## 5 Discussion  
The process of adoption of a specific type of technology takes place when there is a degree of harmony between various factors that have an impact on human behavior. It is a complex process that involves personal, interpersonal, and social factors that directly influence whether a particular subject interacts with the technological device [37; 39]. Based on this idea, the present work determined as a fundamental objective to study which factors influence the degree of acceptance of IoT in Higher Education teachers. To do so, it was framed in the conceptual bases established by the UTAUT model in terms of socio-demographic variables that participate in this decision process. Thus, the predictive power of the model was 43% of the variance, which was adequate, while taking into account that in most of the researches that have applied this model have reached values between 20% and 70% [38]. The main findings of the research determined that, on the basis of gender, men scored higher overall than women. In addition, through the t test the differences presented in EE, FC, and ATUT were found to be significant. In this sense, a line is established that coincides with other studies in the scientific literature that have applied the UTAUT model and in which gender was presented as an influential factor [37]. Therefore, the constructs that received the highest scores were EE and ATUT, parameters that are associated with intrapersonal perception in attitudinal and competency terms towards the technological device, results previously obtained in previous UTAUT models [12; 28]. In other words, higher education teachers show a positive predisposition towards the use of IoT, as well as confidence that they can correctly interact with this type of technology and extract optimum performance from it. The results showed that teachers aged 36 and older had higher scores in UTAUT model (except FC) than those who were younger. The \(t\)-test found that those differences were significant for PE and EE. This finding is surprising, as it undoubtedly shows a sense of change in the multiple studies that determine how there is a direct correlation between age and the attitude and predisposition to use technology, as well as the belief in the difficulty of using it [35]. Accordingly, it changes the trend highlighted in previous studies, where the fact of being younger has been associated with having a greater predisposition towards the use of technology. On the other hand, the configuration of the UTAUT model through the path analysis showed how seven hypotheses out of the 15 proposed at the beginning of the research were supported by the obtained results. Firstly, with respect to PE, it was shown that it directly influenced behavioral intention (H1), as indicated by previous research using this same type of model [36; 37]. It is, therefore, essential that teachers perceive the usefulness and the many educational opportunities that reside in IoT in order to make use of it in the future. Likewise, age was presented as a significant factor affecting PE and EE (H3, H6). Similarly, FC is presented as a powerful factor affecting the behavioral intention to use IoT (H10). Thus, the idea is established that the use of IoT will be greater by teachers if there is a technical infrastructure that supports the use of IoT. Therefore, it will be essential that when using IoT in universities, the relevant channels and tools are available to guarantee optimum use of the technology. In relation to this same construct, the model made it possible to know that gender has a significant effect on FC (H11). Finally, it allowed extracting that ATUT has a significant effect in behavioral intention to use IoT and that the gender factor is determinant in presenting a greater predisposition of use or not. In contrast, the remaining construct of the model, SI, did not present any significance in the model and, therefore, it can be stated that they do not present any significance when it comes to influencing the adoption of IoT in this study.