JOURNAL ARTICLE

Can you change my generosity towards future others? The impact of observability on intertemporal pro‐social decisions.

  • Published In: Journal of Behavioral Decision Making, 2024, v. 37, n. 1. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Hong, Mei; Liang, Dapeng; Lu, Teng 3 of 3

Abstract

Research has demonstrated that delays in realizing pro‐social decisions significantly influence pro‐social choices. However, the impact of time delay may vary by context. A key contextual factor is decision observability (i.e., the visibility of one's decision to others). Using a dictator game task with delayed rewards, the current study examined the effects of observability on intertemporal pro‐social choices. Subjects were randomly assigned to either an Anonymous group, where payment was private, or an Observable group, involving public payment. They had to decide between the selfish option (which only benefited the subject) and the generous option (sharing money with another person in a specific delay condition). Our data revealed that subjects were less eager to forgo money when time delay increased and showed more selfishness toward specific people, independent of decision observability. This pattern was aligned with a hyperbolic discounting model. Notably, observability mitigated the impact of time delay; subjects were more inclined to donate to temporally distant individuals when their decisions were observable instead of anonymous. In addition, we discuss the practical implications of observability for designing intertemporal donation appeals. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Behavioral Decision Making. 2024/01, Vol. 37, Issue 1, p1
  • Document Type:Article
  • Subject Area:Literature and Writing
  • Publication Date:2024
  • ISSN:0894-3257
  • DOI:10.1002/bdm.2365
  • Accession Number:175057334
  • Copyright Statement:Copyright of Journal of Behavioral Decision Making 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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