Promised hassle time management in online retailing with fraudulent and regretful customers

Abstract

Fraudulent customer return behavior, called “wardrobing,” incurs significant product return costs for online retailers. However, customers can also behave regretfully in online retailing, causing high product return costs. Consequently, we explore the combined effect of fraudulent and regretful customers in product returns. Research suggests implementing restrictive return policies to curb huge return costs due to fraudulent and regretful customer behaviors. One of the ways to implement a restrictive policy is by introducing hassle time. Consequently, retailers commit promised hassle times to customers, during which the retailers can approve the product returns and issue refunds. As a result, we determine how an online retailer can respond to regretful and fraudulent customers through promised hassle time while considering the stochastic nature of hassle time. In some situations, an online retailer might know that hassle time follows a specific probability distribution; in others, a retailer might not know the distribution. Therefore, we determine the optimal promised hassle time an online retailer must promise to regretful and fraudulent customers in each situation. For the first situation, we determine that a specific ratio, which we call the customer ratio, determines how the optimal promised hassle time changes with customers' fraud and regret levels. If the customer ratio is one, the optimal promised hassle time does not change; if it is greater than one, the optimal promised hassle time increases; and if it is less than one, the optimal promised hassle time decreases, with an increase in fraud and a decrease in regret levels. For the second situation, we propose a novel decision-making tool for online retailers, enabling them to optimize the promised hassle time. Finally, we generalize the results of our study to order cancellations with regretful customers, and shared mobility services.