# Artificial Intelligence Software Adoption in Manufacturing Companies
## 4 Results
### Logistic Regression Results
In connection with the readiness for Industry 4.0 and the probability of using AI, the results of the logistic regression can be found in Table 10. Since the results show that for I4.0 readiness the value in the Sig. column is below the maximum significance threshold of 0.1 (and even 0.05), we can interpret the values of the beta coefficient, exponentiated beta \(\text{Exp}(\beta)\), and LB and UB. Given that the independent variable is now continuous, each unit increase in I4.0 readiness raises the log odds of AI adoption by 0.19, with an approximate standard error of 0.09. The \(\text{Exp}(\beta)\) value of 1.21 indicates that for each unit increase in I4.0 readiness, the odds of AI adoption rise by a factor of 1.21, or 21%. This means that higher levels of I4.0 readiness are associated with a 21% increase in the odds of adopting AI. The 95% confidence interval for the odds ratio, provided by the LB and UB columns, ranges from 1.02 to 1.43. This suggests that the true odds ratio is likely between these values, and since the interval is entirely above 1, it confirms the statistical significance of this variable. In conclusion, these results demonstrate that I4.0 readiness is a significant predictor of AI adoption, with higher readiness levels leading to increased odds of adopting AI. This version maintains the essential details while improving clarity and readability. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & \(\beta\) & **S.E.** & **Wald** & **df** & **Sig.** & **Exp(\(\beta\))** & **LB** & **UB** \\ \hline I4.0 & 0.19 & 0.087 & 4.783 & 1 & **0.029 \({}^{*}\)** & 1.21 & 1.02 & 1.43 \\ \multicolumn{1}{c}{\
\begin{tabular}{c} Constant \\ \end{tabular} } & \(-1.91\) & 0.241 & 62.390 & 1 & \(<\)0.001 & 0.15 & \\ \hline \multicolumn{1}{c}{
\begin{tabular}{c} _Model information:_ \\ \end{tabular} } & \multicolumn{2}{c}{\(X^{2}\) = 4.771} & \multicolumn{2}{c}{_Sig. = 0.029_} & \multicolumn{2}{c}{_Nagelkerke \(r^{2}\) = 0.02_} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Logistic regression results for Industry 4.0 readiness. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & \(\beta\) & **S.E.** & **Wald** & **df** & **Sig.** & **Exp(\(\beta\))** & **LB** & **UB** \\ \hline Low & & & 2.579 & 3 & 0.461 & & & \\ Low-medium & 0.180 & 0.332 & 0.296 & 1 & 0.587 & 1.198 & 0.625 & 2.293 \\ Medium-high & 0.073 & 0.382 & 0.036 & 1 & 0.849 & 1.076 & 0.509 & 2.273 \\ High & 0.817 & 0.525 & 2.425 & 1 & 0.119 & 2.265 & 0.809 & 6.336 \\ Constant & \(-1.644\) & 0.265 & 38.513 & 1 & \(<\)0.001 & 0.193 & & \\ \hline \multicolumn{1}{c}{
\begin{tabular}{c} _Model information:_ \\ \end{tabular} } & \multicolumn{2}{c}{\(X^{2}\) = 2.398_} & \multicolumn{2}{c}{_Sig. = 0.494_} & \multicolumn{2}{c}{_Nagelkerke \(r^{2}\) = 0.01_} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Logistic regression results for technology intensity (reference category: low technology intensity). This model is statistically significant and indicates that readiness for I4.0 has a positive effect on the use of AI, i.e., it increases the likelihood of using AI. For this reason, we can also analyze the probability of AI usage at different levels of readiness. Figure 1 visualizes the results of the logistic regression. It shows the probabilities of using AI at each readiness level and shows that the higher the level of readiness for I4.0, the higher the probability of AI use in manufacturing companies.