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Business Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence.

The BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness.

The findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities.

The single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework.

The experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.

The study assessed the role of entrepreneurial self-efficacy (ESE) in the performance of graduate-owned small and medium-sized enterprises (SMEs). Specifically, the paper focused on how graduate owners’ confidence in identifying and commercializing new business opportunities and create new commodities according to the market demand influences both, the financial and non-financial performance of graduate-owned SMEs.

The study employed a cross-sectional survey design, in which 228 SME graduate owners majored in entrepreneurship were involved through online structured questionnaires. The relationship between the variables was determined by using partial least squares structured equation modeling (PLS-SEM).

The results indicate that entrepreneurial self-efficacy of graduate SME owners influence the financial and non-financial performance of their SMEs. Specifically, financial performance values have a ß-value of 0.576 and a p-value of <0.0001, non-financial performance values have a ß-value of 0.275 and a p-value of <0.0001.

In order to improve the performance of graduate-owned SMEs, universities should strive to equip graduates with self-confidence, which will enable them to utilize their entrepreneurial competences acquired through the entrepreneurship course programs. This will enable them to tackle business environmental challenges and be able to run successful businesses.

The results of this study extend the use of the self-efficacy theory in explaining the performance of graduate-owned SMEs in Tanzanian. Further, the study focused on ESE as a specific domain developed from entrepreneurship learning for entrepreneurial outcomes. Therefore, the study engaged graduate entrepreneurs who majored in entrepreneurship, which has not been the focus in most studies.

This paper introduces a novel method to improve building safety management by leveraging building information modeling (BIM) and adaptive information retrieval techniques. The integration aims to overcome the limitations of traditional safety management methods in connecting construction processes with risk management efficiently.

The proposed method involves developing industry foundation classes (IFC) ontologies and integrating them with a safety document ontology to form a comprehensive BIM-based safety context framework. Custom reasoning rules and an inference engine are constructed to enable automatic context-aware safety information retrieval. The methodology is demonstrated through an adaptive information retrieval system using job hazard analysis (JHA) documents.

The implementation of the BIM-based adaptive information retrieval system shows significant improvements in identifying and managing construction risks. By mapping job-specific risks to corresponding safety measures, the system enhances risk detection and management tailored to particular construction tasks. The results indicate a marked improvement in the precision and accuracy of safety assessments and recommendations, aligning them closely with planned construction activities and conditions.

This paper offers an innovative approach to construction safety management through the development of a BIM-facilitated context-aware information retrieval system. This approach provides a more intelligent and automated framework for identifying and managing risks in construction projects. By focusing on specific job steps and related risks, the system enhances the effectiveness and accuracy of safety measures, contributing to better overall building safety management.

Construction organizations' investment in effective talent-development programs is a key strategy in attracting, developing and retaining staff. Such programs are especially important given the current challenges in the construction workforce, including labor shortages, an aging workforce, generational differences in the workforce, supply chain disruptions and the need to effectively train staff in the skills that are essential in a constrained labor environment. To address these challenges, this study proposes a performance measurement strategy that construction companies can use as input to design their talent development programs.

The strategy intends to assess the performance of project managers and develop criteria that define categories of their performance, including the top performers' category. This enables construction organizations to provide each project manager with individualized training that addresses areas of weakness and in turn, develops the skills that correspond with being top performers. The proposed strategy was developed and tested by surveying the immediate supervisors of 187 project managers working for general and specialty contractors in the United States. Principal component analysis was used to develop a single performance construct from seven performance criteria.

This construct was used to organize the project managers into the categories of top, above-average and below-average performers. According to the findings, top-performing project managers have well-rounded skills in the areas of leadership, communication, technical proficiency and overall job knowledge.

The outcomes of this study can help construction organizations focus their talent-development programs on the skills most associated with PMs being top performers.

This study provides construction organizations with a comprehensive performance-measuring construct to focus their talent-development programs on the skills most associated with top-performing project managers. Researchers can use this study as a foundation for further understanding how performance is related to various construction professions.

