How to interpret inconsistent Beta values in different steps of hierarchical regression analysis? I did hierarchical regression analysis on my data due to having moderation effects in my research model.

R2 increased from .695 in model1 (main effect only) to .734 in model2 (main &interaction effects)(sig. F change = .000). All the assumptions for the regression analysis have been met.
I have two problems with the "coefficients" table: 


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*As u can see in the table, the insignificant beta value of ZSC in model1 became significant in model2! Is it ok? I'm confused! Which value should i consider to reject/accept the related hypothesis? B of model1 (which rejects the hypothesis) or 2 (which confirms it!!)?

*Although the Beta value for ZSC_X_CS is significant, its positive sign is against the hypothesis! it's supposed to have a negative sign according to the literature & also logic! How should i treat this hypothesis? Accept? Reject? Partially accept?!!!
TQ.

 A: First, when you ask different questions, you get different answers. Your two models ask different questions. One asks about a model that includes an interaction, one asks about a model that does not. There's no reason to expect consistency. 
Second, the difference between significance and non-significance is not significant. (With a hat tip to Andrew Gelman, who came up with that sentence).
Third, since you don't provide any context, it's hard to know what's going on regarding what's "supposed" to happen. 
And finally, when dealing with interactions, it's often helpful to make a plot of the predicted outcomes at different levels of the IVs. 
A: For simplicity, I will ignore the variables other than ZSC and ZSC, and will call them $X$ and $Y$ (their names are too similar for me). You obtained two regression models: $$CRI = 5 + 0.7 X - 0.06 Y$$ and $$CRI = 5 + 0.72 X - 0.1 Y + 0.15 X Y $$
The meaning of the coefficient of $Y$ in the first model is the following: if $Y$ is increased by one unit, then regardless of the value of $X$, the outcome is changed by $-0.06$. 
Its meaning in the second model is: if $Y$ is increased by one unit and $X=0$, then the outcome is changed by $-0.1$. But, if say $X=1$, then the effect of one unit of change in $Y$ is $-0.1+0.15=0.05$, and if $X=-1$ then the effect is $-0.25$. 
The moral is that the meaning of the "main effect" term changes when you add interaction, so there should be no surprise if its significance changes, especially if the interaction is non-negligible. In a certain sense, when you fitted the first model, you averaged over all the possible values of $X$ in $-0.1 Y + 0.15 X Y$. Also, the first model is clearly wrong (because it assumes no interaction when there is one), so any inference from it is wrong as well.
Perhaps rewriting the right side as $= 5 + 0.72 X + (0.15 X - 0.1) Y$ would help. The effect of $Y$ could be positive or negative depending on whether $0.15X < 0.1$ or not. You have to put these values into the context of the actual problem. What does $X=0$ mean? what does $Y=0$ mean? etc.
