## 4 Results
### EFA Analysis  
Factor analysis was used to analyze the major barriers to BIM adoption in Nigeria's construction industry. This analysis explored and detected the relationship among variables and categorized the factors in a concise and comprehensive form. Table 3 shows that the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) value of 0.916 retrieved from the data was adequate and satisfactory for factor analysis and Bartlett's Test of Sphericity \begin{table}
\begin{tabular}{l l l} \hline \hline
**S/N** & **Information** & **Frequency Percentage (\%)** \\ \hline \hline A. Highest academic qualification & & \\ \hline OND/HND & 10 & 9.8 \\ \hline B.SC/B.TECH & 42 & 41.2 \\ \hline M.SC/M.TECH & 49 & 48.0 \\ \hline OTHERS & 1 & 1.0 \\ \hline TOTAL & 102 & 100.0 \\ \hline \hline B. Years of experience & & \\ \hline
1–5 years & 23 & 22.5 \\ \hline
6–10 years & 40 & 39.2 \\ \hline
11–15 years & 22 & 21.6 \\ \hline
15–20 years & 15 & 14.7 \\ \hline Above 20 years & 2 & 2.0 \\ \hline Total & 102 & 100.0 \\ \hline \hline C. Numbers of project currently engaged on & & \\ \hline
1–5 projects & 21 & 20.6 \\ \hline
6–10 projects & 25 & 24.5 \\ \hline
11–15 projects & 39 & 38.2 \\ \hline
16–20 projects & 12 & 11.8 \\ \hline Above 20 projects & 5 & 4.9 \\ \hline Total & 102 & 100.0 \\ \hline D. Professional body of respondents & & \\ \hline RIBA & 6 & 5.9 \\ \hline NICS & 8 & 7.8 \\ \hline CIBSE & 22 & 21.6 \\ \hline ICE/COREN & 41 & 40.2 \\ \hline PMI & 25 & 24.5 \\ \hline Total & 102 & 100.0 \\ \hline E. Membership status & & \\ \hline Graduate & 14 & 13.7 \\ \hline Probationer & 25 & 24.5 \\ \hline Corporate/Associate & 42 & 41.2 \\ \hline Fellow & 21 & 20.6 \\ \hline Total & 102 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the respondents. for correlation adequacy between the variables indicating a \(p\)-value \(<\) 0.05 was highly significant and considered suitable for factor analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{3}{c}{**Initial Eigenvalues**} & \multicolumn{3}{c}{**Rotation Sums of Squared Loadings**} \\ \cline{2-7}  & **Total** & **\% of Variance** & **Cumulative\%** & **Total** & **\% of Variance** & **Cumulative\%** \\ \hline  
\hline \hline \end{tabular} Extraction Method: Principal Component Analysis.  
\end{table} 
Table 4: Total variance explained for the major barriers to the adoption of BIM technology in the construction industry. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Kaiser-Meyer-Olkin Measure**} \\ \hline Kaiser–Meyer–Olkin Measure of Sampling Adequacy & 0.916 \\ \hline \multirow{3}{*}{Bartlett’s Test of Sphericity} & Approx. Chi-Square & 2311.112 \\ \cline{2-3}  & Df & 595 \\ \cline{2-3}  & Sig. & 0.000 \\ \hline \end{tabular} The rotated component matrix has 35 variables that constitute the major barriers to adopting BIM technology in Nigeria’s construction industry (Table 4). The first component revealed that the principal factor account for 17.417% of the total variance, and the second component constitute 12.00% of the total variance.  
\end{table} 
Table 3: Kaiser–Meyer–Olkin measure of sampling adequacy. Table 3 also suggests a rotation sums of squared loadings of 67.573, which is above 50%, indicating the suitability of using EFA. Table 4 strongly influences each of the barriers based on the varimax rotation; therefore, it is essential to identify these factors before interpreting the seven extracted BIM barriers. The seven extracted components were named as follows: BIM literacy among the construction professionals, BIM collaboration and standard, cost impact of BIM, accessibility to current updates of BIM development, problem of standardization, competitive mentality among the stakeholders, and BIM Reliability and Contract condition. Although no specific procedure was followed in naming the factors in Table 5, the names were justified based on the background and the level of knowledge of the researcher.