AbstractIdeology plays a central role in consumer decisions, actions, and practices. While there have been numerous studies of ideological formations in specific consumption contexts, an integrative theoretical framework on consumption ideology has been missing. The theoretical framework presented in this article integrates systemic, social group, and social reality perspectives from social theory with prior consumer research to conceptualize consumption ideology as ideas and ideals that are related to consumerism and manifested in consumer behavior. Consumption ideology originates from conflicts between consumer desires and the system of consumerism. It is reflected in consumers’ lived experiences and expressed in social representations and communicative actions related to status-based consumption, brand affinity and antipathy, performed practices, and political consumption. By adapting to the market, consumers confirm the system, but when they resist, they accelerate conflicts in consumer experiences unless resistance is ideologically co-opted by the market. Three illustrative cases—upcycling, Zoom backgrounds, and the commercialization of TikTok—exemplify how the framework may be used to analyze consumption ideology and generate new research questions. The article concludes with future research programs that move beyond micro-theorizations to illuminate the broader role of ideology in contemporary consumerist society.

Abstract Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products. This research compares high-adaptivity algorithms, which can learn and adapt, versus low-adaptivity algorithms, which are entirely pre-programmed, and explore their impact on consumers' product preferences. Six empirical studies show that, in general, consumers prefer products with high-adaptivity algorithms. However, this preference depends on the desired level of product outcome range—the number of solutions a product is expected to provide within a task or across tasks. The findings also demonstrate that perceived algorithm creativity and predictability drive the observed effects. This research highlights the distinctive role of algorithm types in the perception of consumer goods and reveals the consequences of unveiling the mind of the machine to consumers.