About: The 7 Ps marketing mix of home-sharing services: Mining travelers’ online reviews on Airbnb

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	The 7 Ps marketing mix of home-sharing services: Mining travelers’ online reviews on Airbnb
Creator	Tang, Yingying Kwok, Linchi Yu, Bei
Source	Elsevier; Medline; PMC
abstract	The 7 Ps model is a very useful tool in helping service firms solve managerial issues in marketing. Guided by the 7 Ps marketing mix framework, a big-data, supervised machine learning analysis was performed with 1,148,062 English reviews of 37,092 Airbnb listings in San Francisco and New York City. The results disclose similar patterns in both markets, where travelers shared their experience about Service Product and Physical Evidence most often; Price and Promotion were the least mentioned elements. Furthermore, through a series of comparisons of Airbnb’s 7 Ps marketing mix among the listings managed by different types of hosts, multi-unit and single-unit hosts seem to offer similar services with a small observable difference; whereas superhosts and the ordinary hosts deliver different services. This study makes valuable methodological contributions and provides practical marketing insights for hoteliers and the hosts and webmasters on home-sharing websites. Policymakers should pay special attention to multi-unit hosts.
has issue date	2020-07-15(xsd:dateTime)
bibo:doi	10.1016/j.ijhm.2020.102616
bibo:pmid	32834353
has license	no-cc
sha1sum (hex)	483ddaeb27657fb1a36a6a3eed3ffb3325b00275
schema:url	https://doi.org/10.1016/j.ijhm.2020.102616
resource representing a document's title	The 7 Ps marketing mix of home-sharing services: Mining travelers’ online reviews on Airbnb
has PubMed Central identifier	PMC7361117
has PubMed identifier	32834353
schema:publication	Int J Hosp Manag
resource representing a document's body	covid:483ddaeb27657fb1a36a6a3eed3ffb3325b00275#body_text
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