Emotional design engineering for packaging of olive oil using machine learning techniques

Consumer behaviour, and therefore purchase intentions, are affected by the visual elements of packaging. This is particularly important for products in the agri-food sector, especially for olive oil. In this work, the perception of different packaging options by olive oil users is analysed. For this...

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Detalles Bibliográficos
Autores: Heras García de Vinuesa, Ana de las, Zamora-Polo, Francisco, Ferramosca, Antonio, Luque Sendra, Amalia
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/176996
Acceso en línea:https://hdl.handle.net/11441/176996
https://doi.org/10.1080/23311916.2025.2555340
Access Level:acceso abierto
Palabra clave:Kansei engineering
Machine learning
Sustainable design
Engineering design
Olive oil packaging
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spelling Emotional design engineering for packaging of olive oil using machine learning techniquesHeras García de Vinuesa, Ana de lasZamora-Polo, FranciscoFerramosca, AntonioLuque Sendra, AmaliaKansei engineeringMachine learningSustainable designEngineering designOlive oil packagingConsumer behaviour, and therefore purchase intentions, are affected by the visual elements of packaging. This is particularly important for products in the agri-food sector, especially for olive oil. In this work, the perception of different packaging options by olive oil users is analysed. For this, machine learning tools are employed in the synthesis phase of the Kansei Engineering (KE) methodology. On the one hand, four properties (material, colour, price, capacity) were considered for the definition of the property space. Subsequently, for the determination of the semantic space, a literature search was first performed, and an affinity analysis was then carried out, followed by a pilot survey to reduce the number of Kanseis. The semantic space consisted of 17 and 6 Kanseis, respectively. The final survey was given to a sample of 100 Andalusian citizens. Machine learning techniques (linear regression, ridge regression SVR) were employed for the synthesis phase. The results show that KE can be used as a tool to optimise the design of olive oil packaging by using machine learning tools in the synthesis phase. This study can provide the basis for other studies of other agri-food products and for the use of other artificial intelligence tools.Taylor and FrancisIngeniería del DiseñoTEP990: Proyectos de IngenieríaUniversidad de Sevilla2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/176996https://doi.org/10.1080/23311916.2025.2555340reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésCogent Engineering, 12 (1), 2555340.GOYA/ VII Own Research and Transfer Plan 2023/00000378GOYA/ VII Own Research and Transfer Plan 2023/00000390https://www.tandfonline.com/doi/full/10.1080/23311916.2025.2555340info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1769962026-06-17T12:51:07Z
dc.title.none.fl_str_mv Emotional design engineering for packaging of olive oil using machine learning techniques
title Emotional design engineering for packaging of olive oil using machine learning techniques
spellingShingle Emotional design engineering for packaging of olive oil using machine learning techniques
Heras García de Vinuesa, Ana de las
Kansei engineering
Machine learning
Sustainable design
Engineering design
Olive oil packaging
title_short Emotional design engineering for packaging of olive oil using machine learning techniques
title_full Emotional design engineering for packaging of olive oil using machine learning techniques
title_fullStr Emotional design engineering for packaging of olive oil using machine learning techniques
title_full_unstemmed Emotional design engineering for packaging of olive oil using machine learning techniques
title_sort Emotional design engineering for packaging of olive oil using machine learning techniques
dc.creator.none.fl_str_mv Heras García de Vinuesa, Ana de las
Zamora-Polo, Francisco
Ferramosca, Antonio
Luque Sendra, Amalia
author Heras García de Vinuesa, Ana de las
author_facet Heras García de Vinuesa, Ana de las
Zamora-Polo, Francisco
Ferramosca, Antonio
Luque Sendra, Amalia
author_role author
author2 Zamora-Polo, Francisco
Ferramosca, Antonio
Luque Sendra, Amalia
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería del Diseño
TEP990: Proyectos de Ingeniería
Universidad de Sevilla
dc.subject.none.fl_str_mv Kansei engineering
Machine learning
Sustainable design
Engineering design
Olive oil packaging
topic Kansei engineering
Machine learning
Sustainable design
Engineering design
Olive oil packaging
description Consumer behaviour, and therefore purchase intentions, are affected by the visual elements of packaging. This is particularly important for products in the agri-food sector, especially for olive oil. In this work, the perception of different packaging options by olive oil users is analysed. For this, machine learning tools are employed in the synthesis phase of the Kansei Engineering (KE) methodology. On the one hand, four properties (material, colour, price, capacity) were considered for the definition of the property space. Subsequently, for the determination of the semantic space, a literature search was first performed, and an affinity analysis was then carried out, followed by a pilot survey to reduce the number of Kanseis. The semantic space consisted of 17 and 6 Kanseis, respectively. The final survey was given to a sample of 100 Andalusian citizens. Machine learning techniques (linear regression, ridge regression SVR) were employed for the synthesis phase. The results show that KE can be used as a tool to optimise the design of olive oil packaging by using machine learning tools in the synthesis phase. This study can provide the basis for other studies of other agri-food products and for the use of other artificial intelligence tools.
publishDate 2025
dc.date.none.fl_str_mv 2025
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/11441/176996
https://doi.org/10.1080/23311916.2025.2555340
url https://hdl.handle.net/11441/176996
https://doi.org/10.1080/23311916.2025.2555340
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Cogent Engineering, 12 (1), 2555340.
GOYA/ VII Own Research and Transfer Plan 2023/00000378
GOYA/ VII Own Research and Transfer Plan 2023/00000390
https://www.tandfonline.com/doi/full/10.1080/23311916.2025.2555340
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 Taylor and Francis
publisher.none.fl_str_mv Taylor and Francis
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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score 15,812429

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