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With the global rise of the fourth industrial revolution, the rapid advancement of big data, digitization, and cloud computing technologies has led to a substantial influx of data (Petrillo et al., 2018; Xia et al., 2023; Xu et al., 2018). Algorithmic management, which refers to utilizing advanced data analytics for automating managerial decision-making and employee supervision, represents a pivotal shift in contemporary organizational operations (Jarrahi et al., 2023; Tomprou & Lee, 2022). In recent years, algorithmic management has garnered widespread adoption in various business sectors due to its potential to enhance operational efficiency and overall organizational performance (Cheng & Foley, 2019; Duggan et al., 2020; Meijerink & Bondarouk, 2023; Parent-Rocheleau & Parker, 2022).
However, despite the enthusiasm for this management paradigm, its underlying operational patterns have become clear: It serves as a rigid mechanism that allows managers to control workers and limit employee autonomy, potentially leading to inflexibility in organizational structures and processes (Meijerink & Bondarouk, 2023). While streamlining certain operations, this control-centric approach can inadvertently create an environment where employees are less able to exercise discretion or engage in creative problem-solving (Benlian et al., 2022). Under these controlling and restrictive conditions, employees face the significant challenge of independently synthesizing information to innovate since their creative thinking is limited by algorithm-driven directives (Kellogg et al., 2020). Previous research has demonstrated that such rigid situations can elicit unfavorable employee emotional responses, such as feelings of detachment or powerlessness (Lee, 2018), reduce trust and engagement among them (Kellogg et al., 2020; Morse et al., 2021), and induce work overload due to inflexible task assignments and performance metrics (Wood et al., 2019). However, our understanding of how algorithmic management specifically influences employee creativity remains limited. Considering the apparent contradiction between businesses’ implementations of algorithmic management and their demand for employee innovation to thrive in a rapidly evolving environment, it is imperative to delve into the nuanced impact of algorithmic management on employee creativity and to explore how algorithmic management is stifling workplace dynamics.
Moreover, as a situational factor, the impact of algorithmic management on work is not only determined by its characteristics but also influenced by the interaction with individual factors of employees (Parker & Grote, 2022). Algorithms may struggle to fully understand and adapt to the diversity and complexity of individual human factors when processing them, which can be related to the impact on employee motivation and abilities (Lee et al., 2015). Thus, we will focus on exploring the impacts of how algorithmic management and other factors (i.e., employee ability and motivation) influence employee creativity, which remains a largely unexplored realm of inquiry.