Eugenio Felipe Merlano, Regina Frei, Danni Zhang, Ekaterina Murzacheva and Steve Wood
The expansion of online shopping aligned with challenging economic conditions has contributed to increasing fraudulent retail product returns. Retailers employ numerous…
Abstract
Purpose
The expansion of online shopping aligned with challenging economic conditions has contributed to increasing fraudulent retail product returns. Retailers employ numerous interventions typically determined by embedded perspectives within the company (supply side) rather than consumer-based assessments of their effectiveness (demand side). This study aims to understand how customers evaluate counter-fraud measures on opportunistic returns fraud in the UK. Based on the fraud triangle and the theory of planned behaviour, we develop an empirically informed framework to assist retail practice.
Design/methodology/approach
We collected 485 valid survey responses about consumer attitudes regarding which interventions are effective against different types of returns fraud. First, a principal component section evaluates the policies' effectiveness to identify any policy grouping that could help prioritise specific sets of policies. Second, cluster analysis follows a two-stage approach, where cluster size is determined, and then survey respondents are partitioned into subgroups based on how similar their beliefs are regarding the effectiveness of anti-fraud policies.
Findings
We identify policies relating to perceived effectiveness of interventions and create customer profiles to assist retailers in conceptualising potential opportunistic fraudsters. Our product returns fraud framework adopts a consumer perspective to capture the perceived behavioural control of potential fraudsters. Results suggest effectiveness of different types of interventions vary between different types of consumers, which leads to the development of propositions to combat the fraud.
Originality/value
This study is unique in assessing the perceived effectiveness of a range of interventions based on data collection and advanced analytics to combat fraudulent product returns in omnichannel retail.