Skip to content
naulon
Not checkedNot checked for notices yet.

Evolutionary Game Analysis of Online and Offline Drug Retailer Competition Based on Network Externalities

Chenshuo Li, Zhe Huang

IEEE Access · 2019

doi:10.1109/access.2019.2929272 (opens in a new tab)

We email you when a new notice or a question about this paper appears.

Abstract

Network externality is an important feature of the era of network economy. The competition between online and offline retail pharmacies caused by network externality is getting fiercer and fiercer. In this context, we studied the long-term market competition evolution of two-channel retail pharmacies under the network external environment, in order to obtain higher profits, two-channel retail pharmacies prefer to choose profit maximization marketing strategy or market share maximization marketing strategy. Using evolutionary game theory, an evolutionary game model of two-channel retail pharmacies was established. Through the establishment of the model, obtain the payment matrix of different marketing strategies selected by the two-channel retail pharmacies and the evolution and stability of the selection of marketing strategies. The results show that when the strength of the network externality is within a certain range, the traditional retail pharmacies with higher marketing cost choose to pursue the profit maximization strategy, while online pharmacy with lower marketing costs choose market share maximization strategy to achieve greater profits; when the network externality is large, in order to maintain a certain market share in the long-term market evolution, the traditional retail pharmacy with high marketing cost chooses to pursue the market share maximization marketing strategy in order to obtain greater profits, while the online pharmacy with low marketing cost chooses to profit from the profit maximization marketing strategy.

Abstract licence: CC BY-NC-ND

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.