Challenges and Opportunities for Group Learning and Group Learning Researchers

Kush, Jonathan; Williamson, Courtney; Argote, Linda

doi:10.1108/s1534-0856(2012)0000015012

Cited by 3 publications

(9 citation statements)

References 104 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…45,46 However, computer-mediated communication by distributed teams also presents significant challenges to information sharing for complex and interdependent tasks. 32,47,48 In particular, Kush et al noted that being a member within multiple work groups can lead to weaker group identities. 32 And while geographically separated members can add novel knowledge to a group, the reliance on technology…”

Section: Harnessing Big Data and The Complexities Of Assessing Health...mentioning

confidence: 99%

“…32,47,48 In particular, Kush et al noted that being a member within multiple work groups can lead to weaker group identities. 32 And while geographically separated members can add novel knowledge to a group, the reliance on technology…”

Section: Harnessing Big Data and The Complexities Of Assessing Health...mentioning

confidence: 99%

“…[25][26][27] In particular, organizational research of communication and information processing has identified challenges confronted by complex MTSs. 22,[28][29][30][31][32] As teams grow, a major challenge is ensuring inclusive communication.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Patient care in complex Sociotechnological ecosystems and learning health systems

Tu,

Garcia,

Zhu

et al. 2024

Learning Health Systems

View full text Add to dashboard Cite

The learning health system (LHS) model was proposed to provide real‐time, bi‐directional flow of learning using data captured in health information technology systems to deliver rapid learning in healthcare delivery. As highlighted by the landmark National Academy of Medicine report “Crossing the Quality Chasm,” the U.S. healthcare delivery industry represents complex adaptive systems, and there is an urgent need to develop innovative methods to identify efficient team structures by harnessing real‐world care delivery data found in the electronic health record (EHR). We offer a discussion surrounding the complexities of team communication and how solutions may be guided by theories such as the Multiteam System (MTS) framework and the Multitheoretical Multilevel Framework of Communication Networks. To advance healthcare delivery science and promote LHSs, our team has been building a new line of research using EHR data to study MTS in the complex real world of cancer care delivery. We are developing new network metrics to study MTSs and will be analyzing the impact of EHR communication network structures on patient outcomes. As this research leads to patient care delivery interventions/tools, healthcare leaders and healthcare professionals can effectively use health IT data to implement the most evidence‐based collaboration approaches in order to achieve the optimal LHS and patient outcomes.

show abstract

Section: Harnessing Big Data and The Complexities Of Assessing Health...mentioning

confidence: 99%

Section: Harnessing Big Data and The Complexities Of Assessing Health...mentioning

confidence: 99%

See 1 more Smart Citation

Patient care in complex Sociotechnological ecosystems and learning health systems

Tu,

Garcia,

Zhu

et al. 2024

Learning Health Systems

View full text Add to dashboard Cite

show abstract

“…Team learning can be defined as change in the team that occurs as a function of experience and can manifest itself in changes in processes, cognitions, routines, or performance (Fiol and Lyles, 1985; Argote et al, 2001; Argote and Miron-Spektor, 2011; Kush et al, 2012). Team learning has also been defined as a change in the group’s repertoire of potential behavior that occurs over time in group interaction (Wilson et al, 2007; Schippers et al, 2013).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…While the advantages of cognitive diversity for short-term problem solving and innovation have been demonstrated (Hong and Page, 2001; Ellemers and Rink, 2016), the impact of such diversity for longer-term outcomes, such as team learning, are less clear. Research looking at team learning captures the way in which teams change as a function of experience and can manifest itself in changes in processes, cognitions, routines, or performance, and has major implications for sustained organizational outcomes (Fiol and Lyles, 1985; Argote et al, 2001; Argote and Miron-Spektor, 2011; Kush et al, 2012). It furthers our understanding of teams as complex, adaptive, dynamic systems that are embedded in constantly changing contexts (Ilgen, 1999; McGrath Joseph et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Cognitive Style Diversity on Implicit Learning in Teams

et al. 2019

View full text Add to dashboard Cite

Organizations are increasingly looking for ways to reap the benefits of cognitive diversity for problem solving. A major unanswered question concerns the implications of cognitive diversity for longer-term outcomes such as team learning, with its broader effects on organizational learning and productivity. We study how cognitive style diversity in teams—or diversity in the way that team members encode, organize and process information—indirectly influences team learning through collective intelligence, or the general ability of a team to work together across a wide array of tasks. Synthesizing several perspectives, we predict and find that cognitive style diversity has a curvilinear—inverted U-shaped—relationship with collective intelligence. Collective intelligence is further positively related to the rate at which teams learn, and is a mechanism guiding the indirect relationship between cognitive style diversity and team learning. We test the predictions in 98 teams using ten rounds of the minimum-effort tacit coordination game. Overall, this research advances our understanding of the implications of cognitive diversity for organizations and why some teams demonstrate high levels of team learning in dynamic situations while others do not.

show abstract

The Effects of Communication Networks and Turnover on Transactive Memory and Group Performance

Argote

Aven

Kush

2018

Organization Science

View full text Add to dashboard Cite

We theorize that the effect of membership turnover on group processes and performance depends on a group’s communication network. We describe two mechanisms through which communication networks affect group performance: (1) the number of direct communication paths and (2) the clarity of the coordination logic. These mechanisms map onto two network dimensions: density, which affects a group’s behavior through the number of available communication paths, and centralization, which affects a group’s behavior through the clarity of the coordination logic. We empirically analyze the effects of turnover on the performance of fully connected all-channel networks and hub-and-spoke or wheel networks in an experiment of 109 four-person groups performing two collaborative problem-solving tasks. The greater number of direct communication paths enabled fully connected groups with stable membership to develop stronger transactive memory systems (TMSs) and perform better than fully connected groups that experienced turnover. By contrast, the clear coordination logic of perfectly centralized groups that experienced turnover facilitated more frequent dyadic communication, which enabled them to strengthen their TMSs, incorporate the contributions of new members, and improve their performance. Thus, our results indicate that communication networks condition the effect of membership turnover on group processes and performance. The online appendix is available at https://doi.org/10.1287/orsc.2017.1176 .

show abstract

Challenges and Opportunities for Group Learning and Group Learning Researchers

Cited by 3 publications

References 104 publications

Patient care in complex Sociotechnological ecosystems and learning health systems

Patient care in complex Sociotechnological ecosystems and learning health systems

The Impact of Cognitive Style Diversity on Implicit Learning in Teams

The Effects of Communication Networks and Turnover on Transactive Memory and Group Performance

Contact Info

Product

Resources

About