Cracking the consumers' code: A framework for understanding the artificial intelligence-consumer interface.

This review presents a framework for understanding how consumers respond to artificial intelligence (AI) and related technologies, such as robots, algorithms, or chatbots. Drawing on a systematic review of the literature (N = 111), we describe how AI technologies influence a variety of consumer-relevant outcomes, including consumer satisfaction and the propensity to rely on AI. We also highlight the important role that consumer characteristics along with contextual characteristics (i.e., the micro and macro context) play in shaping the AI-consumer interaction. We then discuss novel theoretical perspectives that could shed light on the psychological processes triggered by AI-consumer interactions. We conclude by adopting a meta-scientific perspective and discussing how AI may change the process of scientific discovery.

Language matters: humanizing service robots through the use of language during the COVID-19 pandemic.

Service robots are emerging quickly in the marketplace (e.g., in hotels, restaurants, and healthcare), especially as COVID-19-related health concerns and social distancing guidelines have affected people's desire and ability to interact with other humans. However, while robots can increase efficiency and enable service offerings with reduced human contact, prior research shows a systematic consumer aversion toward service robots relative to human service providers. This potential dilemma raises the managerial question of how firms can overcome consumer aversion and better employ service robots. Drawing on prior research that supports the use of language for building interpersonal relationships, this research examines whether the type of language (social-oriented vs. task-oriented language) a service robot uses can improve consumer responses to and evaluations of the focal service robot, particularly in light of consumers' COVID-19-related stress. The results show that consumers respond more favorably to a service robot that uses a social-oriented (vs. task-oriented) language style, particularly when these consumers experience relatively higher levels of COVID-19-related stress. These findings contribute to initial empirical evidence in marketing for the efficacy of leveraging robots' language style to improve customer evaluations of service robots, especially under stressful circumstances. Overall, the results from two experimental studies not only point to actionable managerial implications but also to a new avenue of research on service robots that examines customer-robot interactions through the lens of language and in contexts that can be stressful for consumers (e.g., healthcare or some financial service settings). Supplementary Information: The online version contains supplementary material available at 10.1007/s11002-022-09630-x.