Consistent Partial Least Squares for Nonlinear Structural Equation Models

Psychometrika ◽  
2013 ◽  
Vol 79 (4) ◽  
pp. 585-604 ◽  
Author(s):  
Theo K. Dijkstra ◽  
Karin Schermelleh-Engel
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Nonlinear Structural ◽  
Nonlinear Structural Equation ◽  
Consistent Partial Least Squares

Nonlinear structural equation modeling: is partial least squares an alternative?

2010 ◽  
Vol 94 (2) ◽  
pp. 167-184 ◽  
Author(s):  
Karin Schermelleh-Engel ◽  
Christina S. Werner ◽  
Andreas G. Klein ◽  
Helfried Moosbrugger
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Equation Modeling ◽  
Nonlinear Structural ◽  
Nonlinear Structural Equation

scholarly journals Comparison of Spa Choice between Wellness Tourists and Healthcare/Medical Tourists

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 544
Author(s):  
Jorge Pelegrín-Borondo ◽  
Noelia Araújo-Vila ◽  
Jose A. Fraiz-Brea
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Purchase Intention ◽  
Structural Equation ◽  
Choice Models ◽  
Equation Modeling ◽  
Performance Expectancy ◽  
Influential Variable ◽  
Consistent Partial Least Squares

Spa tourism is considered one of the most important segments of the $639 billion wellness market. The literature refers to two types of spa tourists: wellness tourists and healthcare/medical tourists. However, virtually no studies have compared spa choice models between these two segments. The present study uses the Cognitive-Affective-Normative (CAN) model to compare the variables that explain purchase intention in relation to spas between the two segments. Data were collected through a questionnaire administered to a sample of 810 potential Spanish spa-goers, and consistent partial least squares (PLSc) structural equation modeling (SEM) was used. Contrary to what might be expected, no major differences were found between the spa choice models for wellness tourists and for healthcare/medical tourists. The results show that R2 and Q2 were similar for both models. The most influential variable was performance expectancy, and differences were only found in the influence of the pleasure variable.

scholarly journals Structural equation models - PLS in engineering sciences: a brief guide for researchers through a case applied to the industry

Athenea ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 5-18
Author(s):  
Juan Enrique Villalva A.
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Structural Equations ◽  
Partial Least Square ◽  
Equation Modeling ◽  
Engineering Sciences ◽  
The Impact

Modeling using structural equations, is a second generation statistical data analysis technique, it has been positioned as the methodological options most used by researchers in various fields of science. The best known method is the covariance-based approach, but it presents some limitations for its application in certain cases. Another alternative method is based on the variance structure, through the analysis of partial least squares, which is an appropriate option when the research involves the use of latent variables (for example, composite indicators) prepared by the researcher, and where it is necessary to explain and predict complex models. This article presents a brief summary of the structural equation modeling technique, with an example on the relationship of constructs, sustainability and competitiveness in iron mining, and is intended to be a brief guide for future researchers in the engineering sciences. Keywords: Competitiveness, Structural equations, Iron mining, Sustainability. References [1]J. Hair, G. Hult, C. Ringle and M. Sarstedt. A Primer on Partial Least Square Structural Equation Modeling (PLS-SEM). California: United States. Sage, 2017. [2]H. Wold. Model Construction and Evaluation when Theoretical Knowledge Is Scarce: An Example of the Use of Partial Least Squares. Genève. Faculté des Sciences Économiques et Sociales, Université de Genève. 1979. [3]J. Henseler, G. Hubona & P. Ray. “Using PLS path modeling new technology research: updated guidelines”. Industrial Management & Data Systems, 116(1), 2-20. 2016. [4]G. Cepeda and Roldán J. “Aplicando en la Práctica la Técnica PLS en la Administración de Empresas”. Congreso de la ACEDE, Murcia, España, 2004. [5]D. Garson. Partial Least Squares. Regresión and Structural Equation Models. USA. Statistical Associates Publishing: 2016. [6]D. Barclay, C. Higgins & R. Thompson. “The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration”. Technology Studies. Special Issue on Research Methodology. (2:2), pp. 285-309. 1995. [7]J. Medina, N. Pedraza & M. Guerrero. “Modelado de Ecuaciones Estructurales. Un Enfoque de Partial Least Square Aplicado en las Ciencias Sociales y Administrativas”. XIV Congreso Internacional de la Academia de Ciencias Administrativas A.C. (ACACIA). EGADE – ITESM. Monterrey, México, 2010. [8]J. Medina & J. Chaparro. “The Impact of the Human Element in the Information Systems Quality for Decision Making and User Satisfaction”. Journal of Computer Information Systems. (48:2), pp. 44-52. 2008. [9]D. Leidner, S. Carlsson, J. Elam & M. Corrales. “Mexican and Swedish Managers’ Perceptions of the Impact of EIS on Organizational Intelligence, Decisión Making, and Structure”. Decision Science. (30:3), pp. 633-658. 1999.[10]W. Chin. “The partial least squares approach for structural equation modeling”. Chapter Ten, pp. 295-336 in Modern methods for business research. Edited by Macoulides, G. A., New Jersey: Lawrence Erlbaum Associates, 1998. [11]M. Höck & C. Ringle M. “Strategic networks in the software industry: An empirical analysis of the value continuum”. IFSAM VIIIth World Congress, Berlin 2006. [12]J. Henseler, Ch. Ringle & M. Sarstedt. Handbook of partial least squares: Concepts, methods and applications in marketing and related fields. Berlin: Springer, 2012. [13]S. Daskalakis & J. Mantas. “Evaluating the impact of a service-oriented framework for healthcare interoperability”. Studies in Health Technology and Informatics. pp. 285-290. 2008. [14]C. Fornell & D. Larcker: “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error”, Journal of Marketing Research, vol. 18, pp. 39-50. Februay 1981. [15]C. Fornell. A Second Generation of Multivariate Analysis: An Overview. Vol. 1. New York, U.S.A. Praeger Publishers: 1982. [16]R. Falk and N. Miller. A Primer for Soft Modeling. Ohio: The University of Akron. 1992. [17]M. Martínez. Aplicación de la técnica PLS-SEM en la gestión del conocimiento: un enfoque técnico práctico. Revista Iberoamericana para Investigación y el Desarrollo Educativo. Vol. 8, Núm. 16. 2018. [18]S. Geisser. “A predictive approach to the random effects model”. Biometrika, Vol. 61(1), pp. 101-107. 1974. [19]J. Cohen. Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum, 1988. [20]GRI (2013). G4 Sustainability Reporting Guidelines. Global Reporting Initiative. Available: www.globalreporting.org

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