The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines

In this article a methodology, based on the Kano model, to prioritize the features of a product or service is proposed. Instead of using detailed, lengthy, burdensome, time-demanding, and biased-prone questionnaires, enquiring the user satisfaction with each product feature, a simplified survey aski...

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Authors: Luque Sendra, Amalia, Mazzoleni, Mirko, Heras García de Vinuesa, Ana de las, Ferramosca, Antonio, Previdi, Fabio, Carrasco Muñoz, Alejandro
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/165630
Online Access:https://hdl.handle.net/11441/165630
https://doi.org/10.1109/ACCESS.2024.3492385
Access Level:Open access
Keyword:Kano model
Wine quality
Machine learning
Feature clustering
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spelling The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of WinesLuque Sendra, AmaliaMazzoleni, MirkoHeras García de Vinuesa, Ana de lasFerramosca, AntonioPrevidi, FabioCarrasco Muñoz, AlejandroKano modelWine qualityMachine learningFeature clusteringIn this article a methodology, based on the Kano model, to prioritize the features of a product or service is proposed. Instead of using detailed, lengthy, burdensome, time-demanding, and biased-prone questionnaires, enquiring the user satisfaction with each product feature, a simplified survey asking for the overall satisfaction with the product is used. The proposed method starts by training a machine learning (ML) model using a dataset of different instances of the product and the corresponding perceived quality. This model is then employed to derive the relationship between each feature and the satisfaction associated with them. The shape of this relationship is interpreted according to a Kano model placing each attribute in a bidimensional Kano map which is later partitioned using ML clustering techniques. This methodology has been applied to an open dataset containing the physicochemical characteristics of hundreds of wines and the corresponding scores obtained in a blind tasting evaluation. The research has shown that ML models get very remarkable results predicting the perceived quality of a wine and is able to build a Kano map of the wine features. The ML clustering techniques employed partitioning this Kano map has clearly overperformed conventional rectangular or polar segmentation. It has also been shown that using four categories of features, as it is proposed in the Kano model, is the most reasonable partition from an ML clustering perspective.IEEE. Institute of Electrical and Electronics EngineersIngeniería del DiseñoTecnología ElectrónicaTEP990: Proyectos de IngenieríaUniversidad de Sevilla2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/165630https://doi.org/10.1109/ACCESS.2024.3492385reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Access, 12, 169733-169747.Cátedra Goya-Antonio Unanue de Ingeniería en la Industria AgroalimentariaVII Own Research and Transfer Plan 2023 Project 2023/00000378https://ieeexplore.ieee.org/document/10745272info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1656302026-06-17T12:51:07Z
dc.title.none.fl_str_mv The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines
title The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines
spellingShingle The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines
Luque Sendra, Amalia
Kano model
Wine quality
Machine learning
Feature clustering
title_short The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines
title_full The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines
title_fullStr The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines
title_full_unstemmed The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines
title_sort The Role of Kano Model in Revealing the Most Significant Physicochemical Properties of Wines
dc.creator.none.fl_str_mv Luque Sendra, Amalia
Mazzoleni, Mirko
Heras García de Vinuesa, Ana de las
Ferramosca, Antonio
Previdi, Fabio
Carrasco Muñoz, Alejandro
author Luque Sendra, Amalia
author_facet Luque Sendra, Amalia
Mazzoleni, Mirko
Heras García de Vinuesa, Ana de las
Ferramosca, Antonio
Previdi, Fabio
Carrasco Muñoz, Alejandro
author_role author
author2 Mazzoleni, Mirko
Heras García de Vinuesa, Ana de las
Ferramosca, Antonio
Previdi, Fabio
Carrasco Muñoz, Alejandro
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería del Diseño
Tecnología Electrónica
TEP990: Proyectos de Ingeniería
Universidad de Sevilla
dc.subject.none.fl_str_mv Kano model
Wine quality
Machine learning
Feature clustering
topic Kano model
Wine quality
Machine learning
Feature clustering
description In this article a methodology, based on the Kano model, to prioritize the features of a product or service is proposed. Instead of using detailed, lengthy, burdensome, time-demanding, and biased-prone questionnaires, enquiring the user satisfaction with each product feature, a simplified survey asking for the overall satisfaction with the product is used. The proposed method starts by training a machine learning (ML) model using a dataset of different instances of the product and the corresponding perceived quality. This model is then employed to derive the relationship between each feature and the satisfaction associated with them. The shape of this relationship is interpreted according to a Kano model placing each attribute in a bidimensional Kano map which is later partitioned using ML clustering techniques. This methodology has been applied to an open dataset containing the physicochemical characteristics of hundreds of wines and the corresponding scores obtained in a blind tasting evaluation. The research has shown that ML models get very remarkable results predicting the perceived quality of a wine and is able to build a Kano map of the wine features. The ML clustering techniques employed partitioning this Kano map has clearly overperformed conventional rectangular or polar segmentation. It has also been shown that using four categories of features, as it is proposed in the Kano model, is the most reasonable partition from an ML clustering perspective.
publishDate 2024
dc.date.none.fl_str_mv 2024
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/11441/165630
https://doi.org/10.1109/ACCESS.2024.3492385
url https://hdl.handle.net/11441/165630
https://doi.org/10.1109/ACCESS.2024.3492385
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Access, 12, 169733-169747.
Cátedra Goya-Antonio Unanue de Ingeniería en la Industria Agroalimentaria
VII Own Research and Transfer Plan 2023 Project 2023/00000378
https://ieeexplore.ieee.org/document/10745272
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 IEEE. Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv IEEE. Institute of Electrical and Electronics Engineers
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
repository.mail.fl_str_mv
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