Visual totality of rewards-based crowdfunding pitch videos: disentangling the impact of peak negative affective visual expression on funding outcomes

• Main Authors: Huang, Yi, Uy, Marilyn A., Liu, Chang, Foo, Maw-Der, Li, Angelina Zhuyi
• Other Authors: Nanyang Business School
• Format: Article
• Language: English
• Published: 2023
• Subjects: Business::General; Visual Totality; Machine Learning
• Online Access: https://hdl.handle.net/10356/168946
• Institution: Nanyang Technological University

In this study, we introduce visual totality of a crowdfunding pitch video which considers not only visual segments with human faces but also segments without human faces. Drawing from Emotions as Social Information (EASI) theory and expression theory, we analyze more than 4 million frames in 3184 Indiegogo rewards-based crowdfunding pitch videos using the ResNet 50 deep neural network. Results indicate that the impact of peak negative affective visual expression on funding performance is stronger than that of its positive counterpart for both segments with and without human faces. Additionally, the influence of peak negative affective visual expression from human faces is stronger in the first half (vs. the second half) of the pitch video. Further, we found a substitute moderating effect between the peak negative affective visual expression from segments with and without human faces on funding performance. We conducted an additional data collection to ascertain that pain points serve as the underlying mechanism through which negative affective visual expressions related to funding outcome. We discuss the theoretical and practical implications of our study to the crowdfunding literature and the broader research on entrepreneurial resource acquisition.