Paper ID
2207
Description
As cooperation is becoming increasingly important for complex knowledge work tasks, so is the need to understand how to support it. This study contributes to this aim by manipulating flow experience intensities for individuals working alone or in digitally-mediated small groups. Contrary to related work it is found that flow is not experienced as more intense in groups. Two possible reasons are identified, either an optimization of difficulty through more/less autonomy when working alone/together, or a limitation for sharing intense experiences by working in a communication-restrictive digitally-mediated environment. Furthermore, empiric explorations reveal (i) positive correlations for flow with group performance, satisfaction with and growth of the group, and (ii) that lower/higher flow intensities in digitally-mediated small groups can be classified via machine learning on combinations of ECG, self-report, and task-related data, thus further advancing the development of flow-supportive IS.
Recommended Citation
Knierim, Michael; Nadj, Mario; Li, Maximilian; and Weinhardt, Christof, "Flow in Knowledge Work Groups – Autonomy as a Driver or Digitally Mediated Communication as a Limiting Factor?" (2019). ICIS 2019 Proceedings. 7.
https://aisel.aisnet.org/icis2019/future_of_work/future_work/7
Flow in Knowledge Work Groups – Autonomy as a Driver or Digitally Mediated Communication as a Limiting Factor?
As cooperation is becoming increasingly important for complex knowledge work tasks, so is the need to understand how to support it. This study contributes to this aim by manipulating flow experience intensities for individuals working alone or in digitally-mediated small groups. Contrary to related work it is found that flow is not experienced as more intense in groups. Two possible reasons are identified, either an optimization of difficulty through more/less autonomy when working alone/together, or a limitation for sharing intense experiences by working in a communication-restrictive digitally-mediated environment. Furthermore, empiric explorations reveal (i) positive correlations for flow with group performance, satisfaction with and growth of the group, and (ii) that lower/higher flow intensities in digitally-mediated small groups can be classified via machine learning on combinations of ECG, self-report, and task-related data, thus further advancing the development of flow-supportive IS.