Non-Monetary Leniency of Return Policies
Restricted (Penn State Only)
- Author:
- Chatterjee, Punya
- Graduate Program:
- Business Administration
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 25, 2022
- Committee Members:
- Nicholas Petruzzi, Co-Chair & Dissertation Advisor
J. Andrew Petersen, Outside Unit & Field Member
Jason Acimovic, Major Field Member
Aydin Alptekinoglu, Co-Chair & Dissertation Advisor
Brent Ambrose, Program Head/Chair - Keywords:
- Return Leniency
Strategic Consumer Behavior
Omnichannel Retailer
Consumer Learning
Stackelberg Game - Abstract:
- Due to the ongoing and dramatic growth in the volume of consumer returns, retailers continue to struggle with the trade‐off in returns service strategies between implementing stricter return policies to lower operational costs and lenient return policies to positively stimulate consumers' patronage purchase intentions. This has evidently driven operations management, economics, and marketing scholars to recognize and study consumer returns as an important managerial lever in a retail environment. However, the preponderance of this academic research has focused on monetary leniency of return policies, whereas, in practice majority of the retailers abstain from offering anything but full refund and started to manipulate non monetary leniency of return policies. In this light, the work contained in this dissertation is intended to explore two potential non-monetary levers of the return policies: effort and time leniency. In the first chapter, we investigate the pricing and store return policy of an omnichannel retailer selling n substitutable products to consumers who are uncertain about their valuation of the products. To reflect the capacity constraint of a physical store, we assume that all n products are available online, but only m (<= n) of them are carried in the physical store. Consumers search through all or a subset of the products, one at a time, by either visiting the store or shopping online, and (possibly) returning the mismatched product(s). We require prices to be identical across channels, and endogenize consumers' search and return decisions. By capturing the consumers’ store exchange behavior, our analysis helps explain why majority of the omnichannel retailers allow store returns of online purchases even when stores are not easily accessible. Additionally, by modeling the hassle of handling store returns of online purchases that are not otherwise carried in the store, we are able to explain why a flexible store return policy can be appropriate for some products and not for others. We also study the effect of store return policies on the store assortment structure by considering products with different popularity levels. Interestingly, we find that, compared to a no-store-return policy, a flexible store return policy, when optimal, can induce a different store assortment. Thus, omnichannel retailers should carefully consider their store assortment when offering a flexible store return policy in order to fully extract its benefits. In the second chapter, we study a profit-maximizing retailer's optimal return policy when selling an experience good to a market of heterogeneously informed consumers. The retailer offers either a return time window at full price (and full refund in the event of a return) or no returns at a price discount or both. We focus on the effect of learning through consumption in the consumers' return behavior and the attendant return policy decisions by the retailer. We find that salvage value of returns is a crucial factor in whether the retailer’s optimal return policy would employ return windows and if it offers consumers a choice between a return window at full price and no-return at a discounted price.