스토리텔링형 기부 독려 메시지의 수혜자 표현 방식에 따른 효과: 기만성 인식의 매개 효과를 중심으로

초록

Objectives: Traditional fundraising approaches prominently featuring real beneficiaries have raised ethical concerns, such as privacy invasion. Consequently, the use of actors and AI-generated models has expanded as alternatives. However, the effects of these alternative approaches have not been sufficiently examined. This study aims to investigate the effects of beneficiary representation in storytelling fundraising messages on perceived deception, attitude toward the message and the organization, and donation intention, while also examining differences by emotional appeal type. Methods: An online experiment was conducted with a sample of 300 Korean adults aged 20–69. A factorial design was used, incorporating three beneficiary representations (real beneficiary vs. actor vs. AI-generated model) and two emotional appeals (positive vs. negative). Participants were randomly assigned to one of the six experimental conditions and subsequently evaluated perceived deception, attitude toward the message, attitude toward the organization (i.e., authenticity, expertise, and trustworthiness), and donation intention. Results: The results indicated that messages using an actor or an AI-generated model elicited higher levels of perceived deception than those using a real beneficiary. The indirect effects of beneficiary representations on attitudes toward the message and the organization, mediated by perceived deception, were significant only under negative emotional appeal conditions. Conclusions: This study is significant because it empirically examined the effects of using actors and AI-generated models as alternatives to address ethical concerns associated with real beneficiaries in storytelling fundraising messages. The findings indicate that perceived deception varies depending on the type of beneficiary representation. They also suggest that when the use of actors or AI-generated models is unavoidable, employing positive emotional appeals rather than negative ones is recommended.

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