Researching Teams: Nothing's Going to Change Our World

DeCostanza, Arwen H.; DiRosa, Gia A.; Rogers, Sean Edmund; Slaughter, Andrew J.; Estrada, Armando

doi:10.1111/j.1754-9434.2011.01400.x

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…A recent review highlights some of the novel tools being used to measure team dynamics, including trust (Delice, Rousseau, & Feitosa, ). For instance, there have been multiple calls to assess team dynamics with unobtrusive measures in the context of long duration spaceflight missions (Salas et al, ) as well as the military (Decostanza, Dirosa, Rogers, Slaughter, & Estrada, ) to address potential practical limitations.…”

Section: Future Researchmentioning

confidence: 99%

Measuring team trust: A critical and meta‐analytical review

et al. 2020

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Team trust is gaining attention in research and practice due to its benefits for team performance, yet clarity about the intricacies of its measurement is needed.Therefore, we meta-analyzed 118 studies (N = 7,738) to untangle the role of measurement features by investigating the degree to which they influence the trustperformance relationship. Results showed that the trust-performance relationship is contingent upon time lag and source of measurement. Specifically, cross-sectional and single-source studies produced higher effect sizes than time-lagged and different-source studies. In contrast, the moderating roles of conceptualizationoperationalization alignment and referent of trust measures were not supported. Post hoc analyses revealed that affective trust is more strongly related to global, versus specific team outcomes, and that mixed-referent items are particularly effective within intact teams, whereas the trust-performance relationship is constrained when direct consensus items are used within ad hoc teams. Furthermore, we provided a critical review that highlights the importance of composites, multilevel forces, and item content and wording. Finally, we clarified key gaps in the literature, calling for research where needed. This review serves as a bridge between conceptualization and measurement and lays the groundwork for advancing knowledge of team trust. K E Y W O R D Strust, teams, measurement, meta-analysis

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Section: Future Researchmentioning

confidence: 99%

Measuring team trust: A critical and meta‐analytical review

et al. 2020

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“…We agree that the types of entities that Wageman et al (2012), DeCostanza et al (2012), and Keeton et al (2012) discussed require extensive teamwork and coordination—but they are something more than teams per se. They more closely resemble what Mathieu, Marks, and Zaccaro (2001) and Zaccaro, Marks, and DeChurch (2011) have referred to as multiteam systems (MTSs) or networks of teams.…”

Section: What Are Teams?mentioning

confidence: 82%

“…No doubt this is largely attributable to the difficulty of sampling a sufficient number of teams for an empirical investigation, let alone the difficulty of sampling them across contexts that vary in meaningful ways. Along these lines, our initial article as well as commentaries by Klimoski (2012), DeCostanza et al (2012), and Wageman et al (2012) highlight that environments are far more complex and dynamic than they were in the early days of team research. Many of today's teams cross organizational boundaries and operate simultaneously in multiple environments.…”

Section: Unifying Themesmentioning

confidence: 91%

“…Wageman, Gardner, and Mortensen (2012) advocated breaking beyond “old school thinking” and embracing the novelty and complexity of new emerging organizational forms such as those that deal with large‐scale crises (e.g., hurricane relief), wildfires, and epidemics. DeCostanza, DiRosa, Rogers, Slaughter, and Estrada (2012) described provincial reconstruction teams and other complex organizational entities as involving a network of interdependent teams. Keeton, Slack, Schmidt, and Malka (2012) noted how the National Aeronautics and Space Administration (NASA) needs to facilitate “multiteam teams” such as flight control teams at multiple sites supporting the International Space Station.…”

Section: What Are Teams?mentioning

confidence: 99%

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On Teams: Unifying Themes and the Way Ahead

Tannenbaum

Mathieu

Salas

et al. 2012

Ind. organ. psychol.

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The commentaries to our focal article were both interesting and stimulating. As we generally agreed with the major points raised in the commentaries, we use this response to frame an on‐going tension point or challenge regarding team definitions, highlight a few unifying themes that weave through our initial article and the commentaries, and discuss the transition from research to informed practice. The past few decades have been exciting times for team researchers and practitioners, and the time is ripe for new energies and approaches.

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“…In this paper, we investigate the brain networks involved in the implicit learning of community structure for social networks. Navigating interwoven layers of social connections is critical for success in a broad range of social interactions with co-workers, friends, family and strangers [46][47][48][49] . Although there is an extensive literature examining how people learn relational information in the domains of language learning 16,19 , motor sequence learning 12,20,21 , and statistical learning 11,17,26 , little is known about the neural mechanisms underlying how people learn relational information about social networks.…”

Section: Discussionmentioning

confidence: 99%

Functional brain network architecture supporting the learning of social networks in humans

Tompson

Kahn

Vettel

et al. 2019

Preprint

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Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. From the behavioral data in both tasks, we found that learners were sensitive to the community structure of the networks, as evidenced by a slower reaction time on trials transitioning between clusters than on trials transitioning within a cluster. From the neuroimaging data collected during the social network learning task, we observed that the functional connectivity of the hippocampus and temporoparietal junction was significantly greater when transitioning between clusters than when transitioning within a cluster. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions during the social task than during the non-social task. Collectively, our results identify neurophysiological underpinnings of social versus nonsocial network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies. Figure 3. Patterns of functional connectivity in the brain are distinct for social network learning compared to a non-social control condition. (A) Functional connectivity matrix showing edges with significantly different weights for the social network condition (upper triangle in red) versus the non-social control condition (lower triangle in blue). Significance was assessed after an FDR correction at p<0.05 over edges. (B) Whole-brain mapshowing regions with significantly different strength z-scores for trials in the social network condition (red) versus in the non-social control condition (blue). Significance was assessed after an FDR correction at p<0.05 over brain regions. (C) Bar graph showing percentage of network hubs identified in each cognitive system for the social network condition versus the non-social control condition, relative to the percentage identified in a non-parametric null model (gray box plots). (D) By averaging edge weights both within and between putative functional systems, we constructed a system-level connectivity matrix. Here we display that matrix showing systems with significantly different connectivity for the social network condition (red) versus the non-social control condition (blue). Signifi...

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Researching Teams: Nothing's Going to Change Our World

Cited by 9 publications

References 6 publications

Measuring team trust: A critical and meta‐analytical review

Measuring team trust: A critical and meta‐analytical review

On Teams: Unifying Themes and the Way Ahead

Functional brain network architecture supporting the learning of social networks in humans

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