is there any mistake in the data or there is a probability of no connection between the variables? Kindly help me with the interpretation. Also, explain me the R square as well. I have attached the framework too.
In SPSS, when you get a very low (close to zero) or negative correlation coefficient, it indicates a weak or no relationship between your two variables. @Hardeep Gusain
You need to pay attention to the statistical significance of your coefficients. In particular, a negative correlation that is not significant means that it is not different from zero.
It's impossible to say, from what you've provided, whether or not there is "any mistake in the data."
There could be inaccurate values in the data, so that your variables have a very low ratio of true-score variance to measurement error variance (unreliability). Or worse, your variables might not be measuring what you want them to measure (invalidity). Or perhaps your measurement are good enough, but the theoretical framework you are trying to test is wrong. Or maybe both the measures and the theory are fine, but your regression model incorrectly specifies what you hope to test. (I can't tell, from what you've provided, which variables in your framework are meant to correspond to which variables in your regression.)
The bivariate Pearson correlation matrix indicates that most of your variables are only weakly related to one another, if at all. The non-significant ones (p >> .05) may well be (close to) zero in the population from which your sample was drawn.
I don't see what is generally one of the most useful parts of a regression output, the "variables in the equation" table, with the regression coefficients. But what I do see from your regression is that your predictor variables, together, explain at best 6.4% of the variance in your dependent variables (R-squared = .064). The model as a whole is non-significant (p = .154) so the population R-squared could well be (close to) zero. Adjusted for over-fitting in a fairly small sample, the estimated R-squared is only .028, so your predictors, together, are not much related to the dependent variable.
So the results are weak, but again, that could result from any combination of measurement error, faulty theory, and/or misspecified model.