Industry Stakeholders Research Articles

Digital platforms and the construction supply chain: trends and emerging themes in extant AEC research

A substantial body of research has emerged on the conditions that impact how and when industry stakeholders adopt digital platforms. However, in the construction industry in general—and the architecture, engineering and construction (AEC) industry in particular—to date, this research remains fragmented, which hampers our understanding of how digital platforms can enhance productivity and value creation. This paper aims to provide a better understanding of the benefits and challenges of adopting digital platforms and integrating them into the AEC industry. We conduct a systematic literature review on digital platform integration to identify themes that are relevant to the AEC industry. Based on our findings, we develop an integrative model that captures the phases of digital platform adoption and integration and outlines where different industry stakeholders tend to face challenges in this process, and we pinpoint where benefits can be derived. We employ this model to derive a research agenda that will fill the identified knowledge gaps currently existing in our understanding of construction industry stakeholders’ integration within the digital platform space.

Security Challenges and Business Opportunities in the IoT Ecosystem

In the growing IoT environment, security seems to be a dominant topic, because of the broad extent of IoT devices functionalities is expressed in the different sectors. This article scrutinizes the elaborate IoT infrastructure, exposing the common flaws and traceable security incidents, which serve as a background in the appreciation of the need of having reliable security frameworks. At the same time, the research paper pinpoints the vast business prospects that will spawn from the issues, highlighting the fact that these can be tapped through the creation and provision of cutting-edge security systems and consultancy services. The study makes a prediction of the future of IoT based on its current rating and empirical evidence by reviewing literature to assess emerging technologies and market trends, which give way for future innovations in IoT security. In general, this paper proposes that interested actors who are proactive and afraid of the emerging security threats have an opportunity to tap into the growth and innovation in building strong IoT systems. Consequently, the insights offered in this paper are appropriate for both the industry stakeholders and the researchers.

Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies Using AI (QUADAS-AI): Protocol for a Qualitative Study.

Quality assessment of diagnostic accuracy studies (QUADAS), and more recently QUADAS-2, were developed to aid the evaluation of methodological quality within primary diagnostic accuracy studies. However, its current form, QUADAS-2 does not address the unique considerations raised by artificial intelligence (AI)-centered diagnostic systems. The rapid progression of the AI diagnostics field mandates suitable quality assessment tools to determine the risk of bias and applicability, and subsequently evaluate translational potential for clinical practice. We aim to develop an AI-specific QUADAS (QUADAS-AI) tool that addresses the specific challenges associated with the appraisal of AI diagnostic accuracy studies. This paper describes the processes and methods that will be used to develop QUADAS-AI. The development of QUADAS-AI can be distilled into 3 broad stages. Stage 1-a project organization phase had been undertaken, during which a project team and a steering committee were established. The steering committee consists of a panel of international experts representing diverse stakeholder groups. Following this, the scope of the project was finalized. Stage 2-an item generation process will be completed following (1) a mapping review, (2) a meta-research study, (3) a scoping survey of international experts, and (4) a patient and public involvement and engagement exercise. Candidate items will then be put forward to the international Delphi panel to achieve consensus for inclusion in the revised tool. A modified Delphi consensus methodology involving multiple online rounds and a final consensus meeting will be carried out to refine the tool, following which the initial QUADAS-AI tool will be drafted. A piloting phase will be carried out to identify components that are considered to be either ambiguous or missing. Stage 3-once the steering committee has finalized the QUADAS-AI tool, specific dissemination strategies will be aimed toward academic, policy, regulatory, industry, and public stakeholders, respectively. As of July 2024, the project organization phase, as well as the mapping review and meta-research study, have been completed. We aim to complete the item generation, including the Delphi consensus, and finalize the tool by the end of 2024. Therefore, QUADAS-AI will be able to provide a consensus-derived platform upon which stakeholders may systematically appraise the methodological quality associated with AI diagnostic accuracy studies by the beginning of 2025. AI-driven systems comprise an increasingly significant proportion of research in clinical diagnostics. Through this process, QUADAS-AI will aid the evaluation of studies in this domain in order to identify bias and applicability concerns. As such, QUADAS-AI may form a key part of clinical, governmental, and regulatory evaluation frameworks for AI diagnostic systems globally. DERR1-10.2196/58202.

Building consensus on clinical outcome assessments for BCI devices. A summary of the 10th BCI society meeting 2023 workshop.

