Abstract
Bootstrapping is a nonparametric procedure that allows testing the statistical significance of various PLSSEM results such path coefficients, Cronbach’s alpha, HTMT, and R² values.
Brief Description
PLSSEM does not assume that the data is normally distributed, which implies that parametric significance tests (e.g., as used in regression analyses) cannot be applied to test whether coefficients such as outer weights, outer loadings and path coefficients are significant. Instead, PLSSEM relies on a nonparametric bootstrap procedure (Efron and Tibshirani, 1986; Davison and Hinkley, 1997) to test the significance of estimated path coefficients in PLSSEM.
In bootstrapping, subsamples are created with randomly drawn observations from the original set of data (with replacement). The subsample is then used to estimate the PLS path model. This process is repeated until a large number of random subsamples has been created, typically about 10,000.
The parameter estimates (e.g., outer weights, outer loadings and path coefficients) obtained from the subsamples are used to derive the 95% confidence intervals for significance testing (e.g., original PLSSEM results are significant when they are outside the confidence interval). In addition, bootstrapping provides the standard errors for the estimates, which allow tvalues to be calculated to assess the significance of each estimate.
Hair et al. (2022) explain bootstrapping in more detail.
Bootstrapping Settings in SmartPLS
Subsamples
Bootstrapping creates subsamples with observations drawn at random from the original dataset (with replacement). The number of observations per bootstrap subsample is identical to the number of observations in the original sample (SmartPLS also considers the smaller number of observations in the original sample if you use casebycase deletion to handle missing values). To ensure stability of results, the number of subsamples should be large. For an initial assessment, one may wish to choose a smaller number of bootstrap subsamples (e.g., 1000) to be randomly drawn and estimated with the PLSSEM algorithm, since that requires less time. For the final results preparation, however, one should use a large number of bootstrap subsamples (e.g., 10,000).
Note: Larger numbers of bootstrap subsamples increase the computation time.
Do Parallel Processing
If chosen the bootstrapping algorithm will be performed on multiple processors (if your computer offers more than one core). As each subsample can be calculated individually, subsamples can be computed in parallel mode. Using parallel computing will reduce computation time.
Confidence Interval Method
Sets the bootstrapping method used for estimating nonparametric confidence intervals. The following bootstrapping procedures are available (for more details, see Hair et al., 2022):
 Percentile Bootstrap (default)
 Studentized Bootstrap
 BiasCorrected and Accelerated (BCa) Bootstrap
By default, we recommend using percentile bootstrapping. If you have concerns about a nonnormal bootstrap distribution, you can alternatively use biascorrected and accelerated (BCa) bootstrapping.
Test Type
Specifies if a onesided or twosided significance test is conducted.
Significance Level
Specifies the significance level of the test statistic.
References

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLSSEM), 3rd Ed., Sage: Thousand Oaks.

Davison, A. C., and Hinkley, D. V. (1997). Bootstrap Methods and Their Application, Cambridge University Press: Cambridge.

Efron, B., and Tibshirani, R. J. (1993). An Introduction to the Bootstrap, Chapman Hall: New York.
Please always cite the use of SmartPLS!
Ringle, Christian M., Wende, Sven, & Becker, JanMichael. (2022). SmartPLS 4. Boenningstedt: SmartPLS. Retrieved from https://www.smartpls.com