Your R2 depends, of course, on your model and the number of exogeneous constructs. Whether your R2 is good or not also depends on your discipline / field of research. Whereas in some areas a R2 of .25 is considered weak, you may also find it quite good if it is a novel research field. 

However, if you are doing a SEM (covariance based or not??), you may also use different indicators for assessing the quality of your model. According to Hair et al. ("A primer on partial least squares structural equation modeling", 2014), you may also draw on the Stone-Geisser's Q2-value to describe your model's predictive relevance.

What does R-square describes? You can denominate everything as R-square...

In SEM analysis the goodness of fit indicators are RMSEA, CFI, etc. 

In regression analysis R square is the coeficient of determination and inficates the percentage of variance of dependent var8able explained by the independent variables of the model.

Regards

Alma

Your R2 depends, of course, on your model and the number of exogeneous constructs. Whether your R2 is good or not also depends on your discipline / field of research. Whereas in some areas a R2 of .25 is considered weak, you may also find it quite good if it is a novel research field. 

However, if you are doing a SEM (covariance based or not??), you may also use different indicators for assessing the quality of your model. According to Hair et al. ("A primer on partial least squares structural equation modeling", 2014), you may also draw on the Stone-Geisser's Q2-value to describe your model's predictive relevance.

I hope that you can find the answer in these similar questions:

https://www.researchgate.net/post/Can_R_Square_of_15_of_a_full_model_in_SEM_be_acceptable

https://www.researchgate.net/post/What_is_the_acceptable_R-squared_in_the_information_system_research_Can_you_provide_some_references

Thanks a lot, Nolze, Spaho, Menke and Nedyalkov.

Your answers are really helpful to me.