A research team led by Professor Kim Dohyoung (first author) of the KMU International Business School (KIBS) at Kookmin University (President Jeong Seung Ryul) (Co-author: Professor Kang Song-hee of Tech University of Korea; Corresponding Author: Professor Hong Ahreum of Kyung Hee University) has been published in Technovation, a prestigious SSCI-indexed international journal in the field of management (JCR IF 10.9, Top 3% in Management | ABS 3 | ABDC A).

Public research and development (R&D) projects assess research progress through annual and interim reviews to determine whether to continue the project or adjust its direction. However, existing evaluation methods often rely on the subjective judgments of experts, leading to persistent concerns regarding a lack of consistency in evaluation criteria, evaluation bias, and efficiency issues in evaluating large-scale projects.

To address these limitations, Professor Kim Dohyoung’s research team analyzed the potential applications of generative artificial intelligence (Generative AI) and proposed the “MEG (Maturity-Expectation Gap)” framework, which quantitatively analyzes the discrepancy between the actual maturity of the technology and stakeholders’ expectations. The team combined survey data from experts with experience in public R&D evaluation with machine learning-based analysis of academic literature to compare and analyze the level of technological expectations against perceived technological maturity.

The analysis revealed significant differences in expectations regarding Generative AI and perceptions of actual technological maturity across stakeholder groups. It was confirmed that the larger this expectation-maturity gap, the lower the trust in and willingness to adopt AI. Additionally, the research team demonstrated that by diagnosing the feasibility of Generative AI adoption across different evaluation domains, it is possible to distinguish between areas where technology application is relatively easy and those requiring additional preparation.

This study systematically analyzed the gap between expectations and reality that may arise when applying generative AI technology to public R&D evaluation and policy decision-making processes. It is expected to provide important insights for establishing policy evaluation and decision-making systems utilizing artificial intelligence in the public sector in the future.

Professor Kim Dohyoung stated, “Generative AI has the potential to enhance efficiency and consistency in the public R&D evaluation process; however, if the gap between expectations of the technology and its actual maturity is not managed, the adoption process may instead generate distrust and resistance.” He added, “The MEG framework proposed in this study can be utilized to diagnose this gap and establish a phased adoption strategy.”

This achievement is significant in that it presents an analytical framework for systematically applying generative AI technology to public R&D evaluation and policy decision-making processes, and it is expected to contribute to the future establishment of data-driven public R&D management and policy decision-making systems.