Design for the pricing strategy of return-freight insurance based on online product reviews

Geng, Shidao; Li, Wenli; Qu, Xiaofei; Chen, Lirong

doi:10.1016/j.elerap.2017.05.001

Cited by 39 publications

(13 citation statements)

References 37 publications

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“…Lantz and Hjort [16] study the impact of free delivery and free returns on e-customers' purchasing choices. Geng et al [10] calculate the optimal insurance price and return-freight insurance compensation for insurance companies. Chen and Chen [6] suggest how a retailer should define their return policies for both online and offline channels.…”

Section: Literature Reviewmentioning

confidence: 99%

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“Product + logistics” bundling sale and co-delivery in cross-border e-commerce

Niu

Wang

Lee

et al. 2019

Electron Commer Res

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When customers purchase products via cross-border e-commerce, they care about both the product quality and the logistics service quality. Actually, retailers are selling "product + logistics" to customers, although their contracted logistics service provider (LSP) might not be preferred by customers. In practice, it is observed that a retailer selling high-quality products tends to contract with multiple LSPs to ensure higher customer volumes and the overall high quality of "product + logistics". However, interestingly, we find that the LSP's profits might be negatively affected by serving two competing retailers, and preferences of the LSP and the retailer selling high-quality products through logistical cooperation result in two "prisoner's dilemma" regions. We also identify the size of the system's profit pie and the allocation rules among the competing LSPs and retailers. We show that it is possible to observe competing retailers' co-delivery, which benefits both the LSP and the retailer selling high-quality products.

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Section: Literature Reviewmentioning

confidence: 99%

“…Bloomberg reports that Walmart's sales can reach more than 90% of China's customers through collaboration. 10 The above illustrations epitomize two strategies in practice. If a retailer selling high-quality products contracts with two competing LSPs, we refer to it as the retailer's dual-LSP strategy (DS).…”

Section: Introductionmentioning

confidence: 99%

“Product + logistics” bundling sale and co-delivery in cross-border e-commerce

Niu

Wang

Lee

et al. 2019

Electron Commer Res

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show abstract

“…Pasternack () and Emmons and Gilbert (1998) studied manufacturers’ return strategies for retailers’ unsold products. Then some scholars investigated no‐reason‐full‐return policies (Davis et al., ; Ferguson et al., ; Shulman et al., ; Geng et al., ; Borenich et al., ; Guo et al., ). For instance, Cheng et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Pasternack (1985) and Emmons and Gilbert (1998) studied manufacturers' return strategies for retailers' unsold products. Then some scholars investigated no-reason-full-return policies (Davis et al, 1995;Ferguson et al, 2006;Shulman et al, 2011;Geng et al, 2017;Borenich et al, 2019;Guo et al, 2020). For instance, Cheng et al (2017) investigated firms' optimal ordering policies for deteriorating items within a no-reason return period; Choi and Guo (2018) studied the impact of consumer returns on responsive supply in fashion mass customization systems; Zhang et al (2018) explored online-only and omnichannel strategies of firms while considering consumer returns and order cancellation.…”

Section: Literature Reviewmentioning

confidence: 99%

Whether a retailer should enter an e‐commerce platform taking into account consumer returns

Cao

et al. 2020

Int Trans Operational Res

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Considering consumers are increasingly shopping online nowadays and the online sales market is dominated by e‐commerce giants, traditional retailers need to choose whether to enter e‐commerce platforms. Moreover, traditional retailers need to determine whether to offer offline return services considering online return services are very popular. To address these challenges, we explore a retailer's optimal offline return strategy and channel choice of whether or not to enter a platform in the contexts of symmetric information and asymmetric information, respectively. We present conditions for the retailer to share information. Interestingly, we find that the retailer in some conditions has no motivation to improve customer satisfaction rate of offline store. Most important, we find that the retailer's channel choice depends on the magnitude of the annual service fee that is affected by offline return strategy and asymmetric information, and the offline return strategy depends on the magnitude of the average residual value of returned products.

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“…Demand risk has been mitigated in several ways in the literature including efficient network design and information sharing amongst sales and marketing and operations (Sarangi and Srivatsan, 2009). Linear programming, procurement decision models and online product reviews have often been used in the extant studies to mitigate the demand/inventory risk and maximize the profit of electronics, food and return-freight insurance companies (Shu et al , 2017; Sodhi, 2005; Shu et al , 2017; Geng et al , 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Mitigating demand risk of durable goods in online retailing

Ghadge¹,

Bag²,

Goswami³

et al. 2020

IJRDM

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PurposeAn uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost.Design/methodology/approachFour e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer.FindingsMixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction.Research limitations/implicationsLimited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss.Practical implicationsThe analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner.Originality/valueThe study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information.

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Design for the pricing strategy of return-freight insurance based on online product reviews

Cited by 39 publications

References 37 publications

“Product + logistics” bundling sale and co-delivery in cross-border e-commerce

“Product + logistics” bundling sale and co-delivery in cross-border e-commerce

Whether a retailer should enter an e‐commerce platform taking into account consumer returns

Mitigating demand risk of durable goods in online retailing

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