The 10th International Brain Computer Interface (BCI) Society Meeting, 'Balancing Innovation and Translation', was held from the 6th to 9th of June 2023 in Brussels, Belgium. This report provides a summary of the workshop 'Building Consensus on Clinical Outcome Assessments (COAs) for BCI Devices'. This workshop was intended to give participants an overview of the current state of BCI, future opportunities, and how different countries and regions provide regulatory oversight to support the BCI community to develop safe and effective devices for patients. Five presentations and a panel discussion including representatives from regulators, industry, and clinical research stakeholders focused on how various stakeholders and the BCI community might best work together to ensure studies provide data that is useful for evaluating safety and effectiveness, including reaching consensus on clinical outcome assessments (COAs) that represent clinically meaningful benefits and support regulatory and payor requirements. This report focuses on the regulatory and reimbursement requirements for medical devices and how to best measure safety and effectiveness and summarizes the presentations from five experts and the discussion between the panel and the audience. Consensus was reached on the following items specifically related to BCI: (i) the importance of and need for a new generation of COAs, (ii) the challenges facing the development of appropriate clinical outcome assessments, and (iii) that improvements in COAs should demonstrate obvious and clinically meaningful benefit(s). There was discussion on: (i) clinical trial design for BCIs and (ii) considerations for payor reimbursement and other funding. Whilst the importance of building community consensus on COAs was apparent, further collaboration will be required to reach consensus on which specific current and/or novel COAs could be used for the BCI field to evolve from research to market.

Role of Artificial Intelligence in Vegetable Production: A Review

Role of Artificial Intelligence (AI) in vegetable production, emphasizing its potential to address critical challenges such as climate change, population growth, and resource scarcity. AI technologies, including machine learning, computer vision, and robotics, are revolutionizing agricultural practices. AI-driven innovations in crop management, pest control, and soil analysis enhance productivity, reduce labour costs, and ensure sustainable farming practices. Notable advancements include precision spraying by Blue River Technology, significantly reducing herbicide use, and deploying autonomous tractors and drones for efficient farm management. AI applications, such as PEAT's Plantix and Trace Genomics, provide accurate diagnostics for soil health and pest management. Satellite-based solutions like Farm Shots and aWhere offer real-time crop monitoring and weather prediction, optimizing resource use and mitigating risks. The review highlights the importance of making AI technologies more affordable and accessible to farmers, particularly in developing regions. Collaboration between researchers, industry stakeholders, and policymakers is crucial to harness AI's full potential in agriculture. As AI continues to evolve, its integration into vegetable production promises a more efficient, resilient, and sustainable agricultural sector, contributing to global food security and environmental preservation. The aim of the study is to evaluate the impact and effectiveness of Artificial Intelligence (AI) in vegetable production, focusing on how AI technologies enhance productivity, efficiency, and sustainability. The objectives are to assess current AI applications, analyze their benefits and challenges, and provide recommendations for future improvements and wider adoption in the agricultural sector. The research methodology for the study on the role of Artificial Intelligence in vegetable production involves a comprehensive literature review of existing AI technologies and their applications in agriculture, coupled with the analysis of case studies to evaluate real-world implementations. Additionally, expert interviews and surveys with farmers and industry professionals will be conducted to gather insights on the benefits, challenges, and future potential of AI in this sector. The theoretical implications of the study on the role of Artificial Intelligence in vegetable production include advancing the understanding of AI's capabilities in agricultural optimization and contributing to the academic discourse on sustainable farming practices. Practically, the study provides actionable insights for farmers and agribusinesses on implementing AI technologies to enhance crop yields, reduce resource wastage, and improve overall farm management efficiency.

Technology Trends Revolutionizing the World of Work- Auto Industry Perspective based on the SDG

