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Partial Least Squares Path Modeling: Updated Guidelines

In: Partial Least Squares Path Modeling

Author

Listed:
  • Jörg Henseler

    (University of Twente)

  • Geoffrey Hubona

    (Georgia R School)

  • Pauline Ash Ray

    (Thomas University, Business Division)

Abstract

Partial least squares (PLS) path modeling is a variance-based structural equation modeling technique that is widely applied in business and social sciences. It is the method of choice if a structural equation model contains both factors and composites. This chapter aggregates new insights and offers a fresh look at PLS path modeling. It presents the newest developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations (HTMT). PLS path modeling can be regarded as an instantiation of generalized canonical correlation analysis. It aims at modeling relationships between composites, i.e., linear combinations of observed variables. A recent extension, consistent PLS, makes it possible to also include factors in a PLS path model. The chapter illustrates how to specify a PLS path model consisting of construct measurement and structural relationships. It also shows how to integrate categorical variables. A particularly important consideration is model identification: Every construct measured by multiple indicators must be embedded into a nomological net, which means that there must be at least one other construct with which it is related. PLS path modeling results are useful for exploratory and confirmatory research. The chapter provides guidelines for assessing the fit of the overall model, the reliability and validity of the measurement model, and the relationships between constructs. Moreover, it provides a glimpse on various extensions of PLS, many of which will be described in more detail in later chapters of the book.

Suggested Citation

  • Jörg Henseler & Geoffrey Hubona & Pauline Ash Ray, 2017. "Partial Least Squares Path Modeling: Updated Guidelines," Springer Books, in: Hengky Latan & Richard Noonan (ed.), Partial Least Squares Path Modeling, chapter 0, pages 19-39, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-64069-3_2
    DOI: 10.1007/978-3-319-64069-3_2
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    Cited by:

    1. Widaryanti & Wan Abdullah & Riana Sitawati & Luhgiatno, 2026. "Exploring the application of PLS-SEM in business, management, and accounting research: a bibliometric approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(1), pages 2407-2434, February.
    2. Manu Sharma & Navneet Rawat & Sudhanshu Joshi & Abhishek Misra, 2025. "Assessing the role of destination and nature-based destination image in Gen Z solo travellers pro-sustainable tourism behaviour: the moderating role of social media influencers trust," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(6), pages 5469-5489, December.
    3. Mavis Sirri Ngwa & Pelin Bayram, 2026. "Does perceived organizational support lead to organizational citizenship behavior and turnover intention in hotel industry? The indirect roles of work–life satisfaction and employee resilience," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 13(1), pages 1-12, December.
    4. Fareeha Hammad & Fahad Abdul Sattar & Waqar Ahmed & Mohammad Areeb Akram, 2026. "Understanding the influence of strategic reverse logistics management on the sustainable performance of the organization," OPSEARCH, Springer;Operational Research Society of India, vol. 63(1), pages 294-314, March.
    5. Tahani Ali Hakami, 2025. "Enhancing internal audit efficiency in Saudi Arabian SMEs: the impact of cloud adoption determinants," SN Business & Economics, Springer, vol. 5(12), pages 1-32, December.

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