Pricing and Matching in the Sharing Economy
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Sharing economy platforms use crowdsourced suppliers to provide customers with services or goods. Their decision making often revolves around pricing and matching. Platforms like Uber charge the customers a price for using the services or goods and offer the crowdsourced suppliers a wage or pay for providing the services or goods. First, we study how the platform could optimally set the price and the wage for a single service or product in different market conditions, and investigate the performance of the fixed commission contract which uses a fixed commission percentage across all market conditions. Second, even with determined pricing decisions, the platform also faces the task of matching customers with suppliers. We consider a stochastic, dynamic model with multiple demand types to be matched with multiple supply types over a planning horizon. We characterize the optimal matching policy by determining the priorities of the demand-supply pairs, under a sufficient condition on the reward structure. Then, the results are applied to two cases with more specific reward structures; namely, the horizontal reward structure and the vertical reward structure, to better characterize the optimal policy. Finally, we study the joint pricing and matching decision by a platform for a single service or product and take into account suppliers’ and customers’ forward-looking behavior. We propose a simple heuristic policy: fixed price and wage plus waiting compensation, in conjunction with the greedy matching policy on a first-come-first-served basis. This heuristic policy induces forward-looking suppliers and customers to behave myopically and is shown to be asymptotically optimal.
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- Hu M, Zhou Y (2018a) Dynamic type matching, available at SSRN: http://ssrn.com/abstract=2592622
- Hu M, Zhou Y (2018b) Price, wage and fixed commission in on-demand matching, available at SSRN: http://ssrn.com/abstract=2949513