Machine learning insights into recommendation intention: evidence from the fitness industry

Recommendation intention, or the Net Promoter Score (NPS, in the terminology of fitness centers), is a tool that condenses into a single indicator the willingness of customers to recommend a service. It is important because it has become a widely used proxy for loyalty, brand advocacy, and growth, a...

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Autores: Alonso Dos Santos, Manuel, García Fernández, Jerónimo, Fuentes-Solis, Rodrigo, Zarco, Carmen
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:dnet:idus________::5725041950167ec8377b25aec7314a11
Acceso en línea:https://hdl.handle.net/11441/185846
https://doi.org/10.1186/s40537-026-01398-5
Access Level:acceso abierto
Palabra clave:Machine learning
Sports management
Intention to recommend
Fitness centres
Net promotion score
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spelling Machine learning insights into recommendation intention: evidence from the fitness industryAlonso Dos Santos, ManuelGarcía Fernández, JerónimoFuentes-Solis, RodrigoZarco, CarmenMachine learningSports managementIntention to recommendFitness centresNet promotion scoreRecommendation intention, or the Net Promoter Score (NPS, in the terminology of fitness centers), is a tool that condenses into a single indicator the willingness of customers to recommend a service. It is important because it has become a widely used proxy for loyalty, brand advocacy, and growth, and fitness managers are very familiar with its interpretation. Therefore, it is necessary to examine its validity and determinants with greater academic rigor, particularly in experiential services such as fitness, where competition and customer churn are critical. This study applies explainable machine learning techniques to predict recommendation intention using a sample of 15,822 users from 9 fitness center chains in Spain. Five widely established algorithms were employed (Decision Tree, Random Forest, Logistic Regression, Gaussian Naïve Bayes, and Extreme Gradient Boosting), evaluated with classification metrics such as accuracy, sensitivity, specificity, and AUC. The results show that satisfaction, emotions, and renewal intention are the most relevant predictors, achieving accuracy levels above 80%. Furthermore, the application of interpretability techniques offers a clear ranking of key variables, with practical implications for loyalty management and the design of customer-oriented marketing strategies. This work contributes to the literature by combining large-scale empirical evidence from the fitness sector with advanced data analytics and opens the way for replication in other experience-intensive service industries.Springer NatureEducación Física y DeporteSEJ525: Gestión e Innovación en Servicios Deportivos, Ocio y Recreación2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/185846https://doi.org/10.1186/s40537-026-01398-5reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésJournal of Big Data, 13 (1), 37. https://doi.org/10.1186/s40537-026-01398-5info:eu-repo/semantics/openAccessoai:dnet:idus________::5725041950167ec8377b25aec7314a112026-06-17T12:51:07Z
dc.title.none.fl_str_mv Machine learning insights into recommendation intention: evidence from the fitness industry
title Machine learning insights into recommendation intention: evidence from the fitness industry
spellingShingle Machine learning insights into recommendation intention: evidence from the fitness industry
Alonso Dos Santos, Manuel
Machine learning
Sports management
Intention to recommend
Fitness centres
Net promotion score
title_short Machine learning insights into recommendation intention: evidence from the fitness industry
title_full Machine learning insights into recommendation intention: evidence from the fitness industry
title_fullStr Machine learning insights into recommendation intention: evidence from the fitness industry
title_full_unstemmed Machine learning insights into recommendation intention: evidence from the fitness industry
title_sort Machine learning insights into recommendation intention: evidence from the fitness industry
dc.creator.none.fl_str_mv Alonso Dos Santos, Manuel
García Fernández, Jerónimo
Fuentes-Solis, Rodrigo
Zarco, Carmen
author Alonso Dos Santos, Manuel
author_facet Alonso Dos Santos, Manuel
García Fernández, Jerónimo
Fuentes-Solis, Rodrigo
Zarco, Carmen
author_role author
author2 García Fernández, Jerónimo
Fuentes-Solis, Rodrigo
Zarco, Carmen
author2_role author
author
author
dc.contributor.none.fl_str_mv Educación Física y Deporte
SEJ525: Gestión e Innovación en Servicios Deportivos, Ocio y Recreación
dc.subject.none.fl_str_mv Machine learning
Sports management
Intention to recommend
Fitness centres
Net promotion score
topic Machine learning
Sports management
Intention to recommend
Fitness centres
Net promotion score
description Recommendation intention, or the Net Promoter Score (NPS, in the terminology of fitness centers), is a tool that condenses into a single indicator the willingness of customers to recommend a service. It is important because it has become a widely used proxy for loyalty, brand advocacy, and growth, and fitness managers are very familiar with its interpretation. Therefore, it is necessary to examine its validity and determinants with greater academic rigor, particularly in experiential services such as fitness, where competition and customer churn are critical. This study applies explainable machine learning techniques to predict recommendation intention using a sample of 15,822 users from 9 fitness center chains in Spain. Five widely established algorithms were employed (Decision Tree, Random Forest, Logistic Regression, Gaussian Naïve Bayes, and Extreme Gradient Boosting), evaluated with classification metrics such as accuracy, sensitivity, specificity, and AUC. The results show that satisfaction, emotions, and renewal intention are the most relevant predictors, achieving accuracy levels above 80%. Furthermore, the application of interpretability techniques offers a clear ranking of key variables, with practical implications for loyalty management and the design of customer-oriented marketing strategies. This work contributes to the literature by combining large-scale empirical evidence from the fitness sector with advanced data analytics and opens the way for replication in other experience-intensive service industries.
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/185846
https://doi.org/10.1186/s40537-026-01398-5
url https://hdl.handle.net/11441/185846
https://doi.org/10.1186/s40537-026-01398-5
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Big Data, 13 (1), 37.
https://doi.org/10.1186/s40537-026-01398-5
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
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