Counteracting Strategic Purchase Deferrals: The Impact of Online Retailers' Return Policy Decisions

Altuğ, Mehmet; Aydinliyim, Tolga

doi:10.2139/ssrn.2498107

Cited by 10 publications

(17 citation statements)

References 43 publications

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“…As an example, more research is needed to understand under what conditions a full versus partial refund is optimal. For instance, Altug and Aydinliyim () attempt to demonstrate optimality of a full‐refund equilibrium by revising several key modeling assumptions as discussed in Section 4.2.2. Yet, many studies conclude that partial‐refund policies are optimal, as full refunds are overly generous and can subject a retailer to costly opportunistic behavior.…”

Section: Discussionmentioning

confidence: 99%

“…Consider the relationship between competition and return policy leniency. Shulman et al (), Ofek et al (), and Altug and Aydinliyim () suggest that competition may result in lower policy leniency, whereas Chen and Chen () and Chen et al () suggest otherwise. Alternatively, Shang, Pekgün, et al (), using eBay data, do not find a relationship between competitive intensity and MBG adoption.…”

Section: Discussionmentioning

confidence: 99%

“…Also, a special case of this approach is to assume

F (()|, v, p) = v / \overset{v}{true}

, meaning that valuation follows a general uniform distribution (i.e.,

V \sim U ([], 0, \overset{v}{true})

) or a standard uniform distribution in the case of

\overset{v}{true} = 1

. Su (), Akçay, Boyac, and Zhang (), Huang, Gu, Ching, and Siu (), Altug and Aydinliyim (), and Chen and Chen () use a general distribution function for valuation, whereas Davis, Hagerty, and Gerstner () make the standard uniform distribution assumption to derive key insights.…”

Section: Analytical Researchmentioning

confidence: 99%

“…Overall, the majority of the articles suggest that a partial‐refund policy is optimal for retailers, though this seemingly robust finding is not universal in the literature. For example, Altug and Aydinliyim () discuss several conditions that may result in a full‐refund equilibrium. Moreover, many mainstream retailers continue to offer full‐refund policies.…”

Section: Analytical Researchmentioning

confidence: 99%

“…Numerous studies show that a product's salvage value is a critical determinant of a retailer's optimal return policy choices. In particular, these studies find that retailers with more efficient salvaging options can offer more lenient policies (Altug & Aydinliyim, ; Davis et al, ; Su, ).…”

Section: Analytical Researchmentioning

confidence: 99%

See 4 more Smart Citations

Taking stock of consumer returns: A review and classification of the literature

Abdulla

Ketzenberg

Abbey

2019

J of Ops Management

111

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Coincident with the rapid growth of consumer returns and their corresponding importance in the retail marketplace, academic interest in the area of consumer return policy design has significantly increased. In fact, the growth in academic publications has been tremendous, with almost half of the published works appearing within the past 6 years. The influx of new and evolving research spans across multiple disciplines and various methodologies. To provide clarity for the continued evolution of the field, we provide a comprehensive review and classification of the literature predicated on a holistic conceptual framework. The scope of the review includes all peer reviewed journal articles published prior to the end of 2018, along with any working papers cited therefrom, that specifically address (a) managerial decision‐making related to return policies or (b) consumer behavior in response to such decision‐making. Examining the state of the literature and practice on return policy design through the lens of a unified conceptual framework—a framework that spans both analytical and empirical research—reveals numerous managerial and theoretical opportunities for future research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Also, a special case of this approach is to assume

F (()|, v, p) = v / \overset{v}{true}

, meaning that valuation follows a general uniform distribution (i.e.,

V \sim U ([], 0, \overset{v}{true})

) or a standard uniform distribution in the case of

\overset{v}{true} = 1

Section: Analytical Researchmentioning

confidence: 99%

Section: Analytical Researchmentioning

confidence: 99%

Section: Analytical Researchmentioning

confidence: 99%

See 3 more Smart Citations

Taking stock of consumer returns: A review and classification of the literature

Abdulla

Ketzenberg

Abbey

2019

J of Ops Management

111

View full text Add to dashboard Cite

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Equilibrium strategies and optimal pricing in an online retailing queueing system

Wang

Yang

2020

Naval Research Logistics

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This article considers an M/M/1 online retailing queueing model with strategic customers and a monopoly retailer. Customers arrive according to a Poisson process, and upon arrival, they decide whether to purchase the product based on a reward-cost structure. The valuation of the product is determined by its matching probability with the demand. After receiving the purchased product, each customer enters a "virtual" orbit to update the information about the product and then makes her decision on whether to replace or return the product based on the product's assessed value. Accordingly, a two-dimensional decision among customers is formulated and we investigate the equilibrium customer strategies and the optimal pricing strategy for the retailer. Interestingly, in equilibrium, we find that the system's throughput can be nonmonotone in the product's matching probability. In addition, under the equilibrium strategy of customers, we reveal the fact that the retailer's optimal price is not necessarily increasing in the matching probability. From the perspective of the social planner, the returning behavior can worsen the social welfare, and therefore those unsatisfied customers are encouraged to replace the product rather than to return it. Finally, some numerical experiments are conducted to verify our results and to examine the sensitivity of several key performance measures of the system.

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Impact of artificial intelligence adoption on online returns policies

Yang

Tan

2020

Ann Oper Res

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The shift to e-commerce has led to an astonishing increase in online sales for retailers. However, the number of returns made on online purchases is also increasing and have a profound impact on retailers’ operations and profit. Hence, retailers need to balance between minimizing and allowing product returns. This study examines an offline showroom versus an artificial intelligence (AI) online virtual-reality webroom and how the settings affect customers’ purchase and retailers’ return decisions. A case study is used to illustrate the AI application. Our results show that adopting artificial intelligence helps sellers to make better returns policies, maximize reselling returns, and reduce the risks of leftovers and shortages. Our findings unlock the potential of artificial intelligence applications in retail operations and should interest practitioners and researchers in online retailing, especially those concerned with online returns policies and the consumer personalized service experience.

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Counteracting Strategic Purchase Deferrals: The Impact of Online Retailers' Return Policy Decisions

Cited by 10 publications

References 43 publications

Taking stock of consumer returns: A review and classification of the literature

Taking stock of consumer returns: A review and classification of the literature

Equilibrium strategies and optimal pricing in an online retailing queueing system

Impact of artificial intelligence adoption on online returns policies

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