Nudges have been critiqued for being too blunt of a tool. For instance, a retirement savings default may be helpful for a group of employees on average, but subgroups, say under-savers or over-savers, might be helped or harmed by this one-size-fits-all approach. As such, there have been calls to develop a more personalized approach to nudging (see here in our collection: “Imagining the Next Decade of Behavioral Science”). This paper outlines two dimensions that behavioral scientists could consider when designing personalized nudges: choice personalization and delivery personalization. Think of choice personalization as “personalization within nudges”—the method of nudge has been set (say, a default) but is tailored to specific individuals (different default leves of retirement contributions, for those over-savers and under-savers). Think of delivery personalization as “personalization as across nudges”—understanding the most effective method to nudge a certain individual. Personalizing nudges does come with data privacy and legal concerns, but these can be overcome, the paper argues.
Behaviour change 101 series: Five steps to select the right behaviour/s to target - BehaviourWorks Australia
At BehaviourWorks, we often prioritise behaviours using the Impact-Likelihood Matrix (figure below). In this approach, behaviours are prioritised by mapping them based on: The impact they have on the problem they are intended to address. The likelihood of the target audience adopting the behaviour.
Nudge Me Right: Personalizing Online Nudges to People's Decision-Making Styles by Eyal Peer, Serge Egelman, Marian Harbach, Nathan Malkin, Arunesh Mathur, Alisa Frik :: SSRN
NEW URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3324907