## 5 Data Analysis and Results
### Analysis of Neural Network for Cloud Computing Adoption  
The proposed study has combined the two analytical approaches, namely PLS-SEM as a statistical approach and neural network as an artificial intelligence technique. Multiple regression analysis (MRA) and PLS-SEM are considered to be a conventional linear statistical technique, which is used for identifying the linear relationship between variables and simplify the complex decision-making process [191]. To solve this issue, it is suggested to apply the artificial neural network, which can easily recognize the non-linear relationship. According to [PERSON] and [PERSON] [169], the advantage of using the neural network model is that it can learn complex linear and non-linear relationships between predictors and the adoption decision. Also, the ANN is more flexible and can give better prediction accuracy as compared to the linear model(s), and it may surpass the usual statistical technique (such as MRA) [172]. However, because of its \"black-box\" nature, ANN is not suitable for checking the hypothesis and determining the causal relationship [191]. Thus, this paper adopted a two-stages approach, similar to [66]. In the first stage of the study, the research model is tested, and the important hypothesized predictors are analyzed using SEM. The result of the PLS-SEM is then given as input to the model of ANN, which is employed to analyze the relative significance of each predictor variable in the second stage. Hence, the results of selected factors from Smart-PLS analysis were employed to improve ANN analysis. The applied ANN has three layers: \"Input layer, hidden layer, and output layer\". The hidden nodes have no direct connection with the outside world (thus the name \"hidden\") [192]. These nodes are responsible for performing computations and transferring information from the input nodes to the output nodes [193]. As depicted in Figure 3, seven independent substantiation factors, derived from PLS-SEM analysis, are considered as the input section for ANN; whereas one dependent variable (CC adoption) is considered as the output section of ANN (see Figure 3). [PERSON] and [PERSON] [194] recommended that ANNs should be calculated by varying the number of hidden nodes from one to ten. To detect the hidden nodes (H1-H10 in Figure 3), researchers such as [PERSON], [PERSON] [172] have recommended testing the ANN model by modifying the number of hidden nodes from one to ten. The proposed research has been applied to the 10 hidden nodes to create the relative significance of the predictors. The proposed study established ANN by using R programming, as it helps to simplify and give effective results. A multilayer perception training algorithm was applied for the preparation of the ANN model (see Table 8). Hence, 70 % of the data have been used as the train network model, and the remaining 30% of the data were used to test the proposed research model. Seven predicting factors, namely compatibility, competitive pressure, complexity, cost saving, technology readiness, security, and top management support, were tested. The factor \"intention\", which is the dependent variable in the proposed study, has been calculated in the output layer of the ANN model. The root-mean-square error (RMSE) was used to assess the precision of the ANN model that was developed [195] in both the training and testing datasets. As shown in Table 8, the average RMSE values of the training and testing procedures are relatively small at 0.1101 and 0.1022, respectively. Therefore, this confirms that there is an excellent model fit. Besides, the importance of variable is verified based on the number of non-zero synaptic weights connected to relevant hidden parts (see Table 8), which displays that the model has