结构方程建模
偏最小二乘回归
潜变量
路径分析(统计学)
计算机科学
路径(计算)
数学
应用数学
通径系数
人工智能
机器学习
程序设计语言
作者
Marko Sarstedt,Christian M. Ringle,Joseph F. Hair
出处
期刊:Springer eBooks
[Springer Nature]
日期:2017-01-01
卷期号:: 1-40
被引量:1759
标识
DOI:10.1007/978-3-319-05542-8_15-1
摘要
Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.
科研通智能强力驱动
Strongly Powered by AbleSci AI