The purpose of this paper is to develop an optimal charging strategy for a third-party crowdsourcing platform.
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.
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.
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.
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.
Funding: The authors appreciate aid from the National Natural Science Foundation of China under Grant Nos. 71472172 and 71671001; and the Social Science and Humanity Foundation of Ministry of Education of China under Grant No. 14YJA630035.
Chen, J., Liu, Z., Zhang, W. and Gong, B. (2019), "An optimal charging strategy for crowdsourcing platforms", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-03-2019-0173Download as .RIS
Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited