Advanced Applications in Management Using Partial Least Squares Structural Equation Modelling (PLS-SEM)

Introduction

Partial least squares structural equation modeling (PLS-SEM) has recently gained considerable attention in a variety of disciplines, including information systems management (Cepeda Carrión et al., 2024; Sabol et al., 2023; Sharma et al., 2024), international management (Ritcher at al., 2023), logistics management (Wang et al., 2023), mixed methods such as qualitative and. quantitative (Kurtaliqui et al., 2024), and mixing NCA and IPMA (Hauff et al., 2024). PLS is a composite-based approach to SEM, which aims at maximizing the explained variance of dependent constructs in the path model (e.g., Hair et al., 2019). Compared to other SEM techniques, PLS allows researchers to simultaneously estimate complex interrelationships, involving a variety of constructs and indicators with their direct, indirect, or moderating relationships that would otherwise not be easy to disentangle and examine (e.g., Nitzl, Cepeda Carrión & Roldán, 2016; Richter, Cepeda Carrión, Roldán, & Ringle, 2016). 

Recent management research is increasingly concerned with fully understanding and explaining the roles of intervening and contingent variables, as well as relationships among variables (Sarstedt et al., 2024). For instance, there is a growing interest in verifying and measuring the relationships between Human Capital, Labour Conditions, Market and Wages (Sanchez-Cubo, Mondejar-Jimenez and Garcia-Pozo, 2023). Similarly, a common factor model conceptualization acknowledges that effects may not be constant but may decrease or increase. Therefore, researchers need to go beyond linear modeling and consider nonlinear modeling.

The increasing complexity of modeling requirements highlights the critical importance of advanced analytical methods. In the context of Partial Least Squares-Structural Equation Modeling (PLS-SEM), some notable advancements include confirmatory tetrad analysis, which helps evaluate the measurement mode empirically. Additionally, new methods have been developed to test discriminant validity, such as prediction-oriented segmentation analysis, which is useful in identifying and treating unobserved heterogeneity. Finally, invariance testing can be performed using the invariance measurement used in the composite model approach (e.g., Liengaard, 2024; Sarstedt and Moisescu, 2024).

List of topic areas

• Applications and advancements of the original PLS-SEM algorithm (e.g., extended PLS, consistent PLS) 
• Analysis of complex model relationships involving nonlinear effects, multiple mediation, and/or moderated mediation 
• Invariance assessment and multi-group analysis 
• Applications and advancements of latent class procedures (e.g., FIMIX-PLS, PLSGas, PLS-POS, PLS-IRRS) 
• Common method bias assessment 
• Endogeneity assessment and treatment 
• Longitudinal data analysis 
• Model comparisons 
• Use of PLS-SEM in experimental research 
• Application and development of novel prediction metrics 
• Application of PLS-SEM with archival (secondary) data 
• Measurement issues, including confirmatory composite analysis (CCA) 
• Prediction using PLS-SEM 
• Importance-performance map analysis in PLS-SEM

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Key deadlines

Opening date for manuscripts submissions: 21st December, 2024

Closing date for manuscripts submission: 21st March, 2025