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NUDGE; DON’T JUDGE: USING NUDGE THEORY TO DETER SHOPLIFTERS

Authors: Dhruv Sharma;

NUDGE; DON’T JUDGE: USING NUDGE THEORY TO DETER SHOPLIFTERS

Abstract

Crimes defined as “acts attracting legal punishment” are injurious to the community because they violate moral rules (Blackburn, 1993). However, not all crimes are deemed worthy of a custodial sentence. For example, the criminal act of shoplifting usually only results in jail time for repeated offences (Doughty, 2006). And research indicates that the threat of imprisonment may not be an effective deterrent for potential shoplifters (Gonnerman, 2004). The notion that shoplifting is detached from the victim (Wilkes, 1978, Ecenbarger, 1988) and common to all socioeconomic classes affords the perception that shoplifting is a “victimless crime” to many. In this paper we suggest an alternative approach to tackling the problem. We examine whether deterrents engaging ‘nudge theory’ (Thaler and Sunstein, 2008) can discourage shoplifting. We review ‘design against crime’ literature and compare case studies to explore a new approach to preventing crime, using nudge as a theoretical framework. Our paper discusses how ‘rationality’ may influence criminal behaviour; that individuals indulge a “moment” of rational thinking before acting and how contemporary ‘design against crime’ techniques manipulate this thought-process to deter criminal behaviour. We argue that ‘nudge theory’ provides an interesting antithesis. To design against shoplifting using the theory of “nudge” we assert that people make choices non-rationally and can be deterred from situational crimes by designing environments with different contextual cues (Bonell et al., 2011) that deter crime. We call upon the design research community to discuss; debate and design with nudge theory as a preventative approach to shoplifting.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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