Designing Evidence-Based Managerial Decision Support Tools for Older Adult Managers

Evidence-based managerial decision making (EBMDM) is increasingly critical in digital and data-rich organizational environments. At the same time, the managerial workforce is aging, with many senior and experienced managers continuing to make high-stakes decisions well into later adulthood. Prior research suggests that normal age-related cognitive changes can affect how older adults search for, integrate, and synthesize information, particularly in technology-mediated settings. Yet, decision support systems and managerial tools are rarely designed with age-related cognitive variability in mind. This work-in-progress paper presents an ongoing research program that leverages neurophysiological and human–computer interaction methods to understand evidence gathering and decision making among older adult managers at a deeper level and to inform the design of customizable EBMDM tools. By integrating EEG, eye-tracking, physiological and behvioural measures with traditional techniques to observe older adult managers engaged in realistic managerial decision tasks, this research aims to contribute to decsion making theory and develop adaptive decision aids that reduce cognitive burden, mitigate bias, and support high-quality decision making across the adult lifespan.