In light of the inequality in access to farming land and the high prevalence of child malnutrition in Sudan, there is a lack of empirical research on the relationship between land ownership and child nutritional status. This study aims to examine the influence of agricultural landholding on the nutritional status of children under the age of five in rural Sudan.

The study utilizes data from Sudan’s 2014 Multiple Indicator Cluster Survey (MICS), covering a sample of 10,753 rural children. The empirical analysis uses the two-stage least squares (2SLS) approach, adopting various estimation methods and model specifications for robustness check and comparison.

The results demonstrate that agricultural land has a positive and significant effect on reducing child malnutrition, signifying that children from families with agricultural land are less susceptible to malnutrition in Sudan. When examining the male and female sub-samples separately, the findings indicate a positive influence of land ownership on child malnutrition in the female sub-sample, while no significant impact is observed in the male sub-sample. This indicates a gender disparity in the effects of land ownership on child nutrition, with girls benefiting more from access to agricultural land compared to boys.

The study has several significant contributions. First, this is the sole study that examines the impact of agricultural land ownership on child malnutrition in Sudan. Second, considering the gender variations in nutritional status, investigating the influence of land ownership on child nutrition across genders addresses a significant gap in the current literature. Finally, the findings resulting from this study can contribute to achieving the United Nations Sustainable Development Goals (SDGs) to be achieved by 2030, precisely focusing on SDG2 Goal 2: Zero hunger and SDG 10: Goal 10: Reduced inequalities.

The COVID-19 pandemic has profoundly impacted the global economy, disrupting supply chains, causing job losses and altering consumer demand. In Malaysia, the real estate sector has been notably affected, with increased property impairments and overhang due to unprecedented uncertainty. Understanding these effects is crucial for policymakers and investors to prevent real estate and banking crises. This study aims to analyse the relationships between macroeconomic factors during the pandemic on property impairments and overhang, providing insights for maintaining macroeconomic stability. The findings will inform strategies for mitigating economic shocks, identifying opportunities, and guiding real estate policies in Malaysia and potentially globally.

This research article uses a time series ARDL regression analysis to examine pivotal macroeconomic factors including income, housing process, interest rates and unemployment on property loan impairments and property supply overhang in Malaysia. ARDL is effective to measure and analyse time series data, especially to understand the lagged impacts of macroeconomic factors. This can be seen by various economists in analysing macroeconomic factors affecting non-performing loans or the real estate finance using regression analyses both in Malaysia and other regions. The observations are gathered before, during and after the COVID-19 pandemic, spanning a five-year period with monthly frequency from 2018 to 2022.

The study emphasizes the critical importance of effectively managing unemployment and implementing policy interventions, such as moratoriums, to stabilize the economy and reduce the risk of loan impairments during crises like the COVID-19 pandemic. Additionally, this study highlights a significant inverse relationship between income per capita and loan impairments, underscoring the necessity for policies that promote economic growth and income equality. Initiatives targeting job creation, education and skills development can elevate income levels, thereby decreasing loan impairments. Lower lending interest rates during the pandemic also help mitigate the risk of loan impairments by facilitating borrowing, stimulating economic activity and enhancing financial well-being. Furthermore, the study suggests that while lower interest rates incentivize property developers and investors, understanding the intricate interaction between housing prices and supply is crucial for policymakers and stakeholders to effectively manage the housing market and ensure adequate housing supply, especially during crises.

This paper provides insight for policymakers, regulators, investors and property consultants into the dynamic effects of key macroeconomic factors amidst a global recession in how they impact the real estate market with regards specifically to all types of property loan impairments and property supply overhang. The observations are limited to the COVID-19 period, spanning five years with monthly data from 2018 to 2022. This understanding can facilitate the development of targeted strategic monetary policies and investment decisions in case of future recessions.

Policymakers should prioritize initiatives such as moratoriums and job creation programs to mitigate economic downturns. Additionally, financial institutions need to adjust lending practices in response to lower interest rates, while stakeholders in the housing market must understand the complex dynamics between housing prices and supply to ensure a balanced market. Overall, addressing underlying economic factors and implementing targeted policies are essential for building resilience and promoting sustainable economic growth amidst challenging circumstances.

