#SmartPLS4 Webinar Day 2: Structural Model Assessment

Structural model assessment in SmartPLS hinges on two linked tasks: checking whether the measurement quality holds up, and then testing whether hypothesized relationships among latent variables are statistically supported. After confirming measurement-model quality (factor loadings, reliability, and validity), the workflow also includes a common-method-bias check using collinearity statistics (VIF). In the webinar’s example, VIF values are all below 3, which—per the cited guidance—signals no serious common method bias.

With the measurement model cleared, the focus shifts to structural model assessment: determining how constructs relate to one another and whether proposed hypotheses are substantiated. The example simplifies a complex model into a straightforward setup with three predictors—Vision, Development, and Rewards—and one dependent variable, Organizational Performance. To test whether these predictors significantly influence the outcome, the analysis uses bootstrapping, a nonparametric technique suited for non-normal data. The webinar emphasizes practical bootstrapping settings: using a large number of resamples (noting older guidance of 5,000 and newer guidance of 10,000), enabling parallel processing for speed, selecting “complete/slow” only when model-fit and effect-size details are needed, and using bias-corrected and accelerated (BCa) confidence intervals for stability. For hypothesis testing, it recommends one-tailed tests when the direction of effects is specified in advance (e.g., expecting positive impacts), and it uses a 0.05 significance threshold.

Bootstrapping results then determine which paths are significant. For Vision → Organizational Performance, the beta coefficient is positive (0.229) and the p-value is below 0.05, so the relationship is supported. Development → Organizational Performance is also significant. Rewards → Organizational Performance, however, shows a very small beta (below 0.1) with a p-value of 0.335, leading to rejection of a significant effect in this study. The model’s explanatory power is summarized by R²: 58.9% of variance in Organizational Performance is accounted for by Vision, Development, and Rewards.

The webinar then expands from direct effects to more complex causal mechanisms using mediation and moderation. Mediation is framed as a third variable that transmits the effect of an independent variable to a dependent variable, requiring theoretical justification that the mediator is influenced by the IV and in turn influences the DV. The webinar distinguishes full versus partial mediation using significance patterns: full mediation occurs when the direct effect becomes insignificant while the indirect effect remains significant; partial mediation occurs when both direct and indirect effects are significant. It also clarifies how to report mediation in SmartPLS terms—total effects, direct effects, specific indirect effects, and confidence intervals—plus how to interpret mediation tables.

Next comes moderation, where a moderator changes the strength or direction of a relationship rather than pointing to a variable-to-variable causal chain. The example tests whether Role Conflict moderates the link between Collaborative Culture and Organizational Performance; it does not (p > 0.05). Role Ambiguity does moderate the relationship, with a negative interaction effect, meaning higher role ambiguity weakens the positive impact of collaborative culture. Because moderation is assessed through interaction effects, the webinar recommends slope analysis after a significant interaction to interpret how the relationship differs at low, mean, and high levels of the moderator.

Finally, the webinar addresses modeling mechanics in SmartPLS: when moderators are included, measurement-model assessment should be run without the interaction term, then duplicated to build the structural model with the moderation effect. It also introduces the conceptual groundwork for later material on higher-order constructs, distinguishing reflective versus formative measurement logic based on arrow direction and the interchangeability (reflective) versus unique contribution (formative) of indicators.