# Artificial Intelligence Software Adoption in Manufacturing Companies
## 4 Results
### Logistic Regression Results
For the relationship between the role of the manufacturer and AI usage, the category \"Supplier\" was used as a reference group. The results are shown in Table 8. Although the results show slightly negative beta coefficients, the corresponding significant values exceed the maximum threshold of 0.1, with the exception of the variable \"Constant\", which only represents the log odds of AI usage when the role of the manufacturer is not taken into account. This is also confirmed by the small values of the Wald statistic for the variables \"OEM producer\" and \"Both\". Thus, the results show that the role of the manufacturer does not contribute to an increased probability of using AI. Next, the relationship between technology intensity and the probability of AI adoption was analyzed. Table 9 shows the results of the logistic regression. In this case, the companies that fall into the \"low technology intensity\" group were selected as the reference category. When higher intensity groups are compared to the group \"low\", none of the categories show statistical significance due to their Sig. values exceeding the maximum threshold. While the \"high\" technology group has a Sig. value very close to the threshold, there is still \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 Small & & & 2.771 & 2 & 0.250 & & & \\ Medium & 0.201 & 0.323 & 0.387 & 1 & 0.534 & 1.222 & 0.649 & 2.301 \\ Large & 0.605 & 0.373 & 2.626 & 1 & 0.105 & 1.831 & 0.881 & 3.804 \\ Constant & \(-\)1.721 & 0.263 & 42.692 & 1 & \textless{}0.001 & 0.179 & & \\ \hline \multicolumn{2}{c}{_Model information:_} & \multicolumn{2}{c}{\(X^{2}\) = 2.706} & \multicolumn{2}{c}{_Sig. = 0.258_} & \multicolumn{2}{c}{_Nagelfexte \(r^{2}\) = 0.01_} & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Logistic regression results (reference category: small companies). \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 Supplier & & & 0.462 & 2 & & & \\ OEM producer & \(-\)0.192 & 0.284 & 0.457 & 1 & 0.499 & 0.826 & 0.47 & 1.44 \\ Both & \(-\)0.082 & 0.601 & 0.018 & 1 & 0.892 & 0.922 & 0.28 & 2.99 \\ Constant & \(-\)1.365 & 0.229 & 35.635 & 1 & 0.000 & 0.255 & & \\ \hline \multicolumn{2}{c}{_Model information:_} & \multicolumn{2}{c}{\(X^{2}\) = 0.458_} & \multicolumn{2}{c}{_Sig. = 0.795_} & \multicolumn{2}{c}{_Nagelfexte \(r^{2}\) = 0.002_} & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Logistic regression results for role of the manufacturer (reference category: supplier). a high risk of Type I error, and it cannot be claimed that this group of manufacturers is more likely to adopt AI. Based on these results it can be stated that compared to the reference category, no other technology intensity category has a higher probability of adopting AI. Therefore, the manufacturers that fall into either \"low-medium, \"medium-high\" and \"high\" technology intensity groups are not statistically more likely to adopt AI than manufacturers that fall into the group of low technology intensity. Finally, the relationship between readiness for Industry 4.0 and the use of AI was examined. Industry 4.0 readiness is ordinal by nature, as readiness levels follow a ranking from level 0 to level 5. This characteristic indicates that it is an ordinal variable that has a clear ranking, but does not necessarily imply equal distances between categories. If we assume that the distances between the readiness levels are approximately equal, we can treat this variable as continuous and do not need to include the different readiness levels as separate categories. As [PERSON] has shown in his work, treating ordinal variables as continuous still leads to valid results [59]. This allows us to interpret the regression coefficients more easily, i.e., an increase in the predictor variable by one unit corresponds to a change in the log odds of the result.