For most of us, our online retail shopping increased significantly during the pandemic. More than likely, our online shopping returns increased at an even greater rate.
And why not? Most retailers feel compelled to have an easy return policy in order to keep their customers happy.
But as with all things, that comes with a cost. Definitely, a cost that’s been felt by businesses. And almost certainly, a cost that’s being passed on to the customer in one way, shape, or form.
According to the National Retail Federation (NRF), e-commerce accounted for $565 billion of total U.S. retail sales in 2020 – roughly 14% of retail spending. However, it also reported that consumers returned about $102 billion of merchandise purchased online that year – almost 24% of the $428 billion in total retail merchandise returned.
Seen another way, that’s a quarter of their sales that needs to be return-shipped (often at seller’s/our cost), cleaned, repackaged, restocked, and resold.
NRF’s vice president of research development and industry analysis Mark Mathews says, “Retailers view the return process as an opportunity to further engage with customers, as it provides additional points of contact for retailers to enhance the overall consumer experience.”
But isn’t there a better solution?
Enter Yang Wang, Assistant Professor of Information Technology Management – whose research focuses on the intersection of marketing, business analytics, and information systems – and his colleagues Vandana Ramachandran and Olivia R. Liu Sheng from the University of Utah.
The researchers set out to look at whether consumers’ opinions on fit – as captured by online reviews – impacted online product returns.
They collected data from a large online specialty retailer of outdoor goods located in the western United States, focusing on apparel – a category that exhibits much higher rates of online return than all other categories.
The study examined two aspects of fit – fit valence, which, more or less, is a consumer’s assessment of whether the fit was true to size, ran small, or ran large – and fit reference, which allows the reviewer to reference their own body type when submitting a review. For example, a female consumer may indicate that a jacket that she purchased ran large, that she ordered a size 10, and she is 5’6” and weighs 120 pounds. That expanded detail on the buyer’s body type would provide an added dimension to the review for other potential buyers to consider.
The goal of the study, Wang says, was to examine the role of ordinal-fit opinions (fit valance) in product reviews on consumers’ purchase decisions and the conditions under which different elements of fit opinions (fit reference) could help reduce fit uncertainty and, ultimately, reduce product returns.
Since June 2013, the retailer allowed only fit valence (general sizing) evaluations. But in early 2015, they began to allow reviewers to supply fit reference information on their body sizes.
This offered the ideal opportunity for experimental-liked product comparison.
They found a sharp difference in product return rates after the 2015 change to the evaluation system but still had to ferret out the exact reasons for that based on such issues as general timing, comparability of products, and a host of other potentially contributing factors. In total, their study sample included 1,881 products – a control group of 1,511 products that had not received a review with fit reference both before and after the system update, and a treatment group of 370 products that received reviews with fit reference after the change.
One of the most interesting findings of the study was that – unlike a review of a product’s quality attributes, where positive opinions are clearly sought after – negative fit valences were deemed desirable, as well.
Why? “Because future customers can also learn how to choose the right size by interpreting negative fit valences (i.e., runs large or runs small), as long as they are aided by fit reference,” says Wang.
He notes that fit opinions from reviewers with similar body types benefit customers even more and suggests that the development of a “personalized recommender” based on body type could increase purchase satisfaction and further reduce returns.
Wang says that future studies in this stream of research could examine the impact of reviews on “fit uncertainty” as they apply to other product categories, such as apartment rental, food delivery, or even medical services.
The full study, “Do Fit Opinions Matter? The Impact of Fit Context on Online Product Returns,” was published in Information Systems Research, Vol. 32, No. 1, March 2021, pp. 268-289.
| Research@Lubar Faculty scholarship in the Lubar School of Business spans the business fields and beyond through both theoretical and applied research that is published in leading journals. Here are some of our faculty’s most recent publications: |
| Estimating the Impact of “Humanizing” Customer Service Chatbots Information Systems Research Authors: Scott Schanke, Gordon Burtsch, Gautam Ray |
| Together We Rise: How Social Movements Succeed Journal of Consumer Psychology Authors: Gia Nardini, Tracy Rank-Christman, Melissa G. Bublitz, Samantha Cross, Laura A. Peracchio |
| A Performance Analysis of Prediction Intervals for Count Time Series Journal of Forecasting Authors: Annika Homburg, Christian Weiß, Layth C. Alwan, Gabriel Frahm, and Rainer Göb |
| Click here to see more faculty research |