Abstract

Despite heightened interest in brand transgressions among academics and practitioners, the literature remains silent about the influence of a brand’s origin on consumer responses to brand misconduct. This leaves managers unaware of how to adapt post-transgression recovery strategies at home and abroad. Contrary to the in-group country bias literature, we theorize an “origin-backfire” effect: consumers forgive domestic brand transgressions less. Analyzing experimental, social media, and secondary-longitudinal data, we find that consumers treat domestic brand transgressors as home-country traitors deserving punishment. Social identity threats mediate this effect and consumer ethnocentrism attenuates it. Transgressions’ damage on brand reputation and value is larger and takes longer to recover from in domestic markets. Managers can alleviate post-transgression backlash through communication framing that construes the transgression as a response to intergroup threats (in foreign markets) and through collective compensation strategies (in domestic markets). The findings reveal cross-national variability in transgressions’ experience, impact, and recovery and inform post-transgression repair strategies.

Conceptual framework and hypotheses

Country-of-origin (COO) research highlights the influence of a brand’s origin country on consumer decision-making (Hulland et al., 1996). The conceptualization of brand origin is a contentious issue that has attracted significant research interest in the international marketing literature (Magnusson & Westjohn, 2011). Following the surge in globalized value chains, there is considerable ambiguity regarding whether a brand’s COO is determined by where it is manufactured, designed, or sourced. In response, recent developments suggest that a brand’s COO should be defined as the “country which a consumer associates with a product or brand as being its source, regardless of where the product is actually produced” (Jaffe & Nebenzahl, 2006, p. 29). Samiee (2011) suggests that brand origin reflects the country of the brand’s headquarters and is what consumers typically associate with the brand (e.g., Nike is seen as a U.S. brand even though most of production occurs overseas). Thus, we define domestic (foreign) brands as brands that consumers perceive as originating in their own (a foreign) country, regardless of where they are produced or sourced.

Consumers are more favorably predisposed toward domestic than foreign brands, a phenomenon referred to as “domestic country bias” (Balabanis & Diamantopoulos, 2004). Socially, consumers see domestic brands as members of their in-group and support them to promote national interests. Economically, consumers support domestic products because they protect local economic structures, decrease unemployment, and create positive externalities for local economies (Van Ittersum & Wong, 2010). Culturally, domestic brands represent the national culture, unite local communities, protect local traditions, and elicit national pride through their achievements (Özsomer, 2012). Politically, domestic products benefit from consumers’ growing antiglobal sentiment and anticorporatist attitudes (Davvetas et al., 2023; Thompson & Arsel, 2004). Although in some countries consumers show xenocentric tendencies that favor foreign products (Balabanis & Diamantopoulos, 2016), the literature collectively suggests that domestic brands benefit from “in-group bias” while foreign brands suffer from the “liability of foreignness” (Table S2, Web Appendix).

Discussion

Brand transgressions elicit stronger negative sentiment among domestic than among foreign consumers. Domestic consumers’ sentiment toward the brand decreases right after the transgression and improves only in the long term. In contrast, transgressions do not take an equivalent emotional toll on foreign consumers whose sentiment toward the brand is steadily improving over time. The results thus hint at a time-dependent divergence in emotional reactions to domestic and foreign brand transgressions. Due to (1) the inherent biases of OpenAI in sentiment categorization/calculation (Abramski et al., 2023), (2) the noise and correlational nature of Twitter data, and (3) the analysis of tweets only from users who included brand handles in their tweets, the results of Study 1 prevent definitive causal inferences. To address these issues, Study 2 uses data outside Twitter.