In pursuit of satisfaction and the prevention of embarrassment : affective state in group recommender systems

Peer reviewedPostprin

Designing Trustworthy Product Recommendation Virtual Agents Operating Positive Emotion and Having Copious Amount of Knowledge

Anthropomorphic agents used in online-shopping need to be trusted by users so that users feel comfortable buying products. In this paper, we propose a model for designing trustworthy agents by assuming two factors of trust, that is, emotion and knowledgeableness perceived. Our hypothesis is that when a user feels happy and perceives an agent as being highly knowledgeable, a high level of trust results between the user and agent. We conducted four experiments with participants to verify this hypothesis by preparing transition operators utilizing emotional contagion and knowledgeable utterances. As a result, we verified that users' internal states transitioned as expected and that the two factors significantly influenced their trust states

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

Real Loneliness and Artificial Companionship: Looking for Social Connections in Technology

Loneliness among older adults is a problem with severe consequences to individual health, quality of life, cognitive capacity, and life-expectancy. Although approaches towards improving the quality and quantity of social relationships are the prevailing model of therapy, older adults may not always be able to form these relationships due to either personality factors, decreased mobility, or isolation. Intelligent personal assistants (IPAs), virtual agents, and social robotics offer an opportunity for the development of technology that could potentially serve as social companions to older adults. The present study explored whether an IPA could potentially be used as a social companion to older adults feeling lonely. Additionally, the research explored whether the device has the potential to generate social presence among both young and older adults. Results indicate that while the devices do show some social presence, participants rate the device low on some components of social presence, such as emotional contagion. This adversely affects the possibility of a social relationship between an older adult and the device. Analysis reveals ways to improve social presence in these devices

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

Measuring Social Influence in Online Social Networks - Focus on Human Behavior Analytics

With the advent of online social networks (OSN) and their ever-expanding reach, researchers seek to determine a social media user’s social influence (SI) proficiency. Despite its exploding application across multiple domains, the research confronts unprecedented practical challenges due to a lack of systematic examination of human behavior characteristics that impart social influence. This work aims to give a methodical overview by conducting a targeted literature analysis to appraise the accuracy and usefulness of past publications. The finding suggests that first, it is necessary to incorporate behavior analytics into statistical measurement models. Second, there is a severe imbalance between the abundance of theoretical research and the scarcity of empirical work to underpin the collective psychological theories to macro-level predictions. Thirdly, it is crucial to incorporate human sentiments and emotions into any measure of SI, particularly as OSN has endowed everyone with the intrinsic ability to influence others. The paper also suggests the merits of three primary research horizons for future considerations

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

Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes

Artificial intelligence (AI)-enabled voice assistants (VAs) such as Amazon Alexa increasingly assist shopping decisions and exhibit empathic behavior. The advancement of empathic AI raises concerns about machines nudging consumers into purchasing undesired or unnecessary products. Yet, it is unclear how the machine’s empathic behavior affects consumer responses and decision-making outcomes during voice-enabled shopping. This article draws from the service robot acceptance model (sRAM) and social response theory (SRT) and presents an individual-session experiment where families (vs. individuals) complete actual shopping tasks using an ad-hoc Alexa app featuring high (vs. standard) empathic capabilities. We apply the experimental conditions as moderators to the structural model, bridging selected functional, social-emotional, and relational variables. Our framework collocates affective empathy, explicates the bases of consumers’ beliefs, and predicts behavioral outcomes. Findings demonstrate (i) an increase in consumers’ perceptions, beliefs, and adoption intentions with empathic Alexa, (ii) a positive response to empathic Alexa holding constant in family settings, and (iii) an interaction effect only on the functional model dimensions whereby families show greater responses to empathic Alexa while individuals to standard Alexa

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice