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The purpose of this study is to discuss the construction of a structural measurement model utilizing structural equation modelling (SEM) to confirm the link between Industry 4.0 technologies, sustainable manufacturing practices and organizational sustainable performance. Relationship among the paradigm has yet to be fully investigated, necessitating a more conceptual and empirical examination on what impact they have on organizational sustainable performance when used together.

Industry 4.0 and sustainable production practices aim to progress a company's business competitiveness, forming sustainable development that benefits manufacturing companies. The aim of the study is to analyze the relationship between constructs that lead to operational excellence in firms that use Industry 4.0 technologies and sustainable manufacturing techniques. Experts from diverse automotive industries, who are applying both Industry 4.0 and sustainable manufacturing practices, provided data for the study.

Statistical estimations (hypotheses) are created to substantiate the measurement model that has been developed. The structural model was analysed, and the findings were discussed. The statistical estimate is either approved or rejected based on the findings. According to the conclusions of this study, strong link exists between Industry 4.0 technologies and sustainable manufacturing practices that affect organizational sustainable performance environmentally, economically and socially.

The research was conducted in the framework of automobile component manufacturing companies in India. The outcomes of the study are practically feasible.

The authors' novel contribution is the construction of a structural model with Industry 4.0 technologies and sustainable manufacturing practices into account.

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.

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.

Corporate social responsibility (CSR) practices frequently result in increased costs for Chinese international contractors (CICs), where profitability is the primary objective; therefore, internal corporate drivers and external pressures play a crucial role in encouraging them to engage in sustainable CSR practices. This study systematically examines the dynamic impact of internal and external stakeholders on the CSR practices of CICs.

This study adopted a structural equation model (SEM) to identify and validate a correlation between stakeholders and CSR practices. Standardized causal coefficients estimated in SEM were used to construct a fuzzy cognitive map (FCM) model to illustrate the effect of stakeholders on CSR practices with linkage direction and weights. Predictive, diagnostic, and hybrid analyses were performed to dynamically model the variation in stakeholders on the evolution of CSR practices.

The empirical results demonstrate that (1) employee participation in CSR has the greatest impact on CSR practices, followed by CSR strategies, partner and customer expectations, and finally government regulations. (2) In the early stage of CSR fulfillment, CSR strategies have the greatest influence on CSR practices; in the later stage of CSR fulfillment, employee participation in CSR has the greatest influence on CSR practices. (3) In the long run, the most effective and economical integrated interventions are those that address employee participation in CSR, partner expectations and customer expectations, and intervention in CSR strategies is needed if the level of CSR practice needs to be improved in the short term.

This study contributes to the research on the influence mechanisms of CSR practices of CICs and systematically analyzes their dynamic influence on CSR practices of CICs from the perspective of stakeholders.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

Lean construction (LC) consists of very effective techniques; however, its implementation varies considerably from one industry to another. Although numerous lean initiatives do exist in the construction industry, the research topic related to LC implementation is still unexplored due to the scarcity of validated assessment frameworks. This study aims to provide the first attempt in developing a structural model for successful LC implementation.

This study developed a Lean construction model (LCM) by critically reviewing seven previous LC frameworks from different countries, defining 18 subprinciples grouped into 6 major principles and formulating testable hypotheses. The questionnaire was pre-tested with 12 construction management experts and revised by 4 specialized academics. A pilot study with 20 construction units enhanced content reliability. Data from 307 Moroccan construction companies were collected to develop a measurement model. SPSS V. 26 was used for Exploratory Factor Analysis, followed by confirmatory factor analysis using AMOS version 23. Finally, a structural equation model statistically assessed each construct's contribution to the success of LC implementation.

This work led to the development of an original LCM based on valid and reliable LC constructs, consisting of 18 measurement items grouped into 6 LC principles: Process Transparency, People involvement, Waste elimination, Planning and Continuous improvement, Client Focus and Material/information flow and pull. According to the structural model, LC implementation success is positively influenced by Planning and Scheduling/continuous improvement (β = 0.930), followed by Elimination of waste (β = 0.896). Process transparency ranks third (β = 0.858). The study demonstrates that all these factors are mutually complementary, highlighting a positive relationship between LC implementation success and the holistic application of all LC principles.

To the best of the authors’ knowledge, this study is the first attempt to develop a statistically proven model of LC based on structural equation modelling analysis, which is promising for stimulating construction practitioners and researchers for more empirical studies in different countries to obtain a more accurate reflection of LC implementation. Moreover, the paper proposes recommendations to help policymakers, academics and practitioners anticipate the key success drivers for more successful LC implementation.

