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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.

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.

This study aims to investigate the impact of FinTech on money laundering within the context of Nigeria. The motivation stems from observations suggesting that FinTech platforms might be used for illicit money transfers, particularly from developed to developing economies. While existing literature predominantly highlights the positive aspects of FinTech, there's a dearth of studies addressing its potential association with money laundering. Current understanding of this relationship relies heavily on anecdotal evidence derived from reported or convicted cases. Thus, the primary goal of this study is to analyze the influence of FinTech on money laundering while also considering the moderating effects of financial regulation and financial literacy as perceived by users. The research delves into regulatory perspectives concerning money laundering and FinTech.

To fulfill the study's objectives, a quantitative research design is used. A survey of 248 FinTech users in Nigeria is conducted using structured questionnaires. Data collected from the questionnaires is analyzed using partial least square structural equation modeling (PLS-SEM).

The quantitative analysis revealed a significant relationship between FinTech and money laundering and that financial regulation moderates the relationship between FinTech and money laundering in Nigeria, but such was not established with respect to financial literacy. The results of the quantitative approach that uses secondary data are consistent with the qualitative approach. FinTech the results indicate the presence of technology induced money laundering in Nigeria. Regulating technology-based anti-money laundering poses serious challenges for developing countries due to the absence of specific laws that mitigate the threats.

The paper focuses on Nigeria as a case study, which may limit the generalizability of the findings to other countries with different FinTech ecosystems, regulatory frameworks and financial literacy levels.

The finding is useful in developing guidelines and regulations by policymakers and strategies by practitioners in relation to FinTech, money laundering, financial regulation and financial literacy. On the basis of the above, the authors recommend regulation at the national and industry level to mitigate the adverse effect of technology on money laundering. Thus, multilateral partnerships can help in tackling tech-induced money laundering through strengthened cooperation.

Money laundering risks: The study highlights that FinTech, while beneficial, also poses significant risks for money laundering activities, especially in developing countries like Nigeria. Regulatory Importance: It emphasizes the critical role of financial regulations in mitigating the risks associated with FinTech and money laundering. Financial Literacy: The paper suggests that financial literacy does not significantly moderate the relationship between FinTech and money laundering, indicating the need for stronger regulatory measures rather than relying solely on financial literacy. Policy Formulation: The findings are crucial for policymakers to formulate strategies that balance the benefits of FinTech with the need to prevent money laundering and ensure financial system integrity.

This research presents a novel approach to methodology, specifically focusing on the qualitative research design, addressing population, sampling techniques and data collection methods. It emphasizes techniques aimed at ensuring measurement quality and achieving research objectives. Data collection used survey questionnaires, while analysis involved both statistical package for social science (SPSS) and PLS-SEM. SPSS facilitated descriptive and preliminary analyses, while PLS-SEM confirmed measurement quality and tested hypotheses. Ethical considerations were paramount throughout the research process, underscoring the commitment to maintaining originality in research endeavors.

This paper aims to help understand how adopting risk allocation criteria impacts the delivery of public–private partnership (PPP) mass housing in Nigeria with the view of promoting the adoption of PPP housing scheme in Nigeria.

The research design adopts the census sampling approach by using well-structured questionnaires distributed to stakeholders involved in PPP-procured mass housing projects, i.e. consultants, in-house professionals, contractors and the organized private sector, registered with PPP departments in the Federal Capital Territory Development Authority, Abuja, Nigeria. Sixty-three risk factors, nine risk allocation criteria and nine project delivery indices were submitted for the respondents to rank on a Likert scale of 7. Two hypotheses were formulated to test whether the risk allocation criteria impacted PPP mass housing delivery or otherwise. The study adopts partial least square-structural equation modeling to model the effect of risk on risk allocation criteria on project delivery indices and risk severity.

The finding shows that project risk allocation criteria have less effect on project delivery indices than on risk severity. The study concludes that risk allocation principles do not directly affect the delivery of PPP-procured mass housing projects. This is evident by the path coefficient of 0.724 values, which is not statistically significant at a 5% alpha protection value. The study concludes that allocating critical risk factors influences the performance of PPP-procured mass housing projects, as the path coefficient of 0.360 is also not significantly far from 0 and at a 5% alpha protection value.

The study is one of the recent studies conducted in PPP-procured mass housing projects in Nigeria owing to the novelty of procurement option in the sector. It highlights the risk factors that can jeopardize the PPP-procured mass housing project objectives. The study is of immense value to PPP actors in the sector by providing the necessary information required to formulate risk response methods to minimize the impact of the risk factors in PPP mass housing projects.

The present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.

Partial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.

A positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.

The findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.

The study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

The primary objective of this study is to examine the effects of integrating human-centric technology on the overall effectiveness of sustainable building practises. This study intends to address the knowledge gap regarding the influence of human-centered factors on the adoption of technology in the construction industry. Additionally, it wants to establish a strong correlation between the integration of technology and the promotion of sustainability.

This research utilizes Partial Least Square Equation Modeling (SEM) as a comprehensive approach to analyze the intricate interrelationships within the building sector. This study examines a range of aspects, including “Change Management,” “Evaluation and Optimization,” “Integration and Coordination,” “Long-Term Planning,” “Safety and Compliance,” and “Training and User Engagement,” in order to evaluate their impact on the adoption of technology and the achievement of sustainable building outcomes.

