Gender and Counterstereotypical Facial Expressions of Emotion in Crowdfunded Microlending
研究利用计算机辅助面部表情分析，发现众筹小额贷款中，女性企业家展现愤怒和厌恶等男性化表情、男性展现悲伤和快乐等女性化表情能提高贷款成功率，为创业者提供策略参考。
Crowdfunded microlending research implies that both communal and agentic characteristics are valued. These characteristics, however, are often viewed as being at odds with one another due to their association with gender stereotypes. Drawing upon expectancy violation theory and research on gender stereotypes, we theorize that gender-counterstereotypical facial expressions of emotion provide a means for entrepreneurs to project “missing” agentic or communal characteristics. Leveraging computer-aided facial expression analysis to analyze entrepreneur photographs from 43,210 microloan appeals, we show that women benefit from stereotypically masculine facial expressions of anger and disgust, whereas men benefit from stereotypically feminine facial expressions of sadness and happiness.