The widespread use of Voice-Based Assistants (VBAs) in various applications has introduced a new dimension to human-machine communication. This study explores how users assess VBAs exhibiting either excessive or insufficient communication accommodation in imagined initial interactions. Drawing on Communication Accommodation Theory (CAT) and the Stereotype Content Model (SCM), the present research investigates the mediation effect of perceived accommodation on the relationship between warmth and competence of the SCM and evaluations of the VBA as a communicator and a speaker. Participants evaluated the underaccommodative VBA significantly lower with respect to its communication and evaluations of the VBA as a speaker, which were indirectly predicted by warmth and competence stereotype content models via the perceived appropriateness of the communication. The implications of our findings and future research are discussed.

Social robots have the potential to significantly impact human behavior in social settings, presenting both opportunities and challenges. This chapter explores the multifaceted influences of social robots’ cues, behavioral capacities, and affordances on human–robot interactions (HRI) and their implications for human well-being. Social robots employ various cues to engage users, and effective interactions rely on speech and dialogue recognition, visual, audio, and tactile cues, and the ability of robots to move and gesture aids in fusing verbal and non-verbal behaviors. As such, anthropomorphism and helpfulness are pivotal in shaping human perceptions of social robots. Greater anthropomorphism can build rapport and trust, but further research is needed to understand the complex relationship between anthropomorphism, helpfulness, and high-stakes scenarios. The potential benefits of social robots for human well-being are significant, as they can provide emotional support, reduce stress, and help people adopt healthy behaviors. However, it is crucial to balance the advantages and risks of using social robots to complement human interaction rather than replace it.

This paper delves into what the application of authenticity to Human-Machine Communication (HMC) can teach us about authenticity and us as HMC researchers and as a community. Inspired by the 2023 pre-conference “HMC — Authenticity in communicating with machines,” two central questions guide the discussion — How does HMC contribute to our understanding of authentic communication with machines? And how can the concept of authenticity contribute to our sense of self as researchers within the HMC field? Through the collaborative effort of 22 authors, the paper explores the re-conceptualization of authenticity and presents recent areas of tension that guide the HMC research and community. With this paper we aim at offering a gateway for scholars to connect and engage with the evolving HMC field.

With an experimental design, this study examined the effect of source cues (Human vs. AI) on hostile media bias through heuristic machine evaluation of machine and human social media profiles. This study also explored the effects of affective and cognitive involvement as moderators along with media source-self ideological incongruity (source incongruity). A 2 (human vs. AI) x 3 (CNN vs. USA Today vs. Fox News) experimental study was conducted (n = 434). Participants exhibited less hostile media bias when presented with a news story with AI source cues through heuristic machine evaluation. The mitigating effect was stronger for those viewing news from an incongruent news source. Such moderated mediated effect was further moderated by two types of involvement (i.e., affective and cognitive). Implications for future research surrounding the two types of involvement, source incongruity, machine heuristic evaluations, and hostile media bias are discussed in light of our findings.

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