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.

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.

Amid the unfolding Fourth Industrial Revolution, the integration of Logistics 4.0 with agribusiness has emerged as a pivotal nexus, harboring potential for transformational change while concurrently presenting multifaceted challenges. Through a meticulous content analysis, this systematic review delves deeply into the existing body of literature, elucidating the profound capacities of Logistics 4.0 in alleviating supply chain disruptions and underscoring its pivotal role in fostering value co-creation within agro-industrial services. The study sheds light on the transformative potential vested within nascent technologies, such as Internet of Things (IoT), Blockchain, and Artificial Intelligence (AI), and their promise in shaping the future landscape of agribusiness. However, the path forward is not without impediments; the research identifies cardinal barriers, most notably the absence of robust governmental policies and a pervasive lack of awareness, which collectively stymie the seamless incorporation of Industry 4.0 technologies within the realm of agribusiness. Significantly, this inquiry also highlights advancements in sustainable supply chain management, drawing attention to pivotal domains including digitalization, evolving labor paradigms, supply chain financing innovations, and heightened commitments to social responsibility. As we stand on the cusp of technological evolution, the study offers a forward-looking perspective, anticipating a subsequent transition towards Industry 5.0, characterized by the advent of hyper-cognitive systems, synergistic robotics, and AI-centric supply chains. In its culmination, the review presents prospective avenues for future research, emphasizing the indispensable need for relentless exploration and pragmatic solutions. This comprehensive synthesis not only sets the stage for future research endeavors but also extends invaluable insights for practitioners, policymakers, and academicians navigating the intricate labyrinthstry of Logistics 4.0 in agribusiness.

The business life cycle is examined through a comprehensive literature review in this academic study. Our initial approach involves searching for relevant articles on firm life cycle and strategy using the Web of Science and Scopus databases. We conduct bibliometric analyses to identify key contributors and recurring keywords. Subsequently, we select twenty-seven research papers to explore the Theory Development, Characteristics, Context, and Methodology (TCCM) framework for firm life cycle and strategy. Our analysis summarizes corresponding business strategies for each stage, including the use of Initial Management Control Systems (MCS) in the introduction phase. As companies grow, a high inventory-to-sales ratio may hinder effectiveness, but it proves beneficial in the growth and revival stages. Mature companies excel in green process innovation and engage more in Corporate Social Responsibility (CSR) activities. In the decline stage, firms use cost efficiencies, asset retrenchment, and core activity focus for recovery, signaling commitment to a successful turnaround. However, there is a research gap in exploring appropriate global strategies for various life cycle stages, providing an opportunity for additional articles to thoroughly investigate this relationship and assess multinational enterprises’ success trajectories throughout their life cycles.

Infrastructure decision-making has traditionally been focused on the use of cost-benefit analysis (CBA) and multicriteria decision analysis (MCDA). Nevertheless, there remains no consensus in the infrastructure sector regarding a favored approach that comprehensively integrates resilience principles with those tools. This review focuses on how resilience has been evaluated in infrastructure projects. Initially, 400 papers were sourced from Web of Science and Scopus. After a preliminary review, 103 papers were selected, and ultimately, the focus was narrowed down to 56 papers. The primary aim was to uncover limitations in both CBA and MCDA, exploring various strategies for amalgamating them and enhancing their potential to foster resilience, sustainability, and other infrastructure performance aspects. Results were classified based on different rationalities: i) objectivist, ii) conformist, iii) adjustive, and iv) reflexive. The analysis revealed that while both CBA and MCDA contribute to decision-making, their perceived strengths and weaknesses differ depending on the chosen rationality. Nonetheless, embracing a broader perspective, fostering participatory methods, and potentially integrating both approaches seem to offer more promising avenues for assessing the resilience of infrastructures. The goal of this research proposal is to devise an integrated approach for evaluating the long-term sustainability and resilience of infrastructure projects and constructed assets.