## 2 Theoretical Framework and Hypothesis Development
### Cross-Domain Review of Interrelationships Among TAM Constructs  
Over the past 25 years, a substantial amount of meta-analytic, systematic, and context-specific research has consistently confirmed the TAM's theoretical and empirical robustness across various technological, organisational, and educational settings. For instance, the authors of [78] conducted one of the first comprehensive conceptual TAM reviews and studied its empirical application, analysing over 100 studies published since the model's introduction in the mid-1980s. Although their review did not include a statistical meta-analysis, their synthesis of empirical findings provided valuable insights into the consistency of the TAM's core variables across various domains. Specifically, they reported that the relationship between PU and BI was statistically significant in 74% of the studies analysed. While the PEU significantly influenced PU in 69% of cases, its direct effect on BI was notably weaker, with significance observed in less than 60% of the studies. The authors of [31] conducted a meta-analysis synthesising cross-domain TAM data from 26 empirical studies. Their results revealed that PU was the strongest predictor of users' BI, with path coefficients ranging from 0.09 to 0.91 and a mean of \(\beta=0.49\). The findings also indicate that compared to the modest direct influence of PEU on BI (range \(\beta=0.07\)-0.59; mean \(\beta=0.27\)), its effect on PU was more substantial, with coefficients ranging from 0.42 to 0.54 and a mean \(\beta\) of 0.48. Similarly, the authors of [79] expanded upon this work by analysing 88 studies involving over 12,000 participants. Their result supported previous findings confirming that the PU-to-BI relationship remained the strongest, with path coefficients ranging from 0.46 to 0.55 and a mean of \(\beta\) = 0.51. The direct effect of PEU on PU was also consistently significant, with a coefficient ranging from 0.43 to 0.54 and a mean of \(\beta\) = 0.48. Considerably weaker PEU-to-BI relationships, ranging from 0.15 to 0.27 with a mean of \(\beta\) = 0.19, also confirmed the uncertain and often limited direct role of perceived ease of use in predicting behavioural intention to use innovative technology. Focusing on TAM application in e-learning, the authors of [80] conducted a meta-analysis of 42 studies, encompassing a total of 494 effect sizes. Their findings confirmed consistently stronger PU-BI (mean \(\beta\) = 0.40) and PEU-PU (mean \(\beta\) = 0.40) relationships compared to that between PEU and BI (mean \(\beta\) = 0.29). Complementing these findings, the authors of [30] conducted a systematic literature review of 71 studies focused on TAM application in the broader field of education. Their findings reaffirmed the continued dominance of PU in shaping BI, highlighting the indirect role of PEU primarily through its impact on PU. With increasing interest in integrating VR technology into educational environments, the application of the TAM and its extensions has garnered growing attention within the academic community [34; 35; 38; 72; 74; 81; 82; 83; 84; 85]. Across many reviews, the direct and significant effects of PU on BI and the effects of PEU on PU were consistent with evidence in the broader TAM literature. However, the findings present slight variations concerning the direct effect of PEU on BI. Specifically, in earlier studies conducted on less immersive VR platforms [74; 83], PEU was found to directly influence BI; however, as VR technology became more immersive and technologically complex, the direct impact of PEU on BI diminished [34; 35; 72], with other affective variables (e.g., perceived immersion) gaining prominence. Even though the application of TAM has received increasing scholarly attention in the maritime field, including education and training, its implementation remained largely outside fully immersive VR technologies. For instance, ref.