TY - JOUR
T1 - Pricing and wage strategies for an on-demand service platform with heterogeneous congestion-sensitive customers
AU - Zhong, Yuanguang
AU - Pan, Qi
AU - Xie, Wei
AU - Cheng, T. C.E.
AU - Lin, Xiaogang
N1 - Funding Information:
The authors would like to thank the Editor and two anonymous referees for their valuable comments and suggestions, which significantly improved the quality and presentation of this paper. This work was supported by the National Natural Science Foundation of China [Grant 71871097 , 71971085 , 71520107001 , 71501077, 71731006 ], the Guangdong Natural Science Foundation [Grant 2020A1515011270 , 2018A030313760 ], and the GDUPS (Yuanguang Zhong, 2017) . Dr. Cheng was supported in part by The Hong Kong Polytechnic University under the Fung Yiu King - Wing Hang Bank Endowed Professorship in Business Administration.
Publisher Copyright:
© 2020
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - In this paper we consider an on-demand service platform connecting self-scheduling service providers with heterogeneous and congestion-sensitive customers. Based on the congestion sensitivity, customers are classified into two types. Two pricing strategies are proposed for the platform to classify the customers. We develop a model that can capture customer heterogeneous with different congestion sensitivities, in which the optimal strategies are analyzed and compared for the platform. In addition, we compare this model with the unclassified model without customer congestion-sensitivity heterogeneity from the perspective of all participants and the whole society. We show that it does not always benefit the platform to serve as many customers as possible, and the platform should switch between the two strategies according to market conditions. When the potential supply is scare, the potential demand is sufficient, the proportion of low congestion-sensitivity customers is high, or the sensitivity difference between two types of customers is significant, the platform should adopt the strategy serving only one type of customers rather than the whole market. Furthermore, we observe that, the classified model always brings more profit, consumer surplus and social welfare than the unclassified one, although it sometimes hurts the agents’ labor welfare.
AB - In this paper we consider an on-demand service platform connecting self-scheduling service providers with heterogeneous and congestion-sensitive customers. Based on the congestion sensitivity, customers are classified into two types. Two pricing strategies are proposed for the platform to classify the customers. We develop a model that can capture customer heterogeneous with different congestion sensitivities, in which the optimal strategies are analyzed and compared for the platform. In addition, we compare this model with the unclassified model without customer congestion-sensitivity heterogeneity from the perspective of all participants and the whole society. We show that it does not always benefit the platform to serve as many customers as possible, and the platform should switch between the two strategies according to market conditions. When the potential supply is scare, the potential demand is sufficient, the proportion of low congestion-sensitivity customers is high, or the sensitivity difference between two types of customers is significant, the platform should adopt the strategy serving only one type of customers rather than the whole market. Furthermore, we observe that, the classified model always brings more profit, consumer surplus and social welfare than the unclassified one, although it sometimes hurts the agents’ labor welfare.
KW - Congestion-sensitivity
KW - Customer heterogeneity
KW - On-demand service
KW - Sharing economic
UR - http://www.scopus.com/inward/record.url?scp=85093121962&partnerID=8YFLogxK
U2 - 10.1016/j.ijpe.2020.107901
DO - 10.1016/j.ijpe.2020.107901
M3 - Journal article
AN - SCOPUS:85093121962
SN - 0925-5273
VL - 230
JO - International Journal of Production Economics
JF - International Journal of Production Economics
M1 - 107901
ER -