Industrial Management & Data Systems· 2016Q1· Review
Using PLS path modeling in new technology research: updated guidelines
- 6,746citations
- Q1SCImago
- 2016year
Short summary
Partial Least Squares (PLS) path modeling is now a viable method for confirmatory analysis in new technology research, thanks to novel developments like consistent PLS and confirmatory composite analysis.
AI-generated from the title and abstract; the full text is not read.
Key points
- PLS path modeling is suitable for models with both factors and composites.
- New developments like consistent PLS and confirmatory composite analysis are highlighted.
- Novel exact fit tests now allow for confirmatory use of PLS path modeling.
- Updated guidelines are provided for using, reporting, and interpreting PLS results.
AI-generated from the title and abstract; the full text is not read.
Abstract
Purpose – Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues. Design/methodology/approach – This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations. Findings – PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible. Originality/value – This paper provides updated guidelines of how to use PLS and how to report and interpret its results.
The authors' abstract, as published at the source. Industrial Management & Data Systems, 2016 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences