Abstract
The increasing use of advanced technologies in human resource management, such as
 big data driven decision making, HR analytics, and AI-enhanced selection processes, is a
 notable trend. Algorithmic HRM refers to the application of advanced algorithms to automate
 tasks within human resource management or to facilitate data-driven decision making. This
 study seeks to investigate the differences in the adoption and utilization of algorithmic
 HRM among organizations based on organizational management orientations. In particular,
 this study posits that companies may vary in their perception of usefulness and ease of use
 for algorithm HRM depending on two distinct management orientations: control structure
 and external orientation. Based on the empirical analysis of 329 companies, our findings
 indicate that organizations with a control structure orientation, compared to those valuing
 flexibility structure, were more likely to adopt and utilize algorithmic HRM. Similarly, our
 results show that companies prioritizing the integration of internal resources, rather than
 relying on external environments, tend to be more engaged with algorithmic HRM. Based
 on these findings, the study discusses theoretical and practical implications and suggests
 future directions for algorithm HRM research.
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