Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality
本文论证偏最小二乘结构方程模型（PLS-SEM）在估计复杂层次模型中的适用性，基于逼真性哲学和软建模假设，并以大数据分析质量领域为例进行实证验证。
The emergence of multivariate analysis techniques transforms empirical validation of theoretical concepts in social science and business research. In this context, structural equation modelling (SEM) has emerged as a powerful tool to estimate conceptual models linking two or more latent constructs. This paper shows the suitability of the partial least squares (PLS) approach to SEM (PLS-SEM) in estimating a complex model drawing on the philosophy of verisimilitude and the methodology of soft modelling assumptions. The results confirm the utility of PLS-SEM as a promising tool to estimate a complex, hierarchical model in the domain of big data analytics quality.