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Optimal strategies for nanostores under competition in community group buying

Lingli Shu (Business School, Sichuan University, Chengdu, China)
Xiaoyan Li (Business School, Sichuan University, Chengdu, China)
Xuedong Liang (Business School, Sichuan University, Chengdu, China)

Kybernetes

ISSN: 0368-492X

Article publication date: 28 April 2023

215

Abstract

Purpose

For nanostores, striving to become the community group-buying leader is gaining prominence. This paper aims to construct Hotelling linear models to investigate whether nanostores should be registered as leaders and their decisions in a competitive environment.

Design/methodology/approach

This paper constructs three Hotelling linear models: neither nanostore registers as community leader, only one nanostore registers as community leader and both nanostores register as community leader. The competitive operation strategies of two general nanostores under three scenarios are solved.

Findings

The study finds that nanostores without a cost advantage may benefit from being the first leader. The nanostore's preferred decisions depend on the investment cost parameters of its own and competitors which may lead to market share competition. Furthermore, consumers' sensitivity to community group-buying service has a negative effect on nanostores' profit.

Originality/value

The study is one of the few to consider the competition between community leaders. Besides, the study considers that the utilities functions of consumers are concurrently impacted by the service decisions, along with the price in different nanostores. It can provide nanostores useful implications in the dynamic industry.

Keywords

Acknowledgements

The work was partially supported by research grants from MOE (Ministry of Education in China) Project of Humanities and Social Sciences (20XJC630003) and Fundamental Research Funds for the Central Universities (YJ201950).

Citation

Shu, L., Li, X. and Liang, X. (2023), "Optimal strategies for nanostores under competition in community group buying", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-09-2022-1220

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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