The role of Islamic workplace spirituality and grit in predicting teachers' career commitment: A structural equation modeling approach.

Background: In the context of rapid urbanization and rising municipal solid waste generation, solid waste segregation is crucial for effective management; however, poorly researched behavioral issues in the developing world, including Nepal, hinder its implementation. Purpose: This research examines the psychological, social, and ethical drivers of household waste-segregation behavior in Itahari Sub-Metropolitan City, utilizing a combined framework that integrates the Extended Theory of Planned Behavior (ETPB) and the Norm Activation Model (NAM). Design/methodology/approach: A cross-sectional survey was conducted, in which 502 households were chosen by using stratified random sampling from urban and semi-urban wards. The Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, using SmartPLS4, was employed to analyze both measurement and structural models. Findings: Based on its findings, it has been observed that behavioral attitude, subjective norms, perceived control over behavior, moral obligation, and moral obligation judgment play a critical role in determining the willingness to segregate waste, which, in turn, significantly influences the observability of segregation behavior. Knowledge of environmental outcomes has no substantive effect on intention. It does not have a positive impact on behavior, but a weak yet significant negative relationship with practice indicates the existence of an awareness-behavior gap. These findings underscore the importance of developing moral responsibility, social norms, and perceived self-efficacy rather than relying solely on awareness-based interventions. Conclusion: The paper thus concludes that sustainable household waste segregation behavior in Nepal can be promoted through culture-based, psychologically informed, and infrastructure-based initiatives that extend beyond typical educational campaigns. Keywords: Solid waste, Segregation, Intention Behavior, PLS-SEM

Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)In view of its essential role in knowledge creation, multivariate data analysis prevails in the social sciences literature. The field of tourism is not an exception, specifically in the widely adoption of structural equation modeling (SEM), a multivariate technique, by tourism researchers over the past decade. While there are two major types of SEM including covariance-based SEM (CB-SEM) and variance-based SEM (PLS-SEM), the former dominated previous tourism research. However, increasing use of PLS-SEM in tourism research has been witnessed in recent years. This upward trend is likely to persist in the near future given the growing popularity of PLS-SEM in other social sciences domains like marketing, strategic management, and management information system, as specified in the preface of the book. Indeed, PLS-SEM, in relative to CB- SEM, provides more flexibility in handling of data. For instance, PLS-SEM is well-suited for accommodating small sample sizes and complex model, fortesting a model containing both formative and reflective constructs, and for handling single-item measures. To this end, the timely introduction of the book A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) helps tourism researchers stand at the front edge of the SEM technique and make effective use of the PLS-SEM in data analysis. Additionally, the book illustrates the application of PLS-SEM with a free downloadable software namely SmartPLS which is essential to extend the application of PLS-SEM in tourism research.Authored by Hair, Hult, Ringo, and Sarstedt, the book consists of eight chapters. To equip the readers with the basic knowledge of PLS- SEM, Chapter 1 delineates the meaning of SEM and its relationship with multivariate data analysis, followed by a description of the major elements in multivariate data analysis. Then the basic elements of PLS-SEM are explained. Finally, PLS-SEM is distinguished from its counterpart namely CB-SEM while the major characteristics of PLS-SEM and the conditions where the PLS-SEM are more adequate than CB-SEM and vice versa are discussed. To step in the application of PLS- SEM, Chapter 2 firstly explicates the concepts in structural model specification including mediation, moderation, and higher-order models. Then specification of measurement model is explained with a special focus on the differences between reflective and formative measures. After that, the issues that need to be addressed after data collection are discussed. The chapter ends by creating the model in the SmartPLS is illustrated. With an established model, Chapter 3 focuses on model estimation. The chapter explains the algorithm underpinning the estimation and the statistical properties of the PLS-SEM method, as well as the options and parameter settings for running the algorithm. Following that, the issues about interpretation of results are explained. The final section illustrates the execution of model estimation in the SmartPLS.Based on the model estimation, empirical measures of the measurement and structural models are derived, where evaluation of the models takes place. Chapter 4 exhibits the major steps in model evaluation in the beginning. Thereafter, the chapter explains the evaluation of reflective measurement models according to three major criteria including internal consistency reliability, convergent validity, and discriminant validity, followed by an illustration with the SmartPLS. Chapter 5 explains the assessment of formative measurement models with respect to the criteria of convergent validity, collinearity, and significance and relevance of the formative indicators. The chapter also elucidates the basic concepts of bootstrapping which is used to examine the statistical significance of estimates in PLS- SEM. An illustration of the assessment of formative measurement model in the SmartPLS follows. Chapter 6 continues the topic on model evaluation by focusing on the assessment of structural model. …

