If I understand your question correctly, the non-standardized beta (B) is the value you would multiply your raw scores by if you were writing out an regression equation for raw scores. The standardized beta (β) is the relative weight of each predictor in predicting the criterion. Because the standardized beta converts each predictor into the same metric (z-scores), it allows one to directly compare the strength of one predictor to the others.

Rhianon Allen thank you for your response ,

my question is how can I determine if the outcomes of my analysis of PLS are standardised or unstandarized , meaning if they reflect covariance or correlation ? cause non of the terms are mentioned in the output tables ? can you hep please .

i couldn't interpret until now cause I don't know if the numbers of (original sample/ and sample mean) reflect the magnitude or the correlation.

if any one expert with PLS output tables please help

We use the standardize beta in our calculation. If the predictor and criterion variables are all standardized, the regression coefficients are called beta weights. A beta weight equals the correlation when there is a single predictor. If there are two or predictors, a beta weights can be larger than +1 or smaller than -1, but this is due to multicollinearity. So, we work in terms of standardized beta. Usually we would make the mean zero and the standard deviation 1.

The standard deviation is the deviation from the mean and is standardized if you look at formula. Standardized values (also called standard scores or normal deviates). It tells us how far from the mean we are in terms of standard deviations. Standard deviation is: the square root of the variance, where the variance is the mean squared deviation from the mean.