One of the novel concepts in the management literature is intimate co-creation. Considering it as the outcome of workplace persuasion, this study examines its effect via team-member exchange and ethical climate for the assessment of multigroup analysis. Finding a relationship among variables is not the core objective of the study. The core objective was to assess multigroup analysis for examining measurement scales' uniformity or perceptual differences across the male and female groups using measurement invariance.

This was a quantitative study for a survey of faculty members from the top 10 Pakistani universities. It employed state-of-the-art statistical techniques, including the application of the foundational social exchange theory and the utilization of multigroup analysis in structural equation modeling (SEM) with the Analysis of Moment Structure (AMOS). The research methodology was designed to investigate the relationships between workplace persuasion, ethical climate, team member exchange and intimate co-creation. A specific emphasis was placed on assessing whether gender influences these relationships consistently across male and female groups, as determined by measurement invariance tests.

This study underscores the significant impact of ethical persuasion in the workplace on enhancing intimate co-creation among individuals, offering invaluable insights for organizational leaders. Importantly, it emphasizes that gender dynamics do not influence this relationship, underscoring the imperative of addressing gender-related workplace issues to optimize intimate co-creation. This holds particular relevance for service-based organizations, such as universities in this case.

This study makes a significant contribution by exploring the concept of intimate co-creation within the realm of organizational science, while also highlighting the crucial importance of considering workplace gender dynamics. It offers fresh insights into how these dynamics influence group creativity, guiding human resource practices toward fostering innovation within gender-inclusive workplaces. These insights gain added relevance in the evolving post-COVID-19 era and in the context of AI integration. Notably, a distinctive contribution of this study to social exchange theory lies in its innovative application of multigroup analysis to variables related to gender.

This study aims to investigate the relationship between prevention and panic of COVID-19 and distress tolerance, happiness and emotional intelligence (EI) in undergraduate and postgraduate accounting students in Iraq. In other words, this study seeks to answer whether or not different types of prevention and fear of COVID-19 can lead to improved distress tolerance, happiness and EI.

The study’s statistical population comprises 298 undergraduate and 138 postgraduate students in Iraq who were selected as the sample size using the Cochran sampling method. In this study, partial least squares regression (PLS) have been used to investigate the effect of independent variables on the dependent variable.

The results showed a positive and significant relationship between COVID-19 prevention and distress tolerance and happiness, but no significant relationship was observed between COVID-19 prevention and EI. Also, no significant relationship was observed between fear of COVID-19 and distress tolerance and happiness, but there was a positive and significant relationship between fear of COVID-19 and EI.

The present study’s results can provide valuable information to everyone and help the development of science and knowledge because so far, and to the best of the authors’ knowledge, no research has examined the impact of prevention and panic of COVID-19 on distress tolerance, happiness and EI in students.

The purpose of this research study is to empirically investigate the Figma prototyping technology adoption factors among entrepreneurship and innovation libraries for providing support to startups by developing and evolving the prototype solutions in collaboration with health libraries.

This study uses the technology adoption model (TAM) as a framework and the partial least squares structural equation modeling (PLS-SEM) method of structural equation modeling (SEM) using SmartPLS 3.2.9 software version to investigate the prototyping adoption factors among entrepreneurship and innovation libraries for rural health innovations. A total of 40 libraries, spread over 16 entrepreneurship and innovation libraries, participated in this survey, including participants from Europe (35%), Asia (15%) and USA (50%).

The findings show that previous experience, social impact, brand image and system quality have a significant positive impact on entrepreneurship and innovation libraries' perceived usefulness (PU) of prototyping technology. Perceived ease of use of prototype technology is positively influenced by usability, training materials and documentation, experience and self-efficacy. Together, perceived usefulness and perceived ease of use have a significant influence on behavioural intention. Behavioural intention is positively impacted by minimal investment and shallow learning curve. Technology adoption is furthered by behavioural intention. The control variables, for instance location, gender and work experience (as librarian), were found not having any impact on Figma technology adoption.

Through strategic partnerships with other libraries (including health libraries), policymakers, and technology providers, the adoption of prototype technology can be further accelerated. The important ramifications for policymakers, technology providers, public and entrepreneurship and innovation libraries to create a self-reliant innovation ecosystem to foster rural health innovation based on entrepreneurship are also listed in the article.

This research is distinctive since it integrates several areas of study, including entre, advances in rural healthcare and libraries. A novel idea that hasn't been thoroughly investigated is the collaboration between entrepreneurship and innovation libraries and health libraries for supporting businesses. This study offers insights into the factors that drive technology adoption and offers practical advice for policymakers and technology providers. It also advances understanding of the adoption of Figma prototyping technology among libraries for rural health innovation. Overall, this study provides a novel viewpoint on the nexus between different disciplines, showing the opportunity for cooperation and innovation in favour of rural health.

Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.

This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.

The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.

This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.