Vincenzo Esposito Vinzi

PLS path modeling

A presentation of the Partial Least Squares approach to Structural Equation Modeling (or PLS Path Modeling) is given together with a discussion of its extensions. This approach is compared with the estimation of Structural Equation Modeling by means of maximum likelihood (SEM-ML). Notwithstanding, this approach still shows some weaknesses. In this respect, some new improvements are proposed. Furthermore, PLS path modeling can be used for analyzing multiple tables so as to be related to more classical data analysis methods used in this field. Finally, a complete treatment of a real example is shown through the available software.

Handbook of Partial Least Squares

The new volume of Computational Statistics represents a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. The handbook covers the broad area of PLS methods -from regression to structural equation modeling applications, software and interpretation of results. It features papers on the use and the analysis of latent variables and indicators by means of the PLS path modeling approach from the design of the causal network to model assessment and improvement. Within the PLS framework, the handbook also addresses advanced topics such as the analysis of multi-block, multi-group and multi-structured data, the use of categorical indicators, the study of interaction effects, the integration of classification issues, the validation aspects and the comparison between the PLS approach and covariance based structural equation modeling. Most chapters comprise a thorough discussion of applications to marketing and related areas, some tutorials focus on key aspects of PLS analysis with a didactic approach. This handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.

PLS generalised linear regression

PLS univariate regression is a model linking a dependent variable y to a set X = {x1 ;:::; xp} of (numerical or categorical) explanatory variables. It can be obtained as a series of simple and multiple regressions. By taking advantage from the statistical tests associated with linear regression, it is feasible to select the signi3cant explanatory variables to include in PLS regression and to choose the number of PLS components to retain. The principle of the presented algorithm may be similarly used in order to yield an extension of PLS regression to PLS generalised linear regression. The modi3cations to classical PLS regression, the case of PLS logistic regression and the application of PLS generalised linear regression to survival data are studied in detail. Some examples show the use of the proposed methods in real practice. As a matter of fact, classical PLS univariate regression is the result of an iterated use of ordinary least squares (OLS) where PLS stands for partial least squares. PLS generalised linear regression retains the rationale of PLS while the criterion optimised at each step is based on maximum likelihood. Nevertheless, the acronym PLS is kept as a reference to a general methodology for relating a response variable to a set of predictors. The approach proposed for PLS generalised linear regression is simple and easy to implement. Moreover, it can be easily generalised to any model that is linear at the level of the explanatory variables. c

Is environmental management an economically sustainable business

This paper investigates whether environmental management is an economically sustainable business. While firms invest in green production and green supply chain activities with the primary purpose of reducing their environmental impact, the reciprocal relationships with economic performance need to be clarified. Would firms and suppliers adjust their environmental strategies if the higher economic value that environmental management generates is reinvested in greening actions? We found out that environmental management positively influences economic performance as second order (long term) target, to be reached conditioned by higher environmental performance; in addition, firms can increase their performance if they reinvest the higher economic value gained through environmental management in green practices: While investing in environmental management programs is a short term strategy, economic rewards can be obtained only with some delays. Consequently, environmental management is an economically sustainable business only for patient firms. In the evaluation of these reciprocal relationships, we discovered that green supply chain initiatives are more effective and more economically sustainable than internal actions.

New Perspectives in Partial Least Squares and Related Methods

New Perspectives in Partial Least Squares and Related Methods shares original, peer-reviewed research from presentations during the 2012 partial least squares methods meeting (PLS 2012). This was the 7th meeting in the series of PLS conferences and the first to take place in the USA. PLS is an abbreviation for Partial Least Squares and is also sometimes expanded as projection to latent structures. This is an approach for modeling relations between data matrices of different types of variables measured on the same set of objects. The twenty-two papers in this volume, which include three invited contributions from our keynote speakers, provide a comprehensive overview of the current state of the most advanced research related to PLS and related methods. Prominent scientists from around the world took part in PLS 2012 and their contributions covered the multiple dimensions of the partial least squares-based methods. These exciting theoretical developments ranged from partial least squares regression and correlation, component based path modeling to regularized regression and subspace visualization. In following the tradition of the six previous PLS meetings, these contributions also included a large variety of PLS approaches such as PLS metamodels, variable selection, sparse PLS regression, distance based PLS, significance vs. reliability, and non-linear PLS. Finally, these contributions applied PLS methods to data originating from the traditional econometric/economic data to genomics data, brain images, information systems, epidemiology, and chemical spectroscopy. Such a broad and comprehensive volume will also encourage new uses of PLS models in work by researchers and students in many fields.

