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Mental Imagery, Impact, and Affect: A Mediation Model for Charitable Giving

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  • Stephan Dickert
  • Janet Kleber
  • Daniel Västfjäll
  • Paul Slovic

Abstract

One of the puzzling phenomena in philanthropy is that people can show strong compassion for identified individual victims but remain unmoved by catastrophes that affect large numbers of victims. Two prominent findings in research on charitable giving reflect this idiosyncrasy: The (1) identified victim and (2) victim number effects. The first of these suggests that identifying victims increases donations and the second refers to the finding that people’s willingness to donate often decreases as the number of victims increases. While these effects have been documented in the literature, their underlying psychological processes need further study. We propose a model in which identified victim and victim number effects operate through different cognitive and affective mechanisms. In two experiments we present empirical evidence for such a model and show that different affective motivations (donor-focused vs. victim-focused feelings) are related to the cognitive processes of impact judgments and mental imagery. Moreover, we argue that different mediation pathways exist for identifiability and victim number effects.

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  • Stephan Dickert & Janet Kleber & Daniel Västfjäll & Paul Slovic, 2016. "Mental Imagery, Impact, and Affect: A Mediation Model for Charitable Giving," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-15, February.
    • Handle: RePEc:plo:pone00:0148274
    DOI: 10.1371/journal.pone.0148274
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    References listed on IDEAS

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    4. Brañas-Garza, Pablo & Bucheli, Marisa & Espinosa, María Paz, 2020. "Altruism and information," Journal of Economic Psychology, Elsevier, vol. 81(C).
    5. Russell N. James, 2018. "Increasing charitable donation intentions with preliminary importance ratings," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 15(3), pages 393-411, September.
    6. Leily Farrokhvar & Azadeh Ansari & Behrooz Kamali, 2018. "Predictive models for charitable giving using machine learning techniques," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-14, October.
    7. Butts, Marcus M. & Lunt, Devin C. & Freling, Traci L. & Gabriel, Allison S., 2019. "Helping one or helping many? A theoretical integration and meta-analytic review of the compassion fade literature," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 16-33.
    8. Abhishek Bhati & Ruth K. Hansen, 2020. "A literature review of experimental studies in fundraising," Journal of Behavioral Public Administration, Center for Experimental and Behavioral Public Administration, vol. 3(1).
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