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Abstract: This study introduces an unsupervised machine learning approach to predict Technology Readiness Levels (TRLs) using bibliometric data. Traditional TRL assessments often depend on expert opinions, which can be subjective and resource intensive. By analysing metrics such as publication counts, patent filings, and grant funding, the proposed model classifies technologies into low, medium, and high readiness categories. Notably, publication-related metrics emerged as the strongest predictors, accounting for over 60% of the model's predictive power. Various unsupervised machine learning models were applied during the study, and among them, the MDBSCAN model achieved the highest accuracy of 84.9%. This data-driven methodology offers a scalable and objective alternative to conventional TRL assessments, enhancing decision-making in research and development management.
Keywords: Technology Readiness Levels; Unsupervised Machine Learning; Bibliometric Data; Technology Maturity Assessment; Publication Metrics; Patent Metrics; Grant Funding Metrics; Innovation Forecasting; Data-Driven Decision Making; Research and Development Management
1. Introduction
The accurate and timely assessment of technological maturity is a critical challenge for organizations across industries and research domains. Effective technology readiness assessment enables informed decision-making in R&D prioritization, investment strategies, and policy formulation. However, traditional methods for evaluating technology readiness levels (TRLs) are often limited by subjectivity, high costs, and scalability issues (Ernst, 2002).
The Technology Readiness Level (TRL) framework, initially developed by NASA in the 1970s, provides a systematic approach to assessing the maturity of technologies, categorizing them on a scale from TRL 1 (basic research) to TRL 9 (commercialization) (Martínez-Plumed et al., 2021). While the TRL framework has been widely adopted, conventional approaches to TRL assessment continue to rely heavily on expert opinions, which can introduce biases and inconsistencies.
In an era characterized by rapid technological advancement and increasing complexity, there is a pressing need for more objective and data-driven approaches to forecast technological readiness Gao et al. (2013). Recent advances in bibliometrics, machine learning, and data analytics offer promising opportunities to automate TRL prediction and enhance the efficiency and scalability of technology assessment (Kostoff et al., 2004).
This study addresses the limitations of traditional TRL assessment methods by proposing an unsupervised machine learning framework that leverages comprehensive bibliometric data to predict technology readiness levels. By integrating insights from publications, patents, grants, and clinical trials, our approach aims to provide a more objective, data-driven assessment...