PLS-SEM Results Report Template
Software and Estimation
The PLS-SEM analysis was conducted using SmartPLS 4. The PLS-SEM algorithm was used to estimate the model, and bootstrapping with [number] subsamples was used to assess significance.
Measurement Model Assessment
The reflective measurement model was assessed using indicator loadings, internal consistency reliability, convergent validity, and discriminant validity.
Reliability and Convergent Validity
| Construct | Loading Range | Cronbach's Alpha | rho_A | rho_C | AVE | Decision |
|---|---|---|---|---|---|---|
| PU | ||||||
| PEOU | ||||||
| TRUST | ||||||
| SERVICE_QUALITY | ||||||
| SATISFACTION |
Discriminant Validity
HTMT values were below [0.85/0.90], supporting discriminant validity.
Structural Model Assessment
Predictor collinearity was assessed using VIF. All VIF values were [below/above] the selected threshold of [threshold].
| Hypothesis | Path | beta | t | p | 95% CI | Decision |
|---|---|---|---|---|---|---|
| H1 | PU -> SATISFACTION | |||||
| H2 | PEOU -> SATISFACTION | |||||
| H3 | TRUST -> SATISFACTION | |||||
| H4 | SERVICE_QUALITY -> SATISFACTION |
Explanatory Power
The model explained [R2] of the variance in [endogenous construct].
| Endogenous Construct | R2 | Adjusted R2 | Interpretation |
|---|---|---|---|
| SATISFACTION |
Prediction Assessment
PLSpredict was used/not used because [reason]. The Q2_predict values were [positive/non-positive], and RMSE/MAE comparisons indicated [interpretation].
Conclusion
The structural results indicate that [summary of supported hypotheses]. The results should be interpreted in light of [limitations].