Implications of Observability for the Theory and Measurement of Emergent Team Phenomena

Abstract

Many of the most pivotal mechanisms of team success are emergent phenomena-constructs with conceptual origins at the individual level that coalesce over time through members' interactions to characterize a team as a whole. Typically, empirical research on teams represents emergent mechanisms as the aggregate of members' self-report perceptions of the team. This dominant approach assumes members have developed a perception of the emergent property and are able to respond accurately to survey items. Yet emergent phenomena require sufficient time and team interaction before coalescing as perceptible team properties. Attempting to measure an emergent property before it is perceptible can result in inaccurate assessments and substantive conclusions. Therefore, a key purpose of this study is to develop a better understanding of the underlying characteristics of emergent team phenomena that give rise to their emergence as perceptible and, thus, accurately measurable team characteristics. We advance a conceptual framework that classifies emergent team properties on the basis of the degree to

Measuring team trust: A critical and meta‐analytical review

“…Recent work highlights the idea that it takes time for emergent constructs, like trust, to develop enough to be perceived by team members as a shared team characteristic (Carter, Carter, & DeChurch, ). With this in mind, we expanded on analyses related to the referent of team trust measurement to consider the notion that different referents may be more or less appropriate depending on the extent to which trust has sufficiently emerged as a shared team property.…”

Can constructive deviance be empowered? A multi-level field study in Australian supermarkets

“…1 Second, we exclude respondents who do not fit our selection criteria, as they do not belong to the teams of interest (n = 535), are from stores where the manager did not complete the survey (n = 685), and are from stores where the manager had been in her or his role for less than six months (n = 548). This last point deserves note: We consider only those store managers who had been in their roles in their current stores for six months or more, as it takes time for store managers to become accustomed to their roles and stores, for teams to form a consistent view of their supervisors' leadership styles (Carter, Carter, & DeChurch, 2015), and to change their practices in response, and for these changes to have an impact on their stores' performance. Moreover, our performance measures are based on six-month growth data.…”

Time to go wild: How to conceptualize and measure process dynamics in real teams with high-resolution

“…While progress is on the horizon to use automatic or machine-learning for obtaining meaningful team measures from high resolution data (e.g., Bonito & Keyton, 2018;Lehmann-Willenbrock, Hung, & Keyton, 2017), the majority of video-based approaches still relies on trained human observers who need to have sufficient context knowledge, cognitive capabilities, and understanding of the focal team process. Moreover, while behavioral and verbal processes are more easily observable for external observers, cognitive and affective team processes may be less visible for external observers (Carter et al, 2018). Importantly, in order to understand team dynamics, it is crucial that the selected high resolution method also repeatedly time stamps the focal phenomenon.…”