## 4 Data Analysis
### EFA and Model Development  
The factor structure of 25 items related to the IoT was identified using exploratory factor analysis (EFA). A large number of well-established correlation factorability criteria, including Kaiser-Meyer-Olkin (KMO), have been identified. The KMO, which measures the homogeneity factor, is often used to verify least-squares correlations between variables. For a reliable factor analysis, the KMO index has to be at least 0.600 [13; 61] but may go as high as 1. Similarly, when \(p<0.05\) was used as the threshold for significance, the Bartlett Sphericity test showed that the correlation matrix was equal to the Sphericity test matrix. Initially, the KMO sample adequacy metric was higher than the suggested value of 0.600, at 0.893 [2; 66]. The Sphericity test performed by [PERSON] was significant (x2 (320) = 1135.510, \(p<0.05\)). The diagonals of the anti-image correlation matrix were greater than 0.50, making them suitable discrete variables for factor analysis. Lower numbers (<3) indicate variables that did not fit the factor solution. In this investigation, the significance of all initial similarities exceeded the criterion. All factor loadings are more than 0.500 [71]. During the analysis, the minimum Eigen value was set to 1, by which all the constructs indicated in the EFA have greater than 1 Eigen value. Furthermore, in total, 58.660% of the variance is explained by the constructs identified from the analysis, as indicated in Table 3. Cross loading was observed in the case of five variables, such as F12, F17, F18, F24, and F25. All of these factors were removed for further analysis. For increasing accuracy, it is important that the alpha value should also increase to more than 0.750. Less than 0.600 is not acceptable in any case because it indicates poor reliability of results [88]. All the values observed in analysis are greater than 0.600, which indicates acceptable reliability statistics. The average correlation of variables across all objects is greater than 0.300, indicating stable internal variables [61]. After performing the exploratory factor analysis, the next step is to categorize all the factors that are grouped together in a component on the basis of their factor loading criteria, which are interlinked with how the actual questionnaire has been filled. Table 4 shows the grouped factors from the output of the EFA analysis, and a proper name is assigned to each category on the basis of their grouped factors relationship. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Safety Phase** & **Assigned Code** & **Activities** \\ \hline \multirow{4}{*}{Technology Barriers} & F1 & The absence of technological integration \\  & F8 & Limited technology implementation scope \\  & F9 & Limitations on hardware and software as well as a lack of standardized standards \\  & F14 & Implementation of heterogeneous distributed IoT system \\ \hline \multirow{4}{*}{Database Barriers} & F11 & Absence of publicly accessible big data \\  & F13 & Deficiencies in the capture of data onsite \\  & F7 & Database synchronization process modification \\  & F4 & The need for constant surveillance \\  & F2 & Complexity of data architecture \\ \hline \multirow{4}{*}{Privacy Barriers} & F10 & Data privacy problems \\  & F16 & Insecure connection between devices in network \\  & F21 & Legal and regulatory requirements \\  & F20 & Absence of a Knowledge administration system \\ \hline \multirow{4}{*}{Operational Barriers} & F6 & Low cost benefit ratio \\  & F15 & Energy and Device Management \\  & F19 & Safety risks \\ \hline \multirow{4}{*}{Management Barriers} & F23 & Obstacles resulting from physical encounters. \\  & F22 & The need for technical education \\ \cline{1-1}  & F3 & The necessity for enough lighting for effective operation \\ \cline{1-1}  & F5 & Deficiency of IoT service supplies \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Categorization of factors generated from EFA.