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Higher-Order PLS-PM Approach for Different Types of Constructs

Author

Listed:
  • Corrado Crocetta

    (University of Foggia)

  • Laura Antonucci

    (University of Foggia)

  • Rosanna Cataldo

    (University “Federico II”)

  • Roberto Galasso

    (University “Federico II”)

  • Maria Gabriella Grassia

    (University “Federico II”)

  • Carlo Natale Lauro

    (University “Federico II”)

  • Marina Marino

    (University “Federico II”)

Abstract

Partial least squares path modeling (PLS-PM) has become very popular in recent years, for measuring concepts that depend on different aspects and that are based on different types of relationships. PLS-PM represents a useful tool to explore relationships and to analyze the influence of the different aspects on the complex phenomenon analyzed. In particular, the use of higher-order constructs has allowed researchers to extend the application of PLS-PM to more advanced and complex models. In this work, our attention is focused on higher-order constructs that include reflective or formative relationships. Even if the dispute between formative models and reflective models is not exactly recent, it is still alive in current literature, for the most part within the context of structural equation models. This paper focuses attention on theoretical and mathematical differences between formative and reflective measurement models within the context of the PLS-PM approach. A simulation study is proposed in order to show how these approaches fit well in different modeling situations. The approaches have been compared using empirical application in a sustainability context. The findings from the simulation and the empirical application can help researchers to estimate and to use the higher-order PLS-PM approach in reflective and formative type models.

Suggested Citation

  • Corrado Crocetta & Laura Antonucci & Rosanna Cataldo & Roberto Galasso & Maria Gabriella Grassia & Carlo Natale Lauro & Marina Marino, 2021. "Higher-Order PLS-PM Approach for Different Types of Constructs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(2), pages 725-754, April.
    • Handle: RePEc:spr:soinre:v:154:y:2021:i:2:d:10.1007_s11205-020-02563-w
    DOI: 10.1007/s11205-020-02563-w
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    5. Laura Grassini & Alessandro Magrini & Enrico Conti, 2023. "Formative-reflective scheme for the assessment of tourism destination competitiveness: an analysis of Italian municipalities," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3523-3548, August.
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    7. Ines Djokic & Nikola Milicevic & Nenad Djokic & Borka Malcic & Branimir Kalas, 2024. "Students’ Perceptions of the Use of Artificial Intelligence in Educational Service," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(65), pages 294-294, February.
    8. Maurizio Carpita & Paola Pasca & Serena Arima & Enrico Ciavolino, 2023. "Clustering of variables methods and measurement models for soccer players’ performances," Annals of Operations Research, Springer, vol. 325(1), pages 37-56, June.

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