This paper proposes a methodology for developing a Research Impact Index (RII), a benchmarking tool designed to evaluate research impact across multiple domains. By integrating Data Envelopment Analysis (DEA) with Ordered Weighted Averaging (OWA), the RII provides a versatile platform for assessing the efficiency of research-innovation programs. The methodology’s broad applicability across sectors underscores its global relevance, positioning it as a valuable resource for institutions aiming to enhance their contributions to national and regional innovation ecosystems. A key feature of this methodology is its structured incorporation of subjectivity through the OWA operator, which reflects decision-makers' (DM’s) preferences when prioritizing performance indicators. This ensures that the RII remains adaptable to different institutional goals, cultural contexts, and policy environments. By tailoring the model’s sensitivity to varying decision-making preferences, research institutions can better account for the diverse interests of stakeholders, including government agencies, private industry, and funding bodies. Tested on a sample of research projects in agriculture and food sciences, the RII unveiled that two thirds of the projects, are found to be performing efficiently in terms of the expected research outcomes, regardless of the DM’s optimism level. Meanwhile, the inefficient projects are required to expand the proxy outputs, if willing to reach the RII frontier. These include the perceived outcomes, expected impacts, research efficacy, innovative performance and target users with proportions exceeding 11.92%, 18.46%, 13.87%, 11.89% and 30.69%, respectively,. These findings suggest that it might be more demanding to improve the performance of inefficient projects through Target users rather than Innovative performance. Hence, a prioritization scheme that would handle the expansion tasks on the basis of increasing output rates would be recommended.
Oukil et al. (Wed,) studied this question.