Knowledge that Transforms

Abstract Platforms present various certifications to signal the quality of their offerings to prospective consumers. For example, Airbnb.com designates some hosts as “Superhosts” to distinguish properties that provide superior experiences. Platforms also present user-generated ratings—typically elicited and presented as “star ratings”—from their customers for the same purpose. This research investigates the interaction of these signals of quality and suggests a potential downside to platform-provided certifications: They decrease subsequent ratings. In an analysis of over 1,500,000 ratings from Airbnb.com and three follow-up studies, we find that properties with the superhost designation receive lower ratings. We assess the robustness of this result in several ways, including comparing ratings on Airbnb with those for the same property on Vrbo. In three follow-up experiments, we find that the net effect of certifications can lead to reduced choice share: The positive effect of signaling quality is more than offset by the negative effect of reduced ratings. This suggests that consumers are not sufficiently aware of this effect of quality certifications on ratings when choosing.

Abstract Care is increasingly marketized. Previous marketing and consumer research has focused on specific tensions underlying marketized care provision and the ways in which consumers navigate them. In contrast, this conceptual article draws on interdisciplinary research on care to develop a cumulative understanding of marketized care, that is, based on those effects that build up over time when a critical mass of consumers routinely addresses care needs via markets. Defining marketized care as attending to the welfare needs of human and nonhuman others through the market, we identify four negative cumulative effects: individuating effects on consumer subjectivities, alienating effects on care relationships, responsibilizing effects on consumers as opposed to other institutional actors of care provision, and exploitative effects generated in global care and supply chains. We also outline four principles that can mitigate these effects: interdependent consumer autonomy, affective reconnections, proportionate responsibilization, and market reconfiguration. Our conceptualization moves the literature on marketized care forward by outlining its cumulative nature as well as offering potential solutions that are neither demonizing nor celebratory of markets. In doing so, we offer a series of generative insights for research on marketized care that contribute to addressing collective human and nonhuman flourishing.

Abstract The use of generative AI (genAI) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more. However, their role in creative ideation—a cornerstone of consumer research—remains underexplored. Drawing on the human creativity literature, we propose that ideation with genAI is facilitated by its productivity and semantic breadth, which are psychologically analogous to the dual pathways of persistence and flexibility in human ideation. Further, we distinguish between the utility of genAI as a key ideator versus humans as key ideator, conceptualized through the genAI ideation roles of Designer and Writer and of Interviewer and Actor. While genAI excels in generating incremental improvements, its potential for groundbreaking innovation could be unlocked by leveraging its ability to prompt human creativity. This article advances the theoretical and practical understanding of genAI in ideation for consumer research, offering numerous practical guidelines for integrating generative AI into research while emphasizing human–AI collaboration to achieve radical insights.

Abstract We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapse occurs when GenAI outputs lose human sensibilities. Data and model challenges arise as democratization enhances data representation while also embedding real-world biases. The average trap, caused by next-token prediction models, leads to generic outputs that lack individuality. Additionally, model collapse occurs when GenAI increasingly learns from its own outputs, amplifying machine bias and diverging from human behavior. To address these challenges, researchers can leverage democratization to study marginalized consumers and prioritize human-centered research over purely data-driven methods. The average trap can be mitigated by fine-tuning models with task-specific and marginalized consumption data while engineering responses for uniqueness. Preventing model collapse requires integrating human–machine hybrid data and applying theories of mind to realign AI with human-centric consumption. Finally, we outline three future research directions: preserving data distribution tails to support consumption democratization, countering the average trap in next-token prediction, and reversing the trajectory from democratization to model collapse.

Abstract Brands are expanding their market to new consumers by displaying cultural diversity in marketing campaigns—but the risks are unclear. This article explores the role of the brand’s characteristics in multicultural diversity. Our findings suggest that when brands launch a new multicultural diversity campaign, it may be well-received if the brand’s original focus was on the needs of nonmarginalized consumers. However, for brands with a focus on the needs of marginalized consumers (i.e., marginalized-focused brands), such a campaign may be perceived as selling out, thereby negatively impacting consumers’ reactions. Six studies with Black and White U.S. consumers explore this novel “sellout effect” across various product categories and for consequential behaviors. Inclusion is identified as a key mechanism, mitigation strategies are explored, and implications for theory and practice are discussed. Findings highlight the importance of diversity marketing, understanding marginalization for consumers and brands, and the need for research that considers race and ethnicity.

