Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality

Shahriar Akter, Samuel Fosso Wamba, Saifullah Dewan

Research output: Contribution to journalArticle

28 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1011-1021
Number of pages11
JournalProduction Planning and Control
Issue number11-12
Publication statusPublished - 1 Jan 2017


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