Artificial intelligence (AI) is central to digital platforms, particularly in personalized recommendation systems to enhance user interactions. This study investigates how AI recommendation strategies—presenting alternatives and providing reasoning—impact user responses, considering the moderating role of agency perception (control over one’s actions) and the mediating role of perceived assistance (the sense of being supported by AI). An online experiment manipulated the presence of alternatives and reasoning to measure perceived agency and assistance. Results show that presenting alternatives had a direct negative impact on users with high agency, viewing alternatives as intrusive or misaligned. In contrast, users with low agency experienced positive outcomes, perceiving greater assistance and showing improved user engagement and satisfaction. These findings reveal a dual-pathway mechanism, where the same AI strategy elicits divergent responses based on agency perception, emphasizing the need for personalized recommendation designs.

Three dimensional immersive virtual spaces, also known as the metaverse, offer a new way for people to attend concerts. Virtual concerts held in the metaverse allow viewers to experience immersive visuals and interactive communications. However, viewer intentions and reasons behind attending these virtual concerts are far from clear. In this study, we proposed a conceptual model integrating Technology Acceptance Model, prior metaverse experience and parasocial relationship factors. We surveyed 519 metaverse users from South Korea. Multiple regression modeling was used to establish the relationships between the examined variables. Results suggest that perceived ease of use and perceived usefulness were significant predictors of audiences’ intentions. Experienced metaverse platform users prioritize perceived ease of use, while novice users prioritize perceived usefulness. Furthermore, the desire to see the artist is an important reason behind viewers’ intention of attending virtual concerts.

The watching-eye effect proposes that others’ eyes cause people to behave in a prosocial manner. The current study tested this in the context of an online news website, by investigating whether a watching-eye icon influences users’ attention to themselves and expressions of their opinions in a comment section. In an online experiment, participants (N = 741) used an online news website in the presence (vs. absence) of a watching eye as a visual cue for an imagined audience, who reportedly presented their opinions in a comment section. Results showed that the watching eye did influence participants’ private and public self-awareness and the quality of their comments. Presence of the visual cues, compared to its absence, increased female participants’ self-awareness, specifically when others’ opinions revealed in the comment section were mixed or opposed to the news article topic. This increased private self-awareness was positively associated with the comment quality. These findings indicate the importance of social cues on interfaces in mitigating the negative consequences of anonymity in online environments.

The online environment for video conferencing lacks cues compared to offline, so one can hear the interlocutor's criticism more sensitively, and the fear of presenting in front of the camera can hinder participation in the meeting. It is known that interface design affords a role in improving public speaking and has a possibility of changing user behavior. To examine how the interface design of video conferencing affects video debating participation, 2 (visual anonymity: avatar vs. face) × 2 (self-affirmation vs. no self-affirmation) between-subjects experiment was conducted. Results showed that using an avatar, when properly used together with self-affirmation, has a positive effect on active participation in discussions, but derogating others’ critical messages. These results indicate unique underlying mechanisms of the effects of the avatar; the deindividuation effect of visual anonymity, and the effects of improving participation when customizing self-value reflected avatars.

Contrary to the traditional concept of artificial intelligence, explainable artificial intelligence (XAI) aims to provide explanations for the prediction results and make users perceive the system as being reliable. However, despite its importance, only a few studies have investigated how the explanations of an XAI system should be designed. This study investigates how people attribute the perceived ability of XAI systems based on perceived attributional qualities and how the power status of the XAI and anthropomorphism affect the attribution process. In a laboratory experiment, participants (N = 500) read a scenarios of using an XAI system with either lower or higher power status and reported their perceptions of the system. Results indicated that an XAI system with a higher power status caused users to perceive the outputs of the XAI system to be more controllable by intention, and higher perceived stability and uncontrollability resulted in greater confidence in the system’s ability. The effect of perceived controllability on perceived ability was moderated by the extent to which participants anthropomorphised the system. Several design implications for XAI systems are suggested based on our findings.

The current study examined how voice control and a virtual agent in a smart TV interplay in influencing users’ evaluation of the TV. In a 2 (input modality: voice control vs. remote controller) X 2(agent type: realistic vs. cartoonlike agent) between-subjects experiment, participants (N = 64) used and evaluated a smart TV in an educational context. Results revealed negative effects of voice control, especially when it accompanied a cartoonlike agent. Those who used the voice control to the cartoonlike agent rated the agentless humanlike, attractive, intelligent and intimate than did those who used the remote controller to the cartoonlike agent. Also, those who used voice control reported that watching the TV was less involving and enjoyable than those who used the remote controller. The negative effects were mitigated, however, when they interacted with a realistic agent. These results suggest the importance of the matching between input modality and visual interface to reduce potential negative effects.

