Leader driven platform helps experts collaborate across domains effectively
A Leader-Driven Open Collaboration Platform for Exploring New Domains
Human-Computer Interaction
Summary
Exploring new topics often starts with a few specialists, which can lead to biased views. The authors created a platform where experts from different fields can work together to avoid this bias and get fresher ideas. But when many people contribute, their ideas can be very different, making it hard to put everything into one clear story. The authors found that having a leader guide the collaboration helps keep things organized and balanced. This approach helps reduce bias while managing the differences in contributions.
What this means in practice
- •For innovation teams: Support diverse experts to collaboratively develop initial insights in emerging technology areas with guided leadership to organize contributions.
- •For policy development groups: Enable subject matter experts from multiple fields to jointly draft coherent policy documents while reducing individual biases through leader coordination.
Authors
Michael Weiss, Ibrahim AbuAlhaol, Mohamed Amin
Abstract
This paper describes the design and initial evaluation of a leader-driven open collaboration platform for exploring new domains. The goal of this platform is to enable the collaboration of subject matter experts across knowledge boundaries. Traditionally, new domains are explored from within a single specialist or a focused group perspective. However, this often introduces bias. Collaboration helps reduce such bias by providing access to a broader range of information sources, increasing the chances for producing new insights in a new domain. However, it also introduces a new problem: variance between the contributions made. Variance makes it difficult to produce a coherent document. In this paper, we report on our observations from developing an initial prototype of the open collaboration platform, and derive propositions about how leader-driven open collaboration helps reduce bias while containing variance.