Journal of Consumer Behaviour最新文献

The discount strategy has become a widely used approach by managers to boost sales and expand customer bases. However, despite the potential benefits of this strategy, the frequent implementation of discounts can negatively influence consumer behavior and lead to significant marketing expenses. To date, research has not explored effective strategies for withdrawing these discounts. Based on the benefit congruency framework, this study examined the effect of matching discount adjustments with product type on consumers' willingness to purchase. The results of three 2 (discount adjustments: frequency withdrawal vs. depth withdrawal) × 2 (product type: utilitarian vs. hedonic) between-subjects experiments confirmed the positive effect of this matching. Specifically, for hedonic (utilitarian) products, withdrawing discount depth (frequency) led to higher purchase intentions. Additionally, we demonstrated the reasons for the effects of discount withdrawal in terms of depth (perceived loss of utilitarian value) and frequency (perceived loss of hedonic value). This study contributed to the discounting literature by discovering that perceived value is the theoretical mechanism that determines the effectiveness of matching discount adjustments to product types. The research findings offer recommendations for managers seeking to adjust their pricing strategies and offer new insights into how to withdraw discounts while simultaneously retaining consumers.

Researchers have associated a dark side with metaverse adoption, suggesting that metaverse use may hurt the natural environment, society, and users' health. This study proposes a framework to measure the users' perceptions about the negative impacts of the metaverse on the environment, society, and users' health. The study also examined the effect of negative perceptions on metaverse adoption intention for three distinct segments of consumers, i.e., general consumers, technology enthusiasts, and socio-environmentally conscious consumers. Three studies were conducted. Study 1 was aimed at developing scales to measure the perceived negative impact of metaverse use on the environment (PIME), society (PIMS), and users' health (PIMH). Study 2 examined the role of PIME, PIMS, and PIMH in the consumers' metaverse adoption. Study 3 revalidated the proposed model on two segments of technology enthusiasts and socio-environmentally conscious consumers separately. PIMH was observed to be crucial for the metaverse adoption by general consumers and socio-environmentally conscious consumers. PIMS was observed to be a significant predictor of the metaverse adoption by socio-environmentally conscious consumers. The three study constructs were not observed to be significant predictors of metaverse adoption by technology enthusiasts.

Advancements in artificial intelligence (AI) are reshaping how consumers interact with advertising content, transforming their perceptions of reality, and redefining viewer engagement. However, research into consumer reactions to the use of AI in advertising remains limited. Drawing on Mandler's theory of value, this research investigates consumers' emotional and cognitive responses to the use of AI-generated deceased celebrities in advertising. A model was developed and empirically tested to examine the role of emotions in shaping consumers' perceptions of deception, attitudes, and behavioral intentions toward the advertised brand upon initial exposure to these digital recreations. This framework was tested across two studies using structural equation modeling and a representative sample of US consumers. The results supported the proposed model, showing that perceived incongruity triggers physiological arousal, which shapes consumers' positive and negative emotions. Positive emotions reduce perceived deception while negative emotions enhance it, and perceived deception negatively impacts attitude toward the brand and purchase intent. The moderating roles of celebrity attachment and brand heritage were explored. This research contributes to the growing body of literature on the psychological effects of AI applications in marketing communications. The results provide insights to managers who are considering utilizing deceased celebrity likenesses through AI.

In the sharing economy, consumers are routinely rated based on their behavior in marketplace transactions. Increasingly, these ratings are migrating across platforms—for example, individuals may display their Uber rating on a dating site. This growing practice, further enabled by recent advances in data portability, raises a critical question: How does the disclosure or concealment of sharing economy ratings influence judgments in unrelated digital contexts? The current research investigates the broader implications of quantifying and evaluating consumer performance through sharing economy ratings across domains. In four experiments, we find that concealing (vs. revealing) one's sharing economy rating (e.g., Uber rating) on an unrelated platform reduces one's trustworthiness and appeal to a level at or even lower than that of a revealed low rating. However, these effects emerge only when the sharing economy rating is perceived as diagnostic. Concealing other, less diagnostic information (e.g., streaming platform preferences) does not decrease trustworthiness or appeal. This research advances sharing economy, privacy, and signaling theory by revealing that sharing economy ratings function as diagnostic signals beyond their native platforms, that withholding personal data can incur reputational costs when such data is perceived as diagnostic, and that data portability—despite its consumer-centric promise—may unintentionally expose individuals to reputational harm. We conclude with a discussion of the implications for consumers, platforms, and policymakers.

The rapid rise of AI assistants is providing users with a more engaging and intelligent service experience. However, what drives the attractiveness of these multi-modal anthropomorphic AI agents and how they influence users' mindset metrics (attitudes, perceptions, and intentions), remains unclear. This study aims to identify the service attractiveness components of AI assistants using big data text analysis techniques, and reveal their manipulative effects on users' mindset metrics (satisfaction, perceived service quality and brand liking) from a time-considered dynamic perspective. To this end, 4584 valid users' reviews from Chinese car evaluation websites have been collected and analyzed. Conclusions show that: (1) the service attractiveness of AI assistants consists of Functional attribute (service process and service outcome), Relational attribute (sociability and friendliness) and Physical attribute (human-likeness and multi-modal); (2) All three attributes can significantly and positively manipulate users' mindset metrics; (3) car usage time exerts a differential impact on the positive manipulation of the three attributes of service attractiveness. In addition, the potential causes of the differential impact have been explored by constructing structural topic models to identify real-time users' concerns about service attractiveness in review texts. Our research provides new insights into the service attractiveness enhancement of AI assistants.

Mall personality and perceptions significantly influence relationship quality in the retail agenda, with consumer mindsets playing a critical role in shaping perception, evaluation, and behavior. However, no effort has been made so far to grasp the moderating mechanism of fixed (vs. growth) shopper mindsets in the interrelationships of mall brand gender (MBG), mall perception, and relationship quality. To address this gap, we conducted three follow-up studies: two online surveys with shoppers in China (N = 1087) and a 2 × 2 between-subjects design experiment in Study 3. The results offer robust empirical proof of the influence of MBG dimensions on mall perceptions and their subsequent impact on mall relationship quality. Importantly, the analysis shows that fixed-shopper mindsets significantly moderate the link between mall competence (vs. warmth) and mall relationship quality rather than growth-shopper mindsets. The findings also show that fixed shopper mindsets moderate the effect of mall brand masculinity on mall competence, while their moderating influence on the association between mall brand femininity and mall warmth is non-significant compared to growth shopper mindsets. Furthermore, the findings indicate that growth-shopper mindsets significantly moderate the influence of mall brand masculinity on mall warmth and mall brand femininity on mall competence rather than fixed-shopper mindsets. Finally, the paper evaluates its significant theoretical and practical implications.