# Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
## 4 Results
### Interpretation of the Results  
The results of this study allow the following conclusions to the hypotheses:  
**Hypothesis H\({}_{\mathbf{A.1}}\)**: _Insufficient training has a negative impact on the performance of an immersive assistance system._  
**Proof of Hypothesis H\({}_{\mathbf{A.1}}\)**:** is confirmed and, thus, Hypothesis H\({}_{\mathbf{A.0}}\) is rejected. \(\square\)  
In detail, it can be shown with significance that the mean OP between OT and MT decreases by 21.46% (\(p=0.009\); Co'd = \(-1.049\)) and between DT and MT by 16.86% (\(p=0.050\); Co'd = \(-0.825\)). From the perspective of TOTP, however, the mean from OT to MT can be significantly increased by 593.3 s (\(p<0.001\); Co'd = 2.230) and from OT to DT by 511.9 s (\(p<0.001\); Co'd = 1.924). The mean of the index performance is significantly reduced by the switch from OT to MT by 2.47%/min (\(p<0.001\); Co'd = \(-1.828\)) and from OT to DT by 1.88%/min (\(p<0.001\); Co'd = \(-1.392\)). Assuming that the MT has an inferior training quality and thus represents faulty training, the post hoc comparisons of the OP, TOTP, and performance show a statistically significant deterioration between the optimised and faulty training scenarios. Due to the high statistical power and high effect sizes, Hypothesis H\({}_{\mathbf{A.1}}\).can be confirmed.  
**Hypothesis H\({}_{\mathbf{B.1}}\)**: _Insufficient training has a negative impact on the acceptance of an immersive assistance system._  
**Proof of Hypothesis H\({}_{\mathbf{B.1}}\)**:** is confirmed and, thus, Hypothesis H\({}_{\mathbf{B.0}}\) is rejected. \(\square\)  
The key significance for acceptance lies in the BI item, whose ANOVA F(2, 48) = 5.99; \(p=0.005\); \(\eta^{2}\)P = 0.200; and power = 0.89 shows a significant difference within the independent variable QoT. In detail, a significant reduction in the mean by 1384 units (based on a seven-point Likert scale) from the factor level OT to MT was demonstrated via post hoc tests. This statement is also supported by significant differences in other subscales of UTAUT2 (see Tables 3-6, A1 and A2. and Figure 8). The faulty training scenario also reduces the subscales (compared to OT) of the PE (Mean_Diff = \(-0.847\); \(p=0.030\); Co'd = \(-0.903\)), EE (Mean_Diff = \(-0.780\); \(p=0.032\); Co'd = \(-0.898\)), FC (Mean_Diff = \(-1.014\); \(p=0.005\); Co'd = \(-1.143\)), and HM (Mean_Diff = \(-0.898\); \(p=0.009\); Co'd = \(-1.068\)). This leads to the conclusion that the user expects lower performance (output and quality) and less enjoyment combined with greater effort with a worse training scenario. According to the subscale FC,  
Figure 10: Boxplots of the (**a**) HR_Diff (BPM) and (**b**) SCL_Diff (\(\upmu\)S)–QoT.  
a poorer training scenario also means that conditions such as resources, expertise, previous experience, and troubleshooting are assessed as poorer. As expected, the subscales SI and HT show no statistically significant differences, which is also logically understandable. At this time, the quality of training should have no influence on the social environment or the habits of the probands. HT and SI can only arise when the participants are working with AR and in conversation with each other.