Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
In this study, we present a novel method for investigating the digital traces of collaborative user activity in large-scale Enterprise Collaboration Systems (ECS). Guided by existing research, we developed a classification metric (Collaborative Work Codes) to describe the type of work that can be identified in the event logs of collaboration software. Following a Design Science Research approach, we developed a computational technique that assigns the codes to event records based on a mapping table. In two evaluation cycles, the computational technique was applied to two ECS datasets (the first provided by a research group, the second by a large German manufacturing company). The combined data was imported into a dashboard and used to evaluate the coding method and the suitability of the codes for analysis. The findings show that the codes appropriately reflect the type of work carried out by the users.
Recommended Citation
Schubert, Petra; Williams, Susan; Just, Martin; Alberts, Jens; and Bahles, Sebastian, "How Are Employees Using Collaboration Software to Support Their Work? A Method for Analyzing Digital Traces in Enterprise Collaboration Systems" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 8.
https://aisel.aisnet.org/hicss-58/dsm/data_analytics/8
How Are Employees Using Collaboration Software to Support Their Work? A Method for Analyzing Digital Traces in Enterprise Collaboration Systems
Hilton Waikoloa Village, Hawaii
In this study, we present a novel method for investigating the digital traces of collaborative user activity in large-scale Enterprise Collaboration Systems (ECS). Guided by existing research, we developed a classification metric (Collaborative Work Codes) to describe the type of work that can be identified in the event logs of collaboration software. Following a Design Science Research approach, we developed a computational technique that assigns the codes to event records based on a mapping table. In two evaluation cycles, the computational technique was applied to two ECS datasets (the first provided by a research group, the second by a large German manufacturing company). The combined data was imported into a dashboard and used to evaluate the coding method and the suitability of the codes for analysis. The findings show that the codes appropriately reflect the type of work carried out by the users.
https://aisel.aisnet.org/hicss-58/dsm/data_analytics/8