Task Pricing Optimization Model of Crowdsourcing Platform

Li Lin, Xiangyue Chen, Yiying Lou, Weijian Zhang, Ru Zhang


In this paper, we established a task pricing optimization model by the Logistic and anti-resolve thought to work out the problem of unequal spatial distribution and overall low of the task completion rate in the crowdsourcing platforms. Combining with the actual application information, we use scatter diagram, contour map, etc. to make a qualitative study and find that the reason why some of the tasks are not accepted is because the enterprise failed to take the total task quotas around the task into consideration while pricing the task. Then, combined with the influencing factors of traditional pricing model and results of qualitative analyses, the optimization model of crowdsourcing platform is built. Next, we select an ending project in an app of “make money” in China as the example to evaluate the effectiveness of our model. We applied the method of computer simulation to solve the model, and we find that, under the new pricing plan, the task completion rate has been significantly improved, which proves the conclusion of our qualitative analysis and the validity of the optimal model.

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DOI: https://doi.org/10.11114/bms.v4i3.3384


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Business and Management Studies     ISSN 2374-5916 (Print)     ISSN 2374-5924 (Online)

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