The Metaverse, characterized as an interactive and immersive 3D virtual world, is widely recognized for its considerable potential across a range of industries. However, the long-term viability and success of the Metaverse are contingent upon the extent to which users accept and adopt it. Despite this critical aspect, there is a scarcity of research that investigates the factors influencing user acceptance of the Metaverse. To address this research gap, the present study expands upon the technology acceptance model by integrating social and psychological constructs such as social interaction, social presence, conformity, emotional attachment, flow, and perceived enjoyment. The data for this study were obtained through an online survey. A total of 418 responses were collected from Metaverse users, with a response rate of 84%. Partial least squares structural equation modeling was used to analyze the survey data. Results showed that: (1) perceived ease of use (β = 0.214, p < 0,001), emotional attachment (β = 0.375 p < 0,001), and enjoyment (β = 0.194, p < 0,001) could have a positive effect on perceived usefulness; (2) emotional attachment (β = 0.142, p = 0.018), enjoyment (β = 0.179, p = 0.008), and social presence (β = 0.183, p = 0.008) could have a positive effect on perceived ease of use; (3) perceived usefulness (β = 0.432, p < 0,001), perceived ease of use (β = 0.106, p = 0.011), emotional attachment (β = 0.209, p < 0,001), and social interaction (β = 0.209, p < 0,001) have a positive effect on attitudes toward using the Metaverse; (4) perceived usefulness (β = 0.174, p = 0.001), attitudes (β = 0.35, p < 0,001), flow (β = 0.128, p = 0.011), and social interaction (β = 0.106, p = 0.015) could positively influence users’ intention to use the Metaverse. However, social presence could not significantly influence perceived usefulness (β = 0.078, p = 0.141). These results offer important implications for developers and practitioners looking to design and promote the utilization of the Metaverse.

The effectiveness of online meeting platforms is highly associated with users’ acceptance. Nevertheless, few studies have been committed to the roles of social isolation, technostress, and personality in online meeting platform acceptance. This study aimed to investigate the influence of social isolation, technostress, and personality on users’ acceptance of online meeting platforms within the technology acceptance model (TAM) including perceived ease of use, perceived usefulness, attitude towards technology use, and behavioral intention. A total of 975 responses were collected via an online survey. The results revealed that there were positive relationships among four core constructs. But more importantly, social isolation negatively influenced users’ favorable attitudes towards online meeting platforms, and technostress negatively influenced the perception of the usefulness of online meeting platforms. Users with different personalities had different degrees of acceptance of online meeting platforms. The study provides a deep insight into influencing factors in users’ acceptance of online meeting platforms during the rampant COVID-19 pandemic. This study is, therefore, useful for designers and practitioners to optimize the online meeting platforms. In addition, this study adopted TAM to investigate users’ acceptance of online meeting platforms, supporting TAM’s reliability and validity in the online meeting platform-based learning context. Future studies could extend TAM by including specific sociocultural and psychological constructs stemming from the COVID-19 pandemic.

As a relatively new biometric modality, palmprint attracts much attention for its rich intrinsic features and high commercial prospects. Most existing palmprint recognition methods only extract features from the region of interest (ROI) and neglect the features of other regions. Hence, the recognition performance of these methods highly relies on ROI localization, and it requires users' high cooperation. To relieve these problems, we propose a novel end-to-end recognition framework for contactless whole-palm region-based recognition in this paper, dubbed as Channel Groupwise Drop Network (CGDNet). CGDNet consists of a trunk branch and a part exciting branch. The trunk branch extracts the global representations, and the global scale (GS) module is designed to measure the similarity from feature positions to obtain global knowledge. In the part exciting branch, the features are split into two fine-grained parts equally along the horizontal direction. Then, the channel group-wise drop (CGD) module is designed to aggregate the same part features of the object and cooperates with the CGD mask, which randomly drops the same region in the channel group to excite diverse fine-grained features for learning. Finally, fine-grained features and the global features are concatenated as the final feature. The proposed CGDNet achieves competitive performance in two bench-mark datasets compared with the state-of-the-art methods.