Applications, Challenges, and Implications of data processing models in Human Capital Management

Abstract

Human Capital Management (HCM) is the cohesive set of strategy and processes aimed at managing an organization’s employees in the most efficient manner. HR managers make decisions that directly impact an organization’s work culture, competitiveness and ability to meet goals. A qualitative study by Buyens and De Vos (2006) [1] concluded that the value of the HR manager as perceived by top managers and line managers extend beyond just the formulation and execution of HR strategy to delivering real topline business impact. Not to mention, weak HCM can cost companies real dollars. For example, the Department of Labor (US) estimated the cost of a bad hire at ~30% of their year-1 earnings. There already exist several papers establishing a positive causal relationship between the use of data processing and machine learning models and improved HCM outcomes [2] (Mark Tomassen, 2016). Moreover, there exist multitudes of research (Chui et al., 2015) [3] answering the question “Can technology replace humans in HCM functions?” and the resounding conclusion has been that this is highly unlikely in the near future. In fact, a study by Frey and Osbourne (2017) [4] estimated the probability of computerization for over 700 occupations and ranked the HR manager position at 28/702 implying that the function would be near impossible to replace by technology. Therefore, we begin our paper with the foundation that while AI, ML and other computer science and data processing models are not poised to or even intended to replace the HR manager, there is value to be derived from leveraging these tools for better decision making