Journal of Consumer Psychology最新文献

Despite the growing interest in studying the psychology of scarcity and its effect on consumers, little research has investigated whether the effect of resource scarcity depends on the type of scarce resource. Across six studies, we identify the distinct psychological and decision-making consequences of two types of resource scarcity: time and money. We hypothesize that time and money scarcity can differentially influence consumer preferences by altering self-perceptions. Time (vs. money) scarcity leads to perceiving the self as a scarce resource, and these self-perceptions result in time- (vs. money-) scarce consumers drawing more favorable assessments of self-value and preferring options that reflect their heightened self-value. Furthermore, we demonstrate that the differential effect of time versus money scarcity only holds for consumption choices reflecting self-value. We also identify an important moderating condition: the source of the experience of scarcity, specifically whether it was self-chosen or not.

Agents help consumers make decisions. While agents have traditionally been human (e.g., sales associate, real estate agent, financial advisor), artificial intelligence (AI) agents are becoming more prevalent. We find that the type of agent, AI versus human, has an influence on intertemporal judgment. Specifically, when an agent is identified as AI, the concept of fast processing becomes more accessible, which makes time delays seem subjectively longer and encourages impatient behavior. These results have implications for how to conceptualize the influence of AI agents on judgment, the impact of time perception on intertemporal choices, and the sources of impatient behavior.

Large Language Models have been incorporated into an astounding breadth of professional domains. Given their capabilities, many intellectual laborers naturally question to what extent these AI models will be able to usurp their own jobs. As behavioral scientists, we performed an effort to examine the extent to which an AI can perform our roles. To achieve this, we utilized commercially available AIs (e.g., ChatGPT 4) to perform each step of the research process, culminating in an AI-written manuscript. We attempted to intervene as little as possible in the AI-led idea generation, empirical testing, analysis, and reporting. This allowed us to assess the limits of AIs in a behavioral research context and propose guidelines for behavioral researchers wanting to utilize AI. We found that the AIs were adept at some parts of the process and wholly inadequate at others. Our overall recommendation is that behavioral researchers use AIs judiciously and carefully monitor the outputs for quality and coherence. We additionally draw implications for editorial teams, doctoral student training, and the broader research ecosystem.

Below are comments on Tomaino, Cooke, and Hoover by four teams of collaborative reviewers that helped clarify and focus its original version. Their comments on the refined version articulate how the fast-moving world of generative AI can alter authors, readers, reviewers, and consumer behavior journals. In the first comment, Blythe, Kulis, and McGraw propose that Generative AI requires substantial effort to generate research that is fast, cost-effective, and of high quality. They articulate three recommendations: to ask, to train, and to check the system. Asking builds on GenAI's ability to reveal its own capabilities at different stages of the research process. Training allows the system to be customized with relevant context, domain-specific documents, and tailored examples, enhancing its accuracy and reducing errors. Checking is strongly advised to validate that the outputs are both reasonable and robust. Haenlein, Hewett, and Yoo build on the capabilities of Large Language Models that go beyond the research practices central to consumer psychology. They outline strategic prompting strategies: starting broadly and gradually narrowing to specific domains, downloading information from relevant articles and data that is unlikely to be part of the current corpus, and evoking specific theories, methods, or presentation formats. They also elaborate on the ways the apparent magic of GenAI may raise learning or ethical challenges. The third comment by Stacy Wood focuses less on the capabilities of GenAI and more on how its adoption will depend on researcher feelings—in other words, how different aspects of its use may alter researchers' experiences of doing research and their identities as scholars. GenAI has the potential to both build (through increased productivity or increased accessibility) and limit (through loss of agency or faster production) pride of purpose in research. She argues that feelings from using GenAI are likely to differ across research steps, from developing novel concepts, processes, analyses, and writing of the paper. Wherever GenAI may lessen the excitement, satisfaction, motivation, and perceived status of the researcher, barriers to its use are likely to be erected. Finally, Vicki Morwitz identifies new AI capabilities beyond those explored in Tomaino et al. Those include the ability to generate synthetic data that can guide empirical experiments, a facility to create audio and visual stimuli, a capability to study group behavior, and a capacity to reliably interpret complex human statements. The comment then closes with important questions for editorial policies, raising issues about limitations on AI use by authors, its appropriate applications by review teams, and possible publishers' restrictions on uploading copyrighted articles.

