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

Shahriar Akter; Samuel Fosso Wamba; Saifullah Dewan

doi:10.1080/09537287.2016.1267411

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

Shahriar Akter, Samuel Fosso Wamba, Saifullah Dewan

Canberra Business School

Research output: Contribution to journal › Article › peer-review

296 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	1011-1021
Number of pages	11
Journal	Production Planning and Control
Volume	28
Issue number	11-12
DOIs	https://doi.org/10.1080/09537287.2016.1267411
Publication status	Published - 1 Jan 2017

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10.1080/09537287.2016.1267411

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

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