# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
## 5 Discussion  
To understand the external variables that could influence the acceptance of a 3D learning environment for farmers in Europe, we consulted previous TAM applications in e-learning, agriculture, and virtual reality sectors. TAM extensions on e-learning technologies have been a research topic for more than a decade [6]. On the contrary, significantly fewer studies investigated the acceptance of VR technologies, and that gap of knowledge is also \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Research Categories** & **Num. of Studies** & \
\begin{tabular}{l} **Studies with (\(\beta\))** \\ **w.r.t. PU** \\ \end{tabular} &
\begin{tabular}{l} **Studies with (\(\beta\))** \\ **w.r.t. PEOU** \\ \end{tabular} \\ \hline Technology acceptance: theoretical models & 7 & 3 & 3 \\ Acceptance of a bundle of technologies & 6 & 2 & 2 \\ Acceptance of a methodology & 6 & 3 & 3 \\ Acceptance of a device & 8 & 6 & 6 \\ Acceptance of a service/platform/system & 30 & 16 & 17 \\ Comparison of the acceptance among different tasks/technologies & 3 & 2 & 0 \\ Technology acceptance: the effect of certain moderating variables & 7 & 4 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of papers reporting path coefficients (\(\beta\)). Figure 5: Path Coefficients. mentioned in recent work by [PERSON] et al. [128]. To facilitate the analysis, we considered the effects of the external variables in PU and PEOU separately. The impact of each factor per category is depicted in Figures 6 and 7. As reported in Table 4, only two of the three researches belonging to the category: \"Comparison of the acceptance among different tasks or technologies\" provided information about path coefficients. However, none of them delved into the determinants of PEOU. On this basis, we excluded the mentioned category from the QFD matrix of PEOU. Considering that in the TAM, PEOU is also a determinant of PU [7], we included the construct PEOU within the PU matrix to analyze its effect. In general, the findings suggest that the best predictor of PU is PEOU, followed by content and system quality, perceived enjoyment, self-efficacy, social norm, facilitating conditions, experience, individual innovativeness, and finally, anxiety. As for PEOU, it is mainly predicted by self-efficacy, followed by perceived enjoyment, experience, system and content quality, anxiety, social norm, and finally, individual innovativeness. Therefore, we can confirm that the external variables have different effects on PU and PEOU. PEOU is not considered as external variable because it is one of the TAM's two main determinants. Studies from the categories measuring the acceptance of a new methodology, a device, and a system or service, reported a strong predictive power of PEOU on PU. That was also the case for studies analyzing the moderating effect of some variables. A weaker but still significant influence of PEOU on PU was found in theoretical models and in cases-study evaluating the acceptance of a bundle of technologies. This study confirmed the presence of a strong and significant impact of the PEOU on PU in most cases, which validates the significance of the relationship established by [PERSON] [7] in the original TAM. Figure 6: Quality Function Deployment (QFD) matrix for Perceived Usefulness. Figure 7: QFD matrix for Perceived Ease of Use. The concept of ANX is often associated with negative feelings of nervousness, fear, and uncertainty about the outcomes of using innovations [58]. [PERSON] et al. [129] pointed out that in software applications, the effect of ANX is often analyzed, and that is evident in our study. Only works evaluating the acceptance of a device and a system or service, and theoretical extensions of the model, reported the causal link among ANX and the two main TAM determinants. The average effect size evidenced a weak relationship of anxiety with PU and a medium impact on PEOU.