The study reveals a robust and affirmative correlation between the use of human-centric technology and the attainment of success in the realm of sustainable building. The text elucidates the primary factors that influence the adoption of technology and underscores the need of adopting a comprehensive strategy that encompasses change management, adherence to safety standards, and optimization of resources.

This study contributes novelty by placing emphasis on the human-centric dimensions of technology adoption within the context of sustainable building. The results of this research provide a valuable contribution to the ongoing development of sustainable building practises and the adoption of new technologies in this sector.

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.

The purpose of this study is to investigate the determinants that likely influence the intention of using digital payment systems such as the Jordan Mobile Payment (JoMoPay) system among public sector employees in Jordan. To achieve the purpose of the current study, the authors developed a new research model based on the extended unified theory of acceptance and use of technology (UTAUT2), with one of Hofstede’s cross-cultural dimension scales [uncertainty avoidance (UA)] to provide a further understanding of the JoMoPay system acceptance in Jordan.

A partial least squares-structural equation modeling approach was used to analyze the data collected by self-administration from the 270 employees working in the Jordanian public sector located in Amman city, the capital city of Jordan. Because most main public sectors are located in Amman and because of the cost and time considerations, the current study applied a non-probability sampling with the purposive sampling technique.

The empirical results reveal that the evident drivers of behavioral intention to use the JoMoPay system are significantly and positively influenced by social influence, UA, performance expectancy, price value and effort expectancy; therefore, the H1, H2, H3, H5 and H6 were supported. Conversely, the results show no significant relationship between facilitating conditions and the behavioral intention to use the JoMoPay system, and hence, the related hypothesis (H4) was not supported.

The results of this study provide beneficial information to the Central Bank of Jordan and other service providers in Jordan about employee intentions to adopt JoMoPay system and increase decision-makers’ knowledge on factors that have an important impact in UTAUT2 model.

The results of this study enable policymakers to understand the important factors that will enhance savings, investments and living standards, create job opportunities as well as reduce the poverty, the paper money printing cost, risks of money transportation cost and the risk of human errors.

The outcomes obtained will help both practitioners and researchers elucidate and understand the situation of digital payment systems acceptance among Jordanian public sector employees, as well as help them formulate plans to expedite the adoption process of digital payment systems in the case of UA.

The urgency to address climate change and its devastating consequences has never been more pressing. As societies become increasingly aware of the detrimental impact of traditional housing on the planet, there is a growing demand for eco-friendly housing solutions that prioritize energy efficiency, resource conservation and reduced carbon emissions. Therefore, this study aims to investigate the factors that influence customers’ priority toward eco-friendly house purchasing intention.

This study collected 386 data using a quantitative research strategy and purposive sampling method. This study uses a hybrid analysis technique using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approaches to identify the influencing factors.

The PLS-SEM analysis found that attitude toward the eco-friendly house, subjective norms, performance expectancy, environmental knowledge and environmental sensitivity have a positive influence on eco-friendly house purchasing intention. However, perceived behavioral control and willingness to pay were found to have insignificant effect on customers’ intention to purchase eco-friendly houses. The fsQCA results further revealed complex causal relationships between the influencing factors.

This research will not only contribute to academic knowledge but also provide practical guidance to real estate developers, policymakers and individuals looking to make environmentally responsible choices. By understanding the factors that influence consumers’ intentions to purchase eco-friendly houses, we can pave the way for a more sustainable and resilient future.

This study has used a hybrid analysis technique, combining PLS-SEM and fsQCA, to enhance the predictive accuracy of eco-friendly house purchase intentions among individuals residing in densely populated and highly polluted developing countries, such as Bangladesh.

This paper studies the critical factors from the perspectives of technological quality, personal compatibility and organisational commitment using the technological adoption framework (TAF). The proposed TAF studies the critical factors that influence the intention to use building information modelling (BIM) taking into consideration of the “Perceived Ease of Use (PEU)” and “Perceive Usefulness (PU).”

The proposed study is a quantitative research study using the TAF model and the statistical analysis using “Partial Least Squares-Structural Equation Modelling (PLS-SEM).” The questionnaires are developed based on the literature review study and disseminated to the stakeholders in the Malaysian construction industry, including consultants, contractors, and clients. The data collected are analysed using PLS-SEM to identify the correlation between the critical factors influencing BIM adoption and the moderation influence of the PEU and PU towards the “Intention to Use (IU)” BIM.

The data collected from 185 construction industry stakeholders in Malaysia was utilised to develop the structural equation model. The measurement model was analysed in terms of composite reliability, discriminant validity, and collinearity issues. Subsequently, the SEM is analysed, and the findings on the hypothesis on the correlation between the critical factors and the intention to use BIM are examined. The study also examines the mediation effects of the PEU and PU towards the BIM adoption in the Malaysian construction industry.

This research conceptual framework, TAF, is derived from the integration of the existing underpinning theories of the technological adoption model and the technology–organisation–environment framework. This new TAF can be used for the study of new technology adoption. This cross-sectional research study is in line with the “Construction 4.0 Strategic Plan” in Malaysia to establish the current BIM adoption scenario and formulate the framework to promote incentives to promote BIM adoption.