## 7 Discussion and Limitation  
### Discussion  
This research found a conceptual model with the base of technology acceptance model which examined the SEM analysis on projected conceptual model for online games adoption. The research model was prepared with the support of previous studies related to online games [28; 29; 64; 69]. The empirical-based finding was conducted in the study. The outcome of study is majorly focused on individual behavior, symmetry in relations between responses, intention, understanding, and attitude towards various online games available on the Internet. The data of the research was good fit in nature. Therefore, the data was best suitable for measurements, and the result of the theoretical model was supportive, which justifies accurate results related to people's behavior who are using online games or having a habit to play online games. The study reveals a symmetric relationship among the constructs. Therefore, the result found that symmetric social interaction has a positive significant impact on use context and perceived enjoyment. The outcome of this relationship is supported by Reference [70]. Another relation, i.e., use context, has found to have an impeccable effect on perceived enjoyment and perceived ease of use, which is supported by Reference [57]. Perceived ease of use is also found positive impact on perceived usefulness. Hence, results reinforced by previous study [38]. Next, perceived enjoyment reveal optimistic significant symmetric relationship with flow while playing online game. On the other hand, it is insignificant in relation to perceived usefulness and attitude, but results are supported by many researchers, whereby perceived usefulness has a substantial impact on attitude. The last relation of this study is with the dependent variable, i.e., behavior intention, in which the study found the positive relation to attitude, flow,  
Figure 12: The ROC curve for ridge regression model. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PU (\%)** & **Attitude(\%)** & **Flow(\%)** \\ \hline Logistic & 88.67 & 77.12 & 66.33 \\ SVM & 81.33 & 73.88 & 62.85 \\ Ridge & 61.45 & 54.99 & 48.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative result of perceived usefulness, attitude, and flow. and perceived usefulness, when comparing our observations with other literature work. Therefore, it can be implied that, in the field of online games, the study demonstrates that young consumers like to play online games frequently because they feel it easy, convenient, joyful, fun, and enjoyable, anytime and anywhere, without any performance consequences and trouble. The TAM model is very good in the implementation of quantitative problem of statistical analysis. For further evaluation machine learning approach is implemented to trigger the most prominent parameters indulge in the evaluation of online games complex problem. From this, three popular approaches are considered, i.e., logistic regression, ridge regression, and support vector machine. All these are worked efficiently to solve these kinds of problems. From Figures 7-9, the performance of the models over the dataset is shown. These figures elaborates the training and testing accuracy on the dataset. Similarly, the ROC curve is also calculated that provide the region area to give the performance. The higher the region in ROC curve that better will be the model. The ROC curve is represented in the Figures 10-12. Along with this, Table 9 gives the detailed view of performance in terms of Precision, Recall, F1-Score, and Accuracy. From all these most prominent factors evaluated is perceived usefulness, attitude, and flow, which affect high performance the most. Hence, the significance to apply machine learning approach is to reveal the most affective factors for online mobile games. Social interaction is also increased with this as most of the games on mobile devices are multiplayer games, where you can play with your friends, as well as strangers.