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You have to use the bootstrapping algorithm.

We cannot provide individual counselling on how to build an appropriate model. We can only answer general methodological questions that might also be interesting for other researchers. If you need more input on your specific model, you may either purchase the enterprise license which comes with 8 ho...

PLS algorithms use normal (Pearson) correlations and regression. Thus, they assume metric or at least quasi-metric data. Most researchers assume quasi-metric data for their questionnaire responses if it has sufficient categories (usually at least 5 or better 7 or more) and has equidistant categories...

If you want to report the bias-corrected and accelerated bootstrapping confidence intervals you should report the values under "Confidence Intervals Bias Corrected" otherwise they will not be bias-corrected and accelerated, but normal percentile intervals.

Are you running a PLS model or a LISREL/AMOS/MPLUS analysis?

There likely will be a problem somewhere with your model or data set. You could send the project (including the data set and model) to our support team and they might have a look at it.

If you have no measurement model (only single indicators) there is nothing to calculate and the assumption for single indicators is perfect reliability (i.e., the construct is the same as the indicator). So this is ok. However, the question is, whether using only single indicator constructs is in ge...

You can include control variables as single-item constructs into your PLS model.

As far as I remember, FAC-SEM are procedural guidelines on how to combine different PLS results to analyze factorial SEMs. There is no specific algorithm for this in SmartPLS.

First, thresholds are never right or wrong. They are always arbitrary and to some degree based on the opinions of the authors or common practice in the field. The thresholds in the course slides that you show are based on the PLS Primer book 2nd edition. However, we do not use this slide anymore in ...

You do not need to standardize the data prior to importing it. SmartPLS will always standardize the data in the algorithm. In fact, it is usually better to import the unstandardized raw data, because standardization might be different if you analyse groups, have missing values, etc. In these cases i...

1) Any incoming or outgoing significant path is sufficient. On first glance, it seems that you always have at least one incoming or outgoing path that is significant. Thus, you should be ok. 2) If you want to test significance in the second stage model, the sample size there is important for suffici...

The problem is not the negative moderation, but the use of PLSc with a model that does not adhere to the common factor model. Please find some description of the problem here: https://www.smartpls.com/documentation/algorithms-and-techniques/consistent-pls-problems You can also search the forum and f...

I think the most important question is sample size. If you sample size is too small, you might not have enough power to detect small to medium effects. Together with some substantial (even though not severe multicollinearity) this might explain the quite ok R² values while having insignificant paths.