Editorial: Perspectives on Partial Least Squares

This Handbook on Partial Least Squares (PLS) represents a comprehensive presentation of the current, original and most advanced research in the domain of PLS methods with specific reference to their use in Marketing-related areas and with a discussion of the forthcoming and most challenging directions of research and perspectives. The Handbook covers the broad area of PLS Methods from Regression to Structural Equation Modeling, from methods to applications, from software to interpretation of results. This work features papers on the use and the analysis of latent variables and indicators by means of the PLS Path Modeling approach from the design of the causal network to the model assessment and improvement.Moreover, within the PLS framework, the Handbook addresses, among others, special and advanced topics such as the analysis of multi-block, multi-group and multistructured data, the use of categorical indicators, the study of interaction effects, the integration of classification issues, the validation aspects and the comparison between the PLS approach and the covariance-based Structural Equation Modeling. Most chapters comprise a thorough discussion of applications to problems from Marketing and related areas. Furthermore, a few tutorials focus on some key aspects of PLS analysis with a didactic approach. This Handbook serves as both an introduction for those without prior knowledge of PLS but also as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.

An Empirical Operationalization of Countries’ Destination Competitiveness Using Partial Least Squares Modeling

Growth in tourism has resulted in escalating competition among destinations. Understanding destination competitiveness and its determinant factors is thus critical to tourism researchers and policy makers. Using partial least squares path modeling (PLSPM) on a cross-sectional sample of 154 countries, this study examines relationships among destination competitiveness and its predictors, including the economy, natural environment, and infrastructure. Results indicate that the economy has a positive, indirect impact on tourism competitiveness mediated through the infrastructure and the environment; moreover, infrastructure and environment have a direct, positive impact on tourism competitiveness. PLSPM was also used to compute composite scores for overall destination competitiveness, thus assigning rankings to the 154 countries assessed. This study contributes to extant theories on destination competitiveness, presenting important implications for policymakers on how to strengthen destination competitiveness, and providing an empirically based tool to help benchmark a country’s competitiveness in relation to other destinations.

Bootstrap-based Q̂kh2 for the selection of components and variables in PLS regression

Abstract The aim of this paper is to suggest a bootstrap-based method for choosing the number of components in Partial Least Squares Regression (PLSR). Cross-validated Q h 2 statistic is used, for which is intended to derive a bootstrap distribution and to perform a hypothesis testing. Monte Carlo approximation is adopted. Applications on both artificial and real data are presented.

Two-step PLS regression for L-structured data: an application in the cosmetic industry

The present paper proposes a PLS-based methodology for the study of so called “L” data-structures, where external information on both the rows and the columns of a dependent variable matrix is available. L-structures are frequently encountered in consumer preference analysis. In this domain it may be desirable to study the influence of both product and consumer descriptors on consumer preferences. The proposed methodology has been applied on data from the cosmetic industry. The preference scores from 142 consumers on 9 products were explained with respect to the products’ physico-chemical and sensory descriptors, and the consumers’ socio-demographic and behavioural characteristics.

PLS Typological Regression: Algorithmic, Classification and Validation Issues

Classification, within a PLS regression framework, is classically meant in the sense of the SIMCA methodology, i.e. as the assignment of statistical units to a-priori defined classes. As a matter of fact, PLS components are built with the double objective of describing the set of explanatory variables while predicting the set of response variables. Taking into account this objective, a classification algorithm is developed that allows to build typologies of statistical units whose different local PLS models have an intrinsic explanatory power higher than the initial global PLS model. The typology induced by the algorithm may undergo a non parametric validation procedure based on bootstrap. Finally, the definition of a compromise model is investigated.