Organizations conduct series of face-to-face meetings aiming to improve work practices. In these meetings, participants from different backgrounds collaboratively design artifacts, such as knowledge or process maps. Such meetings are orchestrated and carried out by facilitators and the success of the meetings almost solely depends on the experience of the facilitators. Previous research has mainly focused on approaches that support facilitators and participants in the upfront planning of such events. There is however, little guidance for facilitators and participants once a meeting has started. One critical aspect – among others – is that during a meeting, the facilitator and participants need to decide for how long the iterative process of discussion and design should continue. We argue that we can provide support for such decisions based on the evolution of artifacts collaboratively created during such meetings. This paper presents a multi-level, multi-method analysis of artifacts based on experts’ observations in combination with network analytics. We study the use of automated analytics to assess the evolution of collaboratively created artifacts and to indicate maturity and established consensus of the collaborative practice. We propose a computational approach to support facilitators and participants in deciding when to stop face-to-face meetings.

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https://hci.top/en/papers/cscw/6525/2018

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CSCW
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2018
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