JOURNAL ARTICLE

From Stigma to Support: "Black-Owned" Labels and Expertise Stereotypes in Cannabis and Psychedelics Markets.

  • Published In: Journal of Consumer Research, 2026, v. 52, n. 5. P. 1000 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Achar, Chethana; Agrawal, Nidhi; Lock, Keyaira 3 of 3

Abstract

This article investigates consumer responses to "Black-owned" labeling on cannabis and psychedelic products within the context of legalized drugs, focusing on the interplay of racial identity, stigma, and expertise stereotypes. Through four studies—including surveys of Black industry professionals, online experiments with Black and White consumers, and field advertising tests—the research finds that while Black entrepreneurs expect in-group support from Black consumers for such labeling, actual consumer responses vary by product category and racial identity. Specifically, White consumers show increased interest in Black-owned cannabis and psychedelics products consistent with positive expertise stereotypes, whereas Black consumers exhibit in-group support only for non-stigmatized products like candy, not for cannabis. The findings highlight that expertise stereotypes influence out-group consumer behavior in high-risk product categories, whereas in-group support may be constrained by stigma, offering nuanced insights into social identity branding and ownership labeling strategies in emerging legalized drug markets.

Additional Information

  • Source:Journal of Consumer Research. 2026/02, Vol. 52, Issue 5, p1000
  • Document Type:Article
  • Subject Area:Ethnic and Cultural Studies
  • Publication Date:2026
  • ISSN:0093-5301
  • DOI:10.1093/jcr/ucaf022
  • Accession Number:191385612
  • Copyright Statement:Copyright of Journal of Consumer Research is the property of Oxford University Press / USA 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.