RT info:eu-repo/semantics/article
T1 Value proposition operationalization in peer-to-peer platforms using machine learning
A1 Gutiérrez Taño, Desiderio
A1 Ramos-Henríquez, Jos´é M.
A1 Díaz-Armas, Ricardo J.
A2 Dirección de Empresas e Historia Económica
K1 Value proposition
K1 Machine learning
K1 Peer-to-peer platforms
K1 Classification
K1 Features selection
AB The purpose of this paper is to operationalize the value proposition in peer-to-peer platforms, by analyzing from all the variables which ones contribute the most for being an Airbnb Superhost. Authors use two different Machine Learning methods: Boruta for feature selection and SVM classification for prediction. More than 250 variables from 5136 listings were analyzed in the Canary Islands region. Results indicate that the Peer-to-Peer Platform Value proposition can be decomposed into three components: shared resources, value package and communications. Value proposition operationalization shows the possibilities and contribution of Machine Learning in the field of Tourism and Marketing. As practical implications for hosts, relevant variables help to have an understanding of the potential not addressed in their own value proposition. For Airbnb, relevant variables could be highlighted in search results or filters. For other companies, relevant variables of the value proposition can help to operationalize.
SN 0261-5177
YR 2021
FD 2021
LK http://riull.ull.es/xmlui/handle/915/39628
UL http://riull.ull.es/xmlui/handle/915/39628
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 27-dic-2024