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\n[https://www.emerald.com/insight/0960-0035.htm](https://www.emerald.com/insight/0960-0035.htm)  \nProgress in partial least squares structural equation modeling use in logistics and supply chain management in the last decade:  \na structured literature review  \nSiqi Wang  \nBusiness School, Guangdong Ocean University, Yangjiang, China  \nJun-Hwa Cheah  \nNorwich Business School, University of East Anglia, Norwich, UK  \nChee Yew Wong  \nLeeds University Business School, University of Leeds, Leeds, UK, and  \nT. Ramayah  \nSchool of Management, Universiti Sains Malaysia, Gelugor, Malaysia  \nAbstract  \nPurpose – This study aims to evaluate the usage of partial least squares structural equation modeling (PLSSEM) in journals related to logistics and supply chain management (LSCM) .  \nDesign/methodology/approach – Based on a structured literature review approach, the authors reviewed 401articles in the field of LSCM applying PLS-SEM published in15 majorjournals between2014and2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.  \nFindings – LSCM researchers sometimes did not clarify the reasons for using PLS-SEM, such as sample size, complex models and non-normal distributions. Additionally, most articles exhibit limited use of measurement models and structural model evaluation techniques, leading to inappropriate use of assessment criteria. Furthermore, progress in the practical implementation of advanced analysis techniques is slow, and there is a need for improved transparency in reporting analysis algorithms.  \nOriginality/value–This study contributes to the field ofLSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.  \nKeywords Partial least squares, Structural equation modeling, PLS-SEM, Logistics and supply chain management, Structured literature review Paper type Research paper  \n1. Introduction  \nPartial least squares structural equation modeling (PLS-SEM; Lohm€oller, 1989; Wold, 1982), also known as partial least squares path modeling (PLSPM), has served as a powerful multivariate data analysis method for analyzing the intricate relationships between observed variables and latent variables [1] . Guenther et al. (2023) show the use of this method has grown exponentially in the past decade, not only in business fields such as marketing (Sarstedt et al., 2020), human resource management (Ringle et al., 2020), operations management (Peng and Lai, 2012), supply chain management (Kaufmann and Gaeckler, 2015), tourism industry (do Valle and Assaker, 2016) and hotel industry (Ali et al., 2018), but also other fields of scientific inquiry like ecology, agricultural science, engineering and medicine (Sarstedt et al., 2022a) . The advancement of intuitive graphical user interface software of PLS-SEM (i.e. SmartPLS, WarpPLS andADANCO), application guideline articles  \nProgress in PLS-SEM use in LSCM  \n673  \nReceived 2 June 2023 Revised 16 August 2023  \n30 August 2023 Accepted 30 August 2023  \nInternational Journal of Physical Distribution & Logistics Management  \nVol. 54 No. 7/8, 2024  \npp. 673-704 © Emerald Publishing Limited  \n0960-0035 DOI 10. 1108/IJPDLM-06-2023-0200  \nIJPDLM (Cheah et al., 2021; 2023a, b; Hair et al., 2019; Sharma et al., 2023) and manual textbooks  \n54,7/8 n(eo.gn.-Hair ettechnicaall.P, 2021LS u;sRamersayah et al., 2018; Wong, 2019) have made the method available for Among others, the articles by Peng and Lai (2012; citation count of 1,743) and Kaufmannand Gaeckler (2015; citation count of 167) may have had a significant impact on logistics and supply chain management (LSCM) disciplines [2] . Their review and evaluation of the PLSSEM applications, plus recommendations and guidelines, are very useful. Since then, thePLS- ","cbCaigpAfoThLVDw","https://ap.wps.com/l/cbCaigpAfoThLVDw","pdf",563211,32,"English","# Introduction\n## Purpose and research questions\n# Review approach and scope\n## Literature review method and dataset\n# Findings\n## Reasons for using PLS-SEM\n## Measurement and structural model evaluation\n## Advanced techniques and reporting practices\n# Methodological background\n## PLS-SEM development and software tools\n# Conclusions and contributions\n## Criteria and steps for using PLS-SEM","[{\"question\":\"What is the main purpose of this study?\",\"answer\":\"To evaluate how partial least squares structural equation modeling (PLS-SEM) is used in journals related to logistics and supply chain management, and to assess application practices and guidelines.\"},{\"question\":\"How was the literature review conducted?\",\"answer\":\"The review used a structured literature review approach, analyzing 401 articles applying PLS-SEM in 15 major LSCM journals published between 2014 and 2022.\"},{\"question\":\"What key problems did the study find in existing LSCM research using PLS-SEM?\",\"answer\":\"Researchers sometimes did not clarify why they used PLS-SEM, often showing limited use of measurement-model and structural-model evaluation techniques, which can lead to inappropriate assessment criteria.\"}]","Progress in partial least squares structural equation modeling use in logistics and supply chain management in the last decade - A structured literature review | PDF",1792540618,81]