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Yes, it is possible to estimate such a model in SmartPLS. However, without any reflective or single item measures it is hard to assess the convergent validity in a reasonable way.

First, you need to model a direct effect from gender on mas brand image.
Second, you right click on mas brand image and choose to choose "Add Moderating Effect".

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Generally, both approaches should lead to more or less the same conclusion.
I would use the interaction approach if I have only one path that is affected by gender and the multi-group approach if I assume that several paths in my model are affected by gender.

I would always be careful with excluding outliers. You need to explain, why you think that the outliers are not meaningful and not just extreme cases of the population. Just because there are some very unsatisfied customers in a customer satisfaction survey does not make the worth excluding. You wou...

Yes and Yes.

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The Preacher and Hayes procedure is naturally build into PLS analysis as we always use bootstrapping to assess significance of effects. If you use SmartPLS 3 you get all the information you need in the bootstrapping procedure. It already computes the indirect effects and bootstrapps it. As long as y...

The questions is, which test is the one you are interested in. The test above is for the F test of a regression. It assesses the significance of the R² at the given effect size level (e.g., effects size 0.15 = R²). Hence, with 138 cases you will find a model with an R² of 0.15 significant at 95% of ...

The procedure is discribed in the paper. It can be easily adapted in SmartPLS 3.

The reviewer thinks in covariance-based SEM terms. However, assessment and assumptions there are different to the PLS world. PLS has other (less stringent) indefinability assumptions. Your measurement model might not be identified in a CBSEM model, but it is in a PLS model. That is one advantage of ...

The first one (i.e., X) is the empirical (observed) correlation. The second one (i.e., Y) is the model implied correlation. These two can differ and usually differ. The magnitude of the difference gives insights about the appropriateness (goodness of fit) of the model. If they strongly differ, this ...

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If you have two drivers with an importance of 0.3 and -0.3, then they are both equally important, but with oposing signs.

You should first think about the reason of the negative loading. For example, if the item is reverse coded, you should switch the order of that item before using it in your model to allign the item with all the other manifest variables of the construct. That should give you better results.

It depends on your multidimensional construct. If it is a reflective-formative (type II) model, it contains itself an inherent mediation hypothesis (that the higher-order construct fully mediated the effect of the lower-order constructs). Hence, you would have a multiple mediator model. Yet, the mos...