Abstract

The overall purpose of organizational staffing is to deliver fresh hires into organizations. Efforts to improve staffing have historically involved pursuing two primary goals: improving job applicant quality and improving the process used to quantify and make decisions about those applicants. Industrial/-organizational (I-O) psychologists, based upon decades of research, have many specific processes they commonly employ to meet these goals. Despite this, a family of technologies commonly referred to as big data has begun to appear in staffing processes without much, if any, validation from I-O psychologists. Data scientists have claimed that such technologies have the potential to “disrupt” the bedrock staffing procedures on which much of modern I-O psychology has been built. The truth of this claim is difficult to determine for many reasons, but most glaringly because data scientists and I-O psychologists come from such different theoretical perspectives that it is often difficult to find common ground even in casual conversation.