Understanding R Square, F Square, and Q Square using SMART-PLS

R-Square

R Square statistics explains the variance in the endogenous variable explained by the exogenous variable(s).
For example, a variable Y influenced by X1, X2, and X3 has a R-Square value of 0.623. This would mean that 62.3% change in Y can be explained by X1, X2, X3.
In order to make it easier to interpret, look for the arrows that are pointing towards the dependent (endogenous) variable.
Falk and Miller (1992) recommended that R2 values should be equal to or greater than 0.10 in order for the variance explained of a particular endogenous construct to be deemed adequate.
Cohen (1988) suggested R2 values for endogenous latent variables are assessed as follows: 0.26 (substantial), 0.13 (moderate), 0.02 (weak).
Chin (1998) recommended R2 values for endogenous latent variables based on: 0.67 (substantial), 0.33 (moderate), 0.19 (weak).
Hair et al. (2011) & Hair et al. (2013) suggested in scholarly research that focuses on marketing issues, R2 values of 0.75, 0.50, or 0.25 for endogenous latent variables can, as a rough rule of thumb, be respectively described as substantial, moderate or weak.

A variable in a structural model may be affected/influenced by a number of different variables.
Removing an exogenous variable can affect the dependent variable.
F-Square is the change in R-Square when an exogenous variable is removed from the model.
f-square is effect size (>=0.02 is small; >= 0.15 is medium;>= 0.35 is large) (Cohen, 1988).

Q-square is predictive relevance, measures whether a model has predictive relevance or not (> 0 is good).
Further, Q2 establishes the predictive relevance of the endogenous constructs.
Q-square values above zero indicate that your values are well reconstructed and that the model has predictive relevance.
A Q2 above 0 shows that the model has predictive relevance.
In order to find out the Q Square value, Run Blindfolding procedure in SMART-PLS.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd Ed.). New York: Routledge.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning, 46(1-2), 1-12.