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Pricing and Matching in the Sharing Economy

  • Yiwei Chen
  • Ming HuEmail author
  • Yun Zhou
Chapter
Part of the Springer Series in Supply Chain Management book series (SSSCM, volume 6)

Abstract

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.

References

  1. Chen Y, Hu M (2018, forthcoming) Pricing and matching with forward-looking buyers and sellers. Manuf Serv Oper Manag. http://ssrn.com/abstract=2859864 Google Scholar
  2. Chen L, Mislove A, Wilson C (2015) Peeking beneath the hood of uber. In: Proceedings of the 2015 ACM conference on Internet measurement conference. ACM, New York, pp 495–508Google Scholar
  3. Hu M, Zhou Y (2018a) Dynamic type matching, available at SSRN: http://ssrn.com/abstract=2592622
  4. Hu M, Zhou Y (2018b) Price, wage and fixed commission in on-demand matching, available at SSRN: http://ssrn.com/abstract=2949513
  5. Xu P, Allgor R, Graves S (2009) Benefits of reevaluating real-time order fulfillment decisions. Manuf Serv Oper Manag 11(2):340–355CrossRefGoogle Scholar
  6. Yu Y, Chen X, Zhang F (2015) Dynamic capacity management with general upgrading. Oper Res 63(6):1372–1389CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Carl H. Lindner College of BusinessUniversity of CincinnatiCincinnatiUSA
  2. 2.Rotman School of ManagementUniversity of TorontoTorontoCanada
  3. 3.DeGroote School of BusinessMcMaster UniversityHamiltonCanada

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