Why PLS-SEM is suitable for complex modeling? An empirical illustration in Big Data Analytics Quality

Authors

Shahriar Akter, University of WollongongFollow
Samuel Fosso Wamba, Toulouse Business SchoolFollow
Saifullah Dewan, University of Canberra

RIS ID

114348

Publication Details

Akter, S., Fosso Wamba, S. & Dewan, S. (2017). Why PLS-SEM is suitable for complex modeling? An empirical illustration in Big Data Analytics Quality. Production Planning and Control, 28 (11-12), 1011-1021.

Abstract

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).