This analysis reveals how housing agents' beauty and e-WOM influence signup rates, suggesting significant customer behavior trends.
Purpose The purpose of this study is to investigate the effects of housing agents’ images facial features and electronic word-of-mouth (e-WOM) on the potential customers’ signup decision-making on an online rental platform. Design/methodology/approach Based on the first impression theory, an artificial intelligence face recognition system was applied, and a regression model was built to analyze 2,797 housing agents’ online images from Ziroom, a Chinese leading O2O rental platform. Findings The results show that the housing agents’ images facial features (beauty, smile, age), and e-WOM have a significant effect on customer signup conversion rates. Housing agents’ beauty weakens the positive effect of housing agents’ smile, age and e-WOM on customer signup conversion rates. Originality/value This research applies artificial intelligence face recognition technology, simultaneously broadening the first impression theory to online long-term rental platforms. It also provides insights into the role of housing agents’ online image facial features, and e-WOM in customer behavior and decision-making.
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Bai et al. (2025) studied this question.
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