Predicting evaluations of entrepreneurial pitches based on multimodal nonverbal behavioral cues and self-reported characteristics

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Abstract

Acquiring funding for a startup venture often involves pitching a business idea to potential investors. Insight into the nonverbal behavioral cues that impact the investment decision making process can help entrepreneurs to improve their persuasion skills and can provide valuable insights to investors and researchers. Previous research on the prediction of investment decisions in entrepreneurial pitches has primarily focused on analyzing (usually unimodal) behavioral cues from pitchers only. To address this gap, in this study we compare the predictive performance of different feature sets consisting of nonverbal behavior cues from different modalities (i.e., facial expressions, head movement, and vocal expressions) from both pitchers and investors and their self-reported characteristics. Our findings show promising results for the prediction of investor's evaluations of entrepreneurial pitches. Multimodal behavioral cues, especially head movement and vocal expressions, were found to be most predictive.

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APA

Stoitsas, K., Ertuǧrul, I. Ö., Liebregts, W., & Jung, M. M. (2022). Predicting evaluations of entrepreneurial pitches based on multimodal nonverbal behavioral cues and self-reported characteristics. In ACM International Conference Proceeding Series (pp. 121–126). Association for Computing Machinery. https://doi.org/10.1145/3536220.3558041

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