Paper Number
1356
Paper Type
Completed
Description
Reimbursement of repair costs is a way to motivate customers to keep defective products instead of returning them. However, there is no research-based guidance on how retailers should frame repair costs reimbursement offers in terms of who decides on the size of the reimbursement and makes the offer—an employee or a machine. To guide further IS research and suggest ways that help e-commerce businesses to improve repair costs reimbursement effectiveness to decrease product return rates, the present research draws on literature on offer sources and on insights from a qualitative and an experimental study. We find that artificial intelligence-based (vs. human-based) repair costs reimbursement offers promote fairness perceptions, which, in turn, affect important customer outcomes—the likelihood to accept the offer and digital negative word of mouth. The results can guide e-commerce businesses’ returns-prevention efforts and IS research.
Recommended Citation
Walsh, Gianfranco and Schaarschmidt, Mario, "Taking Advantage of Algorithmic Preference to Reduce Product Returns in E-Commerce" (2023). ICIS 2023 Proceedings. 3.
https://aisel.aisnet.org/icis2023/emobilecomm/emobilecomm/3
Taking Advantage of Algorithmic Preference to Reduce Product Returns in E-Commerce
Reimbursement of repair costs is a way to motivate customers to keep defective products instead of returning them. However, there is no research-based guidance on how retailers should frame repair costs reimbursement offers in terms of who decides on the size of the reimbursement and makes the offer—an employee or a machine. To guide further IS research and suggest ways that help e-commerce businesses to improve repair costs reimbursement effectiveness to decrease product return rates, the present research draws on literature on offer sources and on insights from a qualitative and an experimental study. We find that artificial intelligence-based (vs. human-based) repair costs reimbursement offers promote fairness perceptions, which, in turn, affect important customer outcomes—the likelihood to accept the offer and digital negative word of mouth. The results can guide e-commerce businesses’ returns-prevention efforts and IS research.
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Comments
22-Digital