https://gphjournal.org/index.php/ams/issue/feed 2025-10-15T07:14:50+00:00 MOHD MUSTAQUE editor@gphjournal.org Open Journal Systems <p style="font-family: 'Segoe UI', sans-serif; font-size: 16px; color: #333;"><strong>GPH-International Journal of Applied Management Science&nbsp;(e-ISSN&nbsp;<a href="https://portal.issn.org/resource/ISSN/3050-9688" target="_blank" rel="noopener">3050-9688</a>)</strong> is a peer-reviewed, open-access journal dedicated to advancing research in management science with a focus on practical applications. The journal publishes original research articles, comprehensive reviews, and case studies in areas such as strategic management, operations, human resource management, information systems, and innovation. By providing a global platform for scholars, practitioners, and policymakers, it fosters interdisciplinary dialogue and promotes the development of effective management practices in today’s dynamic business environment.</p> https://gphjournal.org/index.php/ams/article/view/2113 2025-10-10T10:25:35+00:00 Kyle Ruskin Matutina Porazo kyleruskin.porazo@pit.edu.ph

<p>The accelerating convergence of digital transformation and sustainability imperatives is reshaping global supply chains, driving both technological innovation and responsible business practices. This study maps the intellectual structure and thematic evolution of research at the intersection of digital transformation (DT) and sustainable supply chain innovation (SSCI) through a scientometric analysis of 3,680 Scopus-indexed publications from 2003 to 2026. Using bibliometric indicators and visualization tools (VOSviewer and Bibliometrix in R), the study examined publication trends, prolific authors, influential journals, institutional and country contributions, thematic clusters, and collaboration networks. Results reveal a vibrant, globally distributed, and interdisciplinary field, with China leading in publication output and countries such as Germany, Italy, and the United States achieving higher citation impact per publication. Core themes include “digital transformation,” “sustainability,” and “industry 4.0,” while emerging topics such as ESG, carbon performance, and supply chain resilience reflect growing integration of technological and sustainability imperatives. The collaboration network showcases strong intra-European and cross-continental partnerships, aligning directly with the United Nations Sustainable Development Goals (SDG 9, SDG 12, SDG 13, and SDG 17). While the field is thematically mature, opportunities remain in niche areas such as blockchain integration, Industry 5.0 applications, and digital inclusion, offering directions for advancing both research and practice.</p>

2025-10-10T10:13:26+00:00 ##submission.copyrightStatement## https://gphjournal.org/index.php/ams/article/view/2124 2025-10-15T07:14:50+00:00 Dearielyn Calatrava Maskariño dearielyn.maskarino@pit.edu.ph

<p><strong>Background</strong><br> Artificial Intelligence (AI) has become a transformative force in operational domains, reshaping processes in manufacturing, logistics, scheduling, and cloud-based systems. The rapid proliferation of research output, particularly within the 2025–2026 period, underscores the need for a systematic bibliometric assessment to elucidate emerging thematic trajectories, intellectual structures, and influential contributors in AI-driven operations.</p> <p><strong>Methods</strong><br> A quantitative bibliometric design was employed using the Biblioshiny platform, underpinned by the Bibliometrix R package. Bibliographic data were sourced from Scopus and restricted to publications dated 2025–2026 that explicitly addressed AI applications in operational contexts. The analysis integrated performance indicators—such as publication productivity and citation impact with science mapping techniques, including co-authorship analysis, keyword co-occurrence, thematic clustering, and network centrality metrics.</p> <p><strong>Results</strong><br> Findings reveal a pronounced temporal concentration of publications in 2025, indicative of a hyper-accelerated research front. China emerged as the predominant contributor, with South China University of Technology and other leading institutions demonstrating the highest output. Thematic mapping identified three major clusters reinforcement learning, scheduling algorithms, and smart manufacturing and a smaller emergent cluster on fabrication. Strong inter-thematic linkages highlight the convergence of AI methodologies with operational optimization and Industry 4.0 applications. Owing to the recency of the dataset, traditional citation counts were minimal; thus, PageRank and network-based metrics provided more meaningful indicators of early influence. Several recent publications demonstrated notable structural impact within the emerging knowledge network.</p> <p><strong>Conclusion</strong><br> AI-driven operations research is characterized by rapid expansion, thematic convergence, and significant regional concentration, particularly within Chinese institutions. Reinforcement learning, scheduling algorithms, and smart manufacturing constitute the intellectual core of the field, reinforced by advances in cloud and edge computing. In the context of an emergent research landscape, network-based impact measures are more appropriate than conventional citation metrics. The findings indicate a swift transition from theoretical exploration to applied innovation, necessitating continued monitoring, interdisciplinary collaboration, and strategic policy and industry engagement.</p>

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