Self-Organizing Teams in Online Work Settings

TL;DR

This paper introduces Self-Organizing Teams (SOTs), a human-centered approach to online team formation that enhances collaboration quality and satisfaction by empowering participants to control and guide their teamwork.

Contribution

It proposes a novel human-centered model for online team formation that allows participants to self-organize, contrasting with traditional algorithm-driven methods.

Findings

SOTs produce higher quality results than benchmarks.

Participants report greater teamwork satisfaction in SOTs.

Human SOTs exhibit emergent collective properties similar to machine learning systems.

Abstract

As the volume and complexity of distributed online work increases, the 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 such teams by grouping workers according to a set of predefined decision criteria. This approach micro-manages workers, who have no say in the team formation process. Depriving users of control over who they will work with stifles creativity, causes psychological discomfort and results in less-than-optimal collaboration results. In this work, we propose an alternative model, called Self-Organizing Teams (SOTs), which relies on the crowd of online workers itself to organize into effective teams. Supported but not guided by an algorithm, SOTs 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 SOTs to two benchmarks that do not offer user agency over the collaboration, reveal that participants in the SOTs condition produce results of higher quality and report higher teamwork satisfaction. We also find that, similarly to machine learning-based self-organization, human SOTs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible teammates.