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Technology 4.0 comes with a challenge to understand the degree of users’ willingness to adopt a digital transformation. Metaverse, being a digital transformation, enables real-world activities in the virtual environment, which attracts organizations to adopt the new fascinating technology. This paper thus explores the uses and gratification factors affecting user adoption and recommendation of metaverse from the management perspective.

The study adopts a mixed approach where structural topic modeling is used to analyze tweets about the metaverse, and the themes uncovered from structural topic modeling were further analyzed through data collection using structural equation modeling.

The analyses revealed that social interaction, escapism, convenient navigability, and telepresence significantly affect adoption intent and recommendation to use metaverse, while the trendiness showed insignificance. In the metaverse, users can embody avatars or digital representations, users can express themselves, communicate nonverbally, and interact with others in a more natural and intuitive manner.

This paper contributes to research as it is the first of its kind to explore the factors affecting adoption intent and recommendation to use metaverse using Uses and Gratification theory in a mixed approach. Moreover, the authors performed a two-step study involving both qualitative and quantitative techniques, giving a new perspective to the metaverse-related study.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

The study intends to evaluate students’ intention to shift from cash payment to mobile payment system for academic fee payments through push, pull and mooring framework. Push factors comprise risk and service-related factors, pull factors consist of subjective and aspect-based factors and mooring factors include cost and cognitive factors.

Sample of the study consists of around 296 undergraduate and postgraduate students from different higher educational institutions located in India. The questionnaire for data collection comprises 21 Likert scale-based items distributed among seven constructs. Partial least square structural equation modeling is used to identify the significant factors influencing students’ intentions.

Five of the factors, namely, risk, service, subjective, aspect and cognitive significantly influence student’s intention to switch to mobile payment system for academic fee payments. Moderation analysis indicates that the impact of the push and pull factors on switching intention towards mobile payments has a more positive influence among male students.

This study is probably the only study that tested the specific push, pull and mooring factors influencing intention to switch to mobile payment from cash payment in the Indian education system based on the incentive, Fogg behavior and status quo bias theory for academic fee payment.