Analysis of the Efficiency and Determinants of Regional R&D Investment using the DEA Model

Despite significant central government investment in regional R&D, a persistent problem of decreasing innovation capacity exists in certain regions due to imbalances in regional investment and outcomes.Accordingly, the objective of this study is to combine the Data Envelopment Analysis (DEA) model and regression analysis to measure the efficiency of regional R&D investment by metropolitan area and identify the key factors influencing that efficiency.To achieve this, data from 17 local governments (from 2021 to 2023) were categorized into metropolitan areas.The BCC model was used to analyze pure technical efficiency, while beta regression analysis was employed to analyze the factors affecting regional R&D efficiency.The analysis showed that the Seoul Metropolitan Area, Daegu-Gyeongbuk, Gwangju-Jeonnam, and Gangso areas were efficient, whereas the Busan-Ulsan-Gyeongnam (Bu-Ul-Gyeong) and Chungcheong areas were inefficient.In particular, the Chungcheong area recorded the lowest efficiency, confirming an urgent need for structural improvement.In the analysis of influencing factors, the number of researchers was found to have a statistically significant negative (-) impact on R&D efficiency.Conversely, corporate R&D investment and the number of domestic patent applications and number of government R&D commercializations had a significant positive (+) impact.This suggests that a mere quantitative increase in personnel can lead to a decrease in efficiency, while the voluntary investment of the private sector and activities that create tangible outcomes, such as patents and commercialization, are crucial factors in increasing efficiency.This study applied a two-stage analysis methodology integrating DEA and regression analysis to enhance the objectivity and reliability of R&D efficiency analysis, thereby providing practical implications for policymakers.