Despite increasing attention to alternative market systems, where consumers perform considerable voluntary labor, consumer researchers have a limited understanding of the nature or implications of the emotion work entailed in making such contributions. This paper addresses this gap, focusing on “committed consumers” defined as those who provide extensive volunteer labor to support alternative markets and their principles. It does so based on hermeneutic analysis of ethnographic and netnographic data collected from participants in local food markets (REKO markets) in Norway. The paper identifies four distinct types of institutional emotion work that contribute to perpetuating alternative markets and conceptualizes how committed consumers' own emotions are affected when making such contributions. This paper extends our understanding of consumers' roles in alternative market systems and of the socially constituted and constitutive emotions entailed in consumer volunteerism.

Markets are shaped by innovation and choice. Drawing upon advances in the scientific study of awe, in this article I present a model that details how experiences of this emotion shape innovation and choice. I first detail the latest science on awe, which finds it to be distinct from closely related states, like beauty, interest, admiration, and fear, and that orients individuals to rigorous, systems-based, meaning-making thought, and actions that enhance social integration. I then summarize how awe leads to a mental state of wonder and curiosity, a fertile ground for the creation of cultural forms through acts of innovation. As illustrations, I consider how awe leads to creative representation, symbolic expression, ritualization, and object design. To the extent that these cultural creations are touched by awe, I then reason, they will fare well in terms of choice, a process whose discussion is the concern in the last section of this article.

This article builds on Keltner's conceptual model of awe, innovation, and choice (Keltner, 2025). This article expands on the framework in two main ways by outlining (1) when awe could have positive versus negative consequences for consumer choice and (2) how focusing on distinctive aspects of the consumer behavior setting may further enhance understanding of awe. Building on these themes, this article proposes several areas for research: examining granular aspects of the core appraisals, further characterizing different cognitive functions, considering consequences for different consumer choice domains (e.g., decision making, indulgence, customization), and focusing on how different kinds of relationships (e.g., brand communities), types of prosocial action (e.g., donating vs. volunteering), and forms of brand generated awe (direct vs. indirect) impact consumer behavior. This article offers specific propositions to encourage future research on how awe may impact consumers and brands.

Prior scholarship characterizes awe as an aesthetic emotion, and the rich and growing aesthetics literature can help illuminate the role of awe in consumer psychology. The current commentary draws on this literature, as well as Keltner's conceptual analysis of awe, to highlight findings and remaining questions pertaining to awe in the realm of consumption. Marketing activities such as branding and promotion, store design, and product development present opportunities to awe consumers, yet awesome consumption experiences are rare. The current work discusses characteristics of awe-inspiring products and brands, the tendency of awe to increase or decrease consumption, relevant individual differences between consumers, and the nature and evolutionary background of awe and aesthetics.

Despite evidence that people believe that the unhealthier the food, the tastier it is, some studies also suggest the opposing belief—the healthier the food, the tastier it is. A framework is proposed to reconcile this contradiction, and four studies demonstrate that the discrete categorization of foods as healthful versus unhealthful determines which intuition consumers use. When stereotypically unhealthy foods (e.g., candies, ice cream, hot dogs) are encountered, they are automatically categorized as unhealthful and the properties associated with that category (e.g., sweetness, saltiness, fat content) become accessible. Inferences about taste are then based on these properties and the unhealthier the encountered products are (i.e., the higher the sugar and fat content they have), the tastier they are perceived to be (unhealthy = tasty belief). Conversely, when stereotypically healthful foods (e.g., fruits) are encountered, other properties (e.g., freshness, vitamins) become salient, and tastiness is mainly inferred based on these properties, leading to the inference that the healthier these foods are (i.e., the more freshness and vitamins they have), the tastier they are perceived to be (healthy = tasty belief). Marketers and policymakers can leverage these findings to understand better when emphasizing healthiness benefits or hurts taste perceptions.

The deployment of autonomous vehicles (AVs) and the accompanying societal and economic benefits will greatly depend on how much liability AV firms will have to carry for accidents involving these vehicles, which in turn impacts their insurability and associated insurance premiums. Across three experiments (N = 2677), we investigate whether accidents where the AV was not at fault could become an unexpected liability risk for AV firms, by exploring consumer perceptions of AV liability. We find that when such accidents occur, the not-at-fault vehicle becomes more salient to consumers when it is an AV. As a result, consumers are more likely to view as relevant counterfactuals in which the not-at-fault vehicle might have behaved differently to avoid or minimize damage from, the accident. This leads them to judge AV firms as more liable than both firms that make human-driven vehicles and human drivers for damages when not at fault.