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Estimating participants for knowledge-intensive tasks in a network of crowdsourcing marketplaces

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Abstract

Crowdsourcing has become an increasingly attractive practice for companies to abstain on-demand workforce and higher level of flexibility in open contexts. While knowledge-intensive crowdsourcing is expected to be prosperous, most current crowdsourcing calls are still about general and low-priced tasks. An obstacle of conducing knowledge-intensive crowdsourcing is the lack of diversity of expertise and the small scale of crowd in isolated crowdsourcing marketplaces. In this paper, a network of crowdsourcing marketplaces is envisioned for efficient knowledge-intensive crowdsourcing and engagement of massive and diverse participants across different marketplaces. Based on an algorithm for estimating participants for knowledge-intensive crowdsourcing tasks, an experiment with 100 simulations indicates that conducting crowdsourcing tasks in a network of crowdsourcing marketplaces results in higher customer satisfaction than doing that in isolated marketplaces. This finding advocates the development of a network of crowdsourcing marketplaces to open up the potential of knowledge-intensive crowdsourcing.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grand No. 71501145).

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  1. School of Information Management, Wuhan University, Wuhan, Hubei, 430072, People’s Republic of China

    Yiwei Gong

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  1. Yiwei Gong
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Gong, Y. Estimating participants for knowledge-intensive tasks in a network of crowdsourcing marketplaces. Inf Syst Front 19, 301–319 (2017). https://doi.org/10.1007/s10796-016-9674-6

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