Purpose: This study aims to evaluate the usage of partial least squares structural equation modeling (PLS-SEM) in journals related to logistics and supply chain management (LSCM).Design/methodology/approach: Based on a structured literature review approach, the authors reviewed 401 articles in the field of LSCM applying PLS-SEM published in 15 major journals between 2014 and 2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.Findings: 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.Originality/value: This study contributes to the field of LSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.

As artificial intelligence (AI) technology becomes increasingly prevalent in the tourism sector, an in-depth exploration of its opportunities and potential risks for the United Nations Sustainable Development Goals (SDGs) is urgently needed. However, existing research falls short in constructing an integrated knowledge framework that systematically clarifies how AI can effectively advance sustainable tourism, leaving theoretical understanding and practical pathways relatively fragmented. To address this gap, this study employs a systematic literature review following the strict SPAR-4-SLR protocol and integrates a domain-based TCCM (i.e., theories, contexts, characteristics, and methods) analysis framework. A total of 177 core articles from the Scopus and Web of Science databases were rigorously analyzed. This study first examines publication trends, key journals, and the citation impact of AI in tourism. It then systematically synthesizes the theoretical foundations, research contexts, characteristics, and methodologies. Most importantly, this review delves into the antecedents, decision-making processes (including mediating and moderating variables), and outcomes of AI applications in the tourism industry. This study not only delineates a clear direction and agenda for future academic inquiry but also provides theoretical support and practical guidance for policymakers and tourism managers to design and implement AI-driven sustainable tourism strategies.