Team-Flow and Team-Effectiveness in Virtual Teams

• Applicants: Professor Dr.-Ing. Marcin Grzegorzek; Professorin Dr. Corinna Peifer
• Subject Area: Social Psychology, Industrial and Organisational Psychology ; Accounting and Finance ; Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 465142069

Background:Advances in information and communication technology have created the opportunity for virtual (team) work and - due to globalization - more and more teams are collaborating virtually across the globe. In addition, the COVID-19 pandemic has boosted the impementation of virtual teamwork. However, there are some challenges for virtual teams - such as reduced informal communication - with implications for team effectiveness, including team member satisfaction. Research Objectives:Team flow is a concept with high potential for promoting team effectiveness, so we want to explore this further in this project. First, using a qualitative approach, we will identify the conditions of team flow in the context of virtual teams. We will consider conditions at the social, individual, and task levels in both self-managed teams and teams led by a manager. To understand team processes, measuring their dynamics is essential. Furthermore, we will therefore investigate team flow dynamics during the virtual teamwork process. However, capturing team flow dynamics is challenging because traditional flow measurements rely on self-report questionnaires that require interrupting the team process. Therefore, using machine learning, we aim to identify an algorithm based on behavioral and sensor data that is able to predict team flow and assess its dynamics over time.Methodology:Study 1 is designed to identify conditions of virtual teamwork. In a qualitative field study we will survey 400 employees (200 from Poland, 200 from Germany) online. To extract the conditions we will use text mining tools based on machine learning. In Studies 2 and 3, we focus on capturing team flow dynamics and their effects on team effectiveness in virtual self-managed teams (Study 2, 100 participants) and in teams led by a manager (Study 3, 100 participants). Dynamics will be assessed using the newly developed machine learning algorithm. Subsequently, we will develop an input-process-output model of team flow conditions (input), team flow (process/mediator), and team effectiveness (output). Expected Impact:Our project contributes to understanding conditions, dynamics, and consequences of team flow in virtual teams, which will provide promising insights for increasing team effectiveness, including team member satisfaction, which is especially relevant in times of globalization and COVID-19 pandemic. Moreover, virtual team collaboration of participants from Poland and Germany will create a realistic international context for our project objectives and promote German-Polish exchange also on the level of participants.

• DFG Programme: Research Grants
• International Connection: Poland
• Partner Organisation: Narodowe Centrum Nauki (NCN)
• Cooperation Partners: Privatdozent Dr. Olaf Flak; Professorin Dr. Barbara Kozusznik; Dr. Anita Janina Pollak; Dr. Adrian Arkadiusz Pyszka