Abstract
Despite the importance of individuals' data-driven mindset (DDM) in the digital transformation triggered by analytics and artificial intelligence (AI) in organizations, research on this concept remains scant. This study addresses this gap by conceptualizing DDM and shedding light on its antecedents and outcomes. Based on the mindset theory of action phases, we employ the expectancy-value theory to conceptualize DDM and propose its sub-constructs, comprising expectancy beliefs, values, and costs that drive individuals' behavioral intentions and responses. Accordingly, we further explore how individuals' analytics knowledge relates to DDM, influencing their commitment to data-driven approaches and, subsequently, decision quality. By providing a conceptualization and definition of DDM, this study holds original value and enriches the literature on human mindsets. This work also contributes to the digital transformation literature by elucidating the antecedents and outcome variables of DDM. It offers actionable insights into the mechanism enabling organizations to shape their employees' DDM, consequently facilitating data-driven practices and decision-making performance.