## 4 Results and Discussion
### Verification of the Hypothesis of the Study
#### 4.6.1 Factor Analysis  
To determine if the data captured in this study were appropriate for factor analysis, we used the Kaiser-Meyer-Olkin (KMO) and [PERSON]'s Test of Sphericity. From the literature, a KMO value > 0.6 is acceptable for a sample size < 100, while a KMO value between 0.5 and 0.6 is acceptable for sample sizes between 100 and 200 [39; 56]. In this study, the KMO measure of sampling adequacy was 0.706, and [PERSON]'s Test of Sphericity was significant (Sig = 0.000), with a Chi-square value of 785.296 and 231 degrees of freedom (df). Table 4 presents the results generated from the SPSS software for KMO and [PERSON]'s Test of Sphericity. These results demonstrate that the data set is not an identity matrix with zero correlations (i.e., the variables are indeed correlated), thereby confirming the appropriateness of applying factor analysis. In study, we measured 22 items related to the challenges of IoT adoption in the Silicon Mountain tech ecosystem, using a five-point Likert scale to gauge their level of seriousness. The averages of these items were calculated to provide an overview of how respondents rated the different challenges. To focus the analysis on the most significant barriers, we applied a cutoff threshold of 3.5, which allowed us to identify the factors perceived by respondents as serious impediments to IoT adoption in the Silicon Mountain tech ecosystem. After applying this threshold, 13 out of the 22 factors emerged as critical barriers to IoT adoption, and these are displayed in Table 5 along with their average ratings (mean values). \begin{table}
\begin{tabular}{c c c} \hline \hline Kaiser–Meyer–Olkin Measure of Sampling Adequacy & 0.706 \\ \hline \hline  & Approx. Chi-Square & 785.296 \\ \cline{2-3} Bartlett’s Test of Sphericity & df & 231 \\ \cline{2-3}  & Sig. & 0.000 \\ \hline \hline determinant = 0.003. & & \\ \end{tabular}
\end{table} 
Table 4: Kaiser–Meyer–Olkin and Bartlett’s Test of Sphericity. \begin{table}
\begin{tabular}{c c} \hline \hline
**Major Factors that Affect the Adoption of Internet of Things** & **Mean** \\ \hline Organizations often lack an understanding of the benefits of IoT and are generally not motivated to & 3.8973 \\ \cline{2-3}  & invest in it & \\ \hline Ongoing socio-economic crises in the country & 3.8151 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Major factors affecting IoT adoption in the Silicon Mountain technology ecosystem. Table 5 reveals the primary challenges to IoT adoption in the Silicon Mountain based on the mean averages. The major challenges identified include the following:  
* Lack of Understanding and Motivation: organizations typically do not grasp the benefits of the IoT and are often unwilling to invest in it, with a mean score of 3.8973. * Socio-economic Crises: the ongoing socio-economic crises in the country pose a significant barrier, scoring 3.8151. * Inadequate Financial Resources: the inability to secure loans and the lack of funding support from the government and other sponsors are major issues, with a mean score of 3.8014. * Loose Policies: the loose nature of policies is a critical challenge, reflected in a mean score of 3.7877. * Insufficient Skilled Labor: there is a shortage of skilled labor in the IoT, data science, and agriculture, scoring 3.7603. * Client Reluctance: potential clients are hesitant to adopt the IoT due to the need for infrastructure upgrades and new business models, scoring 3.7534. * High Marketing Costs: the cost of marketing IoT solutions is high, making it unaffordable for startups, with a mean score of 3.7534. * Lack of Commercial Understanding: there is a lack of understanding of commercial practices and market regulation, scoring 3.7397. * Brain Drain: the mass movement of graduates from Science, Technology, Engineering, and Mathematics (STEM) fields seeking better opportunities is a significant barrier, scoring 3.7260. * Conflicting Policies: overlapping and conflicting policies also pose a challenge, with a mean score of 3.7192.