A two-sided matching method of resources and tasks in competitive crowdsourcing platform oriented to workers’ and employers’ satisfaction

Due to the most resources organisation methods recommending or matching tasks based on single perspective, this paper proposes a two-sided matching method to maximise workers’ and employers’ satisfaction. First, workers choose tasks for certain motivations. Therefore, this paper establishes the model of task attraction based on motivation theory to measure the extent that tasks satisfy workers’ participation willingness. Rewarding and learning, the two most important motivations which influence participation willingness of workers, are introduced in the model. Considering the impact of job characteristics on workers’ behaviours, job autonomy is introduced in the model of task attraction. Second, because task completion quality and timeliness have an impact on employer satisfaction, the model of resource capability is constructed to measure capability of workers based on task completion quality and timeliness. Reputation model is introduced to evaluate workers’ timeliness and help employers filter malicious workers. Based on these models, evaluation index systems of satisfaction are established to evaluate worker and employer satisfaction. Third, a two-sided matching method is proposed. Gale–Shapley algorithm is adopted to solve two-sided matching problem. Finally, the proposed method is verified by utilising real data from EPWK platform. The results show that the proposed method can effectively improve overall satisfaction.