How to optimize government R&D budget allocation? A review of recent studies and research gaps

Abstract

Purpose Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation. Design/methodology/approach This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process. Findings The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics. Practical implications Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms. Originality/value The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.