The present work proposes a method based on CLV (Clustering around Latent
Variables) for identifying groups of consumers in L-shape data. This kind of datastructure
is very common in consumer studies where a panel of consumers is asked to
assess the global liking of a certain number of products and then, preference scores are
arranged in a two-way table Y. External information on both products (physicalchemical
description or sensory attributes) and consumers (socio-demographic
background, purchase behaviours or consumption habits) may be available in a row
descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this
method is to automatically provide a consumer segmentation where all the three
matrices play an active role in the classification, getting homogeneous groups from all
points of view: preference, products and consumer characteristics.
The proposed clustering method is illustrated on data from preference studies on food
products: juices based on berry fruits and traditional cheeses from Trentino. The
hedonic ratings given by the consumer panel on the products under study were
explained with respect to the product chemical compounds, sensory evaluation and
consumer socio-demographic information, purchase behaviour and consumption habits.
| Identifer | oai:union.ndltd.org:unibo.it/oai:amsdottorato.cib.unibo.it:667 |
| Date | 02 April 2008 |
| Creators | Endrizzi, Isabella <1975> |
| Contributors | Montanari, Angela |
| Publisher | Alma Mater Studiorum - Università di Bologna |
| Source Sets | Università di Bologna |
| Language | English |
| Detected Language | English |
| Type | Doctoral Thesis, PeerReviewed |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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