## 2 Materials and Methods
The Student \(t\)-test for independent samples was applied to test if there were statistically significant differences between the two companies with respect to the following:  
* The average number of days from creation/initiation to planning of change requests. * The average number of days from planning to effective implementation of requests. An ANOVA test was applied to test and to verify if there were:
* Statistically significant differences (only for Company A these data were collected being available) according to whether or not customers are implicated in the change process, referring to the average number of days from creation/initiation to planning of change request and the average number of days from planning to effective implementation of a change request. * In addition, although both companies are active in the automotive industry and have some similarities, the ANOVA test was applied to verify if the data have (or not) important variations around mean values, and whether or not these variations are statistically significant between companies for all the economic performance indicators. Statistically significant differences between the mean values of the two companies refer to all the economic efficiency indicators calculated per employee (i.e., the turnover, net profit, and debts). To identify and verify if there are statistically significant differences between Company A and Company B, regarding the month of creation/initiation of change requests, the month planned for implementation, the month of effective implementation of change requests, and the change request status, we applied the Mann--Whitney U test for independent samples. This statistical test allows us to test if the distribution of each \"dummy\"/categorical variable is the same along each of the companies or if there are statistically significant differences. Boxplots are used as graphical representations for better visualization of the quantitative differences between two companies from the research detailed for each type of process proposed for change. Since the main purpose of this research was to find the best predictor of change request status (considered as the dependent variable from a statistically point of view in this research), a multilinear regression analysis with the Enter method was applied by using the same statistical software, i.e., SPSS 23.0. The following independent variables of the regression model were considered: the month of proposal for a change request, the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance Indicators** & **2017** & **2017** & **2019** & **2020** & **2021** \\ \hline Company A & & & & & \\ \hline Turnover & 1,772,494,278 & 2,245,269,652 & 2,642,519,314 & 2,384,450,466 & 2,719,582,643 \\ Net profit & 75,536,047 & 131,317,375 & 104,699,652 & 5,525,460 & 67,059,975 \\ Own capital & 278,241,373 & 407,843,613 & 514,112,683 & 518,776,213 & 585,836,188 \\ The number of employees & 2718 & 3209 & 3269 & 3006 & 3040 \\ Debts & 719,133,992 & 790,444,495 & 757,731,299 & 772,423,357 & 733,595,299 \\ Fixed assets & 619,127,854 & 749,578,670 & 789,028,236 & 723,823,357 & 693,030,091 \\ \hline Company B & & & & & \\ \hline Turnover & 3,769,456,584 & 4,292,798,906 & 4,097,920,130 & 3,549,865,965 & 3,361,428,353 \\ Net profit & 59,103,202 & 852,506,056 & 134,188,496 & 225,919,057 & \(-\)244,172,664 \\ Own capital & 990,543,637 & 1,849,238,824 & 1,983,427,320 & 2,209,346,377 & 1,976,488,993 \\ Number of employees & 3341 & 3881 & 3890 & 3718 & 3682 \\ Debts & 1,159,496,262 & 1,001,313,179 & 915,120,917 & 1,034,944,775 & 1,630,408,279 \\ Fixed assets & 1,619,664,158 & 2,010,338,748 & 2,168,836,425 & 2,320,589,143 & 2,667,134,091 \\ \hline \hline \end{tabular} (Source: Prepared by the authors based on the information from [23] www.listafirme.ro. Accessed on 30 January 2023).  
\end{table} 
Table 2: Performance indicators for the two companies (in Romanian lei current prices).