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

    23 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 languageEnglish
    Pages (from-to)1011-1021
    Number of pages11
    JournalProduction Planning and Control
    Volume28
    Issue number11-12
    DOIs
    Publication statusPublished - 1 Jan 2017

    Fingerprint

    Social sciences
    Big data
    Partial least squares
    Modeling
    Structural equation modeling
    Industry
    Multivariate Analysis
    Methodology
    Hierarchical model
    Multivariate analysis
    Conceptual model
    Business research

    Cite this

    Akter, Shahriar ; Fosso Wamba, Samuel ; Dewan, Saifullah. / Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality. In: Production Planning and Control. 2017 ; Vol. 28, No. 11-12. pp. 1011-1021.
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    Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality. / Akter, Shahriar; Fosso Wamba, Samuel; Dewan, Saifullah.

    In: Production Planning and Control, Vol. 28, No. 11-12, 01.01.2017, p. 1011-1021.

    Research output: Contribution to journalArticle

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