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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).

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.

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.

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.

Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also increasingly using PLS-SEM, this growing interest calls for guidance.

Based on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals.

The use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method.

This research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals.

The results of this analysis guide researchers who use the PLS-SEM method for their quality management studies.

This is the first article to systematically review the use of PLS-SEM in the quality management discipline.

The partial least squares structural equation modeling (PLS-SEM) approach for construction management (CM) scholars has become the preferred approach for its capability of assessing the complex relationship and relaxed normality and sample size assumptions. This paper systematically maps the structure of knowledge about PLS-SEM in CM using bibliometric analysis. Also, the study employs meta-analysis to explore how data and model characteristics, model evaluation and advanced modeling techniques have been utilized in the CM domain.

This study integrated two methods: bibliometric analysis on a sample of 211 articles identified using the PRISMA framework and meta-analysis on 163 articles identified based on the availability of full-length articles and relevant information.

The results revealed the leading knowledge formation entities (countries, institutions, authors, sources and documents). Also, the study employs full content analysis to identify six research themes, and meta-analysis is used to explore the use of PLS-SEM based on the following criteria: (1) reasons for using PLS-SEM in CM, (2) data characteristics, (3) model characteristics and evaluation and (4) use of advanced modeling and analysis techniques. Further, the study uses regression analysis and identifies “advanced modeling and analysis techniques” as the critical feature responsible for the publication in a journal with high scientific prestige. Finally, the study presented the comprehensive guidelines to be used by construction management scholars who wish to use PLS-SEM in their research work.

To the author’s knowledge, it is the first study of this kind to use PLS-SEM in CM research. This study provides an extensive analysis of the Scopus database and an in-depth review of the data characteristics, model characteristics and use of advanced modeling techniques in CM research.

Given the positive organizational principles associated with total quality management (TQM) – customer focus, continuous improvement, and process management – one would assume TQM's application is universally beneficial across businesses. Generally, research supports that notion. However, given resource limitations and shallow management teams in small businesses, there are multiple challenges in implementing TQM in small and medium-sized enterprises (SMEs). Therefore, small business leaders should benefit from knowledge linking other management practices to TQM’s positive effect on small firm performance, which enhances these leaders' return on TQM investment.

The authors apply partial least squares structural equation modeling (PLS-SEM) to explore TQM’s effect on small business performance and how other management practices enhance that relationship. Specifically, the authors explore how a comprehensive strategic approach (CSA) – a higher-order construct consisting of strategic planning, goal setting, and financial ratio analysis – moderates the relationship between TQM and small business performance. Given the complexity of the authors' model, the application of higher-order constructs, and the exploratory nature of this work, PLS-SEM is well suited for this study.

Consistent with prior research, the authors found that TQM (also a higher-order construct, consisting of seven lower-order constructs) positively impacts small firm performance. In addition, the authors found that CSA positively moderates the relationship between TQM and financial performance.

TQM’s effect on small business performance is enhanced when leaders implement a CSA. In other words, when small business leaders strategically plan, set goals, and analyze financial ratios, TQM's positive effect on firm performance is enhanced. This finding provides business leaders insights for how to maximize the TQM investment return.

The paper provides a step-by-step guide in the guise of a roadmap for service improvement initiatives using importance performance map analysis (IPMA).

To empirically illustrate how IPMA can be applied to any service industry, three sectors are considered; sports and fitness (study A), hospitality (study B) and higher education (study C). Following the proper selection of measuring instruments and their evaluation using structural equation modeling-partial least squares (Smart-PLS), IPMA is applied to identify those attributes having strong total effects (high importance) over the targeted construct (satisfaction) but which also have low average latent variable scores (low performance).

For sports and fitness (study A), the physical aspects and programme quality require managerial attention. For the hospitability sector (study B), it is service commitment, interaction quality and internal sense of happiness. Whereas for higher education (study C), it is administrative quality as well as the element of transformative quality, namely the university’s role in adding to its students’ emotional stability, which needs the attention of the top management.

This study provides researchers and practitioners with a roadmap for applying PLS-SEM and IPMA for continuous service quality improvement. The roadmap extends upon Ringle and Sarstedt’s (2016) work. It highlights critical decisions that need to be considered in the pre-analytical stages of the IPMA application, i.e. at the research design phase in selecting the most appropriate service quality measurement model specifications. It not only contributes to the existing body of knowledge by providing empirical evidence to advance theory development in the quality management field but also has implications for the practitioners in any service sector on where to focus their attention for an effective service improvement.

This study aims to recognize the significant barriers of small and medium-sized enterprises (SMEs) in Bangladesh, hindering the adoption of total quality management (TQM). Additionally, this research intends to explore the interrelations among these barriers to develop essential managerial insights for promoting TQM implementation in SMEs.

The interpretive structural modeling (ISM) approach and Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) a cross-impact matrix multiplication applied to classification show the relationship among the barriers and classification of the barriers to TQM implementation respectively, and partial least squares structural equation modeling (PLS-SEM) is applied for ISM model validation.

This study examined previous literature and conducted interviews with professionals to identify 17 barriers. The study then develops and investigates a model that outlines the relationships and priorities among these barriers and categorizes them based on their impact and interdependence. This analysis can assist SMEs in implementing TQM during their operations successfully.

This research emphasizes the crucial obstacles that greatly affect other barriers and require immediate attention. Furthermore, this study provides valuable information for SMEs to effectively prioritize their resources and efforts to overcome these obstacles.

