Issue |
RAIRO-Oper. Res.
Volume 56, Number 1, January-February 2022
|
|
---|---|---|
Page(s) | 101 - 114 | |
DOI | https://doi.org/10.1051/ro/2021178 | |
Published online | 07 February 2022 |
Fashion retail competition on product greenness with overconfidence
1
School of Management, Hefei University of Technology, Hefei 230009, P.R. China
2
Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, P.R. China
* Corresponding author: chengbayi@hotmail.com
Received:
17
June
2021
Accepted:
29
November
2021
In this paper, we study the impacts of overconfidence in a competitive retailer setting of green fashion. We model a green fashion supply chain comprising one unbiased manufacturer and two biased retailers, to explore how overconfidence affects greenness level of fashion products and expected profit of retailers. An overconfident retailer has a cognitive bias in which it believes consumers are more sensitive to greenness of fashion products than it really is. Our findings show that the competition between two retailers discourages greenness level of fashion products, while overconfidence can provide a counterbalance to the negative impact caused by competition. We also find, a retailer’s overconfidence is not only conducive to the greenness level of its own fashion products, but also can benefit to its rival. Moreover, it shows a low level of overconfidence can be a comparative advantage of the retailer’s profit. Even though one of the retailers is unbiased and has an advantage of information, it can still earn less than its overconfident rival.
Mathematics Subject Classification: 00A06
Key words: Overconfidence / retail competition / green fashion
© The authors. Published by EDP Sciences, ROADEF, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.