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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.

This research aims to explore the factors influencing the adoption intention of eco-friendly smart home appliances among residents in densely populated urban areas of a developing country.

A quantitative research approach was employed to gather data from 348 respondents through purposive sampling. A comparative analysis strategy was then utilized to investigate the adoption of eco-friendly smart home appliances, combining both linear (PLS-SEM) and non-linear (fsQCA) approaches.

The results obtained from PLS-SEM highlight that performance expectancy, facilitating conditions, hedonic motivation, price value, and environmental knowledge significantly influence the adoption intention of eco-friendly smart home appliances. However, the findings suggest that effort expectancy, social influence, and habit are not significantly associated with customers' intention to adopt eco-friendly smart home appliances. On the other hand, the fsQCA results identified eight configurations of antecedents, offering valuable insights into interpreting the complex combined causal relationships among these factors that can generate (each combination) the adoption intention of eco-friendly smart home appliances among densely populated city dwellers.

This study offers crucial marketing insights for various stakeholders, including homeowners, technology developers and manufacturers, smart home service providers, real estate developers, and government entities. The findings provide guidance on how these stakeholders can effectively encourage customers to adopt eco-friendly smart home appliances, aligning with future environmental sustainability demands. The research implications underscore the significance of exploring the antecedents that influence customers' adoption intention of eco-friendly technologies, contributing to the attainment of future sustainability goals.

The environmental sustainability of smart homes, particularly in densely populated city settings in developing countries, has received limited attention in previous studies. Therefore, this study aims to address the pressing issue of global warming and make a meaningful contribution to future sustainability goals related to smart housing technologies. Therefore, this study employs a comprehensive approach, combining both PLS-SEM (linear) and fsQCA (non-linear) techniques to provide a more thorough examination of the factors influencing the adoption of environmentally sustainable smart home appliances.

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.

The COVID-19 pandemic has affected economic activity both globally and nationally, which also has an impact to banking sector and Islamic banking is no exception. This study aims to see how the impact of Islamic bank financing in seven sectors affected by the COVID-19 to the credit risk of Indonesian Islamic banks. In addition, this study also tries to see whether the proportion of mudharabah-musharaka or profit-loss sharing (PLS) financing also affects credit risk in Indonesian Islamic banks.

This study uses fixed effect panel data regression over the period 2011–2020.

The results of this study show that wholesale and retail trade financing will increase credit risk in Indonesian Islamic banks as a policy implication. In terms of the proportion of PLS financing, it shows that a larger share of PLS financing will reduce credit risk in Islamic banks.

This paper demonstrates that despite the industry’s perception of PLS as riskier than murabaha-based instruments. According to the research, PLS financing will lower credit risk in Islamic banks. This study found that PLS contributes to overall economic stability by shifting the function of Islamic banks from a simple lending body to an active market catalyst/manager/consultant to market players seeking financial aid.

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.

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.

Pedagogical leadership (PL) has been regarded as the best leadership style in the education sector. Thus, the aim of this study was to develop and validate a pedagogical leadership scale (PLS).

Two distinct approaches (inductive and deductive) were utilized. First, a review of the literature was conducted, and then qualitative data were collected through interviews, and their responses were categorized into 40 items. These items were thematized using exploratory factor analysis (EFA) by involving 300 participants. To examine the fitness of the scale, the researchers conducted a confirmatory factor analysis (CFA) with 470 participants.

EFA discovered a total variance of 64.766% for four factors. In CFA, RMSEA, NFI, RFI, NNFI, CFI, GFI and AGFI values were accepted. The highest correlation was found among constructs of PL. Path analysis revealed PL affected social, professional, intellectual and academic capitals. The correlations between the PLS and psychological empowerment demonstrated the theoretically predicted relationships with these variables. Thus, with the initial evidence of a valid and reliable PLS, a pool of 32 items under 4 factors (social, academic, professional and intellectual capital) were developed.

Despite the management of childhood education requiring the practice of PL, it is underexplored in childhood schools, particularly to our knowledge, no studies have been conducted to develop and confirm the PLS in Ethiopia.

Blockchain, a groundbreaking technology that recently surfaced, is under thorough scrutiny due to its prospective utility across different sectors. This research aims to delve into and assess the cognitive elements that impact the integration of blockchain technology (BT) within library environments.

Utilizing the Stimulus–Organism–Response (SOR) theory, this research aims to facilitate the implementation of BT within academic institution libraries and provide valuable insights for managerial decision-making. A two-staged deep learning structural equation modelling artificial neural network (ANN) analysis was conducted on 583 computer experts affiliated with academic institutions across various countries to gather relevant information.

The research model can correspondingly expound 71% and 60% of the variance in trust and adoption intention of BT in libraries, where ANN results indicate that perceived possession is the primary predictor, with a technical capability factor that has a normalized significance of 84%. The study successfully identified the relationship of each variable of our conceptual model.

Unlike the SOR theory framework that uses a linear model and theoretically assumes that all relationships are significant, to the best of the authors’ knowledge, it is the first study to validate ANN and SEM in a library context successfully. The results of the two-step PLS–SEM and ANN technique demonstrate that the usage of ANN validates the PLS–SEM analysis. ANN can represent complicated linear and nonlinear connections with higher prediction accuracy than SEM approaches. Also, an importance-performance Map analysis of the PLS–SEM data offers a more detailed insight into each factor's significance and performance.

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 study aims to identify the librarians’ readiness to leverage artificial intelligence for sustainable competence development and smart library services.

This study used a quantitative research design for addressing the objectives. The population consisted of librarians from the public and private sector universities of Pakistan. The data were analyzed by using Smart PLS software.

The analysis consisted of two major parts: first the assessment of measurement model and second the structural equation modeling analysis. A significant positive impact of AI adoption was found on the implementation smart library services. Findings revealed that behavioral intention motivated librarians to adopt AI tools in university libraries for the delivery of smart library services.

We applied quantitative method to carry out the study while future authors may conduct a systematic literature review on the same topic for offering a broader outlook.

It has provided practical contributions by providing a baseline for management bodies to construct policies for the successful adoption of AI in libraries for sustainable competence development of practicing librarians and implementation of smart library services.

The study has social implications too as AI integrated library services prove fruitful for society and digitally skilled librarians play a vital role for the promotion of reading and research culture in society.

To the best of the authors’ knowledge, this is the first study on librarians’ readiness to leverage artificial intelligence for the enhancement of digital literacy skills, sustainable competence development and smart library services in the context of Pakistan.