This study delves into the primary obstacles impeding the integration of TQM in SMEs through a novel approach. Additionally, this study constructs a verified contextual framework that depicts the hierarchies and interconnections among these barriers.

Researchers explore the impact of an intelligent assistant in virtual teams by applying the theoretical lens of a transactive memory system (TMS) to understand the relationships between trust in a specific technology, knowledge sharing and knowledge application.

An online survey was administered to a Qualtrics-curated panel of individual, US-based virtual team members utilizing an intelligent assistant with team collaboration software. Partial least squares structural equation modeling (PLS-SEM) was utilized to examine the hypothesized relationships of interest.

Results suggest that knowledge application is strongly influenced by trust in a specific technology and knowledge sharing. Additionally, a transactive memory system positively increases trust in the intelligent assistant, and similarly, trust in the intelligent assistant has a significant positive relationship with knowledge sharing.

The research model contributes to our understanding of the impact of an intelligent assistant in virtual teams. Although the transactive memory system construct has been explored in various contexts and models, few have explored the impact of an intelligent assistant and trust in a specific technology.

The purpose of this paper is to investigate the mediating role of satisfaction (SAT) in relation to mobile banking service quality (MB-SQ) and continuance intention (CI) among Nepali mobile banking users.

The paper adopted a quantitative approach and cross-sectional survey research design. Data were collected with structured questionnaires from 326 mobile banking users. A partial least squares structural equation modeling (PLS-SEM) and artificial neuro network (ANN) approach were applied to examine hypotheses.

Results confirm a significant positive influence of MB-SQ on SAT and CI of mobile banking adoption. Moreover, MB-SQ partially mediates the relationship between SAT and CI of mobile banking adoption.

Based on the findings of this research, theoretically, this paper attempted to investigate the mediating role of MB-SQ in the CI of mobile banking, and managerially, mobile banking service providers could have insights on designing mobile banking service marketing strategy.

This paper is among the earliest studies to investigate the role of MB-SQ as a higher-order reflective-reflective construct on CI. Moreover, the endogeneity issue has been tested, and ANN has been applied to investigate the predictive relevance of SAT and MB-SQ on CI of mobile banking users. Furthermore, the authors have delved into the ongoing discourse surrounding Generation Y and Generation Z, exploring their implications on CI within the realm of mobile service quality. It provides a critical juncture for understanding continuance intention in the mobile service quality context.

The study aims to identify the extent to which industry 4.0 (IR4.0) adoption impacts the sustainable manufacturing (SM) performance of the manufacturing industry, focusing on the comparative analysis between developed and developing economies amid coronavirus disease 2019 (COVID-19) pandemic.

The study proposes a conceptual model formed on seminal theories and literature using the cross-sectional design. For data collection, a purposive sampling method is used where 154 Malaysian (developing) and Australian (developed) manufacturing firms' data were collected. Partial least square-based structural equation modeling is employed to test the hypothesis and proposed research model.

This study finds that adoption of IR4.0 technologies does not directly influence the sustainability performance of the manufacturing industry, but rather the trajectories of SM (efficiency, flexibility, automation and big data and granularity) fully mediate the relationship between IR4.0 adoption and sustainability manufacturing performance. The comparative analysis between Australia and Malaysia shows no significant difference in the relationships or the framework; hence, the differences between developed and developing countries are not significant in this mechanism.

The study contributes to the insights of the managers regarding COVID-19 and the implementation of IR4.0 in the SM domain. The policymakers would further get better insights since the study pays attention to sustainable development goal, industry, innovation, infrastructure and responsible production.

The mental healthcare is experiencing an ever-growing surge in understanding the consumer (e.g., patient) engagement paradox, aiming to vouch for the quality of care. Despite this surge, scant attention has been given in academia to conceptualize and empirically investigate this particular aspect. Thus, drawing on the Stimulus-Organism-Response (S-O-R) paradigm, the study explores how patients engage with healthcare service providers and how they perceive the quality of the healthcare services.

Data were collected from 279 respondents, and the derived conceptual model was tested by using Smart PLS 3.2.7 and PROCESS. To complement the findings of partial least squares (PLS)-based structural equation modeling (SEM), the present study also applied fuzzy set qualitative comparative analysis (fsQCA) to identify the necessary and sufficient conditions to explore substitute conjunctive paths that emerge.

Findings show that patients’ perceived intimacy (PI), cohesion and privacy enhance the quality of mental healthcare service providers. The results also suggest that patients’ PI, cohesion and privacy have indirect effects on the perceived quality of care (PQC) by the service providers through consumer engagement. The fsQCA results derive that the relationship among conditions leading to patients’ perception of the quality of care in regard to mental healthcare service providers is complex and is best reflected as multiple and conjectural causation configurations.

The findings from this research contribute to the advancement of studies on patients’ experiences by empirically examining the unique dynamics of interaction between consumers (patients) and mental healthcare service providers, thereby enriching both the literature on social interactions and the understanding of the consumer–provider relationship.

The results of this study provide practical implications for mental healthcare service providers on how to combine the study variables to enhance the quality of care and satisfy more patients.

A significant research gap has ascertained the inter-relationship between PI, cohesion, privacy, engagement and PQC from the perspective of mental healthcare service providers. This research is one of the primary studies from a managerial and methodological standpoint. The study contributes by combining symmetric and asymmetric statistical tools in service marketing and healthcare research. Furthermore, the application of fsQCA helps to understand the interactions that might not be immediately obvious through traditional symmetric methods.