Exploring the Future Metaverse: Research Models for User Experience, Business Readiness, and National Competitiveness
November 15, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Amir Reza Asadi, Shiva Ghasemi
arXiv ID
2411.10408
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
This systematic literature review paper explores perspectives on the ideal metaverse from user experience, business, and national levels, considering both academic and industry viewpoints. The study examines the metaverse as a sociotechnical imaginary, enabled collectively by virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. Through a systematic literature review, n=144 records were included and by employing grounded theory for analysis of data, we developed three research models, which can guide researchers in examining the metaverse as a sociotechnical future of information technology. Designers can apply the metaverse user experience maturity model to develop more user-friendly services, while business strategists can use the metaverse business readiness model to assess their firms' current state and prepare for transformation. Additionally, policymakers and policy analysts can utilize the metaverse national competitiveness model to track their countries' competitiveness during this paradigm shift. The synthesis of the results also led to the development of practical assessment tools derived from these models that can guide researchers
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted