Comparison of different clustering methods for investigating individual differences using choice experiments

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Comparison of different clustering methods for investigating individual differences using choice experiments. / Asioli, D.; Berget, I.; Næs, T.

I: Food Research International, Bind 111, 2018, s. 371-378.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Asioli, D, Berget, I & Næs, T 2018, 'Comparison of different clustering methods for investigating individual differences using choice experiments', Food Research International, bind 111, s. 371-378. https://doi.org/10.1016/j.foodres.2018.05.029

APA

Asioli, D., Berget, I., & Næs, T. (2018). Comparison of different clustering methods for investigating individual differences using choice experiments. Food Research International, 111, 371-378. https://doi.org/10.1016/j.foodres.2018.05.029

Vancouver

Asioli D, Berget I, Næs T. Comparison of different clustering methods for investigating individual differences using choice experiments. Food Research International. 2018;111:371-378. https://doi.org/10.1016/j.foodres.2018.05.029

Author

Asioli, D. ; Berget, I. ; Næs, T. / Comparison of different clustering methods for investigating individual differences using choice experiments. I: Food Research International. 2018 ; Bind 111. s. 371-378.

Bibtex

@article{996c0efe41cb44418b919b737af8259f,
title = "Comparison of different clustering methods for investigating individual differences using choice experiments",
abstract = "Different strategies for investigating individual differences among consumers using choice experiments are compared. The paper is based on a consumer study of iced coffee in Norway. Consumers (n = 102) performed a choice task of twenty different iced coffee profiles varying in coffee type, production origin, calorie content and price following an orthogonal design. Consumer factors, such as socio-demographics, attitudes and habits, were also collected. Choice data will be analysed using two different clustering strategies. Strategy one is the most classical approach called Latent Class Logit (LCL) model, while Strategy two uses Mixed Logit (ML) model combined with Principal Component Analysis (PCA) for visual segmentation or with automatic clustering detection using Fuzzy C Means clustering (FCM). The clusters obtained can be interpreted using external consumer factors by using the Partial Least Square – Discrimination Analysis (PLS-DA) model. The different approaches are compared in terms of data analysis methodologies, modelling, outcomes, interpretation, flexibility, practical issues and user friendliness.",
keywords = "Choice experiment, Clustering methods, Consumers, Iced coffee, Method comparison, Norway",
author = "D. Asioli and I. Berget and T. N{\ae}s",
year = "2018",
doi = "10.1016/j.foodres.2018.05.029",
language = "English",
volume = "111",
pages = "371--378",
journal = "Food Research International",
issn = "0963-9969",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Comparison of different clustering methods for investigating individual differences using choice experiments

AU - Asioli, D.

AU - Berget, I.

AU - Næs, T.

PY - 2018

Y1 - 2018

N2 - Different strategies for investigating individual differences among consumers using choice experiments are compared. The paper is based on a consumer study of iced coffee in Norway. Consumers (n = 102) performed a choice task of twenty different iced coffee profiles varying in coffee type, production origin, calorie content and price following an orthogonal design. Consumer factors, such as socio-demographics, attitudes and habits, were also collected. Choice data will be analysed using two different clustering strategies. Strategy one is the most classical approach called Latent Class Logit (LCL) model, while Strategy two uses Mixed Logit (ML) model combined with Principal Component Analysis (PCA) for visual segmentation or with automatic clustering detection using Fuzzy C Means clustering (FCM). The clusters obtained can be interpreted using external consumer factors by using the Partial Least Square – Discrimination Analysis (PLS-DA) model. The different approaches are compared in terms of data analysis methodologies, modelling, outcomes, interpretation, flexibility, practical issues and user friendliness.

AB - Different strategies for investigating individual differences among consumers using choice experiments are compared. The paper is based on a consumer study of iced coffee in Norway. Consumers (n = 102) performed a choice task of twenty different iced coffee profiles varying in coffee type, production origin, calorie content and price following an orthogonal design. Consumer factors, such as socio-demographics, attitudes and habits, were also collected. Choice data will be analysed using two different clustering strategies. Strategy one is the most classical approach called Latent Class Logit (LCL) model, while Strategy two uses Mixed Logit (ML) model combined with Principal Component Analysis (PCA) for visual segmentation or with automatic clustering detection using Fuzzy C Means clustering (FCM). The clusters obtained can be interpreted using external consumer factors by using the Partial Least Square – Discrimination Analysis (PLS-DA) model. The different approaches are compared in terms of data analysis methodologies, modelling, outcomes, interpretation, flexibility, practical issues and user friendliness.

KW - Choice experiment

KW - Clustering methods

KW - Consumers

KW - Iced coffee

KW - Method comparison

KW - Norway

U2 - 10.1016/j.foodres.2018.05.029

DO - 10.1016/j.foodres.2018.05.029

M3 - Journal article

C2 - 30007698

AN - SCOPUS:85047473522

VL - 111

SP - 371

EP - 378

JO - Food Research International

JF - Food Research International

SN - 0963-9969

ER -

ID: 220847857