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Abstract
In the past, work was governed by the natural rhythms of the physical world, but organizations increasingly distribute their work along the temporal dimension. This leads to varying temporal rhythms, which depict recurring patterns of activity in time, among workers, enabled by communication and collaboration technologies. The routine use of technology generates activity log data called digital traces, which promise an opportunity for a data-driven inquiry into temporal rhythms. While research using digital traces is scarce, various vendors claim to identify daily working hours based on email traces. Our study explores the use of email traces for an inquiry into daily and weekly temporal rhythms by triangulating quantitative results with interviews. Contrary to the vendors’ claims, our results show that the usefulness of email traces is limited to identifying aggregated and stable temporal rhythms.
Recommended Citation
Hüllmann, Joschka Andreas and Krebber, Simone, "Identifying Temporal Rhythms using Email Traces" (2020). AMCIS 2020 Proceedings. 17.
https://aisel.aisnet.org/amcis2020/org_transformation_is/org_transformation_is/17
Identifying Temporal Rhythms using Email Traces
In the past, work was governed by the natural rhythms of the physical world, but organizations increasingly distribute their work along the temporal dimension. This leads to varying temporal rhythms, which depict recurring patterns of activity in time, among workers, enabled by communication and collaboration technologies. The routine use of technology generates activity log data called digital traces, which promise an opportunity for a data-driven inquiry into temporal rhythms. While research using digital traces is scarce, various vendors claim to identify daily working hours based on email traces. Our study explores the use of email traces for an inquiry into daily and weekly temporal rhythms by triangulating quantitative results with interviews. Contrary to the vendors’ claims, our results show that the usefulness of email traces is limited to identifying aggregated and stable temporal rhythms.
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