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

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  • Yiwei Gong

    (Wuhan University)

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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  • Yiwei Gong, 2017. "Estimating participants for knowledge-intensive tasks in a network of crowdsourcing marketplaces," Information Systems Frontiers, Springer, vol. 19(2), pages 301-319, April.
    • Handle: RePEc:spr:infosf:v:19:y:2017:i:2:d:10.1007_s10796-016-9674-6
    DOI: 10.1007/s10796-016-9674-6
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    References listed on IDEAS

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    4. Xuefeng Zhang & Bengang Gong & Yaqin Cao & Yi Ding & Jiafu Su, 2022. "Investigating participants’ attributes for participant estimation in knowledge-intensive crowdsourcing: a fuzzy DEMATEL based approach," Electronic Commerce Research, Springer, vol. 22(3), pages 811-842, September.
    5. Pollok, Patrick & Lüttgens, Dirk & Piller, Frank T., 2019. "Attracting solutions in crowdsourcing contests: The role of knowledge distance, identity disclosure, and seeker status," Research Policy, Elsevier, vol. 48(1), pages 98-114.
    6. Marijn Janssen & David Konopnicki & Jane L. Snowdon & Adegboyega Ojo, 0. "Driving public sector innovation using big and open linked data (BOLD)," Information Systems Frontiers, Springer, vol. 0, pages 1-7.

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