Aim/Purpose: Disruptive technologies impact business cycles and work practices, paving the way for new-generation manufacturing trends. Organizations are constantly working to introduce products with a higher degree of innovation, catering to customer demands in the market. Background: The automobile industry plays a significant role in applying disruptive technologies while proving value to all stakeholders. The technological disruptions in the auto sector can potentially change the relationship between consumers and the automobile while impacting business trends and work practices. Methodology: Various scholarly research material was reviewed to understand the impact of technology disruptions on manufacturing methods, work practices, employee engagement, and skill development. Secondary data analysis was conducted for this qualitative and descriptive study. Contribution: This paper helps to understand the impact created by various technologies in the auto sector on employee retention—the role of man- Machine collaboration in creating job satisfaction among employees. Findings: Besides automation and digitalization, AI, Robotics, and Mechatronics play a crucial role in the sector's evolution. The study findings help us understand the impact of technology on manufacturing and business practices. Recommendation for Practitioners: Investment in training the workforce and embracing collaboration helps employees to benefit from the real world of work. Recommendation for Researcher: To ensure the relevance of research studies, practitioners and researchers should maintain a collaborative approach, using research insights to inform business strategies. Impact on Society: Analyze how technologies like collaborative robots (cobots) are integrated into manufacturing processes and their impact on job roles and efficiency. Technology continues to evolve, so societies shall proactively address challenges and leverage opportunities to take advantage of these innovations. It is the role of government policymakers, industry stakeholders, and educators to shape the auto industry's future in a way that maximizes the benefits to society from technological innovation. Future Research: The study's scope is to be done quantitatively by covering OEMs all over India. It discussed disruptive technology in general. Specific technological advancements can be analysed to dive deep into the phenomenon. A similar study could be undertaken in another industry besides the automobile.

SolarPACES Task III Project: Analyze Heliostat Field:

In recent years, great efforts have been made to reach a consensus on heliostat testing best practices. A specific SolarPACES task was launched to provide a Heliostat Testing Guidelines document for single heliostat evaluation with a focus on prototype validation and qualification. Such guidelines are not well-suited for heliostat evaluation in operating commercial heliostat fields. The commercial implementation of the Central Receiver technology is burdened by the lack of a demonstrated cost-effective methodology to test solar fields, particularly during the commissioning and operation phases of the plant. To address heliostat characterization challenges, the SolarPACES funded Project “Analyze Heliostat Field” aims to set the basis towards a SolarPACES guideline for Heliostat Field Performance testing under a common framework. This is by means of a review of the existing methodologies, R&D and industrial stakeholders information sharing and preparation of a future quantitative comparison and validation plan. As part of the development of this project, several meetings and a workshop involving the SolarPACES community was organized to share knowledge and experience in the measurement and characterization of heliostat fields using a range of technologies and procedures. Research centers and companies from 5 different and distant countries have actively participated in these meetings, sharing their experiences, needs and interests. This paper summarizes the outcome of this international collaborative effort and the prospects for future close collaborations sustained over time.

Analysing Oil Prices Fluctuations, Carbon Emissions and Renewable Energy Trends in Uncovering the Eco‐Economic Interplay in China's Trade Liberalization

ABSTRACTThis study evaluates the factors contributing to carbon emissions in China between 2002 and 2021, considering the impact of GDP growth, shocks in oil prices, trade liberalization and energy use. Using the dynamic simulated ARDL (SARDL) model, we indicate a long‐term link between trade liberalization, oil prices (WTI) shocks, GDP growth, energy use and CO2. The novel SARDL revealed a direct relationship between trade liberalization and CO2 in China in the long term. However, the results show a negative impact of WTI shocks on CO2 emissions. The study suggests that renewable energy (RE) significantly and negatively affects China's CO2 emissions. In addition, the findings conclude that, in the case of China, GDP has an insignificant long‐run relationship with CO2 emissions. The outcomes provide valuable insights for policymakers, researchers and industry stakeholders to enhance RE and promote sustainable energy practices.

Motivations, Choices, and Constraints of Italian Transgender Travelers: A Study of Tourism Dynamics within the Rainbow

This study explores the motivations, choices, and constraints shaping tourism behavior among transgender individuals living in Italy. Employing a mixed-methods approach, the research begins with quantitative data collection and analyses, followed by qualitative insights to uncover the multifaceted reasons that drive transgender individuals to engage in tourism. These motivations range from seeking personal authenticity to cultural exploration and community connection. This study also examines the intricate interplay of choice and constraint in shaping transgender travelers’ tourism experiences, highlighting the challenges they face and the strategies they use to cope with the obstacles they face within the tourism context. Given Italy’s persistent stereotypes and prejudices against gender and sexual minorities, this research provides a critical examination of the intersectionality of gender identity and tourism within a challenging cultural and legal landscape. The findings contribute to a deeper understanding of transgender travelers’ tourism experiences and offer valuable implications for industry stakeholders, policymakers, and scholars. By amplifying the voices of Italian transgender travelers, this study aims to foster greater inclusivity and recognition of their diverse needs and experiences within the tourism sector.

