JOURNAL ARTICLE
Consumers' Perspective of Plant‐Based Meat Alternatives—A Systematic Literature Review and Future Research Agenda.
Published In: International Journal of Consumer Studies, 2025, v. 49, n. 2. P. 1 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Majcher, Sylwia 3 of 3
Abstract
This article presents a Theories, Contexts, and Methods (TCM)‐Antecedents, Decisions, and Outcomes (ADO) framework‐based systematic review based on the TCM‐ADO framework to synthesize consumer perspectives of plant‐based meat alternatives (PBMA). This article offers an overview of the TCM presented in the reviewed articles. ADO are examined to delineate the factors influencing consumer choices and their implications. By integrating these perspectives, the review provides a holistic view, setting a foundation for targeted strategies in promoting PBMA adoption. This review identifies 26 antecedents from 53 articles, organized into four main categories: individual‐related antecedents, socio‐demographic factors, sociocultural factors, and product‐related antecedents. Understanding consumers' perspectives on PBMA is crucial to developing strategies that encourage sustainable dietary changes. This review highlights the complexity of consumer decision‐making regarding PBMA and underscores the need for multi‐pronged strategies to enhance their acceptance and purchase intention. Furthermore, the TCM and ADO frameworks are used to identify literature gaps and suggest future research directions. This approach supports stakeholders in developing targeted interventions that facilitate the transition to more sustainable food systems. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Consumer Studies. 2025/03, Vol. 49, Issue 2, p1
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2025
- ISSN:1470-6423
- DOI:10.1111/ijcs.70036
- Accession Number:184043842
- Copyright Statement:Copyright of International Journal of Consumer Studies is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.