This research extends the literature by showing that the positive effects of self-disclosure by an AI voice bot (AVB) vary depending on the identity perception of the bot (human or machine) and the cultural characteristics of the users: when an AVB is perceived as human and engages in personal self-disclosure, users feel more intimate with and trust the bot, and when the bot is perceived as machine and engages in factual self-disclosure, users feel more intimate with and trust the AVB. We also found that South Korean (vs. U.S.) users felt more intimate with and trusted AVBs when they perceived the bot as human and when it engaged in personal self-disclosure. The theoretical and practical implications of the study are discussed.

This study investigates the role and effectiveness of political chatbots’ anthropomorphism in influencing voting intentions. Our findings reveal that participants show higher voting intention when they were exposed to the political chatbot with higher (vs. lower) levels of anthropomorphic visual cues. This research also investigated two moderators underlying this relationship: message type (factual vs. emotional message) and message interactivity (high vs. low interactivity). We found that compared to the less anthropomorphized political chatbot, participants’ voting intention was enhanced when the highly anthropomorphic chatbot delivered emotional (vs. factual) messages. In terms of message interactivity, the participants exhibited higher voting intentions when the political chatbot had lower levels of anthropomorphism and higher (lower) message interactivity. Theoretical and practical implications of these findings are discussed.

Abstract Informed by the construal level theory (CLT) and accounting for anthropomorphism, we investigated the effectiveness of political chatbots in influencing voting intentions. This study employed a three-way analysis of variance test with a 2 (anthropomorphism: anthropomorphism vs. non-anthropomorphism) × 2 (message type: feasibility vs. desirability appeal) × 2 (political ideology: conservatives vs. liberals) between-subjects experiment (n = 360). The findings reveal that participants showed higher voting intention after conversing with a highly anthropomorphic chatbot (vs. non-anthropomorphic chatbot) and when the chatbot delivered desirability (vs. feasibility) appeals. Participants also exhibited a higher voting intention when the chatbot was less anthropomorphic and it delivered feasibility (vs. desirability) messages. Moreover, we identified the three-way interaction effects of anthropomorphism, message appeal type and political ideology on voting intention. These findings are discussed in terms of their theoretical and practical implications.

Many art activities have been trialed in 360-degree virtual reality (VR) and this has become an increasingly representative way toward sustainable consumption of art. However, understanding audience responses to the VR art remains a matter of debate and investigation. By employing the uses and gratifications (U&G) approach, this study explores motives for and consequences of watching 360-degree VR art. The identified motives are three dimensions: pursuing learning from entertainment, pursuing social conformity, and pursuing convenience. Among the three use motives, it was found that pursuing learning from entertainment is the most significant factor influencing audiences’ transportation experience while watching 360-degree VR art, and audiences’ innovativeness functions as a moderator in the relationship between transportation and the attitude toward the VR content. The theoretical and practical implications of this study are discussed.

The authors have developed magnetic resonance imaging-guided breast biopsy robot (MRSON), which is an automated magnetic resonance (MR) guided breast needle biopsy robot that works in an intragantry space with (3+1) degrees of freedom. This robot utilizes a bendable superelastic Nitinol biopsy needle. The MRSON’s needle path is normally parallel to theX-axis of the Cartesian coordinates. However, in some cases, parallel biopsy needle paths are not feasible based on obstacles that are present in front of the target tumor (i.e., compressor grid walls, visible blood vessels, or nontarget tumors). Therefore, a deflected needle path planning method for MRSON was developed in this article to avoid obstacles and accurately perform biopsies. The novelty of the proposed method lies in using a grid with 20×12 holes, through which the robot introduces the biopsy needle and actively deflects the needle against the grid to generate a deflected needle path leading to the target lesion without requiring additional actuators. To calculate the deflection amount and needle insertion depth, the deflected needle is modeled by an$\epsilon$-second-order polynomial, and feasible needle paths are determined through geometry-based analysis. A path-ranking algorithm is then applied to select the best path among all feasible needle paths. Furthermore, to handle with any target lesion movement and coordinate misalignment between the robot and MR image coordinates, a Jacobian-based needle path adjustment method is proposed and verified through numerical simulations. The proposed deflected needle path planning method was verified through 28 in vitro accuracy test and four in vitro validation tests with a linear obstacle, which revealed a root-mean-square targeting error of 2.3 mm.