Persuasive Backfiring: When Behavior Change Interventions Trigger Unintended Negative Outcomes

Abstract

Scholars and research teams focus their efforts on studying ways to improve the lives of individuals, which often brings tangible social benefits. However, there is scarce scientific knowledge available on negative outcomes of behavior change interventions, and possibly even fewer that report a special type of negative outcome, called a backfire. In this paper, we start a wider scientific discussion on intervention backfiring. We introduce a framework to help facilitate the debate of this topic. We provide tools to aid academics in the study of this realm and support practitioners to remain mindful of the potential risks when designing behavior change interventions. We describe taxonomy of persuasive backfiring and propose tools in the form of intention-outcome and likelihood-severity matrices to outline a roadmap for further research and application. We open transparent discussion on backfiring, with an attitude of looking out and coming up with strategies to reduce them whenever identified.

Nudging with Care: The Risks and Benefits of Social Information

“…Another reason why empirical information may backfire is when the messenger is not trusted. Stibe and Cugelman (2016) point to credibility and the suspicion of hidden intentions as reasons why the message may not be effective, and the extensive literature on the failure of legal interventions may be useful in understanding why issues of credibility and trust mar otherwise positive messages (Stuntz, 2000). Credibility is also particularly important in cases of pluralistic ignorance (Miller and McFarland, 1987) where a descriptive norm is misperceived.…”

A machine learning perspective on responsible gambling

“…There are many aspects of any new warning label which should first be empirically tested. Any new warning label should ideally be trialled in both the laboratory and in the field before being introduced into the population, to mitigate the risk of any behavioral backfiring (Stibe & Cugelman, 2016). For example, qualitative research suggests that some young drinkers use ABV% information to find cost-effective ways of maximizing their alcohol consumption (Jones & Gregory, 2009;Maynard et al, 2017).…”

“…We have used the examples of alcohol and electronic gambling machine warning labels to highlight an omission of product risk information for sports bets. However, any new warning label should be trialled experimentally and in field trials before being rolled out at a population level, to ensure consumer understanding and to mitigate the risk of any behavioral backfiring (Stibe & Cugelman, 2016). Any gambling warning label should also ideally leverage insights from the behavioral risk communication literature (Garcia-Retamero & Cokely, 2017), which current UK electronic gambling machine labels do not (Collins et al, 2014;Rowe et al, 2017).…”

Transforming Technology for Global Business Acceleration and Change Management

“…(1) Triangletriadic reciprocal determinism (Bandura, 2005), (2) Curveelaboration likelihood and behavioral modeling (Fogg, 2009), (3) Metricessential components for defining transformation (Stibe & Larson, 2016), (4) Circlessusceptibility to influence and change (Stibe & Larson, 2016), (5) Architecturekey layers of transforming technology design (Stibe & Larson, 2016), (6) Sociumfundamentals of socially influencing systems (Stibe & Cugelman, 2019), (7) Moderationtypology of computer-supported influence , (8) Ethicsdark patterns and persuasive backfiring (Stibe & Cugelman, 2016).…”