How Will Our Retirement Life Be? An Analysis of Elderly Care through the Lens of the Forest Therapy Industry

With the development of the social economy and the arrival of the aging era, the issue of elderly care has increasingly gained social attention. This matter not only concerns the personal interests of individuals but also represents a significant focal point in the construction of social welfare systems, influencing the development direction of multiple industries. As the elderly population continues to grow, there is a pressing need for innovative and sustainable care solutions. Among the diverse elderly care models, the forest therapy industry emerges as a new direction, providing seniors with a comfortable natural environment that promotes physical and mental health. This approach not only enhances the well-being of older adults but also serves as an important avenue for rational and sustainable development through the utilization of forest ecological products. This paper explores the connection between elderly care models and the forest therapy industry, offering insights into the current state and future development of the Chinese elderly care sector. By examining the benefits of integrating forest therapy into existing care frameworks, this study aims to provide practical recommendations. It is hoped that these insights will serve as a reference for stakeholders in the Chinese elderly care industry, promoting the positive role of forest therapy in optimizing and enhancing elderly care models for a healthier, happier aging population.

Action Plans Study: Principles of Green Chemistry, Sustainable Development, and Smart Cities

The sustainability of cities is a challenge due to the growth, functioning, and needs of the population. In this context, the concept of the smart city has gained prominence worldwide in the last decades. In Brazil, it has also drawn attention driven by technological advances and the search for innovative solutions to urban challenges. Several different reports were created aiming to assess and categorize the advancement of cities in this regard. Some of them established their own indicators for this evaluation, whereas others are based on standards ISO 37120 and ISO 37122. The focus of this study revolves around the examination of indicators presently utilized, which may be influenced by initiatives grounded in the principles of green chemistry (PGCs). Furthermore, it explores how these principles can aid in the realization of the targets outlined in the Sustainable Development Goals (SDGs) set forth by the United Nations. Two case studies are presented, the first pointing out how PGCs and SDGs can correlate with smart city reporting indicators. The second is a case study centered on the Brazilian city of Curitiba (PR). We aim to exemplify how the city’s action plans underscore the significance of synergy among the principles of green chemistry, the objectives of sustainable development, and indicators for smart cities. Assessing how chemically green a city can be is a relevant argument for future industrial installations and stakeholders and the influence of this index on the quality of life of its population. Such an approach not only fosters innovation and efficiency but also fosters environmental stewardship, thus contributing to overall sustainability.

Improved Monitoring and Classification of Engine Oil Condition through Two Machine Learning Techniques

<div>This study explores the effectiveness of two machine learning models, namely multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), in advancing maintenance management based on engine oil analysis. Data obtained from a Mercedes Benz 2628 diesel engine were utilized to both train and assess the MLP-NN and ANFIS models. Six indices—Fe, Pb, Al, Cr, Si, and PQ—were employed as inputs to predict and classify engine conditions. Remarkably, both models exhibited high accuracy, achieving an average precision of 94%. While the radial basis function (RBF) model, as presented in a referenced article, surpassed ANFIS, this comparison underscored the transformative potential of artificial intelligence (AI) tools in the realm of maintenance management. Serving as a proof-of-concept for AI applications in maintenance management, this study encourages industry stakeholders to explore analogous methodologies.</div> <section> <h2>Highlights</h2> <div> <ul> <li> <div>Two machine learning models, multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), were employed to predict and classify the performance condition of diesel engines.</div> </li> <li> <div>Among various training algorithms, Levenberg–Marquardt and the Bayesian regularization demonstrated superior classification accuracy, achieving a 95%–96% range.</div> </li> <li> <div>To assess the generalizability of MLP-NN and ANFIS, the training set size was varied from 90% to 10%.</div> </li> <li> <div>The ANFIS model exhibited greater stability than MLP-NN, with a 50% higher performance.</div> </li> </ul> </div> </section> <section> <h2>Graphical Abstract</h2> <div> <img/> </div> </section>

