• A holistic framework of conversational agent (CA) empathy is developed and validated, integrating user insights with established empathy theories. • Open-ended surveys reveal core empathic behaviors in CAs, leading to the identification of four key agent traits: Active Listening, Personalization, Emotional Expressivity, and Persona Attractiveness. • The Interpersonal Reactivity Index (IRI) is applied to examine the influence of these traits on user perceptions of cognitive and affective empathy components. • Structural equation modeling confirms the impact of each trait on user evaluations of perspective-taking, fantasy, empathic concerns, and personal distress, providing guidance for designing empathetic CAs. As Conversational Agents (CAs) increasingly interact with users on social and emotional levels, understanding how these agents convey empathy has become a critical challenge. This paper reports on an exploratory mixed-methods study that propose and empirically explores an initial framework for CA empathic responsiveness. The research proceeds in two sequential studies. Study 1 first conducted a qualitative analysis of open-ended surveys (N=166, U.S. and South Korea) to identify key user-defined empathic behaviors. Through thematic analysis, these insights were integrated with existing empathy theories to derive a framework of four core agent characteristics: Active Listening (AL), Personalization (PE), Emotional Expressivity (EE), and Persona Attractiveness (PA). Study 2 then conducted a quantitative investigation in South Korea (N=200) using Structural Equation Modeling (SEM) to test this framework. Empathic responsiveness was operationalized adopting Agent Empathic Reactivity Index (AERI), a validated CA-specific adaptation of the Interpersonal Reactivity Index (IRI), assessing perspective-taking, fantasy, empathic concerns, and personal distress. SEM results confirmed all 12 hypothesized paths. AL and PE strongly enhanced perspective-taking and empathic concerns, while PE also significantly reduced personal distress. Notably, EE and PA had dual effects: they improved positive dimensions, such as fantasy, but also significantly increased users’ perception of the agent’s personal distress. These findings highlight the delicate balance required in designing emotionally resonant CAs. This work advances the theoretical understanding of multidimensional agent empathy and provides actionable, nuanced guidance for designers aiming to build trust and foster long-term user relationships.

With the advancement of artificial intelligence, conversational agents are now capable of displaying intelligent and emotionally empathetic responses, which are essential for the continued use of AI-based agents. However, there is a scarcity of formal measures that can comprehensively evaluate how well they react to the user’s emotional needs. The objective of this research is to develop and validate a set of new measures, collectively called the agent empathic reactivity index (AERI), an adaptation of the interpersonal reactivity index (IRI) developed for the human-human relationship evaluation to the human-agent interaction context. By rigorously following the measure development procedures suggested by prior research, four dimensions of AERI measures of empathic concern, perspective-taking, fantasy, and personal distress were developed. Multiple pilot tests and surveys involving various conversational agents were conducted to validate the four AERI measures. The study results show that the new measures have strong psychometric properties and nomological validity.

Fueled by the power of AI, chatbots are becoming more personal. Prior research showed that a chatbot has great potential to elicit its user’s self-disclosure because it does not judge the user. However, the chatbot’s features beyond the conversational characteristics in eliciting a user’s self-disclosure are not as well researched. In this study, we have developed a chatbot and implemented two non-conversation features: (1) co-activity (COA), conducting an activity together, and (2) conversation atmosphere visualization (CAV), visually displaying the emotional feelings conveyed in the conversation, to examine their effects on self-disclosure and user experience. We conducted a field study involving 87 participants who were randomly assigned to one of the four experimental conditions (control, COA only, CAV only, CAV + COA) and asked to use the assigned chatbot for 10 days in their natural life setting. Our results from this field study show that both the COA and CAV features have significant effects on a user’s self-disclosure. In addition, interaction effects between COA and CAV have been found to affect a user’s intention to use. Based on the findings, we provide design implications for a user’s self-disclosure and trusting relationship development with a chatbot.

The advent of information and communication technology has made people practice prosocial behavior in social networking services (SNSs) more easily. For this reason, the aim of the study was to identify the social and individual factors that induce prosociality in SNS. The concept of isomorphism for categorizing the characteristics of each social networks was adopted. The study also considered the concept of social presence for representing each individual. The experiment manipulated types of isomorphism (Mimetic, Normative, and Coercive) and degrees of social presence in an experimental SNS context. The study also measured individuals’ intention and activity of prosocial behavior. The experiment results indicate that mimetic and normative isomorphic conditions induce higher levels of prosocial intention and activity than coercive isomorphic condition. Also, a higher degree of social presence induces a higher level of prosocial intention. More interesting, the impact of mimetic condition is stronger when the social presence is higher.