HRM Algorithms: Moderating the Relationship between Chaotic Markets and Strategic Renewal

Abstract

HRM algorithms can profoundly impact organizations in the digital economy era. In the face of turbulent and complex market conditions, strategic renewal is regarded as one of the most important organizational mechanisms for dealing with market uncertainty and a turbulent environment. However, the existing research remains elusive about the relationship between market-level conditions and firm-level strategic renewal. Our paper addresses this important gap by examining the potential enhancement of agile strategic renewal in high-uncertainty environments through the implementation of HRM algorithms. Drawing on Chaos Theory, we argue that HRM algorithms have the potential to support the self-organization capacity of a workforce, supporting better alignment with changing environments. Using covariance-based structural equation modelling on a survey of over 500 Spanish firms, our findings provide partial support for the modelled hypotheses by showing that the use of HRM algorithms positively moderates the relationship between market turbulence and strategic renewal, but does not appear to moderate the relationship between market complexity and strategic renewal. The study contributes to our understanding of the importance of adopting internal business analytics systems to stimulate agility and align the workforce more effectively with changing environments, but also highlights their less substantive role in deciphering complex external factors.