Optimal pricing in on-demand-service-platform-operations with hired agents and risk-sensitive customers in the blockchain era

On-demand service platforms are popular nowadays. Many platforms hire agents to serve customers who are risk sensitive towards the waiting-time. In this paper, we apply the mean-risk theory to analytically explore how the risk attitude of customers affects the optimal service pricing decision of the on-demand platform, consumer surplus (CS) of customers, the expected profit (EP) and profit risk (PR) of the platform (and the hired service agents). In the basic model, assuming consumers are homogeneous, we find that if the customers are more risk averse (risk seeking), the optimal service price will drop (increase). Comparing among the three different risk attitudes of customers, we find that when the customers are risk seeking, the CS and the platform's EP are highest, even though the platform's PR is also highest. While the opposite happens when the customers are risk averse. In the extended model with a market including customers with different risk attitudes, the blockchain technology helps the platform assess the proportion of risk seeking, risk neutral and risk averse customers accurately. We explore the optimal service prices under both the common pricing policy and the customized pricing policy (with-respect-to customer's risk attitude), and derive the value of blockchain technology mediated customized service pricing strategy. We conclude by highlighting that the risk attitudes of customers play a critical role in determining the optimal on-demand service pricing, and the blockchain technology is a valuable technological tool to help. (C) 2020 Elsevier B.V. All rights reserved.