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Successfully scaled solutions need not be homogenous

Published online by Cambridge University Press:  09 July 2020

DILIP SOMAN  and
TANJIM HOSSAIN
DILIP SOMAN*
Affiliation:
Canada Research Chair in Behavioural Science and Economics at the Rotman School of Management, University of Toronto, Toronto, Canada
TANJIM HOSSAIN
Affiliation:
Professor of Marketing in the Department of Management at the University of Toronto Mississauga, Mississauga, Canada Rotman School of Management, University of Toronto, Toronto, Canada
*
*Correspondence to Rotman School of Management, University of Toronto, 105 St. George St., Toronto, ONM5S 3E6, Canada. E-mail: dilip.soman@rotman.utoronto.ca
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Abstract

Al-Ubaydli et al. point out that many research findings experience a reduction in magnitude of treatment effects when scaled, and they make a number of proposals to improve the scalability of pilot project findings. While we agree that scalability is important for policy relevance, we argue that non-scalability does not always render a research finding useless in practice. Three practices ensuring (1) that the intervention is appropriate for the context; (2) that heterogeneity in treatment effects are understood; and (3) that the temptation to try multiple interventions simultaneously is avoided can allow us to customize successful policy prescriptions to specific real-world settings.

Type
Articles
Information
Behavioural Public Policy , Volume 5 , Special Issue 1: Field Experiments and Public Policy , January 2021 , pp. 80 - 89
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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