Algorithmic human resource management: toward a functional affordance perspective

Purpose Organizations are increasingly deploying algorithmic human resource management (HRM) for decision-making. Despite algorithms beginning to permeate HRM practices, our understanding of how to interpret and leverage the functions of algorithmic HRM remains limited. This study aims to review the stock of knowledge in this field of algorithmic HRM and introduce a theoretical perspective of functional affordance to enhance the understanding of the value of algorithmic HRM. Design/methodology/approach A systematic literature review was conducted in this study based on 283 articles. The articles are extracted from the Web of Science and Scopus. The content of the articles was then integrated to formulate the framework for this study. Findings Functional affordance highlights algorithmic HRM can be systematically embedded within the organizational environment, with its characteristics naturally suggesting the functionalities or actions available for HR managers to choose from. The findings of this study demonstrate five features of algorithmic HRM from the perspective of functional affordance: awareness of algorithmic HRM, alignment with business model design, action readiness, adaptation to business context and attribution to individuality. Originality/value This study provides a novel perspective for understanding the insufficiently theorized application of algorithmic HRM within organizations. It presents an integrated framework that elucidates the key features of algorithmic HRM and elaborates on how organizations can better develop algorithm-driven capabilities based on functional affordance.