To see and then to believe: how image affect tenant decision-making and satisfaction on short-term rental platform

Xue Yang () and Miao Tian ()
Additional contact information
Xue Yang: Nanjing University
Miao Tian: JD.Com

Electronic Commerce Research, 2024, vol. 24, issue 4, No 25, 2877-2901

Abstract: Abstract Home sharing is a new industry that was born with the development of sharing economy. The online short-term rental platform is an important carrier for home sharing. On the short-term rental platform, house images are an essential way to display the overall situation of the house, and one of the main channels for tenants to obtain house information. This paper studies the relationship between house images' colors and contents and tenant booking decision-making and satisfaction. By utilizing image mining techniques on the data obtained from a popular short-term rental platform in China, this research reveals that the color richness of house images has a significant negative relationship with both tenant booking decision-making and tenant satisfaction. What's more, both household and leisure content displayed in house images has significant positive relationships with tenant booking decision-making. Our work supplements the research on the impact of house images on home sharing and provides meaningful guidance for both the short-term rental platforms and the landlords.

Keywords: Sharing economy; Online short-term rental; Home sharing; House image; Image mining (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10660-022-09622-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:elcore:v:24:y:2024:i:4:d:10.1007_s10660-022-09622-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10660

DOI: 10.1007/s10660-022-09622-z

Access Statistics for this article

Electronic Commerce Research is currently edited by James Westland

More articles in Electronic Commerce Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().