Evidence map›Paper›PMID 36662816›Full record

ArticlePloS one2023

Optimal trade-in strategy for advance selling with strategic consumers proportion.

Yefeng Wang, Li Zhou, Chuanliang Wu

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yefeng WangCollege of International Economics & Trade, Ningbo University of Finance & Economics, Ningbo, Zhejiang, China.ORCID 0000-0002-9211-1247
Li ZhouUniversity of Greenwich, London, United Kingdom.
Chuanliang WuCollege of Business, Shanghai University of Finance and Economics, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis 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.

findingsThe 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.

Indexed as

CommerceNatural ResourcesCosts and Cost AnalysisKnowledge

Identifiers

PMID36662816
PMCPMC9858020

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.