The Symphony of Team Flow in Virtual Teams. Using Artificial Intelligence for Its Recognition and Promotion

More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork. However, there are challenges for virtual teams – such as reduced informal communication – with implications for team effectiveness. Team flow is...

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Published inFrontiers in psychology Vol. 12; p. 697093
Main Authors Peifer, Corinna, Pollak, Anita, Flak, Olaf, Pyszka, Adrian, Nisar, Muhammad Adeel, Irshad, Muhammad Tausif, Grzegorzek, Marcin, Kordyaka, Bastian, Kożusznik, Barbara
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 08.09.2021
Subjects
collective communication
machine learning
Psychology
team effectiveness
team flow
virtual teams
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Abstract More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork. However, there are challenges for virtual teams – such as reduced informal communication – with implications for team effectiveness. Team flow is a concept with high potential for promoting team effectiveness, however its measurement and promotion are challenging. Traditional team flow measurements rely on self-report questionnaires that require interrupting the team process. Approaches in artificial intelligence, i.e., machine learning, offer methods to identify an algorithm based on behavioral and sensor data that is able to identify team flow and its dynamics over time without interrupting the process. Thus, in this article we present an approach to identify team flow in virtual teams, using machine learning methods. First of all, based on a literature review, we provide a model of team flow characteristics, composed of characteristics that are shared with individual flow and characteristics that are unique for team flow. It is argued that those characteristics that are unique for team flow are represented by the concept of collective communication. Based on that, we present physiological and behavioral correlates of team flow which are suitable – but not limited to – being assessed in virtual teams and which can be used as input data for a machine learning system to assess team flow in real time. Finally, we suggest interventions to support team flow that can be implemented in real time, in virtual environments and controlled by artificial intelligence. This article thus contributes to finding indicators and dynamics of team flow in virtual teams, to stimulate future research and to promote team effectiveness.
AbstractList More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork. However, there are challenges for virtual teams - such as reduced informal communication - with implications for team effectiveness. Team flow is a concept with high potential for promoting team effectiveness, however its measurement and promotion are challenging. Traditional team flow measurements rely on self-report questionnaires that require interrupting the team process. Approaches in artificial intelligence, i.e., machine learning, offer methods to identify an algorithm based on behavioral and sensor data that is able to identify team flow and its dynamics over time without interrupting the process. Thus, in this article we present an approach to identify team flow in virtual teams, using machine learning methods. First of all, based on a literature review, we provide a model of team flow characteristics, composed of characteristics that are shared with individual flow and characteristics that are unique for team flow. It is argued that those characteristics that are unique for team flow are represented by the concept of collective communication. Based on that, we present physiological and behavioral correlates of team flow which are suitable - but not limited to - being assessed in virtual teams and which can be used as input data for a machine learning system to assess team flow in real time. Finally, we suggest interventions to support team flow that can be implemented in real time, in virtual environments and controlled by artificial intelligence. This article thus contributes to finding indicators and dynamics of team flow in virtual teams, to stimulate future research and to promote team effectiveness.More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork. However, there are challenges for virtual teams - such as reduced informal communication - with implications for team effectiveness. Team flow is a concept with high potential for promoting team effectiveness, however its measurement and promotion are challenging. Traditional team flow measurements rely on self-report questionnaires that require interrupting the team process. Approaches in artificial intelligence, i.e., machine learning, offer methods to identify an algorithm based on behavioral and sensor data that is able to identify team flow and its dynamics over time without interrupting the process. Thus, in this article we present an approach to identify team flow in virtual teams, using machine learning methods. First of all, based on a literature review, we provide a model of team flow characteristics, composed of characteristics that are shared with individual flow and characteristics that are unique for team flow. It is argued that those characteristics that are unique for team flow are represented by the concept of collective communication. Based on that, we present physiological and behavioral correlates of team flow which are suitable - but not limited to - being assessed in virtual teams and which can be used as input data for a machine learning system to assess team flow in real time. Finally, we suggest interventions to support team flow that can be implemented in real time, in virtual environments and controlled by artificial intelligence. This article thus contributes to finding indicators and dynamics of team flow in virtual teams, to stimulate future research and to promote team effectiveness.
More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork. However, there are challenges for virtual teams – such as reduced informal communication – with implications for team effectiveness. Team flow is a concept with high potential for promoting team effectiveness, however its measurement and promotion are challenging. Traditional team flow measurements rely on self-report questionnaires that require interrupting the team process. Approaches in artificial intelligence, i.e., machine learning, offer methods to identify an algorithm based on behavioral and sensor data that is able to identify team flow and its dynamics over time without interrupting the process. Thus, in this article we present an approach to identify team flow in virtual teams, using machine learning methods. First of all, based on a literature review, we provide a model of team flow characteristics, composed of characteristics that are shared with individual flow and characteristics that are unique for team flow. It is argued that those characteristics that are unique for team flow are represented by the concept of collective communication. Based on that, we present physiological and behavioral correlates of team flow which are suitable – but not limited to – being assessed in virtual teams and which can be used as input data for a machine learning system to assess team flow in real time. Finally, we suggest interventions to support team flow that can be implemented in real time, in virtual environments and controlled by artificial intelligence. This article thus contributes to finding indicators and dynamics of team flow in virtual teams, to stimulate future research and to promote team effectiveness.
Author Irshad, Muhammad Tausif
Pyszka, Adrian
Kordyaka, Bastian
Kożusznik, Barbara
Flak, Olaf
Nisar, Muhammad Adeel
Peifer, Corinna
Pollak, Anita
Grzegorzek, Marcin
AuthorAffiliation 3 University of Silesia in Katowice , Katowice , Poland
2 Department of Social Science, Institute of Psychology, University of Silesia in Katowice , Katowice , Poland
5 Institute of Medical Informatics, University of Lübeck , Lübeck , Germany
1 Department of Psychology, University of Lübeck , Lübeck , Germany
4 Department of Human Resource Management, College of Management, University of Economics in Katowice , Katowice , Poland
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– name: 5 Institute of Medical Informatics, University of Lübeck , Lübeck , Germany
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– name: 3 University of Silesia in Katowice , Katowice , Poland
– name: 4 Department of Human Resource Management, College of Management, University of Economics in Katowice , Katowice , Poland
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Copyright Copyright © 2021 Peifer, Pollak, Flak, Pyszka, Nisar, Irshad, Grzegorzek, Kordyaka and Kożusznik.
Copyright © 2021 Peifer, Pollak, Flak, Pyszka, Nisar, Irshad, Grzegorzek, Kordyaka and Kożusznik. 2021 Peifer, Pollak, Flak, Pyszka, Nisar, Irshad, Grzegorzek, Kordyaka and Kożusznik
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This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology
Reviewed by: Rita Berger, University of Barcelona, Spain; Charles Walker, St. Bonaventure University, United States
Edited by: Ilaria Durosini, European Institute of Oncology (IEO), Italy
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Snippet More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork....
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SubjectTerms collective communication
machine learning
Psychology
team effectiveness
team flow
virtual teams
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Title The Symphony of Team Flow in Virtual Teams. Using Artificial Intelligence for Its Recognition and Promotion
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https://pubmed.ncbi.nlm.nih.gov/PMC8455848
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