This study explores the dynamic landscape of user experiences in game-based metaverses, a rapidly growing segment of virtual reality environments. The research was motivated by the need to understand how demographic factors such as game type, gender, and age influence user perceptions and interactions in these digital realms. Employing a quantitative approach, the study analyzed responses from 173 users actively engaged in various game-metaverses. Through ANOVA, it assessed the impact of the mentioned demographic variables on user experiences, focusing on aspects such as presence, relatedness, escapism, vicarious satisfaction, enjoyment, and overall satisfaction. The findings reveal significant variations in user experiences based on game type, particularly in terms of perceived authenticity and conviction, with games like Fortnite enhancing realism more effectively. Gender differences were observed in the sense of connectivity within the Metaverse, while age-related variations highlighted differences in the perception of virtual worlds as realistic spaces.

The rapid integration of Generative AI in workplaces has sparked a need to understand its impact on employee performance and technology adoption. This study delves into how AI attributes, namely responsiveness, empathy, and optimism, affect perceived performance and utilitarian benefits, and consequently, technology usage. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from 221 users of Generative AI in various professional settings, the study offers a nuanced understanding of these relationships. The findings indicate that while AI responsiveness positively affects perceived performance, it does not significantly impact utilitarian benefits. Conversely, AI empathy enhances both perceived performance and utilitarian benefits, highlighting its dual role. Technology optimism also shows a positive influence on both aspects. However, utilitarian benefits alone do not directly increase AI usage. Interestingly, organizational culture, while not directly affecting usage, moderates the effects of perceived performance and utilitarian benefits on usage.

As generative artificial intelligence (GAI) tools become increasingly prevalent across various sectors, understanding the factors that influence their usage and performance is essential. This study examines the effects of GAI-task fitness, task knowledge, and self-efficacy on GAI usage and generativity performance, while also exploring the moderating role of metacognition. Data from 245 GAI users were analyzed using structural equation modeling (SEM). The results indicate that GAI-task fitness, task knowledge, and self-efficacy significantly enhance GAI usage, which, in turn, positively influences generativity performance. However, metacognition significantly moderated only the relationship between GAI self-efficacy and usage, showing no notable effects on other pathways. These findings suggest that organizations and educators should prioritize improving task alignment, user knowledge, and self-efficacy to promote the adoption and effective use of GAI tools. Additionally, training that fosters metacognitive skills may help users engage more strategically with GAI, leading to better creative and productive outcomes.

In the burgeoning landscape of metaverse gaming, understanding player behaviour and preferences is crucial for both academic and practical applications. This study delves into the dynamic realm of metaverse gaming, exploring the influence of various factors on user satisfaction and continuance intention. The primary purpose was to examine the roles of self-expression, challenge and achievement, social interaction, violence catharsis, and game difficulty in shaping player experiences in the metaverse. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM) as the analytical method, the study analysed responses from 171 metaverse game users. The findings revealed intriguing patterns: while self-expression significantly impacts continuance intention, it does not affect immediate satisfaction. Challenge and achievement enhance satisfaction but not continuance intention, emphasizing the complex relationship between these constructs. Social interaction emerged as a critical factor positively influencing both satisfaction and continuance intention. Contrary to popular belief, violence catharsis did not significantly affect either satisfaction or continuance intention. Additionally, increased game difficulty was found to negatively impact both satisfaction and continuance intention. Satisfaction was shown to drive continuance intention. These insights bear significant implications for game developers and marketers, highlighting the need for nuanced game design strategies that emphasize social interaction and balanced challenges.

In the ever-evolving domain of artificial intelligence (AI), understanding how users interact with AI-driven solutions, like ChatGPT, has become vital. The purpose of this research is to explore factors influencing user satisfaction and their willingness to subscribe to ChatGPT's superior services. Employing partial least squares structural equation modeling, the study examines responses from 554 ChatGPT users. The results reveal that perceived intelligence and service quality of ChatGPT significantly influence perceived usefulness, knowledge management, and overall user satisfaction. Interestingly, while perceived usefulness notably augments user satisfaction, it doesn’t directly drive willingness to subscribe to paid services. Similarly, knowledge management boosts satisfaction but doesn’t affect payment readiness. Importantly, user satisfaction substantially encourages willingness to pay for advanced features. Personal innovativeness contributes to willingness to pay, but it doesn’t modify the impacts of perceived usefulness, knowledge management, or satisfaction on payment readiness. Finally, perceived risk negatively impacts willingness to pay. These findings illuminate the intricate mechanisms governing user-AI interactions and factors determining their readiness for premium subscriptions. This research lays groundwork for future scholarly investigations of AI-user dynamics.

