## 3 Results
### Descriptive Statistics
Analyzing the statistical significance of predictors that influenced the lifetime dependent variable, we can see that mean CA and AA showed statistical significance of \(p<0.05\) for all periods. The CP variable, on the other hand, is one that has no effect and is not relevant in any given period. Separately analyzing each period, we can see that the periods of one month, two months, and three months showed the significance of the CA, SD, SP and AA variables. [PERSON]'s statistics with results presented in Table 2 showed the highest value with CA in the periods of two weeks month and two months. In the three-month period, [PERSON] pointed to the significance of AA. The influence of predictors on the dependent variable over seven days showed significance in CA, SD, and AA. However, in the 14 day period, only two predictors, CA and AA, showed statistical significance, which affected the product's life expectancy. In the next step, [PERSON] (Figures 2-6) survival probability charts for one month with division for user parameters, and the three user groups were analyzed. The diagrams show the emergence of a growing number of increasingly shorter episodes that, at the border, seek the real function of survival. Figure 6 shows a survival model without division into classes as a general model for divisional and nondivisional variables. The next stages show statistical regression models with division into aggregated groups of adopters, i.e., AG1-AG5. Regarding the explanation of these classes, we can refer to Table 3. Regression analysis was divided into two groups of variables, and product life is a dependent variable. The first group of variables (predictors) include average variable values from CA to AA. The second group include experience-related variables, i.e., MSG_in, MSG_out, FR_in, and FR_out. In Table 3, we can see the statistical-significance parameter (p) and the strength-of-significance factor (f). \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**4G1**} & \multicolumn{2}{c}{**AG2**} & \multicolumn{2}{c}{**AG3**} & \multicolumn{2}{c}{**AG4**} & \multicolumn{2}{c}{**AG5**} \\ \cline{2-11}  & **f** & **p** & **f** & **p** & **f** & **p** & **f** & **p** & **f** & **p** \\ \hline CA & 5.07 & 0.03 & 4.19 & 0.04 & 36.33 & \textless{}0.01 & 29.48 & \textless{}0.01 & 16.95 & \textless{}0.01 \\ SD & 0.62 & 0.43 & 0.10 & 0.75 & 7.97 & 0.01 & 4.27 & 0.04 & 0.39 & 0.53 \\ CP & 0.21 & 0.65 & 9.10 & \textless{}0.01 & 5.74 & 0.02 & 5.36 & 0.02 & 8.25 & \textless{}0.01 \\ SP & 0.52 & 0.47 & 13.98 & \textless{}0.01 & 9.62 & \textless{}0.01 & 9.74 & \textless{}0.01 & 13.47 & \textless{}0.01 \\ AA & 9.78 & \textless{}0.01 & 9.97 & \textless{}0.01 & 0.01 & 0.94 & 0.23 & 0.63 & 0.05 & 0.82 \\ MSG\_in & 0.77 & 0.38 & 2.99 & 0.09 & 14.24 & \textless{}0.01 & 7.18 & 0.01 & 4.44 & 0.04 \\ MSG\_out & \textless{}0.01 & 0.94 & 6.07 & 0.01 & 18.38 & \textless{}0.01 & 9.51 & \textless{}0.01 & 7.67 & 0.01 \\ FR\_in & 1.22 & 0.27 & 6.62 & 0.01 & 13.51 & \textless{}0.01 & 21.22 & \textless{}0.01 & 12.83 & \textless{}0.01 \\ FR\_out & 4.49 & 0.04 & 15.67 & \textless{}0.01 & 30.57 & \textless{}0.01 & 36.94 & \textless{}0.01 & 29.11 & \textless{}0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of regression analysis showing how Activity Factors and Experience Factors are affecting user assignment to adoption group.