A chatbot equipped with a conversational user interface often allows its users to feel as if they are conversing with a human being. The current study examined whether users’ perception of a mind within a chatbot is associated with their feeling of co-presence, closeness, and intention to use and whether the influence of mind perception is reinforced when the chatbot presents social cues in its language. A laboratory experiment (N = 64) revealed that the more participants perceived a mind behind a chatbot, the more co-presence and interpersonal closeness they experienced with the chatbot. The associations with co-presence and closeness became stronger when the chatbot used social cues. Furthermore, mind perception had an indirect effect on intention to use via closeness when social cues were presented. These findings imply the importance of mind perception and social cues in a chatbot’s language in creating a positive chatbot experience.

Sangwon Leea & Young June Saha* a Department of Interaction Science, Sungkyunkwan University, Seoul, South KoreaSangwon Lee is an associate professor in the department of Interaction Science, Sungkyunkwan University. He has obtained his PhD degree in Industrial Engineering at the Pennsylvania State University in 2010. His research interests include human-computer interaction, user experience, affective computing, and user modeling.Young June Sah (PhD, Michigan State University) is an adjunct professor in the department of Interaction Science, Sungkyunkwan University. His research interests include psychological and behavioral effects of media technologies and their cognitive mechanisms.CONTACT Young June Sah [email protected] Department of Interaction Science, Sungkyunkwan University, 25-2, Sungkyunkwan-ro, Jongno-gu, Seoul 110-745, South Korea.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/hihc.ABSTRACTThe present study proposes an approach to measuring learnability, focusing on the learning process from a perspective of extended learnability. In developing a predictive model for time performance without any interruption or error based on NGOMSL (Natural GOMS Language), the concepts of “expertise time,” “actual time,” “learning deficit,” and “learning level” are considered with a few assumptions, and a learning deficit curve for the relationship between learning deficit and learning level is presented. Experimental data on repetitive use in sample users’ first use session of a system demonstrate the application of our approach herein. For exploratory tasks on four simulated websites with different levels of usability and aesthetics, responses from 64 users were obtained in terms of time performance, perceived usability, and user satisfaction. Through this application, we were able to discern some information about the dynamic properties of learning in relation to users’ subjective responses: (1) learning level according to number of uses, (2) the number of uses required to reach a competency level, (3) effects of usability and aesthetics factors on learning level, and (4) learning level in connection with perceived usability and user satisfaction. The model is expected to have potentials as an effective approach to measuring learnability insofar as it provides useful information in ways that differ from existing methods for learnability.

Users of voice user interface (VUI) often encounter errors, such as when a VUI attempts to recognize a user’s voice inputs or execute tasks. Conversation is prone to errors, and in the collaborative perspective, communicators manage common ground together to handle erroneous situations. Adopting a collaborative view of conversation, we propose that a VUI can address different types of errors by providing users with feedback to aid them in developing common ground to communicate more effectively. To test this proposal, we conducted a 2 (error type: recognition vs. execution error) × 2 (feedback elaboration: present vs. absent) mixed-design experiment in which users interacted with a VUI speaker and evaluated its usability in these four modes. Participants reported greater acceptance of feedback and higher usability perception for a speaker returning execution errors than for one returning recognition errors, particularly when the speaker presented feedback articulating reasons for the errors. This finding indicates that a VUI can employ feedback explaining the causes of errors to facilitate the development of common ground and to minimize the negative consequences of errors.

When a robot is said to be a specialist in a particular domain, does it alter the nature and quality of human-robot interaction? This study examines the effects of specialization in robot functions, along with individual difference in immersive tendencies, on users' trust, perception, activity, and memory. In a controlled experiment, 38 participants were taught a physical exercise lesson from either a specialist or generalist humanoid robot for 6 min. Results showed that specialization had effects on the participants' affective trust; and immersive tendency predicted active participation in the interaction and led to better memory. The latter also moderated the effect of the former - users with higher immersive tendency are more likely to make human attributions of specialization, and rate a specialist robot as more intelligent than a generalist robot. These results have theoretical implications for media-equation as well as design implications for human-robot interaction professionals.