Abstract
The existing reputation evaluation mechanism of crowdsourcing platform in China has the problems of single evaluation index, simple evaluation model and poor differentiation ability, which can’t fully reflect the real reputation status of crowdsourcing participants. Under the big data environment, from the perspective of incentive and punishment, collecting of crowdsourcing participant evaluation indexes extensively, this paper construct the multidimensional reputation evaluation model (MREM) of crowdsourcing participants from four dimensions: initial reputation dimension, transaction dimension, evaluation dimension and fraud penalty dimension. Empirical analysis based on the data of participants from “epwk” platform, the validity of the model is verified from two aspects: incentive trading and fraud prevention, the results show that MREM model is more comprehensive, dynamic and accurate than the existing model of crowdsourcing platform, and has stronger practicability.
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