PSII-7 Development and assessment of a mobility scoring system for beef cattle

Abstract Lameness is a disruption of normal gait due to infection, injury, or abnormal conformation, causing pain and discomfort. Several scoring systems have been developed to determine lameness severity; a key indicator in deciding if an animal is fit to withstand transport. However, most scoring systems were developed for dairy cattle and may not be suited for use in feedlot cattle due to the large genetic variation between beef breeds as well as the highly variable environment in which they are housed. Lack of consistency when scoring lameness and reduced ability to detect early lame cattle can result in poor animal welfare and increased costs. The objectives of this study were 1) to develop a mobility scoring system that aids in transport decisions for beef cattle, and 2) to determine observer repeatability using this scoring system. Over a 7-mo period, video cameras were placed in an alley to record the ambulation of individual cattle exiting the processing barn at 4 commercial feedlots in Southern Alberta. Cameras were set to record whole-body videos of cattle walking, running, or trotting. Five researchers trained to use this mobility scoring system, individually evaluated 174 cattle from the collected videos, for fitness for transport and the presence/absence of gait abnormalities: shortness of stride, stiffness (mild, moderate, or severe), limp (mild, moderate, or severe), hip hike, head dropped, head bob, arched back, non-weight bearing, or reluctance to move. The mobility scoring system used was a modified (NCBA and Zinpro) 4-point scale (0-3) for locomotion: 0) an animal with no gait abnormalities; 1) an animal with mild stiffness and shortness of stride; 2) an animal with difficulty taking some steps and one or more of the following: severe stiffness, limp favoring affected limb, limps with a head bob when walking but still bears weight; and 3) an animal reluctant or unable to move, bearing little or no weight on affected limb, the head of the animal is dropped and back arched with pronounced head bob and severe limp detected, or incoordination. Fitness for transport was scored as fit, compromised, or unfit according to the humane transportation guidelines of the Canadian Food Inspection Agency. A kappa statistic was calculated to assess inter-observer reliability for both mobility and transport fitness scoring systems as well as gait abnormalities. The combined mobility scores had substantial agreement between observers (Κ = 0.63; P < 0.001). Mobility score of 2 had the highest agreement among all variables (Κ = 0.88; P < 0.001), followed by head bob (Κ = 0.85; P < 0.001), and arched back (Κ = 0.76; P < 0.001). Results of this study will aid in the development of a mobility score that improves industry stakeholder agreement regarding lameness severity and transport loading decisions.

301 Leveraging genomics to advance the breeding of Canadian livestock

Abstract Implementing breeding strategies for a more sustainable food system is one of the most pressing topics for livestock industries. Although different species face different challenges, large-scale research projects can pave the way for future opportunities in these key sectors. Herein, we present two examples (dairy and poultry) of how research projects can result in meaningful changes to commercial breeding programs. The first example is an international large-scale dairy project, which will deliver a roadmap for greenhouse gas (GHG) management using genomics and nutrition. The project aims to facilitate a 55% reduction in GHG emissions from Canadian dairy at an estimated value of $338M CAD. This systems-level approach will leverage resources developed through previous and current large-scale projects to produce accurate multi-level emission estimates and identify opportunities to mitigate enteric GHG emissions. The results of this project will provide accurate, reliable, and robust data for industry stakeholders, national policy, and GHG inventories. The second example is a research collaboration with a commercial turkey breeding company that will enable breeding strategies for long-term and sustainable genetic improvement in health traits. This project aims to reduce preslaughter mortality by 5% and condemnations due to myopathies and health issues. This reduction alone is expected to result in 7.5M kg more turkey meat at a value of $19.8M CAD. Furthermore, there is also an expected reduction of 2.3M kg of CO2 equivalent per year for each 1% livability improvement due to production efficiencies and reduced inputs wasted for more sustainable and ethical meat production. Overall, ongoing and future research initiatives will continue to deliver solutions for key Canadian livestock sectors to improve animal health and welfare as well as efficiency and sustainability. The strides made in these ongoing collaborative projects are imperative for enhancing the sustainability of animal agriculture to feed the growing population.

Circular economy strategies: a fuzzy DEMATEL decision framework for the fast fashion footwear manufacture

The fast fashion industry significantly impedes sustainability by its rapid production cycles, low-cost products, and heavy carbon footprint. Nowadays, circular economy (CE) has become an interesting concept, which follows the “take-make-dispose” model and allows products, materials, and resources to be in use as long as possible. There is a paucity of research in the identification of circular economy strategies (CES) and mapping them based on their interrelationships, which is very important from a managerial perspective. In this vein, this study identifies nine CES and analyzes their causal relationships using the Fuzzy Decision-Making Trial and Evaluation Laboratory method in the context of fast fashion footwear manufacture. The key contributions include providing actionable insights for implementing CE practices, enhancing the sustainability of the footwear industry, and offering a strategic roadmap for industry stakeholders. The results and implications of this study will guide footwear industry decision-makers in making a strategic map considering the cause-and-effect interrelationships among the CES for implementing CE business practices in the footwear industry.

