Evaluation of Mobile Mixed Reality Simulator (M2RSi) Using Hybrid Technology Acceptance Model (HTAM) in Disaster Mitigation Simulation
Bibliographic record
Abstract
This study discusses the evaluation of aspects of technology acceptance of M2RSi using Hybrid Technology Acceptance Model (HTAM) that is linked with its external factors, including information quality, infrastructure, knowledge and skills, costs, and virtual simulation environments.The four response alternatives on the 4-point Likert scale utilized in this study were (1) Strongly Disagree, (2) Disagree, (3) Agree, and (4) Strongly Agree.Structural Equation Modeling was used to examine the data (SEM).To assess the efficacy of Mixed Reality (MR) technology in crisis management, this study included 100 respondents from two cities in the evaluation process.The research questionnaire included numerous questions about the features of technological acceptability.This research tested 19 hypotheses, and each hypothesis had a significant influence on external variables related to the effectiveness of MR technology in disaster management.There were 5 dominant priority variables for evaluation by developers: Acceptance (ACC), Behavioral Intention (BI), Satisfaction (SA), Perceived Usefulness (PU), and Trialability (TR).In conclusion, developers will gain a more holistic understanding of the effectiveness of Mixed reality (MR) technology in disaster mitigation simulations by prioritizing evaluation of these variables.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".