How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research

被引：1012

作者：

Benitez, Jose ^{[1
,2
]}

Henseler, Jorg ^{[3
,4
]}

Castillo, Ana ^{[2
]}

Schuberth, Florian ^{[3
]}

机构：

[1] Rennes Sch Business, IS, Rennes, France

[2] Univ Granada, Dept Management, Sch Business, Granada, Spain

[3] Univ Twente, Fac Engn Technol, Dept Design Prod & Management, Enschede, Netherlands

[4] Univ Nova Lisboa, Nova Informat Management Sch, Lisbon, Portugal

来源：

INFORMATION & MANAGEMENT | 2020年 / 57卷 / 02期

关键词：

Partial least squares path modeling; Guidelines; Model validation; Composite model; Confirmatory and explanatory information systems research; INFORMATION-TECHNOLOGY CAPABILITY; STRUCTURAL EQUATION MODELS; PLS-SEM; SOCIAL MEDIA; FIRM PERFORMANCE; MANAGEMENT RESEARCH; SAMPLE-SIZE; INNOVATION PERFORMANCE; FORMATIVE MEASUREMENT; CAUSAL INDICATORS;

D O I：

10.1016/j.im.2019.05.003

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Partial least squares path modeling (PLS-PM) is an estimator that has found widespread application for causal information systems (IS) research. Recently, the method has been subject to many improvements, such as consistent PLS (PLSc) for latent variable models, a bootstrap-based test for overall model fit, and the heterotrait-to-monotrait ratio of correlations for assessing discriminant validity. Scholars who would like to rigorously apply PLS-PM need updated guidelines for its use. This paper explains how to perform and report empirical analyses using PLS-PM including the latest enhancements, and illustrates its application with a fictive example on business value of social media.

引用

页数：16

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