One of the challenges faced by students is academic anxiety, which can have negative impacts on their academic activities. The aim of this study was to examine the direct effects of mindfulness, self-compassion, and resilience on academic anxiety, as well as the mediating role of resilience in these relationships. A cross-sectional study was conducted, and a sample of 344 university students from Jambi Province, Indonesia was recruited to complete the Academic Anxiety Scale (AAS), Five Facet Mindfulness Questionnaire (FFMQ), Self-Compassion Scale (SCS), and Brief Resilience Scale (BRS). The structural model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study revealed that there were significant negative effects of mindfulness, self-compassion, and resilience on academic anxiety. Additionally, the findings indicated that resilience acted as a mediator in these relationships. This study has implications for the importance of preventing and managing academic anxiety among students by enhancing mindfulness, self-compassion, and resilience. Counseling services in universities should be maximized to address this issue.

In contemporary research, Partial Least Squares Structural Equation Modeling (PLS-SEM) has emerged as a crucial statistical tool, particularly effective for analyzing complex structural models involving multiple constructs and indicators.This paper aims to elucidate the application of PLS-SEM in quantitative research, highlighting its advantages in extending theories and simultaneously estimating measurement and structural models.The methodological approach is divided into three primary stages: data screening and diagnostic tests, measurement model assessment, and structural model assessment.The data screening ensures dataset suitability by addressing missing data and outliers, while diagnostic tests fulfil normality, linearity, and multicollinearity assumptions.The measurement model assessment validates constructs through composite reliability and average variance extracted (AVE) metrics.The structural model assessment evaluates the significance and relevance of relationships between constructs, determines the coefficient of determination (R and adjusted R), assesses mediating effects, and analyzes the moderating variables.By detailing these methodological steps, the article provides a comprehensive guide for researchers aiming to employ PLS-SEM in their studies, emphasizing its rigour and practicality in handling complex theoretical models.

Voluminous studies use Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data. One of the reasons for using PLS-SEM is when the structural model is complex. Studies employing complex structural models with many constructs and indicators lead to PLS-SEM selection for the analysis. The purposes of assessing the measurement model are to examine basic dimensions for construct variables, validate the dimensions, and determine the number of dimensions for each construct. Assessment of measurement model includes composite reliability and average variance extracted (AVE) to assess reliability and validity, respectively. This study tests the validity and reliability of the attitude construct in the context of compliance behavior of income zakat that other studies can use. This study assesses the measurement model to examine basic dimensions for construct variables, validate the dimensions, and determine the number of dimensions for each construct. Assessment of measurement model includes composite reliability and average variance extracted (AVE) to assess reliability and validity, respectively. This study hopes future research can adapt and adopts the attitude items used in this study in their future research.

Structural equation modeling examines the impact of distribution personalization and content decentralization on brand recall among Gen-Z consumers to analyze the causal relationship between these factors. The research used Covariance Based–Structural Equation Modeling (CB-SEM) and Partial Least Squares–Structural Equation Modeling (PLS-SEM) based on review of related literature. The 385 samples were Gen Z (age 12 to 27 as of year 2024 randomly from Region III, Philippines, comprising the provinces of Aurora, Bataan, Bulacan, Nueva Ecija, Pampanga, and Tarlac. Questionnaires with rating scales were used as research tools. Confirmatory factor analysis was used to analyze model fit, reliability, and validity. A structural model and path analysis were used to examine the links. Causal relationships were analyzed using PLS-SEM. The findings indicated that the model had a reasonable fit with empirical data and exhibited acceptable reliability and validity. The results demonstrate that distribution personalization has a direct positive effect on Gen Z’s ability to recall a brand. In addition, content decentralization acted as a partial mediator or indirect effect on the relationship between distribution personalization and Gen Z brand recall. Based on these results, sellers of clothing brands reliant on short-form video ads may consider employing advanced data analytics technologies to acquire insights into the preferences, behavior, and demographics of the audience and personalize video content to specific audience segments like Gen Z. Furthermore, it is advisable to optimize distribution channels, incorporate user-generated content into distribution strategies, and emphasize usercentric storytelling to create favorable associations with the brand. Future research should be qualitative or mixed methods to obtain a richer qualitative interpretation, and cohort or longitudinal research should be conducted to study these effects eventually.

