A matter of identity: Promoting plant-based food among meat-eaters through a common identity priming
Abstract
Meat production and consumption has been identified as a significant contributor to climate change, however its consumption is not declining. Through four experimental studies we propose an identity-based intervention to promote plant-based food among meat-eaters. In particular, we demonstrated that meat-eaters are more willing to buy plant-based food when it is primed through a common (vs. vegan) identity, because of lower meat-eaters identification and, in turn, lower identity threat. Our results contribute to the understanding of the identity-based factors in influencing food consumption and offer insights for marketers, retailers, and policymakers to encourage a more sustainable diet.
Study specs
Four experimental studies utilizing identity-based interventions to influence food choices among meat-eaters.
- Authors
- C Donato,L Monsurrò,M Di Cioccio
- Institution
- G. D'Annunzio University,Luiss Guido Carli University,University of Modena and Reggio Emilia
- Discipline
- Social Psychology,Consumer Behavior
- Study Type
- Experimental Study
- Year
- 2024
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
The impact of identity priming (common vs. vegan) on meat-eaters' willingness to buy plant-based food.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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