Wang, Yefeng, Zhou, Li ORCID: https://orcid.org/0000-0001-7132-5935 and Wu, Chuanliang (2023) Optimal trade-in strategy for advance selling with strategic consumers proportion. PLoS ONE, 18 (1):e0273124. pp. 1-18. ISSN 1932-6203 (Online) (doi:10.1371/journal.pone.0273124)

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Abstract

Purpose
This study aimed to optimize the trade-in pricing strategy. To leverage market share, many sellers adopt trade-in strategy for advance selling, Customers can return their old products at a discount price when they buy new products. This can help increase the market share and decrease natural resource consumption.
Design/Methodology/Approach
We consider a seller who sells new-generation products over two periods: advance selling and regular selling. Based on the rational expectation equilibrium, we adopt dynamic programming to construct a two-period pricing model with three different trade-in strategies–only in period 2, in both periods, and not at all–explaining the trade-in strategy as a promotion tool used by a monopolist to discriminate for advance selling between new and old customers.
Findings
The results suggest that the optimal price is determined by the proportion of old customers, discount factor and product innovation level. Whether and when to give a trade-in rebate to old customers depends on these parameters. The seller’s choice of optimal trade-in strategy depends on the threshold value of the new customer demand and trade-in demand.
Originality/Value
Most existing literature focuses on advance selling strategies and trade-in strategies. To the best of our knowledge, this is a pioneering study that adopts trade-in as part of the advance selling strategy.

Item Type:	Article
Uncontrolled Keywords:	trade in; strategic consumers; pricing strategy; advance selling
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management H Social Sciences > HF Commerce
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of Systems Management & Strategy Faculty of Business > Networks and Urban Systems Centre (NUSC) Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group Greenwich Business School > Networks and Urban Systems Centre (NUSC) Greenwich Business School > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group (CCRG)
Last Modified:	02 Dec 2024 15:59
URI:	http://gala.gre.ac.uk/id/eprint/38479

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