Abstract Incidental waits are an unavoidable element of the consumer experience on any digital platform. Firms typically utilize no animation or single-speed, repeated animations during these waits. How might the use of such animations and their visual qualities influence perceived waiting time? Can simple design changes by managers influence the customer experience? Although prior research suggests a linear relationship in which faster animations reduce perceived waiting time, we find a convex relationship between animation speed and time perception: moderate-speed animations minimize perceived waiting time compared to no, slow-moving, or fast-moving animations (experiments 1a–1c). We refer to this effect as the convex effect of animation speed. This effect occurs when people use animation speed to infer wait time (experiment 2a) and because moderate-speed animations draw more attention than static images or faster animations (experiment 2b). Animations that introduce dual-attention elements (experiment 3a) or atypical animations (experiment 3b) shift attention away from movement speed, attenuating this effect. Finally, this effect extends to website click-to-landing rates (experiment 4), conversion rates (experiment 5), and product evaluations during mobile shopping (experiment 6). These findings highlight the practical value of optimizing animation speed in user interface design to enhance both customer experience and business outcomes.

Artificial intelligence (AI) is pervading the government and transforming how public services are provided to consumers across policy areas spanning allocation of government benefits, law enforcement, risk monitoring, and the provision of services. Despite technological improvements, AI systems are fallible and may err. How do consumers respond when learning of AI failures? In 13 preregistered studies (N = 3,724) across a range of policy areas, the authors show that algorithmic failures are generalized more broadly than human failures. This effect is termed “algorithmic transference” as it is an inferential process that generalizes (i.e., transfers) information about one member of a group to another member of that same group. Rather than reflecting generalized algorithm aversion, algorithmic transference is rooted in social categorization: it stems from how people perceive a group of AI systems versus a group of humans. Because AI systems are perceived as more homogeneous than people, failure information about one AI algorithm is transferred to another algorithm to a greater extent than failure information about a person is transferred to another person. Capturing AI's impact on consumers and societies, these results show how the premature or mismanaged deployment of faulty AI technologies may undermine the very institutions that AI systems are meant to modernize.

Abstract Consumers’ choices of whom to follow on digital platforms shape their informational landscape. In an era in which the credibility of informational sources is critical, this research examines two key questions: (1) When do consumers prioritize communicator credibility over likability in their decisions to follow? and (2) How do multiple credibility and likability cues interact to influence these decisions? Analyzing four large datasets from popular following-enabled platforms, we find that consumers’ orientation toward content consumption—goal directed (“search”) versus experiential (“scroll”)—is key. Communicator credibility drives following on search-driven platforms (Yelp, Goodreads), whereas likability drives following on scroll-driven ones (Twitter/X, Instagram). Aggregate communicator sentiment across multiple posts serves as a cross-platform indicator of credibility and likability, and its effect on follower count differs by platform type. On scroll-driven platforms, communicators with positive aggregate sentiment benefit from a likability premium, attracting the most followers; this preference for positivity is mitigated by the presence of alternative communicator likability cues (e.g., using sociable language). On search-driven platforms, communicators with mixed aggregate sentiment benefit from a credibility premium, attracting the most followers; this preference for mixed sentiment is mitigated in the presence of alternative credibility cues (e.g., Yelp’s “Elite” badge). Implications for consumer protection and platform design are discussed.