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Cited by 869

The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.

Variance-Based Structural Equation Modeling: Guidelines for Using Partial Least Squares in Information Systems Research

Roldán, José L., Sánchez-Franco, Manuel J.·2012·Research Methodologies, Innovations and Philosophies in Software Systems Engineering and Information Systems
DOI 10.4018/978-1-4666-0179-6.ch010
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Abstract

Partial Least Squares (PLS) is an efficient statistical technique that is highly suited for Information Systems research. In this chapter, the authors propose both the theory underlying PLS and a discussion of the key differences between covariance-based SEM and variance-based SEM, i.e., PLS. In particular, authors: (a) provide an analysis of the origin, development, and features of PLS, and (b) discuss analysis problems as diverse as the nature of epistemic relationships and sample size requirements. In this regard, the authors present basic guidelines for the applying of PLS as well as an explanation of the different steps implied for the assessment of the measurement model and the structural model. Finally, the authors present two examples of Information Systems models in which they have put previous recommendations into effect.

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Metadata

Authors
Roldán, J. L., & Sánchez-Franco, M. J.
Venue
Research Methodologies, Innovations and Philosophies in Software Systems Engineering and Information Systems
Publisher
IGI Global
Published
2012
Pages
193-221
DOI
10.4018/978-1-4666-0179-6.ch010
OpenAlex
W2501702703
Cited by
869
References
80
FWCI
76.30
Type
book chapter
Language
en
Open access
closed

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CoChat. (2026). Citation check: Variance-Based Structural Equation Modeling: Guidelines for Using Partial Least Squares in Information Systems Research. https://app.cochat.ai/papers/10.4018/978-1-4666-0179-6.ch010
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Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-Based Structural Equation Modeling: Guidelines for Using Partial Least Squares in Information Systems Research. Research Methodologies, Innovations and Philosophies in Software Systems Engineering and Information Systems. https://doi.org/10.4018/978-1-4666-0179-6.ch010

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.