Abstract

The sharing economy has risen rapidly in the past decade. The development of shared accommodation encourages more hotels and guesthouses to attract customers through online accommodation-sharing platforms, which has become a meaningful way to fight against the competition of the traditional hotel industry. In this condition, what the hosts are concerned about most is how to attract customers’ attention through the platform display to increase reservations. Based on construal level theory, this paper explores how hosts’ information displayed on online accommodation-sharing platforms determines consumers’ booking behavior by influencing their psychological distance. We use machine learning methods to mine the raw data and extract the representational factors of psychological distance. Based on the data-driven behavior decision-making approach, we collected valid large-scale fine-grained secondary actual consumption data from Airbnb, the world’s leading online accommodation-sharing platform, and scientifically and intelligently processed the data using machine learning methods, then tested the hypotheses using the regression analysis software STATA15. Our findings suggest that both social distance and temporal distance have a negative impact on booking behavior of guests. In detail, subject diversity, perspective taking, and facial attractiveness in the dimension of social distance positively influence guests’ booking behavior; instant bookable in the temporal distance dimension positively affects booking behavior, while response time has a negative effect. This study contributes to the literature by empirically examining psychological distance in the booking behavior of guests in shared accommodation through the processing and analysis of actual consumption data. The findings have important practical implications for how shared accommodation service providers and sharing economy platform managers can operate better.

Full Text
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