Abstract Consumption has always been part of the workplace, yet it has traditionally been seen as nonwork—an activity that depletes rather than creates value. In the knowledge and digital economy, however, consumption and work are becoming increasingly intertwined, calling for a relational perspective on consumption’s productive role. We develop this perspective through a four-year ethnography of coworking spaces across Paris and London, supplemented by post-pandemic archival data. We introduce consumptive work as the instrumentalization of consumption activities in the workplace to generate productive value. Consumptive work emerges within a postindustrial societal context where workplace culture is shaped by consumer ideology, leading to (1) customer entitlement in the workplace, (2) consumer desire toward the workplace, and (3) consumer lifestyle aspirations toward work. Consumptive work is characterized by inconspicuousness, boundarilessness, and communal and market exchange. While it can be empowering, it also fosters neo-normative alienation, particularly through performative play and leisure, and the pursuit of productive wellness. Ultimately, consumptive work reinforces evolving consumer desires and aspirations about office work and workplaces. This study advances interdisciplinary research on consumption and consumption ideology in the workplace, workplace alienation, new ways of working, and consumer research connecting work, home, and leisure.

Abstract With the rise of environmental concerns in recent decades, many companies have joined the initiative to advertise and promote sustainable consumption. The current research examines how providing sustainability cues to consumers might have unintended consequences of which practitioners and policymakers may not be fully aware. One pilot study and 10 main studies, including two real-choice studies, show that a sustainability cue may delay consumption. That is, in an intertemporal choice, a sustainability cue can increase preference for a larger-later option over a smaller-sooner option. This effect occurs because a sustainability cue shifts a consumer’s temporal focus toward the future, leading to a shorter perceived wait time for the larger-later option. The findings further show that the delay does not emerge among those with strong green consumption values and can be circumvented if firms communicate the immediate need or instant payoff of sustainable actions. By investigating how sustainability cues shift consumer preferences between two options separated in time, the current research contributes to the literature on both sustainable consumption and intertemporal choice. The findings offer practitioners and policymakers guidelines to nudge consumers’ sustainable consumption more effectively.

Abstract Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years, featuring unique offerings from individual providers. However, scalable quantification of visual uniqueness and their impacts on platforms like Airbnb remain largely unexplored. We address this gap by developing, validating, and applying an unsupervised machine learning model to automatically extract uniqueness from images and quantify its impact on demand. We first construct a machine learning model, informed by cognitive psychology, to assess visual uniqueness in 481,747 property images, achieving high accuracy and interpretability. Next, we validate our model through three studies involving various participant populations and methods, confirming that the model’s predictions of visual uniqueness align with human judgment. Finally, we apply this model to demand data of Airbnb properties in New York City spanning 13 months. We find an inverted U-shaped relationship between visual uniqueness and demand, with two significant moderation effects: properties with higher response rates or overall ratings benefit more from visual uniqueness. This research provides valuable insights for P2P platforms like Airbnb, highlighting the strategic use of visual uniqueness to enhance visual appeal and market performance. It also offers a new methodological roadmap for integrating psychological insights into the development and validation of unsupervised machine learning models.

Abstract Across six studies, we provide converging and robust lab and field evidence that the fundamental human desire for control motivates consumer engagement in collecting, defined as the act of acquiring items that belong to an existing collection. This is because consumers who desire control seek structure, which is created when interconnected components form a holistic entity. A collection can provide such a structure, as it comprises related items that together create a whole set. Hence, as consumers add items to a collection, they are also manifesting a structure. Indeed, we demonstrate that desire for control’s motivating effect on engagement diminishes when structure-seeking is hindered or when the collection is far from completion. This work contributes to extant consumer research by identifying desire for control as a fundamental motivation of collecting behavior, explaining when and why consumers work toward completing their collections, and explicating the structured nature of collecting. Of practical relevance, we provide implications for the enhancement of consumer well-being; the design, positioning, and communication of collectible products; and the creation of policies regulating the collectibles market.

Abstract Societies create material, social, and moral boundaries that define who and what is dirty. “Dirt” encompasses literal and figurative things—objects, beings, ideas—that transgress these boundaries and thus are “out of place.” Previous research describing how consumers avoid and manage dirt assumes that dirt is aversive. The concept of consumer dirtwork emerged from our examination of self-described “dirtbag” wilderness consumers. Dirtwork reveals the potential usefulness of dirt. Instead of cleaning, dirtworkers redraw dirt boundaries, revealing resources they then work to capture. Boundary redrawing describes a continuum of adjustments to dirt boundaries, ranging from small shifts to complete inversions. Resourcing work describes the efforts required to capture the resources that are uncovered by boundary redrawing. Dirtwork results in challenges and rewards, and offers the possibility of continued dirtwork-resourced consumption. Dirtwork contributes by revealing the process wherein consumers make use of dirt, thus demonstrating the usefulness of dirt and fluidity of dirt boundaries. Dirtwork provides a useful lens for understanding consumer behaviors that do not aspire or cannot conform to socially-imposed cleanliness rules, including stigmatized, mundane, and extraordinary consumption. Dirtwork challenges assumptions that clean is good, socially-valuable, safe, and sustainable, and implicit associations of dirt with danger, stigma, and unsustainability.