Individual Data-Driven Mindset and Decision-Making Performance: The Mediating Roles of Effort and Persistence

Abstract Despite the growing importance of employees’ data-driven mindset (DDM) in enabling data-driven transformation, research on this concept remains limited. This study addresses this gap by developing a DDM instrument and illuminating its influence on individuals’ behaviors and decision-making performance. Grounded in the mindset theory of action phases and expectancy-value theory, DDM is constructed as a composite of expectancy beliefs, values, and perceived costs. This work theorizes that DDM shapes two forms of engagement-related behavior – effort and persistence – which, in turn, influence individual decision quality. Empirical analysis suggests that expectancy beliefs positively affect effort but not persistence, while values positively influence both effort and persistence, and perceived costs negatively impact persistence but not effort. Both effort and persistence are positively associated with decision quality. By introducing the DDM instrument, this study offers a novel and theoretically grounded tool for capturing individual cognitive orientations toward data use, enabling more precise empirical investigations into how such mindsets lead to data-driven behavior and decision quality. The findings enrich the data-driven transformation literature by shedding light on how DDM shapes engagement in data-centric practices and promotes a data-driven culture.