The emergence of multivariate analysis techniques transforms empirical validation of theoretical concepts in social science and business research. In this context, structural equation modeling (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 (BDAQ).
Available for download on Wednesday, July 11, 2018