a team to productively respond to disruptive events [Reference DeRue and Hollenbeck60–Reference Morgeson62], turning episodic disruption into productive activity by providing insight into the event and developing a path forward. “Sense-making” frames a mental image of where the team is and where they are going in order to create an action plan in the face of uncertainty, enabling productive responses to transition points. Leaders are the most important sense-makers who shape followers’ perceived meaningfulness of work-related issues [Reference Rosso, Dekas and Wrzesniewski63], including goal commitment and team identification [Reference Liu and Li45]. Feedback enables adaptation and enhances long-term performance [Reference Einstein and Humphreys58,Reference Kozlowski and Ilgen64,Reference Sivunen65].