It is possible that only one principal component of proportion 0.937 of total variance is available to explain variability within the data. It is very possible.

@Ette Etuk

Thank you so much for assuring me.

Graph shows there are two components/factors your data (Graph is elbow at second factor). But its possible to have only one factor in the data. Which means one component is strong enough to explain the variability in data.

Tahmina Shoaib

Thanks for responding. Yes, the graph shows a little break at the second factor. however, in the analysis output, I got only one component. I was worried that whether it is logical to perform PCA with this dataset.

Since the first component is strong enough and represents much of the variance, it is possible to explain the variability with that as several answers suggest. In my opinion it makes the situation less complex and makes your interpretation easier.

The Eigen value is the measure of importance of principal components (PCs), the PCs with the largest Eigen value are the most important and explain the larger variation in the data set.The PCs which are usually important (eigen values > 1) describe the largest variations in the entire data. The first factorial plane (plot of PC1 against PC2) contained most of the information and therefore primarily considered in explaining the data. PC1 has the largest percent variation compared to other PCs, and it contains the latent variables that explain the major variation in the entire data set.

Yes it can and I agree with Isa Baba Koki 100%

Yes, it is possible. It is advisable to keep more PCs which explain the most variance (70-95%) to make the interpretation easier. More the PCs you include that explains most variation in the original data, better will be the PCA model.

Read more https://www.reneshbedre.com/blog/principal-component-analysis.html

Hello everyone, I also perform PCA. I would like to ask another question about that. Is it normal for a variable to exist in both Dim1 and Dim2 ?