结构方程建模
偏最小二乘回归
验证性因素分析
路径分析(统计学)
差异(会计)
路径(计算)
计算机科学
通径系数
独创性
数据挖掘
管理科学
工业工程
人工智能
机器学习
工程类
心理学
社会心理学
会计
创造力
业务
程序设计语言
作者
Jörg Henseler,Geoffrey S. Hubona,Pauline Ash Ray
出处
期刊:Industrial Management and Data Systems
[Emerald (MCB UP)]
日期:2016-02-01
卷期号:116 (1): 2-20
被引量:3233
标识
DOI:10.1108/imds-09-2015-0382
摘要
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.
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