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Cited by 619

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Market segmentation and willingness to pay for organic products in Spain

Open access
Gil, J·2000·The International Food and Agribusiness Management Review

Abstract

In recent years, consumer concerns on environmental and health issues related to food products have increased and, as a result, the demand for organically grown production has grown. Higher costs of production and retailer margins generate a gap between real prices and those that consumers are willing to pay for organic food. In this article, consumer willingness to pay for organic food in two Spanish regions is analyzed. Markets in both regions are segmented considering consumers lifestyles. Results indicate that consumers concerned about healthy diet and environmental degradation are the most likely to buy organic food, and are willing to pay a high premium. Organic attributes are easily identified in perishable products as the premium consumers would pay for organic meat, fruits, and vegetables is higher.

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Crossref · 10.1016/s1096-7508(01)00040-4

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Metadata

Authors
Gil, J.
Venue
The International Food and Agribusiness Management Review
Publisher
Elsevier BV
Published
2000
Volume
3
Issue
2
Pages
207-226
ISSN
1096-7508 (ISSN-L)
OpenAlex
W2048241521
Cited by
619
References
1
FWCI
1.09
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Market segmentation and willingness to pay for organic products in Spain. https://app.cochat.ai/papers/10.1016/s1096-7508(01)00040-4
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Gil, J. (2000). Market segmentation and willingness to pay for organic products in Spain. The International Food and Agribusiness Management Review. https://doi.org/10.1016/s1096-7508(01)00040-4

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.