Self-organization in online collaborative work settings. Issue 1 (22nd August 2022)

• Record Type: Journal Article
• Authors: Lykourentzou, Ioanna; Vinella, Federica Lucia; Ahmed, Faez; Papastathis, Costas; Papangelis, Konstantinos; Khan, Vassilis-Javed; Masthoff, Judith
• Is Part Of: Collective intelligence. Volume 1:Issue 1(2022)
• Publication Date: 2022-08-22
• Subjects: Online collaborative work -- distributed work -- complex work -- macrotask -- self-organization; Computational intelligence -- Periodicals; Collective behavior -- Periodicals; Swarm intelligence -- Periodicals; 006.3
• DOI: 10.1177/26339137221078005
• Languages: English
• ISSNs: 2633-9137

Abstract

As the volume and complexity of distributed online work increases, collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of online collaborations by grouping workers in a top-down fashion and according to a set of predefined decision criteria. This approach often means that workers have little say in the collaboration formation process. Depriving users of control over whom they will work with can stifle creativity and initiative-taking, increase psychological discomfort, and, overall, result in less-than-optimal collaboration results—especially when the task concerned is open-ended, creative, and complex. In this work, we propose an alternative model, called Self-Organizing Pairs (SOPs), which relies on the crowd of online workers themselves to organize into effective work dyads. Supported but not guided by an algorithm, SOPs are a new human-centered computational structure, which enables participants to control, correct, and guide the output of their collaboration as a collective. Experimental results, comparing SOPs to two benchmarks that do not allow user agency, and on an iterative task of fictional story writing, reveal that participants in the SOPs condition produce creative outcomes of higher quality, and report higher satisfaction with their collaboration. Finally, we find that similarly to machine learning-based self-organization, human SOPs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible collaborators.