JOURNAL ARTICLE

Is Sustainability a Liability? Green Marketing and Consumer Beliefs About Eco-Friendly Products.

  • Published In: Journal of Public Policy & Marketing, 2025, v. 44, n. 2. P. 261 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Chernev, Alexander; Blair, Sean; Böckenholt, Ulf; Mishra, Himanshu 3 of 3

Abstract

This article examines the "sustainability liability" hypothesis, which suggests that consumers perceive sustainable products as underperforming compared to traditional products. Through a large-scale empirical study involving over 6,500 respondents and multiple product scenarios adapted from prior research, the authors find that the sustainability-liability effect is very small and unlikely to have practical significance. They further identify that this effect may only appear modestly for products emphasizing strength-related attributes, while products associated with gentleness may even benefit from sustainability perceptions. Archival linguistic analysis comparing word associations from Google News (up to 2013) and Wikipedia (up to 2021) corpora indicates that over time, sustainability has become more positively associated with product performance, suggesting a shift in consumer beliefs. These findings imply that promoting eco-friendly products does not detract from perceived product quality and support public policies encouraging sustainable product development and marketing without fearing consumer resistance based on performance concerns.

Additional Information

  • Source:Journal of Public Policy & Marketing. 2025/04, Vol. 44, Issue 2, p261
  • Document Type:Article
  • Subject Area:Marketing
  • Publication Date:2025
  • ISSN:0743-9156
  • DOI:10.1177/07439156241264286
  • Accession Number:183576447
  • Copyright Statement:Copyright of Journal of Public Policy & Marketing is the property of American Marketing Association 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.)

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