As the workplace collaboration software market is booming, there is an opportunity to design tools to support reflection and self-regulation of collaboration practices. Building on approaches from personal informatics (PI), we aim to understand and promote the use of data to enable employees to explore their work practices, specifically collaboration. Focused on the preparation stage of PI (deciding to track and tools selection), we invited office workers (N=15, knowledge workers in academia) to identify meaningful aspects of their collaboration experience and report them in a logbook for two weeks. We then conducted semi-structured interviews with participants to identify and reflect on metrics related to collaboration experience. We contribute new insights into employees’ motivations and envisioned metrics reflecting their collaboration, including the personal, social, and organizational considerations for collecting and sharing this data. We derive design implications for self-tracking technologies for collaboration.
CITATION STYLE
Lushnikova, A., Bongard-Blanchy, K., Koenig, V., & Lallemand, C. (2023). Eliciting Meaningful Collaboration Metrics: Design Implications for Self-Tracking Technologies at Work. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14144 LNCS, pp. 643–664). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-42286-7_36
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