This review provides a comprehensive, step-by-step guide to the application of partial least squares structural equation modeling (PLS-SEM) for novice researchers. This is a conceptual and literature-based review that focuses on best practices and PLS-SEM literature. It highlights the rationale for using PLS-SEM, sample size, software tools, and essential metrics in PLS-SEM analysis. Drawing on best practices and recent literature, the review offers a framework for conducting and reporting PLS-SEM analysis. The review presents essential such as outer loadings, Cronbach’s alpha coefficients, average variance extracted (AVE), composite reliability, cross-loadings, Heterotrait-Monotrait ratio of correlations (HTMT), the Fornell-Larcker criterion, variance inflation factor (VIF), and redundancy analysis. Moreover, for more consistent results, the paper emphasizes on researchers to employ 10,000 bootstrap subsamples and Bias-corrected and accelerated (BCa) bootstrap in assessing the structural model. Insights regarding path coefficients, p-values, R-square (R2), f-square (f2), and Q-square (Q2), are also presented. Furthermore, the review underscores the trade-off between predictive power and model fit when applying PLS-SEM. The presented practical insights alert novice researchers in avoiding common pitfalls and enhance the methodological rigor of empirical research that utilizes PLS-SEM. This step-by-step guide supports early-career researchers and contributes to the ongoing debates on improving methodological clarity and transparency.

Despite having a significant socio-economic impact on a country’s growth, one of the biggest contributors to waste generation is the construction sector, thus increasing the necessity for innovative construction waste management strategies. Lean Construction (LC) is a much-adopted methodology that helps manage construction waste efficiently. This study investigates the barriers to LC implementation in the UAE’s building construction sector, with a specific focus on its potential for effective construction waste management. Recognizing the urgent need to adopt sustainable practices in the face of escalating environmental concerns, the study employs a quantitative exploratory approach combining literature synthesis, expert validation, descriptive statistical analysis, Relative Importance Index (RII) ranking, and Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine stakeholder perceptions across eight critical categories: Knowledge/Awareness, Attitude, Management, Government, Financial, Material/Resource, Technical, and Other contextual factors. The RII analysis ranks these barriers based on stakeholder responses, while the PLS-SEM approach models the strength and significance of their interrelationships within a validated structural framework. The findings reveal that while all eight categories significantly influence stakeholder perceptions, Attitude and Management factors exhibit the strongest impact, highlighting the importance of behavioral and organizational readiness in enabling LC adoption. The key deliverable of this research is a validated and empirically supported structural model that provides a strategic roadmap for overcoming resistance to LC implementation. By offering actionable insights into which barriers matter most and how they interact, the study equips policymakers, contractors, and industry stakeholders with evidence-based guidance to design targeted interventions. Ultimately, this research contributes to the growing discourse on sustainable construction by positioning LC as a viable pathway for reducing construction waste and improving efficiency in the UAE context.

Partial Least Squares Structural Equation Modeling (PLS-SEM) serves as a comprehensive methodological framework, critically addressing theoretical underpinnings, rigorous analytical approaches, and state-of-the-art modeling techniques vital for contemporary business research. The methodological discussion includes detailed exploration of reflective and formative measurement models, structural model specification, reliability, and validity assessments, alongside advanced analytical methods such as Confirmatory Tetrad Analysis (CTA-PLS) and Importance-Performance Matrix Analysis (IPMA). Advanced algorithms including bootstrapping and blindfolding procedures are elaborated, emphasizing predictive relevance and methodological precision. Partial Least Squares Structural Equation Modeling further offers robust analytical capabilities to evaluate modern AI-driven innovations, facilitating sophisticated assessment of user trust, perceived accuracy, and satisfaction with recommender systems, voice assistants, autonomous vehicles, AI-driven healthcare diagnostics, personalized educational platforms, and fraud detection technologies. Ethical considerations, reporting best practices, computational tools (SmartPLS, SEMinR), and Explainable AI (XAI) integration enhance the comprehensive nature of this framework. Furthermore, integration of cutting-edge analytical approaches such as moderation, mediation, Multi-Group Analysis (MGA), nonlinear modeling, machine learning integration, and quantum computing potential positions PLS-SEM as indispensable for contemporary business and technology research, ultimately promoting actionable scholarly insights and ensuring maximum methodological impact.

Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example

This study aims to analyse what factors can affect the impact of acid mine drainage on the environment and society. With a sample of research respondents being the community around the company, totalling 200 people. The variables used consist of the environmental variable (X3) as the dependent variable and independent variables, namely acid mine drainage (X1) and community social (X2), with each variable being measured by its indicators. This questionnaire is used to measure Statements compiled using the Likert scale with five respondent answers to consumer satisfaction, namely: Strongly Agree (SS), Agree (S), Hesitant (RR), Disagree (TS), and Strongly Disagree (STS). The data analysis technique used is partial least square-structural equation modelling (PLS-SEM) which is used to predict and develop theory. Partial Least Squares Structural Equation Modelling (PLS-SEM) is a robust and versatile statistical technique commonly employed for data analysis. PLS-SEM is particularly well-suited for exploratory research and when dealing with complex models, small sample sizes, or non-normal data distributions. It allows researchers to simultaneously assess the measurement and structural models, making it an effective tool for understanding complex relationships between variables. Based on the results of the analysis, it is known that Acid mine drainage affects the environment and society with a rms theta model of 0.470.

