Unlocking Global Innovation

In the dynamic landscape of global innovation, researchers increasingly adopt an integrated approach using nonparametric and regression techniques. This study highlights the significance of this method in enabling countries to understand external factors shaping innovation outcomes. Data Envelopment Analysis (DEA) serves as a robust framework for evaluating innovation efficiency, helping countries optimize their innovation processes by scrutinizing resource utilization and identifying areas for improvement. Complementing DEA, Tobit regression analysis offers insights into the nuanced influence of external drivers on innovation. The findings reveal a mixed landscape: while high-income countries dominate innovation efficiency, some lower-middle and low-income countries show notable proficiency. China, classified as an upper-middle-income country, emerges as the most referenced benchmark. Based on benchmarking, inefficient countries can enhance their innovation policies and strategies, helping to bridge the global innovation gap. Despite all input capabilities showing a negative correlation with innovation efficiency, all output variables exhibit a positive correlation. Notably, there was no association between R&D and innovation efficiency in 2020, highlighting the need for judicious use of innovation inputs to avoid wastage. Additionally, the Tobit regression model exhibits a remarkable R-squared value of 0.8523, indicating that the 16 independent factors account for 85.23% of the variation in the innovation efficiency. Amidst technology-driven transformations, leveraging nonparametric analysis methodologies is essential for organizations aiming to thrive in the global innovation arena. This study highlights the crucial role of DEA in assessing innovation efficiency and emphasizes the importance of incorporating nonparametric analysis and regression techniques into strategic decision-making processes to formulate effective innovation policies.