Immersive Cultural Heritage: Exploring Users’ Attitudes and Behavioral Intentions Towards the Dunhuang Digital Museum
Bibliographic record
Abstract
The advent of augmented reality, virtual reality and artificial intelligence technologies and its innovative applications has revolutionized the museum landscape, ushering in a new era that sharply diverges from the past. Although digital museums have attracted the attention of scholars, the relevant research is still insufficient. The study formulates a comprehensive theoretical model based on technology acceptance model, presenting 13 hypotheses that impact users' sustained intent towards digital museums. Using SPSS 26.0 software, this study employed structural equation modeling to analyze the data collected from 382 visitors of the Dunhuang Digital Museum and validate the hypotheses. Results showed that relative advantage, self-efficacy and media richness influence users' attitudes and thus behavioral intentions through perceived ease of use; relative advantage and technostress influence users' attitudes and thus behavioral intentions through perceived usefulness; social mimetism and digital experience not only influence behavioral intentions by influencing attitudes, but also have a direct impact on behavioral intentions. The findings contribute to the enrichment of academic theories and offer professionals a valuable reference for enhancing user experience services in digital museums.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".