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Partial least squares structural equation modeling and complementary methods in business research

Record Type:	Electronic resources : Monograph/item
Title/Author:	Partial least squares structural equation modeling and complementary methods in business research/ by XinYing Chew ... [et al.].
other author:	Chew, XinYing.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xvii, 183 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	Partial Least Squares Structural Equation Modeling -- PLSbSEM Path Model Estimation -- Overview of ANN Analysis -- Business Research Applications in SEM ANN Analysis -- Opportunity for ANN Analysis -- Artificial Neural Network and Theories -- Hybrid SEM and ANN Approach -- Fuzzy set Qualitative Comparative Analysis FsQCA -- Outline of ANFIS Analysis -- Multi Criteria Decision Making -- Machine Learning in Business Research -- Application of Multi Criteria Decision Making Methods.
Contained By:	Springer Nature eBook
Subject:	Business - Research -
Online resource:	https://doi.org/10.1007/978-3-032-01055-1
ISBN:	9783032010551

Partial least squares structural equation modeling and complementary methods in business research
Partial least squares structural equation modeling and complementary methods in business research[electronic resource] /by XinYing Chew ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xvii, 183 p. :ill. (chiefly col.), digital ;24 cm. - Information systems engineering and management,v. 673004-9598 ;. - Information systems engineering and management ;v. 67..

Partial Least Squares Structural Equation Modeling -- PLSbSEM Path Model Estimation -- Overview of ANN Analysis -- Business Research Applications in SEM ANN Analysis -- Opportunity for ANN Analysis -- Artificial Neural Network and Theories -- Hybrid SEM and ANN Approach -- Fuzzy set Qualitative Comparative Analysis FsQCA -- Outline of ANFIS Analysis -- Multi Criteria Decision Making -- Machine Learning in Business Research -- Application of Multi Criteria Decision Making Methods.

This book offers a practical and accessible guide to Partial Least Squares Structural Equation Modeling (PLS-SEM) in business research, while addressing its limitations by integrating complementary methods such as artificial neural networks (ANN), fuzzy-set qualitative comparative analysis (fsQCA), and multi-criteria decision-making (MCDM). It supports early-career researchers, postgraduate students, and practitioners in navigating complex models, predictive analytics, and latent construct measurement. By focusing on emerging business issues like digital transformation, metaverse, and sustainability, this book delivers clear, applied insights. Readers gain not only foundational knowledge of PLS-SEM but also strategies for enhancing research rigor, prediction, and decision-making using hybrid approaches. This is a timely and essential resource for scholars aiming to advance their methodological toolkit for impactful and actionable business research.

ISBN: 9783032010551

Standard No.: 10.1007/978-3-032-01055-1doiSubjects--Topical Terms:

647315
Business
--Research

LC Class. No.: HD30.4

Dewey Class. No.: 650.0721

Partial least squares structural equation modeling and complementary methods in business research
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245 0 0 $a Partial least squares structural equation modeling and complementary methods in business research $h [electronic resource] / $c by XinYing Chew ... [et al.].
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490 1 $a Information systems engineering and management, $x 3004-9598 ; $v v. 67
505 0 $a Partial Least Squares Structural Equation Modeling -- PLSbSEM Path Model Estimation -- Overview of ANN Analysis -- Business Research Applications in SEM ANN Analysis -- Opportunity for ANN Analysis -- Artificial Neural Network and Theories -- Hybrid SEM and ANN Approach -- Fuzzy set Qualitative Comparative Analysis FsQCA -- Outline of ANFIS Analysis -- Multi Criteria Decision Making -- Machine Learning in Business Research -- Application of Multi Criteria Decision Making Methods.
520 $a This book offers a practical and accessible guide to Partial Least Squares Structural Equation Modeling (PLS-SEM) in business research, while addressing its limitations by integrating complementary methods such as artificial neural networks (ANN), fuzzy-set qualitative comparative analysis (fsQCA), and multi-criteria decision-making (MCDM). It supports early-career researchers, postgraduate students, and practitioners in navigating complex models, predictive analytics, and latent construct measurement. By focusing on emerging business issues like digital transformation, metaverse, and sustainability, this book delivers clear, applied insights. Readers gain not only foundational knowledge of PLS-SEM but also strategies for enhancing research rigor, prediction, and decision-making using hybrid approaches. This is a timely and essential resource for scholars aiming to advance their methodological toolkit for impactful and actionable business research.
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650 0 $a Least squares. $3 549973
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Business Mathematics. $3 2088824
650 2 4 $a Data Engineering. $3 3409361
700 1 $a Chew, XinYing. $3 3790935
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830 0 $a Information systems engineering and management ; $v v. 67. $3 3790936
856 4 0 $u https://doi.org/10.1007/978-3-032-01055-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)