# Longitudinal Study on Construction Organization's BIM Acceptance
## 5 Comparisons of BIM Acceptance between 2012 and 2019  
Using an independent sample t-test, the differences in key factors affecting the acceptance of BIM and intent to accept BIM between 2012 and 2019 were validated. The hypotheses were as follows:  
- Key factors affecting the acceptance of BIM in 2019 are greater than those in 2012. - Intent to accept BIM in 2019 was higher than that in 2012. In testing the difference between key factors affecting the 2012 and 2019 intent to accept BIM, and since the t-value resulted in a significant p-value that was less than the set alpha of 0.05 (p \(<\) 0.05, which puts the obtained t in the tail), the null hypothesis was rejected in support of the alternate hypothesis (Table 7). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **PEU** & **PU** & **COA** & **IIA** & **OIA** \\ \hline \multirow{2}{*}{**OC**} & 0.82 & 0.37 & 0.67 & 0.22 & 0.58 \\  & (DE: 0.82, InDE: 0) & (DE: 0.00, InDE: 0.37) & (DE: 0, InDE: 0.67) & (DE: 0, InDE: 0.22) & (DE: 0, InDE: 0.58) \\  & 0.07 & 0.26 & 0.08 & 0.16 & 0.08 \\  & (DE: 0.07, InDE: 0) & (DE: 0.23, InDE: 0.03) & (DE: 0, InDE: 0.08) & (DE: 0, InDE: 0.16) & (DE: 0, InDE: 0.08) \\ \multirow{2}{*}{**PC**} & \multirow{2}{*}{(DE: \(-\)0.08, InDE: 0)} & 0.23 & \(-\)0.04 & 0.15 & \(-\)0.02 \\  & & (DE: 0.27, InDE: \(-\)0.04) & (DE: 0, InDE: 0.04) & (DE: 0, InDE: 0.15) & (DE: 0, InDE: \(-\)0.02) \\ \multirow{2}{*}{**BC**} & 0.16 & 0.20 & 0.14 & 0.26 & 0.18 \\  & (DE: 0.16, InDE: 0) & (DE: 0.13, InDE: 0.07) & (DE: 0.10, InDE: 0.14) & (DE: 0.13, InDE: 0.13) & (DE: \(-\)0.01, InDE: 0.19) \\ \multirow{2}{*}{**PEU**} & \multirow{2}{*}{-} & 0.46 & 0.82 & 0.27 & 0.71 \\  & & (DE: 0.46, InDE: 0.00) & (DE: 0.77, InDE: 0.05) & (DE: 0.04, InDE: 0.23) & (DE: 0.58, InDE: 0.13) \\ \multirow{2}{*}{**PU**} & \multirow{2}{*}{-} & 0.10 & 0.64 & 0.14 \\  & & & (DE: 0.10, InDE: 0) & (DE: 0.65, InDE: \(-\)0.01) & (DE: \(-\)0.13, InDE: 0.28) \\ \multirow{2}{*}{**COA**} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & -0.09 & 0.06 \\  & & & & (DE: 0.09, InDE: 0) & (DE: 0.09, InDE: \(-\)0.03) \\ \multirow{2}{*}{**IIA**} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & - & - & 0.42 \\  & & & & - & (DE: 0.42, InDE: 0) \\ \multirow{2}{*}{**OIA**} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The total effects between constructs. A meaningful difference was observed in all hypotheses; the environment for using BIM, the perceptions of users, and the level of support were higher in 2019 than 2012. Moreover, BIM users showed higher individual and organizational intentions to use BIM in 2019 than in 2012. Using t-tests, we also verified that the differences were significant. Consequently, the mechanism for achieving BIM acceptance will differ over time. The comparison results of the mechanisms implemented in 2012 versus 2019 are shown in Table 8. The results of 2012 in Table 8 are the results of hypothesis validation for only Korean construction organizations based on the 2012 BIM acceptance model in the previous study [29]. On the other hand, the 2019 result is the hypothesis validation result of this study (Figure 2). We confirmed that the key factors affecting the acceptance of BIM were significantly influential factors in both 2012 and 2019. This means that the key factors affecting the acceptance of BIM may be generalized. Furthermore, it was confirmed that the mechanism for achieving BIM acceptance in 2012 differed from the mechanism in 2019. The significant differences between 2012 and 2019 were as follows. First, OC and BC have the most significant and largest impact on PEU. This indicates that when performing individual tasks or cooperative work, PEU of BIM is higher when the organization is more flexible and active in accepting new technology. BC had no direct effect on the intent to accept BIM; however, it did have an indirect influence through PEU.