Security Journal

, Volume 31, Issue 2, pp 428–450 | Cite as

Understanding shoplifting of fast-moving consumer goods: an application of the CRAVED model

Original Article

Abstract

This study examines the variation in theft of shoplifted fast-moving consumer goods. Typically, shoplifting is estimated using shrinkage—a composite of several causes of lost retail merchandise. This study, however, benefits from access to a retailer’s database, in which extraordinary steps are taken to identify and record losses due to shoplifting only. This study is unique because of the more valid measure of shoplifting. A 1-year cross-sectional sample of 7468 products, sold in 204 U.S. chain supermarkets, was drawn from the retailer’s specialized database. Using Clarke’s (Hot products: understanding, anticipating, and reducing demand for stolen goods. Policing and Reducing Crime Unit, Paper 112, Home Office, London, 1999) CRAVED model of theft, products’ theft rates were correlated to the attributes consistent with the most vulnerable targets of theft. The results show that theft rates of products were significantly correlated to the measures for CRAVED. Regression analysis indicated that the measures for CRAVED were significant predictors of theft. Specifically, products were stolen more often when they were more Concealable, less Available, more Valuable, Enjoyable, and more Disposable. The most frequently stolen types of products were several types of cosmetics—primarily small but expensive products (e.g., eye, nail, lip products). Additionally, electronics, toys, and games had high theft rates. Implications for retailers, manufacturers, and governments are discussed. Suggestions for further research are also considered.

Keywords

Shoplifting Shop theft CRAVED Hot products Fast-moving consumer goods Loss prevention 

Notes

Acknowledgements

Many thanks are due to Ron Clarke for his considerable advice and guidance. The retailer is sincerely thanked for allowing access to their unique and valuable database. Robert Chase is thanked for helping to focus the paper early-on. The peer reviewers and editors are thanked for helping to improve the content of the paper.

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Copyright information

© Macmillan Publishers Ltd 2017

Authors and Affiliations

  1. 1.Department of Criminal JusticeUniversity of New HavenWest HavenUSA

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