Image Concerns and Generosity : Field Evidence and Predictions
Prior theoretical and empirical work suggests that behavioral interventions designed to amplify image concerns promote generosity. Consumer elective pricing — where individuals choose how much to pay for products and services — provides a unique opportunity for evaluating the effectiveness of different interventions in the field. We report data from nine field and one lab experiments (N= 3,192) conducted in nonprofit and for-profit settings, where we test how different image concern manipulations that have been previously shown to influence prosocial behavior affect payments. For each of the 10 experiments, we report corresponding forecasts generated by a separate set of participants (N = 1,592) about how the different manipulations influence generosity. In line with the findings from prior literature, lay individuals presented expect large effects under image manipulations. Yet, in contrast with these predictions and with past literature, we find small or no effects of such manipulations. We discuss implications for policymakers and researchers, who may rely on prior findings to make predictions about the effect of behavioral interventions
Year of publication: |
2023
|
---|---|
Authors: | Jung, Minah H. ; Saccardo, Silvia ; Gneezy, Ayelet ; Nelson, Leif D. |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Extent: | 1 Online-Ressource (73 p) |
---|---|
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 13, 2023 erstellt |
Other identifiers: | 10.2139/ssrn.4323906 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014263421
Saved in favorites
Similar items by person
-
Signaling virtue : charitable behavior under consumer elective pricing
Jung, Minah H., (2017)
-
Anchoring in payment : evaluating a judgmental heuristic in field experimental settings
Jung, Minah H., (2016)
-
The Impact of Joint versus Separate Prediction Mode on Forecasting Accuracy
Imas, Alex, (2022)
- More ...