Felipe Merlano

The transition to more sustainable and circular business models (CMBs) is complex, and small and medium-sized enterprises (SMEs) often struggle with the intricacies and additional costs of achieving sustainability. To address these challenges, we developed an interactive web application – the Circularity Radar – in the open-source language for statistical computing and data visualisation R. We used the Shiny package, which provides desirable features for businesses such as a choice between local or online deployment that preserves data privacy, an interactive interface that allows users to specify the conditions under which computations are executed, and dynamic visualisations rendered through input variables. The Circularity Radar can provide low-cost insights to guide SMEs and practitioners in retail and manufacturing industries in exploring the drivers of their sustainability transition, assessing their situations, and understanding the obstacles they must overcome. Our tool incorporates a digital companion framework that involves seven elements of circularity: sustainable materials, sustainable operations, eco-design, product stewardship, R-terms, strategic organisational positioning, and social aspects. Additionally, the Circularity Radar presents alternative instruments and metrics businesses can use to quantitatively and qualitatively assess their progress regarding CBM implementation.

Purpose: The expansion of online shopping aligned with challenging economic conditions have 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 managerial implications 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.