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This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

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.

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 paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose.

This study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group.

The findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design.

Prior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity.

The research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology.

Cryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly.

Given the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.

This study explores the profound influence of social and cultural factors on the financial conduct of indigenous tribes and groups. Anchored in Vygotsky's sociocultural theory, the analysis delves into the intricate interplay between cultural elements, such as bricolage, and the immediate availability of financial resources, illuminating their collective impact on the tribes' financial behaviour. Typically residing in proximity, these communities exhibit homogeneity by forming groups exclusive to their clans, lacking access to conventional financial services and tangible assets that dissuade banks from extending loans. Crucially, the social capital embedded within the group dynamics, often referred to as the peer mechanism, emerges as a pivotal conduit for members to secure capital and bank credit. The synergy of bricolage, representing the adept use of available social capital, facilitates access to finance and credit. Despite the existence of social capital and financial literacy programmes, a stark reality persists – a significant proportion of indigenous people remain financially excluded. This chapter endeavours to scrutinise the ramifications of these factors on tribal financial behaviour, employing the Partial Least Squares Structural Equation Modelling (PLS-SEM) method. Proposing a paradigm shift in financial attitudes, the research underscores the imperative of fostering financial inclusion within indigenous tribes and communities.

This study aims to examine the influence of Audit Committee (AC) composition on Firm Performance (FP) by measuring AC composition (ACC) with a composite score based on the varying effect of each composition-characteristic.

Partial Least Squares- Structural Equation Modeling (PLS-SEM) technique is used to weigh ACC characteristics. Based on 133 companies and covering five years from 2016 to 2020, the study analyses data after controlling endogeneity through the Gaussian Copula approach.

We find a significant positive influence of ACC on Firm Performance. Among the ACC characteristics, the absence of executive directors has the highest positive weight on ACC to influence FP, followed by AC size and Gender diversity. AC independence and members' accounting and financial expertise have no significant weight on its composition.

Apart from the theoretical contribution, the study reveals that each ACC characteristic has a varying effect on AC effectiveness to influence the FP that needs to be considered by regulators while framing regulations on ACC and by BOD while constituting AC for a company.

The study claims originality by being pioneering to reveal that AC composition, with a synergy of its disparate characteristics, positively impacts FP. It highlights that the absence of executive directors and gender diversity in AC (characteristics overlooked by the extant literature) significantly and positively influence FP. Methodologically, it introduces the use of the PLS-SEM algorithm to weigh the characteristics in governance studies. Further, these findings remain relevant amid recent Indian legal reforms, offering contemporary insights for policy consideration.

This study examines the impact of physical environmental factors on customer retention in fast-food restaurants/cafeterias. Furthermore, this study examines the mediating effect of customers’ positive emotions on this relationship.

In this study, the stimulus-organism-response model developed by Mehrabian and Russell (1974) is applied within the context of the fast-food restaurant/cafeteria industry. Data were collected from a sample of 250 consumers who have patronized establishments within this industry. The assessment model entailed employing the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, which involved a two-stage procedure: measurement model and structural model evaluation.

The study’s results underscore the key role of customer positive emotions in influencing customer retention. Notably, physical environmental factors, encompassing elements like table setting, layout, and service staff, emerge as significant determinants in shaping these positive emotions. Furthermore, the findings indicate that these physical environmental factors exert a direct influence on customer retention, with customer positive emotions acting as mediator role in the relationship between these factors and retention.

This study is the first to use physical environment, customer emotions and customer retention variables together in the fast food restaurant/cafeteria industry.

Environmental sustainability orientation has emerged to drive firms into eco-friendly production. Yet, the consequence of this new strategic thinking on firms’ green innovations, especially small- and medium-scale enterprises (SMEs), remains unresolved. Recognizing that the connection between environmental sustainability orientation and green innovation may not always be direct, the study theorizes that dynamic capability and entrepreneurial orientation may form part of the boundary conditions that strengthen its effect on small enterprises’ green innovation. The study adjoins the dynamic capability theory with the entrepreneurial orientation theory to test this relationship among small businesses within a developing economy. Results from the partial least squares–structural equation modeling (PLS-SEM) suggest that environmental sustainability orientation will result in green innovation when the SME’s dynamic capability can develop a creative reconfiguration of knowledge and new distinctive resources to support this new strategic direction. Similarly, findings from the study suggest that environmental sustainability orientation will translate into better green innovation outcomes when the SME entrepreneurial orientation has a solid attraction to protect the ecosystem and does not perceive green innovation as a risky enterprise.

Under the digital wave and the new industrial competition pattern, the automobile industry is facing multiple challenges such as the redefinition of new technologies and supply chain changes. The purpose of this study is to link big data analytics and artificial intelligence (BDA-AI) with digital supply chain transformation (DSCT) by taking Chinese automobile industry firms as a sample and to consider the role of supply chain internal integration (SCII), supply chain external integration (SCEI) and supply chain agility (SCA) between them.

Data were collected from 192 Chinese firms in the automotive industry and analyzed using partial least squares structural equation modeling (PLS-SEM). Importance-performance map analysis is used to extend the standard results reporting of path coefficient estimates in PLS-SEM.

The results indicate that BDA-AI, SCII, SCEI and SCA positively influence DSCT. In addition, this study found that SCII, SCEI and SCA play an intermediary role in BDA-AI and DSCT.

The paper enriches the research on the mechanism of digital resources affecting DSCT and expands the research of organizational information processing theory in the context of digital transformation. The paper explores how the resources deployed by firms change the strategic measures of firms from the perspective of responsiveness. By exploring the positive impact of SCA as a response capability on the DSCT strategy and its intermediary role between digital resources and DSCT, which is helpful to the further theoretical development of logistics and supply chain disciplines.