Advancements and Challenges in Real-Time Electronic Vision Technologies for Canned Fish Quality Inspection: A Comprehensive Review

The global demand for high-quality canned fish products has driven the adoption of advanced inspection technologies to ensure consistency, safety, and compliance with industry standards. This paper provides a comprehensive review of real-time electronic vision technologies employed in the inspection of canned fish quality. It traces the evolution of the canned fish industry from manual inspection methods to sophisticated automated systems, emphasizing the role of technologies such as hyperspectral imaging, machine learning algorithms, and electronic vision systems. The effectiveness of these technologies in detecting defects, assessing quality parameters, and maintaining product integrity is critically analyzed. Despite their benefits, challenges such as high costs, the need for specialized skills, and integration complexities with existing production processes are significant barriers. This review addresses these challenges and proposes solutions, including cost-reduction strategies, workforce training, and the development of adaptable systems. The paper concludes by outlining future research directions, particularly in validating these technologies in real-world scenarios and enhancing their accessibility to the industry. The findings offer valuable insights for researchers and industry stakeholders aiming to advance the quality control of canned fish products through innovative technological solutions.

Shariah Contracts in Private Equity and Its Prospect for Small and Medium Enterprises (SME)

This study explores the significance of contracts in private equity applications, particularly in addressing Shariah compliance concerns inherent in traditional private equity structures. Private equity plays a crucial role in funding and nurturing SMEs, aligning with equity-based activities that are conducive to Islamic finance principles. Using a qualitative Grounded Theory approach, the study involves interviews with various stakeholders in the financial industry. Grounded theory is an iterative and inductive method that systematically analyses data to uncover patterns and themes, generating hypotheses or theories derived directly from the data. The study identifies murabahah, ijarah, mudarabah, and musharakah as suitable contracts for private equity applications. These findings illuminate opportunities for financial industry participants, particularly SMEs, to innovate and access funding through private equity avenues.

Economic impacts of tourism demand on the European food and accommodation industry

Sustainability strategies have become central topics in the literature on tourism development, given the significant increase in tourism demand in Europe. This study examines the economic (cost) impact of increased tourism demand on the European Food and Accommodation Industry. We employed three distinct models — pooled linear regression, flexible nonparametric, and semiparametric smooth coefficients — using data spanning European countries from 2005 to 2020. Our findings reveal the heterogeneous impacts of tourist arrivals and length of stay on costs. Although the direct effects were predominantly positive, the indirect effects were found to be negative, resulting in a minor overall negative effect on the average. Furthermore, we found that the proportion of domestic tourists has a notable impact on costs, revealing that higher proportions are linked to lower direct costs but higher indirect costs on average. These findings provide valuable insights for industry stakeholders and policymakers alike.

Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research

ObjectivesTo identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as...

Building Safer Workplaces: Unveiling the Impact of Safety Leadership Styles in the Construction Industry

This study investigates the most effective leadership style for enhancing safety performance in the construction industry. Through a scientometric analysis of 114 research papers and a meta-analysis of 13 studies involving 7220 participants, it uncovers a growing body of research on safety leadership in construction and identifies safety-specific transformational leadership as the most impactful style for improving safety outcomes. The research highlights the increasing importance of safety leadership in the construction sector and reveals a trend towards safety-specific transformational leadership as the preferred approach. This leadership style emphasizes empowering and motivating employees to prioritize safety through shared values, vision, and goals. The study also emphasizes the need for enhanced collaboration among researchers and institutions, and advocates for the inclusion of studies from developing countries to ensure a comprehensive understanding of safety leadership practices globally. Additionally, the findings align with the Sustainable Development Goals 3, 8, and 11, underscoring the significance of fostering safe and secure working environments and promoting sustainable urbanization. This research serves as a valuable resource for construction industry stakeholders seeking to implement effective safety leadership strategies, and it lays the groundwork for further exploration in this critical area.