With the increasing amount of consumer data, concerns over the seizure of data are a significant issue in contemporary digital society. While existing marketing literature has predominantly focused on firm–consumer interactions, it has paid scant attention to how consumers perceive government collection, access, and use of their data. Using the case of the worldwide WhatsApp data policy crisis, we theorize why and how consumers legitimize government data seizure. Drawing on netnographic data from Turkish consumers on three digital platforms, we identify three key themes: embracing the inevitable, safeguarding national interests, and politicizing privacy risks. Our research extends existing literature by incorporating consideration of the government, a key market actor, and by uncovering the interplay between political ideology, market politics, and data privacy. We also contribute by revealing consumers’ multilevel consumer privacy benefit-risk assessment and the role of political logic in this assessment.

Consumers desire privacy, but their actions often seem to conflict (privacy paradox). Prior research focused on situations where privacy-decisions are focal (e.g., choosing passwords), recommending smoothing frictions or heightening attitude salience via contextual cues to address these inconsistencies. However, in many situations (e.g., shopping), even when consumers face privacy-relevant decisions (e.g., exchanging one’s email for discounts), privacy decisions are not part of the focal task. For these situations, we propose that thoughts about privacy are crowded out by task-related thoughts, even when encountering contextual cues that should trigger privacy thoughts. Across four studies, our findings suggest that thoughts about privacy, assessed by open-ended and indirect measures, are scarce and dwarfed by thoughts about the focal task. Furthermore, contextual cues (e.g., marketer requests for information, browsing situation) alone were generally insufficient to generate spontaneous thoughts about privacy. However, in combination with close-ended measures, contextual cues generated moderate levels of privacy-related thoughts.

Consumer researchers have historically shied away from explicitly assessing the moral “rightness” of consumer decisions. In this article, however, we argue that experimental consumer research often implicitly endorses moral philosophies that do exactly this. We focus on three key philosophical theories: consequentialism, hedonism, and deontology. After providing brief definitions of each, we highlight ways in which they have been implicitly adopted in mature and often moralized areas of consumer research—self-control, prosocial behavior, and sustainable consumption. By considering past research in each domain through a different lens than originally adopted, we see how the recognition of implicit moral philosophies can help generate new inquiries. Finally, we propose that a philosophically aware consumer research may have much to offer future theory and practice: to take consumers’ moral philosophical commitments into account when designing interventions, and to explore truly new questions that arise from the critique of each moral lens.

As more consumption moves to the metaverse, digital goods are becoming a substantial source of revenue for firms. Unlike their physical counterparts, digital goods have minimal supply constraints, and firms must make more deliberate decisions both on how to set the total supply of a product and how to distribute features within a product line. It is thus critical to understand how these decisions impact what consumers are willing to pay for digital goods. In an analysis of over 800,000 sales across 40 major NFT (nonfungible token) collections, we investigate how collection size and feature rarity are associated with an item’s transaction volume and average selling price. We find that, while rare items within a collection sell for more on average, this effect increases for larger collections. In addition, while NFTs in larger collections do not have lower selling prices, on average, larger collections tend to generate more sales volume.

Consumers can use automated prediction algorithms, like navigation apps and recommendation systems, to forecast the outcome of to-be-determined events like how long a drive will take or how much they will enjoy a movie. However, little is known about what drives consumers’ preferences for prediction algorithms. The authors propose that consumers prefer prediction algorithms that replicate the event being predicted by going through the same process that generates event outcomes. In seven studies, the authors find that consumers like replicative prediction algorithms, even when they may lead to worse performance. Furthermore, the authors find that consumers shift toward choosing better performing algorithms after receiving performance feedback but still prefer replicative algorithms holding performance constant. These findings suggest that marketers can increase adoption of prediction algorithms by framing them as replicating (or designing them to replicate) the process that generates event outcomes.

Road traffic accidents are the leading cause of death worldwide for people aged 2–59. Nearly all deaths are due to human error. Automated vehicles could reduce mortality risks, traffic congestion, and air pollution of human-driven vehicles. However, their adoption depends on consumer acceptance, among other factors. In a nationally representative sample of Americans (N=580) and direct replication (N=193), we find consumers prefer lower levels of vehicle automation for themselves than for others. This difference is mediated by self-enhancing comparative evaluations. Relative to automated vehicles, consumers believe they are safer and more trustworthy drivers than other drivers. In a second experiment (N=803), enhanced assessments of self, not different assessments of automated vehicle capabilities, explained different preferences for self and others. Our findings show how biased self-evaluations reduce the acceptance of automated vehicles. This yields practical insights for policymakers and firms seeking to increase acceptance of automated vehicles.