Examining Firms' R&D Strategies through Simulation and Empirical Analysis: An Evolutionary Theory Perspective

Abstract

This paper employs a simulation-based empirical approach to investigate firms' research and development (R&D) strategies, guided by evolutionary theory. Recognizing the dynamic and complex nature of R&D decision-making processes within firms, evolutionary theory offers a valuable framework for understanding how firms adapt and evolve their R&D strategies over time. Drawing on agent-based modeling techniques and empirical data from a diverse set of industries, this study simulates different scenarios to explore the impact of various factors, including market competition, technological uncertainty, organizational capabilities, and environmental dynamics, on firms' R&D investment decisions and innovation outcomes. Through comparative analysis and scenario testing, this research elucidates the evolutionary mechanisms underlying firms' R&D behavior, such as exploration versus exploitation trade-offs, imitation and learning from competitors, and the emergence of technological trajectories. Furthermore, this study conducts empirical analyses using real-world data to validate the simulation results and provide insights into the applicability of evolutionary theory in explaining firms' R&D strategies across different contexts. The findings contribute to advancing theoretical understanding and managerial insights by uncovering the underlying dynamics of firms' R&D decision-making processes and offering strategic guidance for firms seeking to optimize their R&D investments and innovation performance in dynamic and uncertain environments.