GPH-International Journal of Applied Management Science

Scientometric Mapping of the Intellectual Landscape of Digital Transformation and Sustainable Supply Chain Innovation

Kyle Ruskin Matutina Porazo — 2025-10-10

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.

EMERGING TRENDS IN ARTIFICIAL INTELLIGENCE (AI) -DRIVEN OPERATIONS:

Dearielyn Calatrava Maskariño — 2025-10-15

Background
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.

Methods
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.

Results
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.

Conclusion
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.

STRATEGIC LEADERSHIP TO STRENGTHEN AIRPOWER STRATEGY TO SUPPORT NATIONAL DEFENSE SYSTEM

Yulianto Hadi — 2025-10-21

This study investigates how strategic leadership can strengthen airpower strategy to support Indonesia’s national defense system. Indonesia’s geostrategic position in the Indo-Pacific exposes it to airspace incursions, cyberattacks on radar and communications, and the employment of drones for espionage and sabotage, underscoring the need for an adaptive and integrated airpower posture. Using a qualitative phenomenological design, data were gathered through interviews with Indonesian Air Force (TNI AU) leaders, operational observations, and analysis of defense documents. Data were analyzed with Miles, Huberman, and Saldaña’s interactive model, and credibility was reinforced through source- and method-triangulation. Findings indicate that visionary leadership acts as a catalyst for defense modernization, organizational reform, and professionalization of personnel. Cross-agency collaboration with the Ministry of Defense, BRIN, and the national defense industry (DEFEND ID) accelerates mastery of UAVs, passive radar, and C4ISR systems. AI- and big-data–enabled innovations enhance detection speed and response effectiveness against multidomain contingencies, while a whole-of-government approach strengthens societal legitimacy and support for airpower. Comparative lesson-drawing suggests Indonesia can adapt the U.S. acceleration doctrine, South Korea’s research–industry synergy, Turkey’s pursuit of defense industrial autonomy, and Singapore’s Total Defense framework. The study concludes that integrating strategic leadership with a modernized airpower strategy is foundational to an adaptive, responsive, and highly deterrent Indonesian air defense system.

DOMESTIC DEBT FUNDING ELEMENTS AND PUBLIC WORKS EXPENDITURE IN NIGERIA

NWACHUKWU, CHINENYE — 2025-10-21

This study examined the relationship that subsist between Domestic Debt Funding Elements and Public works Expenditure in Nigeria for the period under study (1987 - 2023), utilizing secondary data. Treasury Bills (TB), Treasury Certificates (TC) and Development Stock (DS) are explanatory variables while Public Works Sector Expenditure (PWE) is the dependent variables used to measure Domestic Borrowings in Nigeria for the period under review. From the result of the study all employed variables on the model are stationary at first difference and exhibited a long-run relationship. From the output of model, it was discovered that all employed variables are positive and statistically significant. but treasury certificate was positive and insignificant to public works sector expenditure. We therefore recommended that Government should source for more funds with treasury certificates in other to have the needed suitable funds for infrastructural financing and chunk of domestic borrowing should be deployed to this sub-sector to ensure adequate supervision as well as monitoring and maintenance of public utilities. Finally, government should make known to the public the benefits of domestic borrowing as it leads to economic development.

ARTIFICIAL INTELLIGENCE APPLICATION AND MARKETING EFFECTIVENESS OF ONLINE SHOPPING BUSINESSES IN NIGERIA

Francis Okafor Francis — 2025-10-22

This study investigates the relationship between artificial intelligence application and marketing effectiveness of online shopping firms in Nigeria. The objectives of the study were to investigate the influence of data mining and machine learning on sales volume. Employing the cross-sectional survey design, the study implemented the quasi-experimental research design. A research questionnaire was developed in accordance with the research questions and distributed to the management level employees and IT staff of these online businesses, who comprised the target population of the study. Each of the twenty-four (24) e-commerce companies chosen for this study received five (5) questionnaires from the researcher. Following data cleansing, an overall total of 102 copies of the disseminated questionnaire were recovered. For this analysis, we utilised SPSS Version 25.0 and the PPMCC to test our hypothesis over these copies. The findings indicated that there exist a positive and significant correlation between the volume of sales and data mining, as well as a positive and significant correlation between the volume of sales and machine learning in Nigerian online purchasing firms. The study recommended that; the data mining activities of the online shopping firms should be standardized to ensure that online shoppers can access desired products and services without any challenges or shortcomings; machine learning should be integrative enough to help both the online business and their customers maintain mutual exchange of information and directives that will not be detrimental to the online shoppers and in extension, the organization.