scholarly journals PLS-SEM a 2nd generation technique: Concepts, properties characteristics and phases for its application

2021 ◽  
Vol 3 (2) ◽  
pp. 007-023
Author(s):  
Diana Faviola Olea-Flores ◽  
Alejandra Aldrette-Malacara ◽  
Luis Cuautle-Gutiérrez
Keyword(s):  
Theoretical Model ◽  
Least Squares ◽  
Partial Least Squares ◽  
Second Generation ◽  
Structural Equations ◽  
Multivariate Technique ◽  
2Nd Generation ◽  
Descriptive Research ◽  
Generation Technique

The multivariate technique of partial least squares structural equations (PLS-SEM) considered as second generation, has become more relevant in its application in recent years in various investigations, so this article considers an descriptive research, the which presents some properties characteristic of said technique and through the application of data and values obtained from a case study shows the phases required to validate and evaluate a model with the PLS-SEM technique. With the results obtained, a theoretical model is generated and proposed that could be useful for researchers who starting in the use and application of this technique.

scholarly journals Polymethyl Methacrylate Quality Modeling with Missing Data Using Subspace Based Model Identification

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1691
Author(s):  
Nikesh Patel ◽  
Kavitha Sivanathan ◽  
Prashant Mhaskar
Keyword(s):  
Missing Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Polymethyl Methacrylate ◽  
Model Identification ◽  
Principal Component ◽  
Quality Modeling ◽  
The Impact ◽  
Quality Measurements

This paper addresses the problem of quality modeling in polymethyl methacrylate (PMMA) production. The key challenge is handling the large amounts of missing quality measurements in each batch due to the time and cost sensitive nature of the measurements. To this end, a missing data subspace algorithm that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principal component analysis (PCA) is utilized to build a data driven dynamic model. The use of NIPALS algorithms allows for the correlation structure of the input–output data to minimize the impact of the large amounts of missing quality measurements. These techniques are utilized in a simulated case study to successfully model the PMMA process in particular, and demonstrate the efficacy of the algorithm to handle the quality prediction problem in general.

scholarly journals Do Innovation and Cooperation Influence SMEs’ Competitiveness? Evidence From the Andalusian Metal-Mechanic Sector

Innovar ◽  
2015 ◽  
Vol 25 (55) ◽  
pp. 101-115
Author(s):  
Juan A. Tamayo ◽  
José E. Romero ◽  
Javier Gamero ◽  
Juan A. Martínez-Román
Keyword(s):  
Quality Management ◽  
Least Squares ◽  
Partial Least Squares ◽  
Positive Influence ◽  
Structural Equations ◽  
Financial Resources ◽  
Management Knowledge

This study's main objective is to determine the influence of innovation and cooperation on the competitiveness of SMEs in the metal-mechanic sector of Andalusia (Spain). Using information obtained by interviewing managers of a sample of 80 firms, we proposed a model of structural equations based on the Partial Least Squares (PLS) technique. This model, which explained 37% of the variability of competitiveness, also allowed us to test hypotheses about the positive influence of quality management, knowledge, financial resources and cooperation on innovative outcomes. Along with the contrasted hypotheses, the most noteworthy finding was that cooperation does not significantly influence the innovative outcomes of firms in this sector.

scholarly journals Encountering Suffering at Work in Health Religious Organizations: A Partial Least Squares Path Modeling Case-Study

2020 ◽  
Vol 11 ◽  
Author(s):  
Maria Isabel Sánchez-Hernández ◽  
Eduardo Gismera-Tierno ◽  
Jesus Labrador-Fernández ◽  
José Luis Fernández-Fernández
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Religious Organizations ◽  
Path Modeling

How Do Fans Feel? A Case Analysis of Measuring and Modeling Attitudes Using Partial Least Squares Structural Equation Modeling

2016 ◽  
Vol 5 (1) ◽  
pp. 85-88 ◽  
Author(s):  
Lane Wakefield ◽  
Gregg Bennett
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Equation Modeling ◽  
Sport Organizations ◽  
Sports Teams ◽  
Know How ◽  
Fan Communities

Virtual fan communities (VFC) have become very popular among fans of sports teams. A VFC provides an online place for fans to meet and discuss the team, consume media, and develop friendships. Students will learn, in this case study, how to use partial least squares structural equation modeling (PLS-SEM) to assess fan attitudes toward the VFC and sponsors of the firm. Students will also learn how sport organizations can benefit from leadership with statistical know-how. The case is fictional, but it is based on an actual research study conducted in conjunction with a prominent virtual fan community in which ownership had an interest in fans’ attitudes toward their service.

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

Sign in / Sign up

Export Citation Format

Share Document