This study aims to explore how emotional expressions in startups’ social media posts influence funding outcomes, with a focus on multiple dimensions of emotions using the Circumplex model.
Drawing on the Emotions as Social Information (EASI) theory in social media and the signaling theory, the study empirically explores the importance of emotional content as a strategic tool for startups to attract investors and to secure funding. The study uses the advanced artificial intelligence (AI) model robustly optimized BERT approach (RoBERTa) for emotion analysis and tests hypotheses through causal mediation analysis using the Mediation library in R.
The findings demonstrate that sentiment plays a dual role, with both direct and indirect effects on funding, while emotional arousal influences funding indirectly through likes. Emotional valence, however, shows no significant effect on funding outcomes. Causal mediation analysis revealed that the total effect of sentiment on funding is mediated through likes, highlighting the critical role of social media engagement in translating emotional expressions into tangible financial outcomes.
The study offers actionable insights for startups, emphasizing the strategic importance of crafting emotionally rich social media posts to enhance engagement and funding. The study also significantly contributes to EASI and signaling theory.
The present study is among the first few to apply the EASI to analyze the role of emotional expressions in social media posts on funding outcomes. Furthermore, the study integrates multidimensional emotion analysis and the advanced AI model RoBERTa, offering a novel methodological contribution to the understanding of emotional signaling in digital contexts for startup funding.
