The mobile giving gap: The negative impact of smartphones on donation behavior.

  • Published In: Journal of Consumer Psychology (John Wiley & Sons, Inc. ), 2025, v. 35, n. 2. P. 281 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hock, Stefan J.; Ferguson, Kristen A.; Herd, Kelly B. 3 of 3

Abstract

While charities typically use the same messaging when appealing to consumers on their smartphones and PCs, this approach may backfire. Across three studies, we find consumers are less likely to donate on their smartphones (vs. PCs), a phenomenon we call the mobile giving gap. In study 1, we demonstrate that consumers are less willing to donate real money to a charitable organization. In study 2, we provide process support and demonstrate that the focal effect is mediated by other‐focus. Finally, a field experiment using Google display ads (study 3) replicates the focal effect and demonstrates that the negative impact of smartphones is attenuated when the appeal explicitly focuses on others (vs. the self). This study not only provides additional process support, but also suggests an easily implementable strategy that charities can use to close the mobile giving gap. Taken together, our findings offer theoretical insights related to the mobile mindset and its impact on consumer behavior and highlight that charities should tailor their donation appeals based on device type. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Consumer Psychology (John Wiley & Sons, Inc. ). 2025/04, Vol. 35, Issue 2, p281
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2025
  • ISSN:1057-7408
  • DOI:10.1002/jcpy.1418
  • Accession Number:184199274
  • Copyright Statement:Copyright of Journal of Consumer Psychology (John Wiley & Sons, Inc. ) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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