## 3 Model Construction and Research Assumptions  
### Extraction of User-Perceived Factors of Cultural Education Meta-Universe Systems  
To explore the factors influencing users' willingness to use metaverse technology in cultural heritage, it is essential to consider the complexity of the user experience beyond just reviewing the relevant literature. This requires more in-depth investigation and analysis. Consequently, we have borrowed psychological research methods to extract users' perceptual factors--the \"expressive features\" of the cultural education metaverse system--through feature recognition pathways. This approach aims to provide targeted research variables for the study. To ensure the comprehensiveness of these research variables, data were collected through offline in-depth interviews, and original data were directly gathered from the user evaluation system.  
The GT approach requires an in-depth analysis of original data to summarize hidden inner patterns and connections, and it must be ensured that the selected interviewees are representative and undisturbed during the process [26]. This study interviewed 62 users with experience using the cultural education metaverse system, including 30 males and 32 females. The interviews were conducted in a semi-structured, one-on-one, open-ended format with participants aged 19 to 45. The duration of the interviews varied from 15 to 35 min. With the interviewees' consent, the entire interview was recorded using audio-accelerated recording, which was later transcribed into text to serve as the essential information for coding and analysis.  
Based on the logical coding framework of grounded theory for measuring and identifying research variables [38], this study employed open coding and axial coding to analyze and summarize the logical relationships between perceptual factor features in user evaluations at the conceptual level, ultimately extracting core concepts. The NVivo 11 software was used to facilitate the analysis and design of coding nodes. During the open coding stage, raw data were analyzed sentence by sentence, with a focus on the research theme and purpose. Specific nodes related to feature perception dimensions were refined, resulting in the identification of 18 initial concepts. These concepts were then integrated based on similarities, with repetitive and intersecting concepts summarized. Initial concepts with a frequency of less than two and contradictory concepts were discarded. This process formed nine sub-categories, including user control, social interaction, immersive experience, and feedback mechanisms. Each sub-category was defined by a representative original statement to encapsulate the phenomenon. In the axial coding stage, the nine sub-categories were re-categorized according to the characteristics of augmented reality, allowing for the extraction of three higher-level main categories: interactivity, sense of presence, and narrative, which were identified as the critical user perception factors in the cultural education metaverse system. Finally, the six pieces of initial information reserved beforehand were coded and analyzed, and no new conceptual categories emerged. This indicated that the theory had reached saturation, allowing for the next step of summarization and generalization. The coding process is detailed in Figure 1.  
Figure 1: Refinement of user perception factors (coding results).  
Based on the research findings, three critical performance characteristic factors--presence, interactivity, and narrative--have been identified from the user perspective as crucial in the cultural education metaverse system. However, how these factors are applied within the system and whether they play a significant influential role remains unclear. This uncertainty necessitates further in-depth analysis and research to understand better their impact and effectiveness in the cultural education metaverse system.