The rising demand for AI-powered smart home solutions has produced a recent surge in the adoption of smart home devices (SHDs). SHDs are uniquely situated within private, personal environments, and understanding the impact of device design on users’ parasocial relationship, privacy perceptions, and overall adoption is crucial; however, the literature lacks a satisfactory exploration of this essential facet of integrating intelligent devices into everyday living. This study addresses this deficit by analysing the nuanced interplay between device design features and user adoption intentions. An online experiment was conducted using a 2 (communication style: task-oriented vs. social-oriented) × 3 (embodiment type: application voice vs. virtual animation vs. physical robot) between-subjects design (N = 297). The findings indicate that SHDs employing a social-oriented communication style, while promoting stronger parasocial interactions, are simultaneously correlated with increased perceptions of privacy risk when compared to those utilising a task-oriented communication style. The more embodied the SHDs, the stronger the perceived parasocial interaction. Furthermore, higher perceived privacy risks negatively affect purchase intention. The findings provide novel insights into the design of SHDs that not only address privacy concerns but also create positive user experiences in IoT-based smart homes, thereby fostering long-term adoption.

Social media (SM) use is becoming prevalent among young people. While the research community has recently taken an interest in these evolving phenomena, it is still mostly unknown how early age use of SM affected perception of others (i.e., the feeling of social presence) in SNS and behavioral intention to follow the provided information. Also, how the SM interface and endorser types of effects on early usage of SM have not been investigated. To address this research gap, this study conducted the online experiment involving 534 participants, employing a between-subject design of 2 (channel: SNS vs. web) x 2 (endorser: peer vs. medical doctor). The experiment aimed to investigate the effects of accumulated SM technology usage and the age at which individuals begin using SM while they were exposed to identical health information. The findings revealed that individuals who initiated SNS usage at an age older than 18 experienced sense of social presence (SP) as a result of SNS' channel, rich modality (i.e., video), and source (i.e., peer endorser). Perceivers with an SNS beginning age of less than 18 years old, on the other hand, did not exhibit any relationship between SNS channel, and source endorser. Irrespective of whether the participants began using SNS at a younger or older age, both groups exhibited a stronger perception of face-to-face communication with the endorser with higher media usage behavior. This enhanced SP led to more positive attitude, a stronger subjective norm, and greater perceived behavioral control, resulting in higher intention to act on the endorsed information. The discussion section addresses potential explanations for these findings are addressed.

The gesture interactivity and self-mirroring effects of large digital displays, also known as digital signage, was investigated using the self-presence theory and engagement. By extending the interactive effects of gesture and self-mirroring from the immersive environment to a large interactive display (LID), this study found the implicit mediating roles of social presence and engagement for advertising effectiveness through a 2 (gesture; non-gesture vs. gesture interactivity) x 2 (self-representation; non-self-mirroring vs. self-mirroring interactivity) between-subject design (N = 81) experiment. The results indicated that gesture interactivity enhances a feeling of self-presence, not self-mirroring, and it positively fosters psychological engagement with advertising content. In turn, the heightened engagement results in better advertising effectiveness, such as brand attitude, purchase intention, and recall.

Hyper-parameter optimization is crucial for pushing the accuracy of a deep learning model to its limits. However, a hyper-parameter optimization job, referred to as a study, involves numerous trials of training a model using different training knobs, and therefore is very computation-heavy, typically taking hours and days to finish. We observe that trials issued from hyper-parameter optimization algorithms often share common hyper-parameter sequence prefixes. Based on this observation, we propose Hippo, a hyper-parameter optimization system that reuses computation across trials to reduce the overall amount of computation significantly. Instead of treating each trial independently as in existing hyper-parameter optimization systems, Hippo breaks down the hyper-parameter sequences into stages and merges common stages to form a tree of stages (a stage tree). Hippo maintains an internal data structure, search plan, to manage the current status and history of a study, and employs a critical path based scheduler to minimize the overall study completion time. Hippo applies to not only single studies but multi-study scenarios as well. Evaluations show that Hippo's stage-based execution strategy outperforms trial-based methods for several models and hyper-parameter optimization algorithms, reducing end-to-end training time by up to 2.76X (3.53x) and GPU-hours by up to 4.81X (6.77x), for single (multiple) studies.

As smoking has emerged as a health-risk behavior, communication scholars and practitioners have put many efforts into finding out ways to achieve smoking cessation. In this research, participants (N = 57) were randomly assigned to a spatial augmented reality (SAR) condition (3D projection mapping) and 2D flat screen to be exposed to an anti-smoking message. This research provides insightful evidence that the effects of SAR on people’s behavioral intention to spread anti-smoking messages online could be explained by spatial presence and negative emotions. Implications for research on the potential of SAR in terms of emotions and online viral behavioral intentions are discussed.

This paper presents the propensities of preference for Human Robot Interaction (HRI) according to different personalities and facial expressions of humans and robots. This study is based on two types of personalities: extroverted and introverted. According to the personality, the facial expressions are distinguished to express extroversion and introversion. The design of experiment is a 3 (participant's group of personality: extrovert, intermediate, introvert) x 2 (robot's groups of personality) between-subjects experiment (N=40) in which participants interact with KMC-EXPR robots expressing either extroversion or introversion. The results showed an unprecedented hypothesis, unexpected implications and certain propensities of preferences.