Initiatives aimed at fostering income equality, creating employment opportunities and ensuring housing accessibility contribute to greater social cohesion and well-being. By promoting financial inclusion and building resilience to crises, societies can mitigate the adverse social impacts of economic challenges such as unemployment and housing affordability. Overall, addressing socioeconomic disparities and promoting inclusive growth are essential for fostering a more equitable and resilient society.

The originality and uniqueness of this study lie in its comprehensive analysis of the impact of COVID-19 on loan impairments and housing supply. While previous studies have focused on the pandemic’s effects on specific segments of the real estate market or property prices, this study provides a broad overview of its impact on property loan impairments and housing supply overhang. Finally, this study highlights the social and practical implications. Overall, this study offers a distinctive analysis of COVID-19’s impact on the real estate market and its implications for policymakers, real estate professionals and investors.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

Emotional intelligence (EI) and authentic leadership are essential attributes for effective healthcare management. Understanding their interplay can inform leadership development and improve patient care outcomes. This study aims to determine the relationship between EI and authentic leadership among nurse managers.

Study conducted a cross-sectional study, collecting data from 198 nurse managers using surveys assessing EI and authentic leadership. Correlation and regression analyses were used to explore the relationship and impact of EI on authentic leadership. STROBE checklist used to report the study.

The findings revealed a positive and significant relationship between nurse managers’ EI and their authentic leadership characteristics. Higher EI scores were associated with increased authenticity in leadership. EI, particularly interpersonal EI, plays a crucial role in authentic leadership. Enhancing nurse managers’ EI can foster more authentic leadership, positively impacting healthcare settings.

This research underscores the importance of EI in leadership development and its potential for improving patient care and organizational outcomes.

Construction fall-from-height accidents are not only caused by a single factor but also by the risk coupling between two or more factors. The purpose of this paper is to quantitatively analyze the risk coupling relationships between multiple factors and identify critical factors in construction fall-from-height accidents.

A cause analysis framework was established from the perspective of human, machine, material, management and environmental factors. The definition, the classification and the process of risk coupling were proposed. The data from 824 historical accident reports from 2011 to 2021 were collected on government websites. A risk coupling analysis model was constructed to quantitatively analyze the risk coupling relationships of multiple factors based on the N-K model. The results were classified using K-means clustering analysis.

The results indicated that the greater the number of causal factors involved in risk coupling, the higher the risk coupling value and the higher the risk of accidents. However, specific risk coupling combinations occurred when the number of their coupling factors was not large. Human, machine and material factors were determined to be the critical factors when risk coupling between them tended to pose a greater risk of accidents.

This study established a cause analysis framework from five aspects and constructed a theoretical model to quantitatively analyze multi-factor coupling. Several suggestions were proposed for construction units to manage accident risks more effectively by controlling the number of factors and paying more attention to critical factors coupling and management and environmental factors.

This paper discussed quantifying architect, engineer and contractor (AEC) professionals' cross-work culture productivity by comparing between Malaysian and United Kingdom (UK) projects during industrialized building project delivery. This study addressed the second part of a mixed method research design study.

This study hypothesized that with understanding of cultural work knowledge between professionals during design phase coupled with competent technological support, productivity can be improved. It utilized Cognitive Organizational Theory (COT) protocols to test conceptual models in SimVision®. Organizational structure, project intensity, and statistical validations parameters were performed to obtain the reliability and generalization of the result.

This study found that with Building Information Modeling (BIM) technology intervention, the handling of exception, coordination and decision-making time could be improved, resulting in better project performances. The result also indicated that in choosing organizational fit, national culture factor needed to be considered; otherwise, organizational change would be unacceptable. By changing the operational process from intensive to reciprocal task intensity with BIM technology intervention, the effect on productivity would be similar to changing hierarchical organizational structure to flatter organizational structure.

Project discrepancies issues are limitedly discussed due to companies' confidentiality. The paper only focuses on understanding the effects of human factors during the integrated project delivery phase.

The findings could support developing countries' professionals to collaborate effectively with developed countries' professionals.

The development of the project's cultural knowledge experimentations will provide guidance to teams involved in international projects from developed and developing countries in pursuing joint ventures in project deliveries in either country successfully.