In the burgeoning domain of virtual reality (VR), there’s undeniable progress in technology. However, a distinct gap remains in understanding user engagement with VR devices. This study delves into the factors determining user satisfaction and word-of-mouth (WOM) recommendations related specifically to VR devices. Using partial least squares structural equation modeling, this research analyzes empirical data directly sourced from VR device users. The results indicate that the interface convenience of VR devices significantly impacts emotional value. Moreover, the design and shape of the VR devices resonate profoundly with both emotional and social values. In contrast, the color of the screen displayed by VR devices doesn’t significantly influence these values. The study also uncovers a unique relationship between emotional value and WOM. Additionally, social value has a direct positive influence on both user satisfaction and WOM recommendations. These insights offer a deeper understanding of the consumer mindset within the VR device segment, illuminating key factors driving WOM and enriching our comprehensive understanding of the VR device landscape.

The rapid development and widespread adoption of artificial intelligence (AI) systems like ChatGPT in the workplace underscores the need for a comprehensive understanding of their implications on worker productivity, learning, and the determinants of their usage. This study elucidates the antecedents and outcomes of using ChatGPT among workers, employing a sample size of 351 participants across various industries, aged between 20 and 40. Structural equation modeling (SEM) was employed for the analysis, providing a robust perspective on the relationships between variables. The study discovered that perceived intelligence and self-learning of ChatGPT hold significant positive associations with information support and knowledge acquisition, both of which further influence the utilitarian benefits perceived by users, thereby shaping their intention to use ChatGPT. Utilitarian benefits were found to significantly enhance the intention to use ChatGPT but did not directly impact its actual usage. The intention to use ChatGPT, however, was found to positively correlate with its actual use. Among demographic factors, gender and age were identified as significantly influencing actual use, whereas the industry of users did not hold a significant impact. With these findings, this study offers a more profound understanding of how AI systems like ChatGPT can contribute to workplace productivity, and presents strategic insights for practitioners looking to effectively implement these technologies.

Abstract In an era marked by rapid digital transformation, understanding the factors that influence digital engagement is crucial for bridging the digital divide. This study aims to explore the impact of individual factors such as networking motive, social media use, content service usage, and economic activity on digital transformation awareness and satisfaction. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from 7,000 respondents of the National Information Society Agency (NIA)'s 2022 Digital Divide Survey, this research provides empirical insights into the dynamics of digital engagement. The findings reveal that networking motive significantly predicts social media use, which in turn slightly enhances digital transformation awareness but not satisfaction. Conversely, economic activity positively influences both awareness and satisfaction with digital transformation, underscoring the tangible benefits of digital economic engagement. Life service utilization emerged as a crucial factor, significantly impacting both awareness and satisfaction. These results offer critical implications for policymakers, educators, and digital platform developers, suggesting the need for targeted strategies to enhance digital literacy, promote inclusive digital services, and foster economic opportunities in the digital domain.

Given the rapid development of mobile technologies and the high adoption rates of mobile devices, mobile communications applications (MCAs) are becoming increasingly popular worldwide. In the highly competitive and rapidly changing MCA market, it is becoming important to understand users’ postadoption behaviors toward MCAs. Previous postadoption studies have focused on continuous use, but the success of MCAs is also affected by positive word of mouth. To deepen our understanding of postadoption behaviors in the MCA environment, this study examined the key determinants of continuance intention and recommendation intention, two critical postadoption behaviors. Moreover, this study investigated the effects of dedication and constraint factors on MCA postadoption phenomena from a dual-model perspective. Data collected from 250 users experienced with an MCA were empirically tested against a theoretical framework using partial least squares. The results confirm that the proposed model substantially predicted the postadoption behaviors of MCA users. These findings indicate that both user satisfaction and perceived switching costs play an important role in enhancing users’ continuance and recommendation intentions. Learning and habit were found to be the key antecedents of perceived switching costs. Implications for research and practice are described.