Reputation and Pricing Dynamics in Online Markets

We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers’ reputations which depend on their past transaction histories and ratings from past buyers. Sellers compete in the same market through pricing, while considering the impact of their heterogeneous reputations. We consider sellers with limited as well as unlimited capacities, which correspond to different practical market scenarios. In the unlimited seller capacity scenario, buyers prefer the seller with the highest reputation-price ratio. If the gap between the highest and second highest seller reputation levels is large enough, then the highest reputation seller dominates the market as a monopoly. If sellers’ reputation levels are relatively close to each other, then those sellers with relatively high reputations will survive at the equilibrium, while the remaining relatively low reputation sellers will get zero market share. In the limited seller capacity scenario, we further consider two different cases. If each seller can only serve one buyer, then it is possible for sellers to set their monopoly prices at the equilibrium while all sellers gain positive market shares; if each seller can serve multiple buyers, then it is possible for sellers to set maximum prices at the equilibrium. Simulation results show that the dynamics of reputations and prices in the longer-term interactions will converge to stable states, and the initial buyer ratings of the sellers play the critical role in determining sellers’ reputations and prices at the stable state.

[1]  Mehmet E. Yildiz,et al.  Competitive Targeted Advertising Over Networks , 2016, Oper. Res..

[2]  Randall Berry,et al.  Are imperfect reviews helpful in social learning? , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[3]  Georgios Zervas,et al.  A first look at online reputation on Airbnb, where every stay is above average , 2015, Marketing Letters.

[4]  Juho Hamari,et al.  The sharing economy: Why people participate in collaborative consumption , 2016, J. Assoc. Inf. Sci. Technol..

[5]  Carlos Riquelme,et al.  Dynamic pricing in ridesharing platforms , 2016, SECO.

[6]  Angelos Stavrou,et al.  E-commerce Reputation Manipulation: The Emergence of Reputation-Escalation-as-a-Service , 2015, WWW.

[7]  Donald F. Towsley,et al.  On the Duration and Intensity of Competitions in Nonlinear Pólya Urn Processes with Fitness , 2016, SIGMETRICS.

[8]  Álvaro Enrique Arenas,et al.  A Utility-Based Reputation Model for Service-Oriented Computing , 2007, CoreGRID.

[9]  David H. Reiley,et al.  Pennies from Ebay: The Determinants of Price in Online Auctions , 2000 .

[10]  Audun Jøsang,et al.  A survey of trust and reputation systems for online service provision , 2007, Decis. Support Syst..

[11]  Georgia Perakis,et al.  Non-Linear Pricing Competition with Private Capacity Information , 2015 .

[12]  Paul A. Pavlou,et al.  Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior , 2002, MIS Q..

[13]  R. Srikant,et al.  Revenue-maximizing pricing and capacity expansion in a many-users regime , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[14]  A. Mas-Colell,et al.  Microeconomic Theory , 1995 .

[15]  Asuman E. Ozdaglar,et al.  Price and Capacity Competition , 2006, Games Econ. Behav..

[16]  Chrysanthos Dellarocas,et al.  Analyzing the economic efficiency of eBay-like online reputation reporting mechanisms , 2011, EC '01.

[17]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[18]  Zhengyuan Zhou,et al.  The importance of exploration in online marketplaces , 2014, 53rd IEEE Conference on Decision and Control.

[19]  Richard T. B. Ma,et al.  Enhancing Reputation via Price Discounts in E-Commerce Systems , 2018, ACM Trans. Knowl. Discov. Data.

[20]  R. Srikant,et al.  Economics of Network Pricing With Multiple ISPs , 2006, IEEE/ACM Transactions on Networking.

[21]  Paul Resnick,et al.  The value of reputation on eBay: A controlled experiment , 2002 .

[22]  Ramesh Johari,et al.  Mean Field Equilibrium in Dynamic Games with Strategic Complementarities , 2013, Oper. Res..

[23]  H. Simon Bounded Rationality and Organizational Learning , 1991 .

[24]  Chrysanthos Dellarocas,et al.  Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior , 2000, EC '00.

[25]  Alice Cheng,et al.  Sybilproof reputation mechanisms , 2005, P2PECON '05.

[26]  Yiangos Papanastasiou,et al.  Dynamic Pricing in the Presence of Social Learning and Strategic Consumers , 2017, Manag. Sci..

[27]  MengChu Zhou,et al.  A Novel Method for Calculating Service Reputation , 2013, IEEE Transactions on Automation Science and Engineering.

[28]  Feifei Liu,et al.  Building and managing reputation in the environment of Chinese e-commerce: a case study on Taobao , 2012, WIMS '12.

[29]  J. Pope,et al.  What's Mine is Yours , 2016, Journal of occupational and environmental medicine.

[30]  Benjamin E. Hermalin Leading for the Long-Term , 1998 .

[31]  Xin Li,et al.  Self-selection, slipping, salvaging, slacking, and stoning: the impacts of negative feedback at eBay , 2005, EC '05.

[32]  Xiang Li,et al.  Modeling and Analysis of Collaborative Consumption in Peer-to-Peer Car Sharing , 2015, PERV.

[33]  Paul Resnick,et al.  Reputation systems , 2000, CACM.