# Simultaneous Use of Digital Technologies and Industrial Robots in Manufacturing Firms
## 4 Results and Discussion
### Statistical Tests
When interpreting the logistic regression results, the main focus was on the significance of the predictor variable and the likelihood of using a certain type of robot when a certain enabling technology is utilized. The lower bound (LB) and upper bound (UB) were also reported at the 95% confidence interval. Variable significance (Sig.) indicates that a variable has a significant effect on prediction, while Exp(B) provides an odds ratio or likelihood of using a certain type of industrial robot if a particular digital technology is utilized. Table 5 summarizes the results for logistic regressions for each selected technology and the five types of industrial robots. As expected, technologies that have a significant effect on predicting the use of a certain type of industrial robots are the same as in the association and correlation analysis. All of the selected technologies have a significant effect on predicting the use of industrial robots for handling processes, and only the technologies T1-T3 and T5-T6 have a significant effect on the prediction of the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Technology** & **Statistic** & **R1** & **R2** & **R3** & **R4** & **R5** \\ \hline \multirow{2}{*}{T1} & Pearson \(\chi^{2}\) & **0.008** & **\textless{}0.001** & 0.414 & 0.151 & 0.357 \\ \cline{2-7}  & Phi & **0.245** & **0.317** & 0.075 & 0.132 & 0.085 \\ \hline \multirow{2}{*}{T2} & Pearson \(\chi^{2}\) & **0.011** & **\textless{}0.001** & 0.977 & **0.004** & 0.170 \\ \cline{2-7}  & Phi & **0.235** & **0.304** & 0.003 & **0.267** & 0.123 \\ \hline \multirow{2}{*}{T3} & Pearson \(\chi^{2}\) & **0.017** & **\textless{}0.001** & 0.196 & 0.167 & 0.235 \\ \cline{2-7}  & [PERSON] & **0.220** & **0.364** & 0.119 & 0.127 & 0.109 \\ \hline \multirow{2}{*}{T4} & Pearson \(\chi^{2}\) & 0.188 & **0.004** & 0.355 & 0.338 & 0.501 \\ \cline{2-7}  & [PERSON] & 0.121 & **0.263** & \textless{}0.085 & 0.088 & 0.062 \\ \hline \multirow{2}{*}{T5} & Pearson \(\chi^{2}\) & **0.001** & **\textless{}0.001** & 0.264 & **0.034** & 0.229 \\ \cline{2-7}  & [PERSON] & **0.295** & **0.327** & 0.103 & **0.196** & 0.111 \\ \hline \multirow{2}{*}{T6} & [PERSON] \(\chi^{2}\) & **0.002** & **0.008** & 0.174 & 0.079 & 0.894 \\ \cline{2-7}  & [PERSON] & **0.290** & **0.245** & 0.125 & 0.162 & 0.012 \\ \hline \multirow{2}{*}{T7} & [PERSON] \(\chi^{2}\) & **0.065** & **0.005** & 0.237 & **0.007** & 0.067 \\ \cline{2-7}  & [PERSON] & **0.170** & **0.257** & 0.109 & **0.248** & 0.168 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Association and correlation results for technologies and types of robots. industrial robots for manufacturing process use. In the case of collaborative robots, the use of this type of robot is predicted mainly by the use of ERP systems, near-real-time production control systems and digital visualization (solutions) on the shop floor. Again, no significant effect was found for mobile robots and autonomous robots. To interpret our results, an example of one of the included digital technologies is given: near-real-time production control systems. If a firm is using near-real-time production control systems, then it is 6.7 times more likely to use industrial robots for handling processes, 5.1 times more likely to use industrial robots for manufacturing processes, and 3.1 times more likely to use collaborative robots. When testing the results of association and correlation analysis for readiness levels and types of industrial robots, the Phi coefficient was substituted with the Spearman rank coefficient since readiness level is an ordinal variable (Table 6). The highest correlation coefficient value was found for readiness levels and industrial robots for handling processes.