Evolutionary location and pricing strategies for service merchants in competitive O2O markets

Authored by Zhou He, Jichang Dong, Shouyang Wang, T C E Cheng

Date Published: 2016

DOI: 10.1016/j.ejor.2016.03.030

Sponsors: The Hong Kong Polytechnic University

Platforms: Java Swarm

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

Attracting customers in the online-to-offline (O2O) business is increasingly difficult as more competitors are entering the O2O market. To create and maintain sustainable competitive advantage in crowded 020 markets requires optimizing the joint pricing-location decision and understanding customers' behaviours. To investigate the evolutionary location and pricing behaviors of service merchants, this paper proposes an agent-based competitive O2O model in which the service merchants are modeled as profit-maximizing agents and customers as utility-maximizing agents that are connected by social networks through which they can share their service experiences by word of mouth (WOM). It is observed that the service merchant should standardize its service management to offer a stable expectation to customers if their WOM can be ignored. On the other hand, when facing more socialized customers, firms with variable service quality should adopt aggressive pricing and location strategies. Although customers' social learning facilitates the diversity of services in O2O markets, their online herd behaviors would lead to unpredictable offline demand variations, which consequently pose performance risk to the service merchants. (C) 2016 Elsevier B.V. All rights reserved.

Tags

Agent-based model behavior Complex adaptive systems Word-of-mouth Decision-Making quality uncertainty information Facility location Future-research Single-machine