Partial Least Squares Structural Equation Modeling Approach for Analyzing a Model with a Binary Indicator as an Endogenous Variable

In this paper, we focus on PLS-SEM’s ability to handle models with observable binary outcomes. We examine the different ways in which a binary outcome may appear in a model and distinguish those situations in which a binary outcome is indeed problematic versus those in which one can easily incorporate it into a PLS-SEM analysis. Explicating such details enables IS researchers to distinguish different situations rather than avoid PLS-SEM altogether whenever a binary indicator presents itself. In certain situations, one can adapt PLS-SEM to analyze structural models with a binary observable variable as the endogenous construct. Specifically, one runs the PLS-SEM first stage as is. Subsequently, one uses the output for the binary variable and latent variable antecedents from this analysis in a separate logistic regression or discriminant analysis to estimate path coefficients for just that part of the structural model. We also describe a method—regularized generalized canonical correlation analysis (RGCCA)—from statistics, which is similar to PLS-SEM but unequivocally allows binary outcomes

Key issues on partial least squares (PLS) in operations management research: A guide to submissions

Purpose: This work aims to systematise the use of PLS as an analysis tool via a usage guide or recommendation for researchers to help them eliminate errors when using this tool. Design/methodology/approach: A recent literature review about PLS and discussion with experts in the methodology. Findings: This article considers the current situation of PLS after intense academic debate in recent years, and summarises recommendations to properly conduct and report a research work that uses this methodology in its analyses. We particularly focus on how to: choose the construct type; choose the estimation technique (PLS or CB-SEM); evaluate and report the measurement model; evaluate and report the structural model; analyse statistical power. Research limitations: It was impossible to cover some relevant aspects in considerable detail herein: presenting a guided example that respects all the report recommendations presented herein to act as a practical guide for authors; does the specification or evaluation of the measurement model differ when it deals with first-order or second-order constructs?; how are the outcomes of the constructs interpreted with the indicators being measured with nominal measurement levels?; is the Confirmatory Composite Analysis approach compatible with recent proposals about the Confirmatory Tetrad Analysis (CTA)? These themes will the object of later publications. Originality/value: We provide a check list of the information elements that must contain any article using PLS. Our intention is for the article to act as a guide for the researchers and possible authors who send works to the JIEM (Journal of Industrial and Engineering Management). This guide could be used by both editors and reviewers of JIEM, or other journals in this area, to evaluate and reduce the risk of bias (Losilla, Oliveras, Marin-Garcia & Vives, 2018) in works using PLS as an analysis procedure

The Trade Effects of Endogenous Preferential Trade Agreements

Recent work by Anderson and van Wincoop (2003) establishes an empirical modelling strategy which takes full account of the structural, non-(log-)linear impact of trade barriers on trade in new trade theory models. Structural new trade theory models have never been used to evaluate and quantify the role of endogenous preferential trade agreement (PTA) membership for trade in a way which is consistent with general equilibrium. Apart from this gap, the present paper aims at delivering an empirical model which takes into account both that preferential trade agreement membership is endogenous and that the world matrix of bilateral trade flows contains numerous zero entries. These features are treated in an encompassing way by means of (possibly two-part) Poisson pseudo-maximum likelihood estimation with endogenous binary indicator variables in the empirical model.gravity model, endogenous preferential trade agreement membership, Poisson pseudo-maximum likelihood estimation with endogenous binary indicator variables

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM

Substance Abuse and Health: A Structural Equation Modeling Approach to Assess Latent Health Effects

Background of the Study. Repeated use of a substance (alcohol or drug) may lead to mental and physical sickness, personality changes, insomnia, nausea, mood swings and other disturbances. The number of people addicted to alcohol and/or drug has been increasing every year at an alarming rate. Although the extent of abuse is not directly measurable (i.e., latent), statistical techniques allow us to describe such a hypothetical construct using available information. Objective. There are many factors potentially associated with substance abuse (e.g., smoking, education, cultural background). Although these variables are readily available in many studies, the cause (e.g., a measure of drug or alcohol abuse) is latent, with the observed variables being its manifestations. A measure of a latent health factor index could also be of particular interest. In this study, we investigate the effects of socio-demographic variables on substance (drug and alcohol) abuse and health in the Canadian population. In particular, the objective is to address the following questions: (a) What would be a reasonable hypothesis to explain causes of substance abusive behavior (i.e., cause and effect relationship)? (b) What model would adequately describe the cause-and-effect relationship between the observed variables and health and substance-related latent variables? (c) What covariates are significantly associated with alcohol and drug abusive environments and health status? Method. To describe the cause-and-effect relationship among substance abuse, health and socio-demographic variables, we consider structural equation modeling. One of the appealing features of this technique is that it provides a concise assessment of complex relationships. The idea is to formulate a hypothesis regarding such relationships based on prior knowledge about the problem at hand, and then evaluate this hypothesis using statistical techniques. The main goal is to develop a model/hypothesis which can adequately describe the interrelationships among these variables. Summary Results. The study is based on a survey conducted by Health Canada. We consider 2012 survey data for Saskatchewan and Manitoba, and then develop models to describe the complex relationships among three hypothetical constructs (drug and alcohol abusive environments and heath) and socio-demographic variables. One of the important findings of the study is that an increase in the severity of drug abusive environment may worsen the health of individuals. Another interesting finding is that smoking has no direct effect on health, but it may lead to an environment (alcohol or drug abusive) that could have negative impact on health. Based on our findings, we conclude that substance abuse may significantly deteriorate health. This research will provide policy-makers as well as the public with an understanding of the extent of impacts of substance abuse and relevant socio-demographic variables on health

