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Why Should PLS-SEM Be Used Rather Than Regression? Evidence from the Capital Structure Perspective

In: Partial Least Squares Structural Equation Modeling

Author

Listed:
  • Nur Ainna Ramli

    (Universiti Sains Islam Malaysia)

  • Hengky Latan

    (STIE Bank BPD Jateng
    Petra Christian University)

  • Gilbert V. Nartea

    (University of Canterbury)

Abstract

This study examines capital structure determinants using a simultaneous causal model with interaction effects between manifest and latent variables. Partial Least Squares (PLS) is an approach to Structural Equation Models (SEM) that allows researchers to analyse the relationships simultaneously. It is interesting to compare and contrast this approach in analysing mediation relationships with the regression analysis. In addition to statistical data, logical arguments are presented supported by two case studies from PLS-SEM and regression models. We find that the choice between regression and PLS-SEM matters even with the simplest scenarios per item for constructs. This study’s originality is the provision of new comparative analyses of PLS-SEM versus regression analysis in the context of capital structure determinants. The “indirect” and “mediate” macro syntax normal theory of the Sobel test, and the bootstrapping techniques are compared with PLS-SEM. We find that the PLS-SEM analysis provides less contradictory results than regression analysis in terms of detecting mediation effects.

Suggested Citation

  • Nur Ainna Ramli & Hengky Latan & Gilbert V. Nartea, 2018. "Why Should PLS-SEM Be Used Rather Than Regression? Evidence from the Capital Structure Perspective," International Series in Operations Research & Management Science, in: Necmi K. Avkiran & Christian M. Ringle (ed.), Partial Least Squares Structural Equation Modeling, chapter 0, pages 171-209, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-71691-6_6
    DOI: 10.1007/978-3-319-71691-6_6
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    References listed on IDEAS

    as
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