Purpose Considering the increasing impact of Artificial Intelligence (AI) on financial technology (FinTech), the purpose of this paper is to propose a research framework to better understand robo-advisor adoption by a wide range of potential customers. It also predicts that personal and sociodemographic variables (familiarity with robots, age, gender and country) moderate the main relationships. Design/methodology/approach Data from a web survey of 765 North American, British and Portuguese potential users of robo-advisor services confirm the validity of the measurement scales and provide the input for structural equation modeling and multisample analyses of the hypotheses. Findings Consumers’ attitudes toward robo-advisors, together with mass media and interpersonal subjective norms, are found to be the key determinants of adoption. The influences of perceived usefulness and attitude are slightly higher for users with a higher level of familiarity with robots; in turn, subjective norms are significantly more relevant for users with a lower familiarity and for customers from Anglo-Saxon countries. Practical implications Banks and other firms in the finance industry should design robo-advisors to be used by a wide spectrum of consumers. Marketing tactics applied should consider the customer’s level of familiarity with robots. Originality/value This research identifies the key drivers of robo-advisor adoption and the moderating effect of personal and sociodemographic variables. It contributes to understanding consumers’ perceptions regarding the introduction of AI in FinTech.

Service robots and artificial intelligence promise to increase productivity and reduce costs, prompting substantial growth in sales of service robots and research dedicated to understanding their implications. Nevertheless, marketing research on this phenomenon is scarce. To establish some fundamental insights related to this research domain, the current article seeks to complement research on robots' human-likeness with investigations of the factors that service managers must choose for the service robots implemented in their service setting. A three-part framework, comprised of robot design, customer features, and service encounter characteristics, specifies key factors within each category that need to be analyzed together to determine their optimal adaptation to different service components. Definitions and overlapping concepts are clarified, together with previous knowledge on each variable and research gaps that need to be solved. This framework and the final research questions provide a research agenda to guide scholars and help practitioners implement service robots successfully.

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PurposeService robots are taking over the organizational frontline. Despite a recent surge in studies on this topic, extant works are predominantly conceptual in nature. The purpose of this paper is to provide valuable empirical insights by building on the attribution theory.Design/methodology/approachTwo vignette-based experimental studies were employed. Data were collected from US respondents who were randomly assigned to scenarios focusing on a hotel’s reception service and restaurant’s waiter service.FindingsResults indicate that respondents make stronger attributions of responsibility for the service performance toward humans than toward robots, especially when a service failure occurs. Customers thus attribute responsibility to the firm rather than the frontline robot. Interestingly, the perceived stability of the performance is greater when the service is conducted by a robot than by an employee. This implies that customers expect employees to shape up after a poor service encounter but expect little improvement in robots’ performance over time.Practical implicationsRobots are perceived to be more representative of a firm than employees. To avoid harmful customer attributions, service providers should clearly communicate to customers that frontline robots pack sophisticated analytical, rather than simple mechanical, artificial intelligence technology that explicitly learns from service failures.Originality/valueCustomer responses to frontline robots have remained largely unexplored. This paper is the first to explore the attributions that customers make when they experience robots in the frontline.

Key personal inputs to decision making reside in expectations about whether a purchase or nonpurchase will make one feel better. Integrating several theoretical approaches, this research proposes a holistic framework formed by four kinds of anticipated emotions (AEs) resulting from the crossing of positive‐ or negative‐valenced emotions with action or inaction. Specifically, this research proposes that consumers under a purchase scenario tend to consider positive and negative AEs of both purchase and nonpurchase in their decisions. Research in this area to date has been sparse and focused mostly on AEs with regard to purchase, but not nonpurchase. The results of four studies confirm that AEs influence purchase decisions in a coordinated way depending on their instrumentality, motivating purchase or nonpurchase. AEs also partially mediate the effect of outcome valence on purchase decisions. Taking the status quo bias as a theoretical basis, this work proposes that the amount of information of favorable and unfavorable outcome messages has a greater influence on AEs motivating purchase than AEs motivating nonpurchase. Finally, future research lines are proposed to expand the use of this fourfold framework and more generally to understand the role of forward‐looking emotions in decision processes.

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Because of continuous improvements in their underlying technologies, customers perceive frontline robots as social actors with a high level of humanness, both in appearance and behavior. Advancing from mere theoretical contributions to this study field, this article proposes and empirically validates the humanness‐value‐loyalty model (HVL model). This study analyzes to what extent robots' perceived physical human‐likeness, perceived competence, and perceived warmth affect customers' service value expectations and, subsequently, their loyalty intentions. Following two pretests to select the most suitable robots and ensure scenario realism, data were collected by means of a vignette experimental study and analyzed using the partial least squares method. The results reveal that human‐likeness positively affects four dimensions of service value expectations. Perceived competence of the robot influences mainly utilitarian expectations (i.e., functional and monetary value), while perceived warmth influences relational expectations (i.e., emotional value). Interestingly, and contrary to theoretical predictions, the influence of the robot's warmth on service value expectations is more pronounced for customers with a lower need for social interaction. In sum, this study contributes to a better understanding of customers' reactions to artificial intelligence‐enabled technologies with humanized cognitive capabilities and also suggests interesting research avenues to advance on this emerging field.