An optimal charging strategy for crowdsourcing platforms

• Published in: Kybernetes 2020-06, Vol.49 (6), p.1605-1621
• Main Authors: Chen, Jiumei, Liu, Zhiying, Zhang, Wen, Gong, Bengang
• Format: Article
• Language: English
• Subjects: Charging; Collaboration; Crowdsourcing; Data collection; Game theory; Innovations; Internet; Labor market; Library management; Linux; Literature reviews; Open source software; Optimization; Prediction markets; Product development; Public policy; Risk sharing; Schedules; Solvers; Strategy; Success; Systems theory; Third party
• ISSN: 0368-492X; 1758-7883
• DOI: 10.1108/K-03-2019-0173

Purpose The purpose of this paper is to develop an optimal charging strategy for a third-party crowdsourcing platform. Design/methodology/approach Based on the auction theory, the Stackelberg game theory and the systems theory, this paper presents a new model from the perspective of risk sharing between solution seekers and the crowdsourcing platform, given the utility maximization of the seekers, the crowdsourcing platform and the solvers. Findings Based on the results, this study shows that the menu of fees, which includes different combinations of a fixed fee and a floating fee schedule, should be designed to attract both solution seekers and solvers. In addition, the related prize setting and the expected payoff for each party are presented. Practical implications This study is beneficial for crowdsourcing platform operators, as it provides a new way to design charging strategies and can help in understanding key influential factors. Originality/value To the best of the authors’ knowledge, this study is one of the first to simulate the interactions among the three stakeholders, thereby providing a novel model that includes a fixed fee and a floating commission.