Purpose: This study investigates the extent to which learning motivation and perceived parental financial support influence students’ intention to pursue higher education, focusing on 11th-grade students in a rural Indonesian high school. It addresses a research gap concerning educational aspirations among underserved youth in developing regions. Methodology: A quantitative explanatory design was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. Data were collected from 127 purposively selected students at SMAN 1 X Koto Singkarak. Constructs for learning motivation, perceived parental financial support, and college intention were measured using validated items on a 5-point Likert scale. The analysis assessed the measurement model (validity and reliability) and structural model (path coefficients). Findings: Both learning motivation (β = 0.344; p < 0.001) and perceived parental financial support (β = 0.219; p < 0.01) positively and significantly influence college intention. The model explains 20.2% of the variance in students’ educational aspiration (R² = 0.202). Learning motivation was the stronger predictor, highlighting the critical role of internal psychological drivers despite economic limitations. Originality: This research offers new insights into how psychological and economic factors jointly shape college aspirations in rural settings. By integrating Self-Determination Theory within a socio-economic framework using PLS-SEM, the study contributes both theoretically and methodologically Research limitations: Findings are limited to a single rural school, reducing generalizability. The cross-sectional design limits causal inference, and self-reported data may involve bias. Future studies should use broader samples, longitudinal designs, and explore mediating variables such as peer or institutional influence. Practical implications: The results suggest schools and policymakers should foster student motivation through mentoring and goal-setting, while also enhancing financial access via scholarships and outreach to promote higher education participation in rural areas.

This research aims to investigate the factors that influence the effectiveness of Information Systems (IS) Governance in Higher Education Institutions (IPT) using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The background of this research reflects the importance of IS in supporting operations, management and decision making in a higher education environment that is increasingly complex and dependent on technology.The PLS-SEM method analyzes the relationship between key variables that influence the effectiveness of IS governance at IPT. It is a powerful multivariate statistical approach that allows factor analysis and regression in a single framework, allowing researchers to holistically understand how factors relate to each other. The results of this research will likely provide valuable insight for decision-makers at IPT in improving IS management and utilization. Practical implications include the development of more effective policies, better management strategies, and improved IS infrastructure. In addition, this research is also expected to provide an essential contribution to academic literature in understanding the factors that influence the effectiveness of IS governance in the higher education context. By better understanding the factors that influence the effectiveness of IS governance, IPT can increase its competitiveness, improve the quality of educational services, and support the achievement of its strategic goals. This research is expected to significantly contribute to understanding how IS governance can be implemented and managed more effectively in higher education environments through the PLS-SEM approach.

Ho Chi Minh City is among the top cities in Vietnam with a high proportion of rural to urban migration. This strongly contributes to the economic growth of the city but challenges the infrastructure, social security services, health care, clean water, education, traffic, safety, and social order which negatively impacts the quality of life. The purpose of this study was to explore the life quality of rural to urban migrants in Ho Chi Minh City. A quantitative method was employed to confirm the measurement model and structural model. Probability sampling was applied for the field survey. The final data of 272 migrants have been analyzed, using Partial Least Squares Structural Equation Model (PLS-SEM). Social capital, a special resource of the vulnerable like rural to urban migrants has been investigated. This contributes to the theory that both social capital and quality of life have been approached multi-dimensionally. Bonding, bridging, and linking dimensions have all been approached to construct the social capital measurement model. Five aspects of life quality including work, housing, environment, finance, and social cohesion have been measured to reflect the quality of life multidimensionally. The research results showed the reliability and validity of the measurement model. The positive impact of social capital on quality of life has empirically been confirmed. The findings implied the prompt strategies for

Cost overruns are a global challenge to successfully completing construction projects. Cost overrun has a substantial impact resulting in most construction projects failing to be completed. Several factors have contributed significantly to the industry's decline. The factors were discovered in the literature, assessed, and applied to the construction industry in Pakistan. This study scrutinized and identified the relationships between the factors causing cost overruns in the Pakistani construction industry using the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach. The structural model was created and tested with Smart-PLS software using data from a questionnaire survey of 131 construction practitioners. Six constructs were used to categorise the factors. The model identifies 21 critical factors in Pakistani construction projects, with resource management ranking first. Contract management issues can also contribute significantly to project cost overruns. Model assessment results indicate that the developed model has a substantial power of explaining the factors of cost performance while R2 value showed that 45.7% variance is explained by the model. The model developed model will serve as a jumping-off point for academics, researchers, and practitioners in developing a cost-control system. It is suggested that establishing an efficient and effective contract management protocol among stakeholders throughout the design and supervision stages is extremely beneficial for improving project cost performance and significantly reducing time overruns.