Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to ...two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging.
The advent of blockchain technologies is transmuting the way conventional supply chains are being managed. Due to the complexity of dealing with many actors involved in the supply chain networks, ...contemporary supply chains have limited visibility, transparency, and accountability. Likewise, supply chains are increasingly facing the challenge of integration and sustainability. In this vein, blockchain technologies can play a groundbreaking role in improving the traceability, accountability, and sustainability of complex supply chain networks. The present study examines the instrumentality of blockchain technologies in enabling supply chain mapping and supply chain integration. The study also tests the direct impact of blockchain technologies on supply chain sustainability. Data are collected from 132 Malaysian Electrical and Electronics firms using a close‐ended questionnaire. The study employs Partial Least Squares‐Structural Equation Modelling (PLS‐SEM) and Partial Least Squares‐Multi Group Analysis (PLS‐MGA) for analyzing the hypothesized relationships. The results show that blockchain technologies do not have a direct impact on supply chain sustainability. Nevertheless, this finding reveals a robust indirect effect of BT, through SC integration and SC mapping, on the SC sustainability. The study's findings imply that the notion of the sustainable supply chain can be significantly attained by mapping upstream, midstream, and downstream supply chains. The well‐mapped supply chain can further improve supply chain sustainability. The findings of the study also suggest the adoption of blockchain technologies as a broad‐based strategy to attain multi‐tier goals, for example, supply chain mapping, sustainability, and integration.
Projection to latent structures or partial least squares (PLS) produces output-supervised decomposition on input
X, while principal component analysis (PCA) produces unsupervised decomposition of ...input
X. In this paper, the effect of output
Y on the
X-space decomposition in PLS is analyzed and geometric properties of the PLS structure are revealed. Several PLS algorithms are compared in a geometric way for the purpose of process monitoring. A numerical example and a case study are given to illustrate the analysis results.
Nutrition is one of the important factors that play a major role in the growth and development of children so that they can develop optimally. Child malnutrition, such as stunting, underweight, and ...wasting, is a significant problem in Indonesia. The World Health Organization (WHO) determined that the nutritional status of children under five in Indonesia is in the chronic category, one of which is in Southeast Sulawesi Province. This study examines and analyzes the factors that influence the nutritional status of children under five in Southeast Sulawesi using the Partial Least Square Structural Equation Modeling (SEM-PLS) method and then segments the nutritional status of children under five using Response Based Unit Segmentation Modeling in Partial Least Square (REBUS-PLS) and Finite Mixture Partial Least Square (FIMIX-PLS). The number of observations in this study was 216 sub-districts. From the results of the SEM-PLS analysis conducted, it was concluded that the 10 indicators used were valid and significant in describing the latent variables, and the practice factor variable had an effect on the food factor variable, the food factor variable had an effect on the service factor variable, and the service factor variable had an effect on the under-five nutritional status variable. The REBUS-PLS analysis results in two segments, with one segment of 75 observations and the other segment of 141 observations. The same conclusion is obtained as in the SEM-PLS analysis, but the results of the analysis with REBUS-PLS have a greater value than the results of the SEM-PLS analysis. Key points of the article:
1.Comparing REBUS-PLS and FIMIX-PLS methods for overcoming the case of heterogeneity in dat.2.Combining the SEM-PLS method with the REBUS and FIMIX methods in discussing the factors that influence the nutritional status of children under five in Southeast Sulawesi.
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Partial least squares structural equation modeling (PLS‐SEM) is an essential element of marketing researchers' methodological toolbox. During the last decade, the PLS‐SEM field has undergone massive ...developments, raising the question of whether the method's users are following the most recent best practice guidelines. Extending prior research in the field, this paper presents the results of a new analysis of PLS‐SEM use in marketing research, focusing on articles published between 2011 and 2020 in the top 30 marketing journals. While researchers were more aware of the when's and how's of PLS‐SEM use during the period studied, we find that there continues to be some delay in the adoption of model evaluation's best practices. Based on our review results, we provide recommendations for future PLS‐SEM use, offer guidelines for the method's application, and identify areas of further research interest.
We provide a package called plssem that fits partial least squares structural equation models, which is often considered an alternative to the commonly known covariance-based structural equation ...modeling. plssem is developed in line with the algorithm provided by Wold (1975) and Lohmöller (1989). To demonstrate its features, we present an empirical application on the relationship between perception of self-attractiveness and two specific types of motivations for working out using a real-life data set. In the paper we also show that, in line with other software performing structural equation modeling, plssem can be used for putting in relation single-item observed variables too and not only for latent variable modeling.
Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. ...PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table. When the goal is to find the shared information between two tables, the approach is equivalent to a correlation problem and the technique is then called partial least square correlation (PLSC) (also sometimes called PLS-SVD). In this case there are two sets of latent variables (one set per table), and these latent variables are required to have maximal covariance. When the goal is to predict one data table the other one, the technique is then called partial least square regression. In this case there is one set of latent variables (derived from the predictor table) and these latent variables are required to give the best possible prediction. In this paper we present and illustrate PLSC and PLSR and show how these descriptive multivariate analysis techniques can be extended to deal with inferential questions by using cross-validation techniques such as the bootstrap and permutation tests.
Partial least squares structural equation modeling (PLS-SEM) is one of the most widely used methods of multivariate data analysis. Although previous research has discussed different aspects of ...PLS-SEM, little has been done to explain the attributes of the various PLS-SEM statistical applications. The objective of this editorial is to discuss the multiple PLS-SEM applications, including SmartPLS, WarpPLS, and ADANCO. It is written based on information received from the developers via emails as well as our ongoing understanding and experience of using these applications. We hope this editorial will serve as a manual for users to understand the unique characteristics of each PLS-SEM application and make informed decisions on the most appropriate application for their research.
With the increasing prominence of partial least squares structural equation modeling (PLS-SEM) in business research, the use of latent class analyses for identifying and treating unobserved ...heterogeneity has also gained momentum. Researchers have introduced various latent class approaches in a PLS-SEM context, of which finite mixture PLS (FIMIX-PLS) plays a central role due to its ability to identify heterogeneity and indicate a suitable number of segments to extract from the data. However, applying FIMIX-PLS requires researchers to make several choices that, if incorrect, could lead to wrong results and false conclusions. Addressing this concern, we present the results of a systematic review of FIMIX-PLS applications published in major business research journals. Our review provides an overview of the interdependencies between researchers’ choices and identifies potential problem areas. Based on our results, we offer concrete guidance on how to prevent common pitfalls when using FIMIX-PLS, and identify future research areas.
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