This paper explores the relationship between human factors in construction safety and their effects on operational efficiency. It investigates how safety incidents resulting from human errors influence productivity, project timelines, and overall costs, while examining how strategic safety management can improve organizational performance. The study employs a mixed-methods design that combines quantitative statistical modeling with qualitative case analysis to ensure both empirical rigor and contextual depth (R² = 0.87, p < 0.001). Data were drawn from four construction firms, encompassing a sample of 120 employees across residential, commercial, and infrastructure projects. Variables such as training hours, fatigue levels, safety compliance, and technology adoption were analyzed against key operational performance indicators, including rework hours, schedule adherence, and productivity scores. Statistical analyses were performed using SPSS 29 and AMOS 28, incorporating descriptive statistics, regression analysis, and mediation testing to examine the pathways linking human factors, safety performance, and operational productivity. Reliability and validity were confirmed through Cronbach’s alpha and variance inflation factor (VIF) diagnostics. Results demonstrate that safety compliance acts as a mediating variable connecting training, fatigue, and technology adoption to measurable business outcomes. By providing a quantitative framework that links human factor management to operational efficiency, this research contributes to both construction management theory and practice, emphasizing safety as a strategic driver of performance and competitiveness.

This study simultaneously investigates the effect ‘greening’ has on supply chains and the effect injecting automation has upon these green supply chains. This article adapts the natural resource-based view of the firm, a theoretical perspective prevalent in management literature, to develop a conceptual framework. This study surveys 292 firms from multiple countries to test both relationships. Literature has laid the initial groundwork for multinational manufacturing efficiency studies that we build and expand upon. Multiple techniques were used to mitigate common method bias, including multiple survey responses per firm sampled, confirmatory factor analysis with Chi- square and Harman’s test of common method bias. We find automation generally lends itself to improving aspects of green supply chains regardless of global location of firms. With increasing automation levels, supply chain efficiency improves, thereby improving economic performance. Once automation is employed, there’s a positive effect that cascades through the entire supply chain.

The world’s supply chains are changing as both expected and unexpected environmental pandemics occur. Even though some may be expected, the full extent and ramifications a pandemic will have is an estimate at best. Thus, both flexibility and resiliency are becoming crucial to efficient supply chain systems. This study analyses the recent COVID-19 phenomenon and uses it to gauge reactions, best practices, resilience-based issues and operational performance metrics in order to assist with potential future pandemics. Education, as seen, plays a pivotal role in effectively offering options to combat uncertainty and fluid situations. Such dynamic environments have historically posed a serious problem to operations; however, with proper preparation and care taken options are available today that help marginalize harm of future pandemics.

Human factors remain a leading cause of occupational accidents and fatalities in the construction industry. Although human factors are a major contributor to construction accidents, their relative impact across different construction subsectors has not been fully quantified. This study presents a comparative analysis of accident and fatality data from the U.S. Occupational Safety and Health Administration (OSHA) for three major construction subsectors, NAICS 236 (Construction of Buildings), 237 (Heavy and Civil Engineering Construction), and 238 (Specialty Trade Contractors) from January 2010 through June 2025. Seventy human-factor-related keywords were categorized into eight categories, and accident counts, fatality counts, and fatality rates, defined as the proportion of investigated cases that were fatal, were computed. Twenty-four high-frequency keywords (≥60 investigated accidents) were further analyzed across the three subsectors. Descriptive methods, including risk matrices, Pareto analysis, and category-level fatality rate comparisons, were applied to identify the factors contributing most to fatalities. Differences between subsectors were tested using permutation-based chi-square tests with Benjamini–Hochberg correction for multiple comparisons. Results indicate substantial variation in both frequency and severity among keywords. At the category level, cognitive or perceptual errors, procedural violations and unsafe acts, and judgment and awareness failures in equipment use showed significantly higher fatality proportions in Heavy and Civil Engineering Construction than in the other two subsectors, whereas at the keyword level only lost balance and inattention differed significantly once the correction was applied. The findings provide a data-driven basis for prioritizing targeted safety interventions and tailoring human factor mitigation strategies to subsector-specific risks.