# Artificial Intelligence Software Adoption in Manufacturing Companies
## 4 Results
### Logistic Regression Results  
In this study, a logistic regression was used to assess the relationship of I4.0 readiness levels, company size, role in the supply chain and technology intensity with the likelihood of AI technology adoption. It is important to note that separate logistic regressions were performed to test the hypothesized relationships between the dependent and selected independent variables. Because of this, it is not necessary to test the assumption of collinearity. First, we tested the relationship between company size and the likelihood of using AI. Small companies were chosen as the reference category and are shown in the first row of the table. The last row in the table represents the constant (also known as the intercept), which is the initial level of the dependent variable when the independent variables are equal to zero. The probability of adopting AI was then compared for medium and large companies with that for small companies. The first column \(\beta\) (beta coefficient) represents the change in the log odds of the dependent variable when the independent or predictive variable changes by one unit. If it is positive, this means that an increase in the predictor variable increases the log odds of the dependent variable, and a negative \(\beta\) means the opposite. Column S.E. provides the results of the standard error, which measures the variability or uncertainty of \(\beta\). The next column shows the results for the Wald statistic, which is used to test the \begin{table}
\begin{tabular}{c c} \hline
**Readiness Level** & **Share of Companies [\%]** \\ \hline Level 0 & 17.4 \\ Level 1 & 19.9 \\ Level 2 & 29.5 \\ Level 3 & 14.8 \\ Level 4 & 9.6 \\ Level 5 & 8.8 \\ \hline \end{tabular}
\end{table} 
Table 6: Share of companies for every readiness level. \begin{table}
\begin{tabular}{c c c} \hline
**Technology Intensity** & **Share of Companies [\%]** & **Share of AI Users [\%]** \\ \hline Low & 27.2 & 16.2 \\ Medium-low & 42.7 & 18.8 \\ Medium-high & 24.1 & 17.2 \\ High & 6.0 & 30.4 \\ Total & 100 & \\ \hline \end{tabular}
\end{table} 
Table 5: Share of companies that use AI regarding their technology intensity. null hypothesis that the independent variable (or predictor variable) has no effect or that the coefficient is zero. Larger values of this statistic indicate that the coefficient is actually significantly different from zero. Column df refers to the degrees of freedom or the number of independent values that can vary in the analysis. Column Sig. shows the significance value or p-value, which indicates the probability that the observed effect is due to chance. Common significance levels are 0.01, 0.05 (the most common significance level) and 0.1. The next column, \(\text{Exp}(\beta)\), is the exponentiated beta coefficient, which represents the odds ratio for a one-unit change in the predictor variable. If \(\text{Exp}(\beta)>1\), this means that the probability of the outcome increases with an increase in the predictor. If \(\text{Exp}(\beta)<1\), this means that the probability of the outcome decreases as the predictor increases. If \(\text{Exp}(\beta)=1\) the predictor has no effect on the odds of the outcome. The last two columns LB and UB are the lower bound and the upper bound of the confidence interval for \(\text{Exp}(\beta)\). The results of the logistic regression for the relationship between company size and AI are shown in Table 7. While the results show that medium and large companies have a positive beta coefficient, the Sig. column shows that the statistical significance for both medium and large companies is above the (maximum) threshold of 10% (or 0.1), which means that the risk of a Type I error is too high and the results therefore imply that larger companies are not more likely to use AI than smaller companies. We can interpret this to mean that company size has no statistically significant relationship with the likelihood of AI adoption in manufacturing companies.