Search results

This research aims to examine whether the facial appearances and expressions of Airbnb host photos influence guest star ratings.

This research analyzed the profile photos of over 20,000 Airbnb hosts and the guest star ratings of over 30,000 Airbnb listings in New York City, using machine learning techniques.

First, hosts who provided profile photos received higher guest ratings than those who did not provide photos. When facial features of profile photos were recognizable, guest ratings were higher than when they were not recognizable (e.g. faces too small, faces looking backward or faces blocked by some objects). Second, a happy facial expression, blond hair and brown hair positively affected guest ratings, whereas heads tilted back negatively affected guest ratings.

This research is the first, to the best of the authors’ knowledge, to analyze the facial appearances and expressions of profile photos using machine learning techniques and examine the influence of Airbnb host photos on guest star ratings.

This paper aims to examine the influence of personal distress on donor choice of happy- or sad-faced child in two donation contexts, monetary donations and child sponsorships.

This research conducted two experimental studies, in which participants chose a child to benefit out of eight needy children.

More people chose sad-faced children than happy-faced children in monetary donations, whereas in child sponsorships, the preference for sad-faced over happy-faced children disappeared: people chose happy-faced children as often as they chose sad-faced children.

This research distinguishes between two types of personal distress, experienced and anticipated distress, explaining why donor choices of child differ between monetary donations (where only experienced distress is present) and child sponsorships (where both experienced and anticipated distress are present).