Innovation teams must navigate inherent tensions between different learning activities to produce high levels of performance. Yet, we know little about how teams combine these activities-notably reflexive, experimental, vicarious, and contextual learning-most effectively over time. In this article, we integrate research on teamwork episodes with insights from music theory to develop a new theoretical perspective on team dynamics, which explains how team activities can produce harmony, dissonance, or rhythm in teamwork arrangements that lead to either positive or negative effects on overall performance. We first tested our theory in a field study using longitudinal data from 102 innovation teams at a Fortune Global 500 company; then, we replicated and elaborated our theory in a study of 61 MBA project teams at an elite North American university. Results show that some learning activities can occur within the same teamwork episode to have harmonious positive effects on team performance, while other activities combine to have dissonant negative effects when occurring in the same episode. We argue that dissonant activities must be spread across teamwork episodes to help teams achieve a positive rhythm of team learning over time. Our findings contribute to theory on team dynamics, team learning, and ambidexterity.
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http://dx.doi.org/10.1177/00018392231166635 | DOI Listing |
Mayo Clin Proc Digit Health
December 2024
Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
View Article and Find Full Text PDFJ Clin Transl Sci
December 2024
University of Colorado Clinical and Translational Sciences Institute - Community Engagement and Health Equity Core, Aurora, CO, USA.
The Colorado Immersion Training in Community Engagement (CIT) program supports a change in the research trajectory of junior faculty, early career researchers, and doctoral students toward Community-Based Participatory Research (CBPR). CIT is within the Community Engagement and Health Equity Core (CEHE) at the Colorado Clinical and Translational Sciences Institute (CCTSI), an NIH-funded Clinical and Translational Science award. This Translational Science Case Study reports on CIT's impacts from 2010 to 2019.
View Article and Find Full Text PDFPsychiatry Res
January 2025
Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University, Japan; Artificial Intelligence Ethics and Society Team, RIKEN Center for Advanced Intelligence Project, Saitama, Japan; The General Research Division, Osaka University Research Center on Ethical, Legal and Social Issues, Kyoto, Japan. Electronic address:
Background: Our human volumetric MRI study (Dai et al., 2024) demonstrated that habenula (Hb) volume is associated with psychological resilience, a key protective factor against depression. However, the biological mechanisms underpinning this relationship remain unclear.
View Article and Find Full Text PDFClin Oncol (R Coll Radiol)
December 2024
South West Wales Cancer Centre, Swansea, UK; National Radiotherapy Trials Quality Assurance (RTTQA) Group, National Institute for Health and Care Research, UK; Swansea University Medical School, Swansea, UK.
Aims: The SCOPE2 trial evaluates radiotherapy (RT) dose escalation for oesophageal cancer. We report findings from the accompanying RT quality assurance (RTQA) programme and identify recommendations for PROTIEUS, the next UK trial in oesophageal RT.
Maetrials And Methods: SCOPE2's RTQA programme consisted of a pre-accrual and on-trial component.
J Orthop Surg Res
January 2025
Department of Human Anatomy, Graduate School, Inner Mongolia Medical University, Hohhot, 010010, Inner Mongolia, China.
Purpose: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process.
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