Project overview
Covid-19 has significantly aggravated the problem of high product returns rates, which have been increasing over the last few years. This is a significant challenge for retailers and society, causing economic, social and ecological harm. Returns lead to added complex processing, transportation and wasted resources, as many products cannot be resold and risk going to landfill.
Online shopping thrived during the lockdown, and many retailers extended their returns periods. A surge of product returns arrived when non-essential retailers reopened. Problems that are costly in normal periods (e.g. wardrobing, fraudulent refunds, serial returners) have become worse in this pandemic period. With recent research showing that many customers will retain their new online shopping habits, the problems will stay, too.
We conducted two consumer surveys, interviewed 9 retailers and 5 retail experts to improve our understanding of consumer behaviours in a pandemic. We then modelled this at micro and macro levels using explainable artificial intelligence (AI) techniques to forecast returns and fraud rates in a world dominated by Covid-19 conditions. To mitigate this, we developped a set of measures, indicating the expected effectiveness and environmental impact.
The ultimate goals are to help retailers operate efficiently and thrive in this challenging time, avoiding the need to cut jobs and thereby increasing the financial burden on welfare. This project will uniquely combine behavioural research with the development of explainable AI that retailers can use to mitigate the economic and ecological effects of product returns.
Online shopping thrived during the lockdown, and many retailers extended their returns periods. A surge of product returns arrived when non-essential retailers reopened. Problems that are costly in normal periods (e.g. wardrobing, fraudulent refunds, serial returners) have become worse in this pandemic period. With recent research showing that many customers will retain their new online shopping habits, the problems will stay, too.
We conducted two consumer surveys, interviewed 9 retailers and 5 retail experts to improve our understanding of consumer behaviours in a pandemic. We then modelled this at micro and macro levels using explainable artificial intelligence (AI) techniques to forecast returns and fraud rates in a world dominated by Covid-19 conditions. To mitigate this, we developped a set of measures, indicating the expected effectiveness and environmental impact.
The ultimate goals are to help retailers operate efficiently and thrive in this challenging time, avoiding the need to cut jobs and thereby increasing the financial burden on welfare. This project will uniquely combine behavioural research with the development of explainable AI that retailers can use to mitigate the economic and ecological effects of product returns.
Staff
Other researchers
Collaborating research institutes, centres and groups
Research outputs
Danni Zhang, Regina Frei, Gary Wills, Enrico Gerding, Steffen Bayer & PK Senyo,
2023, Business Strategy and the Environment, 32(7), 4636-4661
DOI: 10.1002/bse.3385
Type: article
Danni Zhang, Regina Frei, P.K. Senyo, Steffen Bayer, Enrico Gerding, Gary Wills & Adrian Beck,
2022, Journal of Retailing and Consumer Services, 70
Type: article