Analysis of green word-of-mouth advertising behavior of organic food consumers.

Machine learning Market segmentation Organic consumers WOM

Journal

Appetite
ISSN: 1095-8304
Titre abrégé: Appetite
Pays: England
ID NLM: 8006808

Informations de publication

Date de publication:
04 Apr 2024
Historique:
received: 02 11 2023
revised: 02 03 2024
accepted: 25 03 2024
medline: 7 4 2024
pubmed: 7 4 2024
entrez: 6 4 2024
Statut: aheadofprint

Résumé

The word-of-mouth (WOM) marketing process is one of the main means by which consumers obtain information. As a communication channel between consumers in economically developing countries, WOM may contribute to the development of the organic food market. The primary objective of this study is to segment organic saffron consumers in Mashhad, Iran, and determine how they engage in WOM marketing. Data were collected through questionnaires from 13 districts of Mashhad using a stratified sampling method. In this study, 400 organic saffron consumers were grouped using a self-organizing map (SOM) neural network based on consumer neobehavioristic theory, and then, using decision trees, consumer behavior rules were extracted for participating in the WOM for each group. According to the results, less than fifty percent of consumers in each of the four market segments are willing to participate in WOM advertising for organic saffron. A lack of awareness of the characteristics of organic saffron is also found to be the main reason for consumers' reluctance to recommend organic saffron to others. Mass-media advertising is an effective way to raise consumer awareness and influence opinion leaders, ultimately resulting in WOM recommendations.

Identifiants

pubmed: 38582136
pii: S0195-6663(24)00125-9
doi: 10.1016/j.appet.2024.107324
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

107324

Informations de copyright

Copyright © 2024. Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Flavio Boccia (F)

Department of Economic and Legal Studies, Parthenope University of Naples, Naples, Italy. Electronic address: flavio.boccia@uniparthenope.it.

Amirhossein Tohidi (A)

Department of Agricultural Economics, Gorgan University of Agricultural Sciences and Natural Resources, 6341773637, Gorgan, Italy. Electronic address: Amirhossein_tohidi@yahoo.com.

Classifications MeSH