I have three important questions concerning moderation in PLS. I have two reflective independent LV's (A,B) that predict one reflective (dependent) LV (C). I created an interaction term (A*B) and used the standardised scores for term creation as recommended by Chin et al. (2003). My questions now are:

(1) Can the path coefficients be interpreted like betas in regression analysis? In my model, I have a

**negative**path coefficient from the interaction term (C) and two positive path coefficients (main effects) of the independent LV's (A,B). So, if my moderating variable increases about 1 std.dev, my predictor-variable decreases about the path coefficient of the path coefficient of the interaction term? Is that correct?

(2) Is PLS similar to moderated regression analysis in the way that the main effects of the predictor- and the moderating variable (A,B) can not be interpreted meaningful?

(3) Another interesting effect I observed is that the

**sign-change-option**has a

**huge impact on the significance of the interaction term**. While for the LV's the sign change option usually had a decent effect in my previous models, it possessed a huge effect on the interaction term. For example, in one model it boosted the t-value from 0.8 to 3.xx when changing from construct-level to individual change... Does anybody have an explanation for that? And, even more important, is individual sign change a suitable approach for these kinds of models?

Any comments would be highly appreciated!!!

Thanks a lot & best regards,

Stephan