Journal of Consumer Behaviour最新文献

Masstige brands (MBs) bridge the gap between traditional luxury brands (TLBs) and mass market brands (MMBs), constituting a rapidly growing market segment. While prior research has examined the effects of brand logo and word of mouth (WOM) on TLBs and MMBs, their impact on MBs remains underexplored. This study investigates how logo presence and WOM valence influence purchase intentions of MBs. Across three studies, we find that both logo presence and positive WOM significantly enhance purchase intentions for MBs. Furthermore, the effect of logo presence for MBs is comparable to that for TLBs and significantly stronger than for MMBs, driven by differences in social value signaling. The impact of attribute-based WOM valence remains stable for MMBs but becomes attenuated for TLBs, reflecting differences in functional value sensitivity. In contrast, experience-based WOM valence exerts a consistent influence across MBs, MMBs, and TLBs, highlighting the universal role of emotional value. These findings clarify value structures in the MB context, extend the literature on logos and WOM, and provide practical insights for managers to design strategies that leverage MBs' position between MMBs and TLBs.

This study investigates how non-elderly consumers psychologically internalize aging risk (AR)—a context-specific perception of future health, financial, and social uncertainty—into private medical insurance (PMI) purchase intentions (PI). Integrating Social Influence Theory (SIT) with the Theory of Planned Behavior (TPB), the study conceptualizes AR as a macro-level antecedent that influences attitudes (AT), subjective norms (SN), and perceived behavioral control (PBC) through informational and normative pathways. Using partial least squares structural equation modeling across three studies in China and Malaysia (N = 899), results show that AR significantly affects all TPB constructs, with AT emerging as the most robust mediator. Although mediation strength varies across countries, moderation tests confirm that the structural pathways linking AR to PI remain cross-culturally stable. These findings advance theory by extending TPB with socially embedded risk perceptions and dual-path influence mechanisms. Practical implications offer tailored strategies for insurers, policymakers, and communicators to address future-oriented health behaviors in aging societies.

The integration of generative AI into the luxury fashion industry is a burgeoning trend with the potential to impact consumer behavior significantly. While prior research has demonstrated the benefits of AI design in the fashion industry, it also suggests that such approaches may not fully align with the distinct values of the luxury segment. Building on prior research, this paper investigates how AI can be more effectively integrated into luxury design. We propose that human-AI co-creation presents a promising approach, as it balances efficiency with the value people place on human effort. Across four empirical studies, we find that human-AI co-creation in luxury design generates more positive consumer responses than AI-only designs. This effect is mediated by perceived design effort. Human-AI co-creation is viewed as involving greater design effort than AI-only design, and human-led co-creation is perceived as requiring more effort than AI-led co-creation. We further examine the moderating roles of co-creation modes and luxury product types, revealing that human-led (vs. AI-led) co-creation enhances perceived design effort and, in turn, improves brand attitudes, particularly for hedonic (vs. functional) luxury products. These findings offer valuable insights for luxury brands navigating the era of intelligent design, highlighting the importance of balancing human creativity with AI capabilities to sustain brand value and foster consumer acceptance.

The present study explores how Generation Z interacts with voice assistants and reacts to their product recommendations when shopping online. This research combines a self-report survey, the Zaltman Metaphor Elicitation Technique, and an eye-tracked product choice experiment, guided by the Technology Acceptance Model, the Media Richness Theory, and the Visual Attention Theory. This design captures the cognitive, emotional, and attentional processes influencing the intention of using voice assistants and the likelihood of following their product recommendations. The survey shows low intention to use voice assistants due to their concerns over perceived usefulness and ease of use. The Zaltman Metaphor Elicitation Technique supports these findings, revealing that while cognitive load for low-enjoyability products was reduced, skepticism persists in product categories where personal preferences and emotional connections are stronger. The product choice experiment further demonstrates that when voice assistants recommend products, those products attract greater visual attention (e.g., a higher number of fixations), especially for low-enjoyability items like detergent and drills. However, participants tend to rely on their own preferences for high-enjoyability products, such as bicycles and couches, despite engaging with the recommendations, as shown by the average fixation duration. These results suggest that voice assistants are most effective in assisting Generation Z's decision-making for products they have limited experience with or interest in. In contrast, recommendations have less impact when Generation Z has emotional or aesthetic preferences. The findings highlight that marketers should tailor voice assistants' recommendations by product category to increase Generation Z's engagement.

