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Personalized nudging

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  • MILLS, STUART

Abstract

A criticism of behavioural nudges is that they lack precision, sometimes nudging people who – had their personal circumstances been known – would have benefitted from being nudged differently. This problem may be solved through a programme of personalized nudging. This paper proposes a two-component framework for personalization that suggests choice architects can personalize both the choices being nudged towards (choice personalization) and the method of nudging itself (delivery personalization). To do so, choice architects will require access to heterogeneous data. This paper argues that such data need not take the form of big data, but agrees with previous authors that the opportunities to personalize nudges increase as data become more accessible. Finally, this paper considers two challenges that a personalized nudging programme must consider, namely the risk personalization poses to the universality of laws, regulation and social experiences, and the data access challenges policy-makers may encounter.

Suggested Citation

  • Mills, Stuart, 2022. "Personalized nudging," Behavioural Public Policy, Cambridge University Press, vol. 6(1), pages 150-159, January.
    • Handle: RePEc:cup:bpubpo:v:6:y:2022:i:1:p:150-159_8
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    Cited by:

    1. Diane Pelly & Orla Doyle, 2022. "Nudging in the workplace: increasing participation in employee EDI wellness events," Working Papers 202208, Geary Institute, University College Dublin.
    2. Mills, Stuart, 2024. "Deceptive choice architecture and behavioral audits: a principles‐based approach," LSE Research Online Documents on Economics 122714, London School of Economics and Political Science, LSE Library.
    3. Mills, Stuart, 2022. "Finding the ‘nudge’ in hypernudge," Technology in Society, Elsevier, vol. 71(C).
    4. S. Mills & S. Costa & C. R. Sunstein, 2023. "AI, Behavioural Science, and Consumer Welfare," Journal of Consumer Policy, Springer, vol. 46(3), pages 387-400, September.
    5. von Zahn, Moritz & Bauer, Kevin & Mihale-Wilson, Cristina & Jagow, Johanna & Speicher, Max & Hinz, Oliver, 2022. "The smart green nudge: Reducing product returns through enriched digital footprints & causal machine learning," SAFE Working Paper Series 363, Leibniz Institute for Financial Research SAFE, revised 2022.
    6. Banerjee, Sanchayan & Picard, Julien, 2023. "Thinking through norms can make them more effective. Experimental evidence on reflective climate policies in the UK," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 106(C).

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