A systematic review of determinants of cultured meat adoption: impacts and guiding insights
Notice bibliographique
Résumé
Purpose The purpose of this article is (1) to carry out an ambivalent analysis of the determinants (benefits/risks) of the adoption of cultured meat, (2) to identify their impacts on consumers’ attitudes (cognitive, affective and conative) and (3) to propose a research agenda. Design/methodology/approach A systematic review of the relevant literature was conducted. The authors selected 86 articles that were coded using NVivo 12 software according to the theoretical framework chosen for this study: (1) consumer attitude ambivalence (benefit–risk) – conflicting presence of positive and negative attitudes in decision-making, (2) the consumer preference theory – choice of consumers based on utility maximisation or best characteristics/determinants and (3) the three-dimensional perspective of attitude – cognitive, affective and behavioural components. The authors followed the methodological steps (formulation of the research question, identification of relevant scientific studies, evaluation of the quality of studies, summary of evidence and interpretation of results) recommended by Lipsey and Wilson (2001) and Tranfield et al . (2003). Several keywords were drawn from a study by Bryant and Barnett (2019) on cultured meat (CM) nomenclature and its impact on consumer acceptance. Findings The identified articles were relatively recent (84/86 articles were published after 2010) and in the fields of agriculture and ethical agriculture (22/86), policy and regulations (12/86) and psychology (11/86). Content analysis helped identify four types of ambivalent determinants for the adoption of cultured meat: ethics, intrinsic, informational and belief. The results suggest the existence of a group of “dominant” determinants for each attitude component. Thus, the dominant determinants of cognitive, affective and conative components are informational, ethical and intrinsic determinants, respectively. Research limitations/implications This research is based on a systematic review of literature and is a review of the narrative literature that provides an overview of what is known about cultured meat adoption. The main weakness of this type of method is the feasibility generally associated with the existence (and a sufficient number) of studies that can be included. Other types of the meta-analytic method could have been used and could have explored different measures and biases (e.g. effect sizes, statistical power, sampling error, measurement error and publication bias). Also, as a food technology whose social acceptability would be influenced by all stakeholders, it would be relevant to expand the analysis to other types of stakeholders. Practical implications Little is still known to the public about the adoption mechanisms of this technology. In terms of behaviour, Siegrist et al . (2018) suggest that new studies should focus on factors that influence the individual differences in the willingness of consumers to eat or purchase cultured meat. By identifying the dominant target influence of informational determinants on cognitive components, that of ethical determinants on affective components and finally that of intrinsic determinants on conative attitudes, this article offers a first avenue of solution to businesses operating in this new industry, as well as to public authorities, to improve the acceptance of cultured meat. Private businesses will benefit from the results of this research by understanding the underlying motivations of consumers to adopt this type of innovation in order to adjust future marketing. Social implications This article, through better understanding of the psychological mechanisms that contribute to its social acceptability amongst the population, has the potential to improve educational campaigns for this technology. The results could thus guide both public policies as well as the regulation of activities related to cultured meat in the coming years, professional orders, private businesses and the general public. It thus provides initial insight needed to understand this public debate. Originality/value Research addressing cultured meat has come primarily from agribusiness and environmental and biological sciences. The authors highlighted the need for interdisciplinary collaboration between biological and social sciences to address ethical issues. This article, via multidisciplinary systematic reviews, links environmental/biological sciences and social sciences, and management.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».