JOURNAL ARTICLE

Bridging Who They Are with Who They Thought They’d Be: The Effects of Gen Zers’ Subjective Well-Being on Their Boycott Responses to Online and Offline Unethical Situations.

  • Published In: Journal of Interactive Marketing, 2023, v. 58, n. 2/3. P. 248 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Tuan, Annamaria; Visentin, Marco; Di Domenico, Giandomenico 3 of 3

Abstract

This article examines how subjective well-being influences Generation Z's (Gen Zers, born 1997–2012) responses to unethical situations encountered online and offline, particularly their intentions to enact or support boycotts. The study integrates moral reasoning domains—ethical idealism, self-congruence, self-expression, and the desire to make a difference—and situational factors such as online versus offline context and first-person versus third-person perspective. Findings indicate that higher subjective well-being among Gen Zers increases their likelihood to actively enact boycotts but decreases their tendency to merely support boycotts, suggesting a preference for authentic action over symbolic gestures. Additionally, unethical situations experienced indirectly (third-person) elicit stronger boycott intentions than those experienced firsthand (first-person), and online contexts do not necessarily increase boycott intentions. The research highlights Gen Z's strong ethical values and identity congruence in consumption choices, with implications for brands aiming to engage this socially conscious generation.

Additional Information

  • Source:Journal of Interactive Marketing. 2023/05, Vol. 58, Issue 2/3, p248
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2023
  • ISSN:1094-9968
  • DOI:10.1177/10949968221136862
  • Accession Number:162962613
  • Copyright Statement:Copyright of Journal of Interactive 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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