The proliferation of charitable organizations in recent years has led to heightened competition for donor contributions. Consequently, there is an increasing imperative to understand how advertising appeals can be optimized to enhance awareness and encourage future donations. One underexplored consumer segment in this context is that of older consumers. In this research, we explore the possibility that older consumers are more likely to donate to advertisement appeals with children than those without children. We draw on research suggesting that especially for older consumers, it is important to help children (rather than adults), since they are likely to outlive adult beneficiaries and effectuate influence beyond the donor's lifetime. Moreover, we posit that the presence of children triggers salience of parenting motivation in older consumers. We tested these hypotheses over four studies, including secondary data (N = 1000 advertising campaigns) and three controlled experiments (N = 627). Our findings demonstrate consistent support that older (versus younger) consumers report greater actual donations and higher donation inclinations to assist refugee beneficiaries with children than those without children. The interaction effect was further substantiated by a distant self (older self) and near self (control) moderator, with participants in the distant-self condition expressing significantly higher donation intentions for refugees with (versus without) children whereas there were no significant differences among participants in the near-self condition. Notably, the effect is mediated by parental motivation. In addition to theoretical understanding, this research offers a novel approach to develop effective advertising and marketing communication.

Some consumers experience identity threats from artificial intelligence (AI) due to its ability to mimic human traits and behaviors, which can negatively impact their perceptions and acceptance of AI. Drawing on the Computers Are Social Actors (CASA) paradigm, exemplar theory, and social identity theory, this research suggests that these identity threats can be reduced when users feel a sense of AI relatedness, a user's feelings of relational connection to AI as a category rather than any single tool. Across three quasi-experiments among users and non-users of generative AI, virtual assistants, and customer service chatbots, results show that users perceive greater AI relatedness, which then reduces perceptions of AI identity threat. However, this effect disappears when (1) consumers use a large number of AI tools and (2) AI use feels mandatory rather than voluntary. Notably, even among forced users, simply using AI reduced perceived identity threats in two of the three contexts. Collectively, this suggests that AI use fosters a broader sense of connection with AI, which can help mitigate identity threats and improve consumers' AI perceptions. This work provides both theoretical implications for understanding consumer-AI relationships and consumers' social identity formation, practical strategies for reducing AI resistance, and societal considerations related to widespread AI use.

With the continuous reshaping of digital marketing by generative artificial intelligence (GenAI), understanding how AI-generated advertising can optimize the responses of consumers is critical for businesses. As GenAI evolves from unimodal models to multimodal models that jointly process texts, images, and audios, the literature still lacks a systematic comparison of their advertising effectiveness. This study aimed to address this gap by exploring the types of products to which unimodal and multimodal GenAI are applicable, and the underlying psychological mechanisms, respectively. A study of real-world data demonstrates that multimodal AI-generated advertisements are more suitable for experience products. Two online studies show that the perceived immersion of consumers can be enhanced by the integration of multimodal AI-generated advertisements with experience products, which thereby generate favorable marketing outcomes. Conversely, the cognitive load of consumers can be reduced by combining unimodal AI-generated content with search products, which thus strengthens their purchase intentions. This research proposes a modality-product type matching effect and extends the existing research stream on the application of GenAI in advertising. It further provides marketers and advertisers with reliable strategies for leveraging GenAI.

As artificial intelligence (AI) service robots become increasingly prevalent, service failures remain a persistent challenge, highlighting the need for effective recovery strategies. In recent years, zoomorphic robots have gained increasing application in service contexts. Zoomorphic design refers to AI service robots that incorporate animal-like physical characteristics, such as ears, tails, or expressive facial elements. This research examines how zoomorphic design influences consumer tolerance of service failures. Across four experimental studies, we demonstrate that zoomorphic robots significantly increase consumer tolerance of service failures compared to non-zoomorphic robots. This effect is driven by perceived warmth, suggesting that animal-like features elicit more favorable emotional responses. Moreover, this effect is moderated by form realism, where high form realism mitigates the positive effects of zoomorphic robots. The findings offer a novel perspective on consumer responses to AI service failures and contribute to the literature on robot design and service recovery.

As generative artificial intelligence (AI) becomes increasingly prevalent in the creative industries, firms are adopting AI to produce artwork. Drawing on social identity theory, this work investigates how consumers respond to AI-generated versus human-generated artwork. Across one field study and three lab experiments, we find that consumers consistently prefer human-generated artwork. In addition, we show that this effect is driven by consumers' reduced empathy with the AI generator, which in turn weakens their identification with the AI generator and lowers preference for AI-generated artwork. Importantly, we identify two interventions that mitigate this effect: increasing consumers' perceived similarity to AI, and framing AI developers as in-group members. These findings reveal that while consumers tend to devalue AI-generated artwork, strategic cues can foster connection and improve acceptance. This research contributes to the emerging literature on the mechanism of consumers' reactions to AI and offers actionable insights for marketers aiming to integrate AI into artistic domains.