## 4 Discussion and Conclusions  
For expanded virtual product usage within online systems, new analytical models and strategies are required. Common phenomena to offline markets are regularly seen in electronic systems and are identified with lifespan, customer habituation, and new-product improvement techniques. This research indicates how the attributes of early adopters to new items can influence user engagement and the survival of virtual goods within dynamic electronic environments. Achieved results, from product classification based on decision trees, showed that it is possible to predict product lifespan with the use of adopter characteristics. Adopter communication activity, represented by Activity Factors, positively affected product survival time. This shows that adopters with high experience factors are the main influencers in the system, and their behavior is adopted by other users. Monitoring of product-usage patterns and adopter characteristics makes it possible to identify products with possible low survival time, and invite additional adopters with the use of incentives and other techniques. Gathered knowledge can be used to reduce the habituation effect and increase product-usage time due to social influence and follower behavior. Results from the conducted study lead to the following main conclusions:  
* characteristics of early adopters related to social activity positively influence product lifespan and the engagement of other users within the system;
* product lifespan can be estimated with the use of initial-audience and early-adopter characteristics;
* the combination of innovators and adopters positively affects the statistical significance of the dependent variable that represents survival time;
* initial-user characteristics can be used to classify products in terms of future usage for the detection of low-potential products, for performance improvement and targeting additional adopters with the desired specifics. Future work will concentrate on a progressive point-by-point evaluation of distribution in the use of social networks and behavior prediction, which is dependent on interpersonal organizations, and the use of conduct forecast, which is dependent on earlier behaviors. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Size of Training Dataset**} \\ \cline{3-8}
**Group ID** & **Rogers Title** & \multicolumn{3}{c}{**Activity Factors**} & \multicolumn{3}{c}{**Experience Factors**} \\ \cline{3-8}  & & **75\%** & **50\%** & **25\%** & **75\%** & **50\%** & **25\%** \\ \hline G1 & innovators & 0.961 & 0.912 & 0.895 & 0.943 & 0.914 & 0.747 \\ G2 & + early adopters & 0.919 & 0.901 & 0.869 & 0.855 & 0.831 & 0.725 \\ G3 & + early majority & 0.919 & 0.905 & 0.865 & 0.849 & 0.821 & 0.729 \\ G4 & + late majority & 0.921 & 0.907 & 0.874 & 0.861 & 0.834 & 0.739 \\ G5 & + laggards & 0.922 & 0.908 & 0.878 & 0.865 & 0.839 & 0.744 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of classification results with the use of Activity and Experience Factors for combined adoption group. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Set**} & \multicolumn{6}{c}{**Number of Adopters Used for Classification**} \\ \cline{2-10}  & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline
75\% & 0.835 & 0.834 & 0.841 & 0.853 & 0.866 & 0.875 & 0.882 & 0.885 & 0.885 \\
50\% & 0.806 & 0.807 & 0.805 & 0.823 & 0.839 & 0.852 & 0.856 & 0.861 & 0.863 \\
25\% & 0.706 & 0.731 & 0.728 & 0.731 & 0.735 & 0.749 & 0.758 & 0.764 & 0.773 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of classification results with the use of Experience Factors for 25%, 50%, 75% training set and 10%, 20%,..., 90% of adopters used. **Author Contributions:** conceptualization conceptualization, K.B., P.P., J.H., P.B. and J.J.; methodology, K.B., P.P., J.H., P.B. and [PERSON]; validation, [PERSON], P.P., P.B. ; investigation, [PERSON], P.P. and P.B. ; resources, P.B. and P.P. ; data curation, P.B.