Empathic voice assistants: Enhancing consumer responses in voice commerce

Artificial intelligence (AI)-enabled voice assistants (VAs) are transforming firm-customer interactions but often come across as lacking empathy. This challenge may cause business managers to question the overall effectiveness of VAs in shopping contexts. Recognizing empathy as a core design element in the next generation of VAs and the limits of scenario-based studies in voice commerce, this article investigates how empathy exhibited by an existing AI agent (Alexa) may alter consumer shopping responses. AI empathy moderates the original structural model bridging functional, relational, and social-emotional dimensions. Findings of an individual-session online experiment show higher intentions to delegate tasks, seek decision assistance, and trust recommendations from AI agents perceived as empathic. In contrast to individual shoppers, families respond better to functional VA attributes such as ease of use when AI empathy is present. The results contribute to the literature on AI empathy and conversational commerce while informing managerial AI design decisions

Affect between Humans and Conversational Agents: A Review and Organizing Frameworks

Conversational agents (CAs), which communicate naturally with humans, are being developed and employed for a variety of tasks. Interactions between humans and CAs induce affect, which is vital to the adoption and performance of CAs. Yet, there is a lack of cumulative understanding of existing research on affect in human-CA interaction. Motivated thus, this article presents a systematic review of empirical IS and HCI studies on such affect, its antecedents and consequences. Besides conducting descriptive analysis of the studies, we also divide them into two broad categories – emotion-related, and those related to other (more persistent) affective responses. We present organizing frameworks for both categories, which complement each other. Through the review and frameworks, we contribute towards attaining a holistic understanding of extant research on human-CA interaction, identifying gaps in prior knowledge, and outlining future research directions. Last, we describe our plan for extending this work to gain additional insights

From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents

[EN] The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be "adapted " when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA(3) architecture.This work is partially supported by the Spanish Government projects PID2020-113416RB-I00, GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.Alfonso, B.; Taverner-Aparicio, JJ.; Vivancos, E.; Botti, V. (2021). From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents. Applied Sciences. 11(22):1-29. https://doi.org/10.3390/app112210874S129112

The Power of Computer-Mediated Communication Theories in Explaining the Effect of Chatbot Introduction on User Experience

Chatbots have increasingly penetrated our lives as their behavior growingly imitates a human interlocutor. This paper examines the effect of different methods of self-presentation of a chatbot on the end-user experience. An interlocutor in a computer-mediated communication (CMC) environment can either introduce itself as a chatbot, a human being, or choose not to identify itself. We conducted an experiment to compare these three methods in terms of end-user experience that comprises of social presence, perceived humanness, and service encounter satisfaction. Our data demonstrate that a chatbot that discloses its virtual identity is scored significantly lower for social presence and perceived humanness than other two choices of self-presentation. Key findings and the associated implications are discussed

Artificial Empathy in Marketing Interactions: Bridging the Human-AI Gap in Affective and Social Customer Experience

Artificial intelligence (AI) continues to transform firm-customer interactions. However, current AI marketing agents are often perceived as cold and uncaring and can be poor substitutes for human-based interactions. Addressing this issue, this article argues that artificial empathy needs to become an important design consideration in the next generation of AI marketing applications. Drawing from research in diverse disciplines, we develop a systematic framework for integrating artificial empathy into AI-enabled marketing interactions. We elaborate on the key components of artificial empathy and how each component can be implemented in AI marketing agents. We further explicate and test how artificial empathy generates value for both customers and firms by bridging the AI-human gap in affective and social customer experience. Recognizing that artificial empathy may not always be desirable or relevant, we identify the requirements for artificial empathy to create value and deduce situations where it is unnecessary and, in some cases, harmful