## 4 Results and Discussion
### Verification of the Hypothesis of the Study
#### 4.6.1 Factor Analysis
* Outdated Educational Curricula: many academic institutions lack dynamic curricula to equip students with the latest developments in IoT technologies, scoring 3.6781. * Favoritism and Corruption: these issues also constitute serious barriers, with a mean score of 3.6507. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Major Factors that Affect the Adoption of Internet of Things**} & **Mean** \\ \hline Inadequate financial resources, difficulty in securing loans, and limited funding support from the government and other sponsors & 3.8014 \\ \hline The loose nature of policies & 3.7877 \\ \hline Insufficient skilled labor in the IoT, data science, and agriculture & 3.7603 \\ \hline Potential clients are hesitant to adopt IoT due to the necessity of setting up or upgrading infrastructure and adopting new business models, which may result in a limited market & 3.7534 \\ \hline The high cost of marketing IoT solutions poses a significant challenge, as many startups are unable to & 3.7534 \\ \hline Lack of understanding of commercial practices and market regulations related to the IoT & 3.7397 \\ \hline The mass movements of graduates from STEM (Science, Technology, Engineering and Mathematics) & 3.7260 \\ \hline Overlapping and conflicting policies & 3.7192 \\ \hline Lack of updated educational curricula with many academic institutions lacking dynamic curricula & 3.6781 \\ that integrate the latest advancements in hardware, software, and communication technologies & 3.6507 \\ \hline Favoritism and corruption & 3.6507 \\ \hline Lack of standardization in IoT as each vendor develops its own hardware, software, and & \\ communication protocols without adhering to common standards, making it challenging to integrate & 3.6370 \\ devices from different manufacturers into a cohesive IoT application & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._* Lack of Standardization: the lack of standardization in the IoT, where each vendor develops their hardware, software, and communication protocols independently, is a hindrance, scoring 3.6370. These challenges highlight significant barriers to the adoption of IoT in the Silicon Mountain area, as indicated by the mean scores derived from the survey. 6.2 Principal Component Analysis of IoT Adoption Challenges in the Silicon Mountain Technology Ecosystem  
The Principal Component Analysis (PCA) method was employed, and the eigenvalues associated with each linear component (factor) before extraction, after extraction, and after rotation are presented in Table 6. Initially, 22 linear components were identified within the data set. Among these, seven primary components were extracted based on their eigenvalues, with only factors having eigenvalues greater than 1 being considered. These seven factors accounted for approximately 60.449% of the variance in the challenges to IoT adoption within the Silicon Mountain technology ecosystem. This percentage is deemed satisfactory, as it exceeds the minimum threshold of 50%. It is recommended that the proportion of the total variance explained by the retained factors should be at least 50% [39]. Consequently, about 39.551% of the variance is attributable to other challenges to IoT adoption that were not considered. Table 7 provides a classification of the various challenges under the seven identified components. This classification facilitates the regrouping of the primary challenges affecting