Exploring Self-organisation in Crowd Teams

Lykourentzou, Ioanna; Liapis, Antonios; Papastathis, Costas; Papangelis, Konstantinos; Vassilakis, Costas

doi:10.1007/978-3-030-39634-3_15

Cited by 1 publication

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under the light of these inherent limitations of fully bottom-up solutions to crowdsourcing TFPs, we also model a blended approach inspired by Prokopenko (2009) who point that self-organization can (and should) be guided by algorithmic top-down mediation. Similar works (Lykourentzou et al, 2010(Lykourentzou et al, , 2019Martius and Herrmann, 2012;Nurzaman et al, 2014;Jarrahi et al, 2020)-either through conceptualization or reallife implementations-have proposed guided self-organization as the ideal strategy linking worker agency with algorithmic optimization. Our implementation of guided self-organization differs in the way it is applied to a simulated collaborative crowdsourcing scenario where workers are recommended by the algorithm whether to change teams or not.…”

Section: Top-down and Bottom-up Models Combined: Hivehybridmentioning

confidence: 99%

“…As for crowd work, guided self-organization is the golden mean between safeguarding worker autonomy and protecting digital work platforms from disintermediation (Jarrahi et al, 2020). In the past, the principles of guided selforganization (albeit under a different name) have touched upon collaborative knowledge production (Lykourentzou et al, 2010) and crowdsourcing teams (Lykourentzou et al, 2019). These studies indicate that guided self-organization is a potentially effective coordination model for crowd collaboration in a manner that is distributed, efficient, and fair.…”

Section: Self-organization In Team Formation: Mediating Through Guidancementioning

confidence: 99%

See 1 more Smart Citation

Crowdsourcing Team Formation With Worker-Centered Modeling

Vinella

Lykourentzou

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

Modern crowdsourcing offers the potential to produce solutions for increasingly complex tasks requiring teamwork and collective labor. However, the vast scale of the crowd makes forming project teams an intractable problem to coordinate manually. To date, most crowdsourcing collaborative platforms rely on algorithms to automate team formation based on worker profiling data and task objectives. As a top-down strategy, algorithmic crowd team formation tends to alienate workers causing poor collaboration, interpersonal clashes, and dissatisfaction. In this paper, we investigate different ways that crowd teams can be formed through three team formation models namely bottom-up, top-down, and hybrid. By simulating an open collaboration scenario such as a hackathon, we observe that the bottom-up model forms the most competitive teams with the highest teamwork quality. Furthermore, we note that bottom-up approaches are particularly suitable for populations with high-risk appetites (most workers being lenient toward exploring new team configurations) and high degrees of homophily (most workers preferring to work with similar teammates). Our study highlights the importance of integrating worker agency in algorithm-mediated team formation systems, especially in collaborative/competitive settings, and bears practical implications for large-scale crowdsourcing platforms.

show abstract