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

被引:1106
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
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
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页数:16
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