The successful execution of large-scale infrastructure projects is essential for economic growth and societal development, but these projects are too often beset with financial risks. The main financial risks related to infrastructure projects, including cost overrun, funding uncertainty, currency fluctuation, and regulatory change are examined in this research. The study identifies and assesses the magnitude and frequency of these risks by combining surveys and analysis of financial reports. The findings show that current risk management strategies, including hedging, contingency funds, and public-private partnerships, are often unsuitable to respond to the specific needs of financial uncertainties. The research suggests the need for an all-encompassing financial risk management framework that relies on real-time data analysis and a cocktail of risk assessment tools. Additionally, the development of strategic tailored approaches to address financial risk recovery depends on proactive stakeholder engagement. This research complements the existing literature on risk management in infrastructure projects by highlighting the financial dimensions of risk management and suggesting future research on advanced financial tools and technologies. Ultimately, large-scale infrastructure project sustainability and success contribute to economic stability and societal well-being can only be achieved through effective financial risk management.

Purpose: This review mainly aims to identify the lean practice conducted in hospitals, determining what problems lean practice can be helpful to solve in the hospitals. Data sources: Four electronic databases (Scopus, Web of science, Medline, and PubMed) were conducted for searching related literature in this review. Study selection: These studies in the hospitals that related lean healthcare practice and contained outcome variables were included. Data extraction: Related information such as research design, countries, lean tools, outcome variables, results were extracted. Results of data synthesis: 20 eligible articles were identified in this review. There was 20% lean practice being conducted in emergency department of hospitals in this review. Six cases have implemented lean in Brazilian hospitals. There were 12 cases implemented lean practice through Value Stream Mapping. Conclusion: Lean practices were highly valued in Brazilian hospitals, and it was frequently implemented in hospital emergency department. Value Stream Mapping and process mapping were the most commonly used lean tool. Waiting time, lead time and Length of Hospital Stay (LOS) were the primary indicators reflecting improvements in this review.

Our study investigates the relationship between firm profitability, board characteristics, and the quality of sustainability disclosures, while examining the moderating effects of financial leverage and external audit assurance. A key focus is the distinction between Big 4 and non-Big 4 audit firms. Using data from Malaysia’s top 100 publicly listed organizations from 2018 to 2020, we analyze sustainability reports based on the Global Reporting Initiative (GRI) standards. Unexpectedly, our results indicate a negative association between firm profitability and board characteristics, challenging traditional assumptions. We find that non-Big 4 audit firms significantly enhance sustainability disclosure quality, contradicting the widely held belief in the superiority of Big 4 firms. Our finding introduces the “Big 4 dilemma” in the Malaysian context and calls for a reassessment of audit firm selection practices. Our study offers new perspectives on the strategic role of board composition and audit firm selection in advancing sustainability disclosures, urging Malaysian organizations to evaluate audit firms on criteria beyond the global prestige of Big 4 firms to improve sustainability reporting.

This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

The sustainability of the creative industry through creative-based tourism in the Laweyan Tourism Village requires the support of a sustainable and inclusive development model for local communities. This research aims to present the design of a tourist village development model that applies the eco-cultural city concept as a Surakarta City Perspective through creative-based tourism towards creative industries. This research uses a qualitative approach with a descriptive exploratory method. Data collection techniques use interviews with key informants. Empirical observation using cultural mapping as identification of physical mapping of spatial layout, build ings and environment, as well as cultural landscapes for tangible and intangible cultural assets of the community in the local landscape in the Laweyan tourist village. Content analysis is applied as a research data analysis method. The research results provide an overview of the design of the creative-based tourism village development model towards a sustainable creative industry including aspects attraction, accessibility, amenities, and ancillary, and green tourism. Model design requires commitment and participation from the government and private sector in collaborating with sustainable tourist village development forums.

Named Entity Recognition (NER), a core task in Information Extraction (IE) alongside Relation Extraction (RE), identifies and extracts entities like place and person names in various domains. NER has improved business processes in both public and private sectors but remains underutilized in government institutions, especially in developing countries like Indonesia. This study examines which government fields have utilized NER over the past five years, evaluates system performance, identifies common methods, highlights countries with significant adoption, and outlines current challenges. Over 64 international studies from 15 countries were selected using PRISMA 2020 guidelines. The findings are synthesized into a preliminary ontology design for Government NER.