<![CDATA[Partial Least Squares Structural Equation Modeling (PLS-SEM) is widely applied to analyze relationships among latent variables using different software tools. This study compared SmartPLS, WarpPLS, and R Studio from quantitative and qualitative perspectives. A synthetic dataset (N=100) with a simple reflective model (X1, X2→Y) was analyzed under equivalent settings, including reflective indicators and bootstrapping with 5,000 resamplings, to ensure structural equivalence and highlight algorithmic differences. Quantitative results showed consistent external loadings above 0.70, with small numerical deviations (overall MAD=0.039) and the largest variation in item Y4 (MaxDiff=0.087). Internal model estimates were stable, with minor differences in path coefficients (MaxDiff≤0.040) and larger variation in R² for R Studio (MaxDiff=0.098), reflecting differences in latent score calculations. Bootstrapping confirmed significance (T>1.96; p<0.05), though variability in T statistics was observed across software. Qualitatively, SmartPLS excelled in usability and visualization, WarpPLS in nonlinear analysis, and R Studio in flexibility and cost-effectiveness. Computationally, SmartPLS consumed the most memory, R Studio was moderate, and WarpPLS was most efficient, with all execution times under five seconds. These findings suggest that software choice should consider not only numerical accuracy but also usability, licensing, and computational efficiency to align with research objectives and user competencies.]]>
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