Factors affecting attitude to use metaverse technology application
Bibliographic record
Abstract
The concept of the “Metaverse” is a three-dimensional virtual world that relies on simulations of reality to represent real-life experiences, and it can be classified as the next generation in using the Internet. In this research, we will examine the factors that may influence user acceptance of metaverse and the relationships between these variables highlight how different factors can be examined. The goal of understanding these factors is to determine how Metaverse developers can improve this technology to meet user expectations and enable users to better interact with this technology. To achieve this goal, a sample of 312 students’ participants from different age groups was selected to respond to an online Likert scale questionnaire ranging from) strongly disagree equal) to (strongly agree equal 5). The study found that perceived enjoyment significantly positively influences technology metaverse application. Moreover, perceived curiosity and perceived self-efficacy positively influence technology application metaverse transitions. In addition, perceived ease of use (PEOU) and perceived usefulness (PU) positively influence the attitude toward using the Metaverse technology/application, which means that all the previous factors have an overall positive effect on the attitude toward using the Metaverse technology application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".