Partial Least Squares Methods for Non-Metric Data

Partial Least Squares (PLS) methods embrace a suite of data analysis techniques based on algorithms belonging to PLS family. These algorithms consist in various extensions of the Nonlinear estimation by Iterative PArtial Least Squares (NIPALS) algorithm, which was proposed by Herman Wold as an alternative algorithm for implementing a Principal Component Analysis. The peculiarity of this algorithm is that it calculates principal components by means of an iterative sequence of simple ordinary least squares regressions. This feature allows overcoming computational problems due to missing data or landscape data matrices, i.e. matrix having more columns than rows. PLS methods were born to handle data sets forming metric spaces. This involves that all the variables embedded in the analysis are observed on interval or ratio scales. In this work we evidenced how NIPALS based algorithms, properly adjusted, can work as optimal scaling algorithms. This new feature of PLS, which had been until now totally unexplored, allowed us to device a new suite of PLS methods: the Non-Metric PLS (NM-PLS) methods. NM-PLS methods can be used with different aims: - to analyze at the same time variables observed on different measurement scales; - to investigate non linearity; - to discard the hard assumption of linearity in favor of a milder assumption of monotonicity. In particular, these methods generalize standard NIPALS, PLS Regression and PLS Path Modeling in such a way to handle variables observed on a variety of measurement scales, as well as to cope with non linearity problems. Three new algorithms are been proposed to implement NM-PLS methods: the Non-Metric NIPALS algorithm, the Non-Metric PLS Regression algorithm, and the Non-Metric PLS Path Modeling algorithm. All these algorithms provide at the same time specific PLS model parameters as well as scaling values for variables to be scaled. Scaling values provided by these algorithms are been proved to be optimal, in the sense that they optimize the same criterion of the model in which they are involved. Moreover, they are suitable, since they respect the constraints depending on which among the properties of the original measurement scale we want to preserve

Contribución del comercio electrónico al desempeño de las PyMEs industriales: un modelo estructural

El rol que juegan las Tecnologías de la Información y comunicación (TIC) para lograr un mejor desempeño organizacional aún requiere de un análisis más profundo entre las pequeñas y medianas empresas (PyMEs) de los países en desarrollo. Este estudio pretende ampliar la literatura empírica sobre la relación entre TIC, comercio electrónico y desempeño de las PyMEs en países en desarrollo. Para alcanzar este objetivo, utilizamos una muestra de 87 empresas manufactureras de la ciudad de Bahia Blanca, Argentina correspondiente al año 2015. Mediante la estimación de un Modelo de ecuación estructura, se obtiene que la adopción del comercio electrónico posee una influencia positiva y significativa en las ventas de las PyMEs la cual es potenciada por el nivel de uso de las TIC. Otros factores organizacionales tales como el tamaño de la empresa y los programas públicos explican el desempeño, pero no son predictores significativos de la adopción del comercio electrónico.The role Information and Communication Technologies (ICT) play in achieving a better organizational performance still needs further analysis among small and medium sized enterprises (SME) from developing countries. This study aims to extend the empirical literature on the relationship between ICT, electronic commerce and SME performance in developing countries. To achieve this goal, we employ a sample of 87 manufacturing firms from the city of Bahía Blanca, Argentina in the year 2015. By estimating a structural equation model, we obtain that electronic commerce adoption has a positive and significant influence on SME sales which is reinforced by the level of ICT use. Other organizational factors such as firm size and public programs explain performance, but are not significant predictors of the electronic commerce adoption.Fil: Alderete, Maria Veronica. Universidad Nacional del Sur. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin

The Trade Effects of Endogenous Preferential Trade Agreements

Recent work by Anderson and van Wincoop (2003) establishes an empirical modeling strategy which takes full account of the structural, non-(log-)linear impact of trade barriers on trade in new trade theory models. Structural new trade theory models have never been used to evaluate and quantify the role of endogenous preferential trade agreement (PTA) membership for trade in a way which is consistent with general equilibrium. Apart from this gap, the present paper aims at delivering an empirical model which takes into account both that preferential trade agreement membership is endogenous and that the world matrix of bilateral trade flows contains numerous zero entries. These features are treated in an encompassing way by means of (possibly two-part) Poisson pseudo-maximum likelihood estimation with endogenous binary indicator variables in the empirical model.Gravity model, Endogenous preferential trade agreement membership, Poisson pseudo-maximum likelihood estimation with endogenous binary indicator variables