Using PLS path modeling in new technology research: updated guidelines
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.
Cite this
Jörg Henseler, Geoffrey S. Hubona, Pauline Ash Ray (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems. https://doi.org/10.1108/imds-09-2015-0382
@article{jrg2016using,
title = {Using PLS path modeling in new technology research: updated guidelines},
author = {Henseler, Jörg and Hubona, Geoffrey S. and Ray, Pauline Ash},
year = {2016},
journal = {Industrial Management & Data Systems},
doi = {10.1108/imds-09-2015-0382}
}Citations in this library
Stored only when both papers are in the corpus — 7,156 citations exist upstream.