How to Validate Formative Model in SmartPLS

Validating formative indicators in SmartPLS starts with a single, practical question: do the indicators suffer from collinearity? In formative measurement models, indicators are not interchangeable—they represent different concepts—so high correlations between them are a red flag. When collinearity is high, SmartPLS weight estimation becomes unstable and the statistical significance of those weights can be distorted, undermining confidence in which indicators truly contribute to the latent construct. The first diagnostic step is therefore to check collinearity using the Variance Inflation Factor (VIF). Common rule-of-thumb thresholds are VIF ≥ 5 (suggesting a potential collinearity problem) and VIF ≥ 3.3 (a more conservative warning level). If all VIF values stay below 5, the formative indicators can proceed without collinearity treatment; if any exceed 5, collinearity issues are present and the formative measurement model should be reconsidered (the transcript notes that handling is outside the scope here).

Once collinearity is cleared, the next validation step targets whether each formative indicator meaningfully contributes to the composite construct. SmartPLS evaluates this through outer weights, which come from a multiple regression framework: the latent variable score acts as the dependent variable, while the formative indicators act as independent variables. Outer weights indicate each indicator’s relative contribution to the latent construct, and their significance is typically assessed via bootstrapping (the transcript mentions 5,000 as a recommended number, though examples use 500 for demonstration). If an indicator’s outer weight is significant, it is treated as contributing and is retained.

But a non-significant outer weight doesn’t automatically mean the indicator must be removed. The workflow then shifts to outer loadings, using a decision logic tied to both magnitude and significance. The transcript uses 0.5 as a practical benchmark for outer loadings: if outer loadings are below 0.5, the indicator’s loading significance is checked. If the outer loading is significant even when below 0.5, the indicator can still be retained. Conversely, if the outer loading is not significant and remains below the threshold, the indicator is deleted. A simple flow emerges: assess VIF first; then test outer weights; if outer weights are insignificant, evaluate outer loadings and their significance; keep indicators that meet the significance criteria.

The transcript then walks through two practical examples. In the first, a formative construct (CSR) has four formative indicators; bootstrapping shows some outer weights are insignificant, but outer loadings for those indicators are significant (including cases where one loading is above 0.5 and another is below 0.5 yet still significant). The result: all formative indicators are retained. In the second example, a higher-order formative construct is validated after confirming VIF values are acceptable and bootstrapped outer weights and outer loadings are significant, leading to a clean validity conclusion.

Finally, reporting is standardized: results are presented in a table listing each formative indicator’s outer weight (with T statistics and P values), outer loading, and the VIF values, along with the construct name(s). This creates an auditable record of collinearity diagnostics and indicator validity decisions for formative measurement models in SmartPLS.