Should Individuals Choose Their Own Incentives? Evidence from a Mindfulness Meditation Intervention

Traditionally, incentives to promote behavioral change are assigned rather than chosen. In this paper, we theoretically and empirically investigate the alternative approach of letting people choose their own incentives from a menu of increasingly challenging and rewarding options. When individuals are heterogeneous and have private information about their costs and benefits, we theoretically show that leaving them the choice of incentives can improve both adherence and welfare. We test the theoretical predictions in a field experiment based on daily meditation sessions. We randomly assign some participants to one of two incentive schemes and allow others to choose between the two schemes. As predicted, participants sort into schemes in (partial) agreement with the objectives of the policymaker. However, in contrast to our prediction, participants who could choose complete significantly fewer sessions than participants that were randomly assigned. Because the results are not driven by poor selection, we infer that letting people choose between incentive schemes may bring in psychological effects that discourage adherence. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. Funding: This work was supported by Zilveren Kruis Zorgverzekeringen N.V. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04157 .

Authors

Institutions

Publication Details

Journal
Management Science
Published
2024-11-28
DOI
https://doi.org/10.1287/mnsc.2021.04157
Citations
4
Primary Topic
Mindfulness and Compassion Interventions
Type
article
Field-Weighted Citation Impact
2.56
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Should Individuals Choose Their Own Incentives? Evidence from a Mindfulness Meditation Intervention

Giorgia Romagnoli, Jan Lindemans, Jonathan N. Cloughesy, Andrej Woerner et al.
4 citations
Management Science
Mindfulness and Compassion Interventions
2.56
article

Should Individuals Choose Their Own Incentives? Evidence from a Mindfulness Meditation Intervention

Giorgia Romagnoli, Jan Lindemans, Jonathan N. Cloughesy, Andrej Woerner, Birgit M. Probst, Nina Bartmann
article en
4 citations

Abstract

Traditionally, incentives to promote behavioral change are assigned rather than chosen. In this paper, we theoretically and empirically investigate the alternative approach of letting people choose their own incentives from a menu of increasingly challenging and rewarding options. When individuals are heterogeneous and have private information about their costs and benefits, we theoretically show that leaving them the choice of incentives can improve both adherence and welfare. We test the theoretical predictions in a field experiment based on daily meditation sessions. We randomly assign some participants to one of two incentive schemes and allow others to choose between the two schemes. As predicted, participants sort into schemes in (partial) agreement with the objectives of the policymaker. However, in contrast to our prediction, participants who could choose complete significantly fewer sessions than participants that were randomly assigned. Because the results are not driven by poor selection, we infer that letting people choose between incentive schemes may bring in psychological effects that discourage adherence. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. Funding: This work was supported by Zilveren Kruis Zorgverzekeringen N.V. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04157 .

Management ScienceVol. 71(8)
University of Southern California (US), Duke University (US), Amsterdam University of Applied Sciences (NL), Technical University of Munich (DE), Ludwig-Maximilians-Universität München (DE), University of Amsterdam (NL)
No poverty
Openalex Percentile: Top 10%
Mindfulness and Compassion Interventions
2.56
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.