Forecasted vs. Actual Generosity in Image-Concern Interventions

Prior research and lay intuition suggest that amplifying image concerns promotes generosity. We test the impact of image-based interventions in consumer elective pricing (CEP), where individuals choose how much to pay for products or services. Across 10 studies (nine field experiments and one laboratory study, n = 3,182), we examine the effects of these interventions on payments and elicit forecasts from independent participants (n = 1,636). Forecasters predict large positive effects, yet behavioral data reveal small and inconsistent impacts. What drives the inconsistency between predictions and actual outcomes? Studies with more than 4,000 forecasters show that neither simulating the decision-making experience nor prior experience with CEP improve prediction accuracy. However, avoiding direct comparisons between experimental conditions—which can artificially amplify perceived differences—reduces overestimation. These findings highlight challenges associated with transferring behavioral insights across contexts and show how forecast elicitation methods shape expectations of intervention effectiveness. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00149 .

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Journal
Management Science
Published
2025-12-30
DOI
https://doi.org/10.1287/mnsc.2023.00149
Primary Topic
Decision-Making and Behavioral Economics
Type
article
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article

Forecasted vs. Actual Generosity in Image-Concern Interventions

Leif D. Nelson, Ayelet Gneezy, Minah Jung, Silvia Saccardo
Management Science
Decision-Making and Behavioral Economics
article

Forecasted vs. Actual Generosity in Image-Concern Interventions

Leif D. Nelson, Ayelet Gneezy, Minah Jung, Silvia Saccardo
article en

Abstract

Prior research and lay intuition suggest that amplifying image concerns promotes generosity. We test the impact of image-based interventions in consumer elective pricing (CEP), where individuals choose how much to pay for products or services. Across 10 studies (nine field experiments and one laboratory study, n = 3,182), we examine the effects of these interventions on payments and elicit forecasts from independent participants (n = 1,636). Forecasters predict large positive effects, yet behavioral data reveal small and inconsistent impacts. What drives the inconsistency between predictions and actual outcomes? Studies with more than 4,000 forecasters show that neither simulating the decision-making experience nor prior experience with CEP improve prediction accuracy. However, avoiding direct comparisons between experimental conditions—which can artificially amplify perceived differences—reduces overestimation. These findings highlight challenges associated with transferring behavioral insights across contexts and show how forecast elicitation methods shape expectations of intervention effectiveness. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00149 .

Management Science
University of California San Diego (US), New York University (US), Carnegie Mellon University (US), University of California, Berkeley (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 26%
Decision-Making and Behavioral Economics
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Forecasted vs. Actual Generosity in Image-Concern Interventions — Leif D. Nelson, Ayelet Gneezy, et al. · Management Science (2025) | TGRS Research Map | TGRS