## 4 Data Analysis and Findings
### Hypotheses Testing Measurements
#### 4.3.2 Coefficient of Determination and Predictive Relevance  
The Coefficient of Determination (R\({}^{2}\)) utilises the combined effects of the independent variables to explain predictive ability through the level of variance in each dependent variable. R\({}^{2}\) is measured between 0 and 1 based on Equation (1) below (Coefficient of determination, where SS is the sum of squares), and denoted as weak (<0.25), moderate (0.26 to 0.5) or strong (>0.5) [46]. \[R^{2}=\frac{S_{ab}^{2}}{SS_{a}-SS_{b}} \tag{1}\]  
Though the evaluation of R\({}^{2}\) is a criterion for predictive analysis, research recommends checking the Predictive Relevance (Q\({}^{2}\)) value, which evaluates the predictive validity of independent to dependent variables in the entire model based on Equation (2) where SSE is the sum of squares due to error and SS is the sum of squares [46]. The predictive relevance Q\({}^{2}\) of each dependent variable should be greater than 0 so that the model is considered capable of predicting influence over that variable. However, a dependent variable with a Q\({}^{2}\) of 0 or less, is considered irrelevant for prediction and accordingly insignificant to the study. \[Q^{2}=1-\frac{SSE}{SS} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Variables** & **Ind indicators** & **AT** & **BI** & **BU** & **CI** & **KA** & **LN** & **OL** & **OS** & **PE** & **PT** & **PU** & **SF** & **TT** & **UC** \\ \hline \multirow{4}{*}{Attitude} & AT1 & **0.895** & 0.525 & 0.483 & 0.19 & 0.324 & 0.126 & 0.004 & 0.171 & 0.42 & 0.315 & 0.718 &  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Variables** & **Indicators** & **AT** & **BI** & **BU** & **CI** & **KA** & **LN** & **OL** & **OS** & **PE** & **FT** & **PU** & **SF** & **TT** & **UC** \\ \hline \multirow{4}{*}{Organisational Support} & OS1 & 0.092 & 0.118 & 0.55 & 0.473 & 0.309 & 0.50 \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Variables** & **Ind indicators** & **AT** & **BU** & **BI** & **CI** & **KA** & **LN** & **OL** & **OS** & **PE** & **PU** & **PT** & **SF** & **TT** & **UC** \\ \hline Attitude & AT & **0.872** & & & & & & & & & & & & \\ \hline BM Uptake & BU & 0.519 & **0.880** & & & & & & & & & & & \\ \hline Behavioural Intention to Use & BI & 0.54 & 0.386 & **0.956** & & & & & & & & & \\ \hline CI/Kaizen & CI & 0.182 & 0.562 & 0.264 & **0.944** & & & & & & & & \\ \hline Knowledge Acquisition & KA & 0.33 & 0.301 & 0.361 & 0.421 & **0.868** & & & & & & & \\ \hline Learn & LN & 0.074 & 0.502 & 0.147 & 0.519 & 0.303 & **0.004** & & & & & \\ \hline Organisational Learning & OL & -0.043 & 0.416 & 0.141 & 0.372 & 0.234 & 0.649 & **0.857** & & & & \\ \hline Organisational Support & OS & 0.092 & 0.495 & 0.151 & 0.369 & 0.335 & 0.632 & 0.504 & **0.804** & & & \\ \hline Perceived Ease of Use & PE & 0.326 & 0.18 & 0.319 & 0.162 & 0.325 & 0.234 & -0.013 & 0.408 & **0.902** & & & \\ \hline Perceived Usefulness & PU & 0.754 & 0.507 & 0.518 & 0.404 & 0.444 & 0.111 & 0.011 & 0.128 & 0.408 & **0.909** & & \\ \hline Process Training & PT & 0.398 & 0.355 & 0.472 & 0.497 & 0.577 & 0.189 & 0.209 & 0.189 & 0.212 & 0.525 & **0.873** & & \\ \hline Social Factors & SF & 0.228 & 0.511 & 0.191 & 0.227 & 0.244 & 0.572 & 0.428 & 0.598 & 0.33 & 0.228 & 0.176 & **0.750** & \\ \hline Technical Training & TT & 0.436 & 0.46 & 0.539 & 0.54 & 0.514 & 0.308 & 0.359 & 0.294 & 0.291 & 0.561 & 0.774 & 0.262 & **0.889** \\ \hline User Competency & UC & 0.494 & 0.331 & 0.59 & 0.398 & 0.599 & 0.195 & 0.123 & 0.328 & 0.467 & 0.519 & 0.539 & 0.219 & 0.602 & **0.905** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Formell-Larker results. Accordingly, this research considers the combination of _t_-value, _p_-value, R\({}^{2}\), and Q\({}^{2}\) as the means of analysis to satisfy significance-based hypotheses testing.