Maghsoudi, Mehrdad, Ahmadi, Sadra, Motameni, Alireza, Otarkhani, Ali. (1405). Computational Discovery of Research Investment Patterns in Supply Chain Management: An Unsupervised Learning Framework for Funding Theme Extraction and Evolutionary Trajectory Analysis. , 18(4), 104-142. doi: 10.22059/imj.2026.413257.1008311
Mehrdad Maghsoudi; Sadra Ahmadi; Alireza Motameni; Ali Otarkhani. "Computational Discovery of Research Investment Patterns in Supply Chain Management: An Unsupervised Learning Framework for Funding Theme Extraction and Evolutionary Trajectory Analysis". , 18, 4, 1405, 104-142. doi: 10.22059/imj.2026.413257.1008311
Maghsoudi, Mehrdad, Ahmadi, Sadra, Motameni, Alireza, Otarkhani, Ali. (1405). 'Computational Discovery of Research Investment Patterns in Supply Chain Management: An Unsupervised Learning Framework for Funding Theme Extraction and Evolutionary Trajectory Analysis', , 18(4), pp. 104-142. doi: 10.22059/imj.2026.413257.1008311
Maghsoudi, Mehrdad, Ahmadi, Sadra, Motameni, Alireza, Otarkhani, Ali. Computational Discovery of Research Investment Patterns in Supply Chain Management: An Unsupervised Learning Framework for Funding Theme Extraction and Evolutionary Trajectory Analysis. , 1405; 18(4): 104-142. doi: 10.22059/imj.2026.413257.1008311

Computational Discovery of Research Investment Patterns in Supply Chain Management: An Unsupervised Learning Framework for Funding Theme Extraction and Evolutionary Trajectory Analysis

Industrial Management Journal
دوره 18، شماره 4، 2026، صفحه 104-142    اصل مقاله (1.46 M)
نوع مقاله: Original Research Article
شناسه دیجیتال (DOI): 10.22059/imj.2026.413257.1008311
نویسندگان
Mehrdad Maghsoudi1؛ Sadra Ahmadi* 2؛ Alireza Motameni3؛ Ali Otarkhani3
1Ph.D. Candidate, Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
2Assistant Prof., Cyberspace Research Institute, Shahid Beheshti University, P.O. Box 1983963113, Tehran, Iran.
3Assistant Prof., Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
چکیده
Objective: This study maps global research investment patterns in supply chain management by identifying latent funding themes and their temporal trajectories.
Methodology: This study analyzed 7,956 quality-assured grant records retrieved from Web of Science in June 2026, covering award years from 1963 to 2026. The framework combines systematic deduplication and quality filtering, multilingual transformer embeddings, K-means clustering with multi-metric validation, cluster-level TF-IDF, GERD normalization, right-censoring correction, and lifecycle sensitivity analysis.
Results: The optimal solution contains 13 thematic clusters. Five themes show GERD-normalized growth above 67%: bioenergy value chains (172%), quantum infrastructure (95%), battery supply networks (94%), additive manufacturing (83%), and cybersecurity systems (67%). Portfolio analysis assigns 37.6% of grants to emerging themes and 7.6% to mature themes, compared with the study benchmark of 25% and 30%, respectively.
Conclusion: Grant-based science mapping provides forward-looking intelligence on supply chain research priorities. The results indicate rapid investment in sustainability and advanced technologies while suggesting a relative shortage of mature-stage translational research.
کلیدواژه‌ها
Supply chain research investment؛ grant portfolio analysis؛ unsupervised machine learning؛ research funding strategy؛ temporal trajectory analysis
مراجع
آمار
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