## 4 Further Analysis with Discussions and Implications  
### The Principal Factors Affecting High-tech Companies' Global Patenting Strategies  
The PCA model is constructed according to Equation (2) by using the pairwise correlational numbers in Table 2 as the model parameters, and then solved for an optimal fit, after which the factor loadings (\(a_{ij}\)) associated with each variable (\(x_{1}\),..., \(x_{9}\) for all technological powers) are assessed (estimated). This establishes a model wherein the main components are identified that, in turn, enables further analysis. After the modelling work, a threshold of 85% is set. That is, if one or more component(s), \(z_{i}\), can successfully explain 85% of the total variance of the data, the component(s) is chosen as the principal component(s).  
After modelling in R [28], it was observed that two principal components were sufficient to explain enough of the total variance. The first principal component explains a major part of the variance, which is as high as 82.14% (with a standard deviation of 2.719). Furthermore, the second principal component also explains a relatively minor but still quite large part (9.93%, which is almost 10%) of the variance, with a standard deviation of 0.945. As a result, up to 92.07% of the total variance can be explained when these two principal factors are considered. Therefore, the suitable PCA model is obtained as follows:  
\[\begin{split}\mathrm{z}_{1}=0.311x_{CAN}+0.357x_{CHN}+0.304x_{ GER}+0.355x_{\mathrm{EU}}+0.337x_{\mathrm{GBR}}+0.328x_{\mathrm{JPN}}\\ +0.336x_{KOR}+0.317x_{TWN}+0.351x_{USA}\end{split} \tag{10}\]  
\[\begin{split}\mathrm{z}_{2}=0.509\mathrm{x}_{CAN}+0.182x_{CHN}-0.2 81x_{GER}+0.198x_{\mathrm{EU}}+0.367x_{\mathrm{GBR}}-0.343x_{\mathrm{JPN}}\\ -0.312x_{KOR}-0.479x_{\mathrm{TWN}}+0.104x_{USA}\end{split} \tag{11}\]  
The established model reveals the overall scenario of the patenting strategies taken by the high-tech companies in the main technological powers. Since the influence of the first principal factor, \(z_{1}\), explains 82.14% of the total variance, which is far greater than the amount explained by the second one (\(z_{2}\)), the knowledge which can be derived from Equation (10) is the main focus here.  
On the RHS (right-hand side) of Equation (10), one can easily observe that CHN, the EU and the USA are the three main economies that the technological powers may think are important because they have higher estimated weights (or 'factor loadings') than the other countries associated with their variables (i.e., 0.357, 0.355 and 0.351, respectively).  
However, if the RHS of Equation (10) is examined, it is also observed that the factor loadings of these technological powers are all above 0.3, and the differences among them are not so substantial. In this sense, it can be seen that every country in the list is deemed as important by all the other countries, so that overall, no specific country dominates the others. Nevertheless, the companies in these countries utilised various patent strategies according to their individual technological strengths (i.e., the STSs) and business/marketing considerations. This has been shown in Section 3.4.