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Performance ranges from 0 to 100. Values around 65 - 71 are therefore medium to high. Importances are standardized regression coefficients. They usuall range between 0 and 1 (but can also exceed 1 in some situtaions, for example, in cases of large collinearity). The interesting aspect of IPMA is the...

There can be potentially many reasons. I would guess that the new variables do not have sufficient variance for one of the subgroups. That can especially happen with moderators that are very similar to your grouping variable. Within the groups you may have a variance of zero for those variables (onl...

1) Usually, the more the better if you are using bootstrapping or permutation based approaches, because the estimates become more stable. 2) There is not much literature about the OTG approach or reporting guidelines. Usually, it is the best to look at some recent applications or the paper that prop...

Your calculations seem to be generally correct. However, a problem with the VAF is that it does not work with negative effects (indirect or direct). Some people propose to only use the absolute effects to circumvent these problems, but the main question is: what do you want to measure with the VAF a...

If you have a moderating effect in your model, you should receive the simple slope results in your PLS output.

First, in the indicator view should be some variables. You do not have any variables (items) appearing there, hence you may first check whether they are just hidden by clicking on the grey filtering symbol on top of that view. If there are still no indicators you need to check you data file again. S...

A high p-value indicates the same as a confidence interval including zero that is a non-significant (not different from zero) parameter. You are estimating tetrads that are covariance components. If they are all non-significant you likely have a reflective model, if you have at least one significant...

Your latent variable scores should have as many rows as you have observations in your data set. Each row represents an observation in the same order as in your dataset.

Yes, you can count the number of non-missing values from the indicator data results and also look at the latent variable scores from the PLS output to see how many missings you would have at the latent variable level.

Yes, of course.

I am concluding that there is moderation effect, sig at 10%. My understanding - M on its own is not significant to affect Y, but when X and M interact, together their impact is sig on Y. Is my understanding correct? Not quite correct. You have a significant moderating effect. That implies that the ...

As you are using the two-stage approach you import the standardized LOC variables into your dataset. Hence, you have the problem and cannot specify the 1-7 range. If all your variables for the LOC cover the full range from 1 to 7 you can just trust the empirical min and max for the standardized LOC ...

You should use 1-7 if the data is in that scale and not standardized. If you import standardized data (and your numbers suggest something like this) you need to set the standardized min and max value. The software automatically identifies the empirical min and max. Of course it cannot know the impli...

The output should provide all the information. In addition, you should inform yourself reading one of the books on PLS: https://www.smartpls.com/documentation/literature/books

Plase also see this response: viewtopic.php?f=11&t=19015