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A meta-analytic study of the factors driving the purchase of organic food

Open access
Massey, Maria, O'Cass, Aron, Otahal, Petr·2018·Appetite

Abstract

Interest in the consumption of organic food has steadily risen over the past two decades. Yet after considerable research addressing a range of issues related to organic food consumption no research systematically examines which factors explain consumers' perceptions and purchase of organics. Through a meta-analysis we examine factors underpinning the purchase of organic food using a sample of 124,353 consumers reported in 150 manuscripts over the period from 1991 to 2016. The results demonstrate that credence attributes of organic food are valued more than search and experience attributes. This shows that the market is guided by the perceived benefits of organic over conventionally grown food. These findings do not diminish the importance of search and experience attributes, but suggest that credence attributes have a prominent role in consumer organic food purchases. From the perspective of organic producers and sellers an understanding of consumer perceptions, set within search, experience and credence attributes, has the potential to offer a unique selling proposition and point of differentiation in the market.

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Metadata

Authors
Massey, M., O'Cass, A., & Otahal, P.
Venue
Appetite
Publisher
Elsevier BV
Published
2018-06
Volume
125
Pages
418-427
ISSN
0195-6663 (ISSN-L)
OpenAlex
W2791767703
Cited by
252
References
16
FWCI
19.62
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A meta-analytic study of the factors driving the purchase of organic food. https://app.cochat.ai/papers/10.1016/j.appet.2018.02.029
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Massey, M., O'Cass, A., & Otahal, P. (2018). A meta-analytic study of the factors driving the purchase of organic food. Appetite. https://doi.org/10.1016/j.appet.2018.02.029

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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.