디지털 기부 플랫폼에서의 감정 이미지 역효과:인간ㆍ동물 대상에 따른 감정의 차별적 효과 분석

This study examines donation behavior on digital platforms by analyzing how text- and image-based cues influence the number of donors in 1,040 campaigns on Naver HappyBean, using the Elaboration Likelihood Model (ELM) as the analytical framework. Text length was significantly associated with an increase in donor counts, confirming the role of informational cues. In contrast, visual cues such as colorfulness showed partial effects in the overall model but did not exhibit consistent influence when human and animal campaigns were analyzed separately. Emotional imagery was generally associated with a decrease in donor counts across both negative and positive emotions, with this effect particularly pronounced in animal-targeted campaigns. These findings indicate that emotional cues may be interpreted differently depending on the target type, reflecting perceived psychological and moral distance between humans and animals. By applying AI-based emotion analysis to online donation images, this study provides a refined understanding of visual persuasion mechanisms and empirically demonstrates differentiated response patterns between human- and animal-focused campaigns, offering theoretical and practical implications for designing effective donation imagery.