The integration of chatbots in the financial sector has significantly improved customer service processes, providing efficient solutions for query management and problem resolution. These automated systems have proven to be valuable tools in enhancing operational efficiency and customer satisfaction in financial institutions. This study aims to conduct a systematic literature review on the impact of chatbots in customer service within the financial sector. A review of 61 relevant publications from 2018 to 2024 was conducted. Articles were selected from databases such as Scopus, IEEE Xplore, ARDI, Web of Science, and ProQuest. The findings highlight that efficiency and customer satisfaction are central to the perception of service quality, aligning with the automation of the user experience. The bibliometric analysis reveals a predominance of publications from countries such as India, Germany, and Australia, underscoring the academic and practical relevance of the topic. Additionally, essential thematic terms such as “artificial intelligence” and “advanced automation” were identified, reflecting technological evolution in this field. This study provides significant insights for future theoretical, practical, and managerial developments, offering a framework to optimize chatbot implementation in highly regulated environments.

The global shortage of nurses has resulted in the demand for their services across different jurisdictions causing migration from developing to developed regions. This study aimed to review the literature on drivers of nurses’ migration intentions from source countries and offer future research directions. A search strategy was applied to ScienceDirect, Web of Science, and Scopus academic databases to find literature. The search was limited to peer-reviewed, empirical studies published in English from 2013–2023 resulting in 841 papers. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a systematic review of 35 studies after thorough inclusion and exclusion criteria. In addition, the VOSviewer software was utilized to map network visualization of keywords, geographic and author cooperation for bibliometric understanding. The findings revealed various socio-economic, organizational, and national factors driving nurses’ migration intentions. However, limited studies have been conducted on family income, organizational culture, leadership style, infrastructure development, social benefits, emergency service delivery, specialized training, and bilateral agreements as potential drivers for informing nurses’ migration intentions. Moreover, a few studies were examined from a theoretical perspective, mainly the push and pull theory of migration. This paper contributes to the health human resources literature and shows the need for future studies to consider the gaps identified in the management and policy direction of nurse labor migration.

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.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

This paper aims to explain the administrative and the Environmental, Social and Governance (ESG) of the Indonesian Spaceport Project in Biak, Papua, Indonesia, under the Public-Private Partnerships (PPP) scheme, particularly from the protest to fear of environmental damage and traditional rights. This paper analyzes the factors that cause the local society’s reluctance to accept the development of Indonesia’s very first commercial spaceport. This paper uses a doctrinal methodology, which examines changes in the trend of ESG in implementing PPP projects. The method used is a qualitative systematic review of national and international studies. This paper finds that the lack of legal certainty for administrative and ESG as the main factor contributing to the pitfall of the PPP project in Biak Papua. No clear Government Contracting Agency (GCA), plus the fact that the Indonesian government puts too much weight on business consideration in PPP while Papuan people need more ESG, especially considering the historical conflict in the region, has been the epicenter of the problem. Given the ESG-PPP regulatory failure of spaceport development in Biak, more focused studies using comparative study methodology are needed to propose a more robust and customized ESG in PPP regulations in a politically and historically sensitive area. The authors forward a regulatory reform to balance administration, ESG, and business considerations in PPP projects for a spaceport.