BUSINESS MANAGEMENT IN ALGORITHMIC ENVIRONMENTS: REDEFINING ACCOUNTABILITY, GOVERNANCE, AND PERFORMANCE

Abstract The increasing integration of algorithmic systems into organizational decision-making has fundamentally altered the landscape of business management. In many contemporary organizations, key managerial decisions are no longer made solely by human judgment but are mediated, informed, or partially executed by algorithms. This shift has created algorithmic environments in which traditional management concepts—such as accountability, governance, and performance—are no longer clearly defined. While existing research often approaches algorithms from technological or ethical perspectives, this paper argues that algorithmic environments represent a core business management challenge rather than a purely technical one. Adopting a management-centered perspective, this study examines how algorithmic decision systems reshape managerial roles, responsibilities, and authority. It argues that when decisions are produced through human–algorithm interaction, conventional models of accountability become insufficient, as responsibility is distributed across managers, systems, and organizational structures. Similarly, governance mechanisms designed for human-centered decision-making struggle to provide transparency, oversight, and control in algorithmically mediated contexts. Performance measurement is also destabilized, as traditional output-based metrics fail to capture decision quality, value alignment, and systemic impact in algorithmic organizations. The paper develops a conceptual framework that redefines accountability, governance, and performance for business management in algorithmic environments. Rather than treating algorithms as autonomous decision-makers or neutral tools, the framework positions them as embedded elements of managerial systems that require deliberate design and oversight. The study demonstrates that effective management in algorithmic environments depends on preserving managerial judgment, redesigning governance structures, and aligning performance metrics with organizational value rather than algorithmic efficiency alone. This research contributes to business management scholarship by reframing algorithmic decision-making as a managerial design problem. It provides theoretical insights and practical implications for organizations seeking to integrate algorithms into management systems without eroding responsibility, control, or strategic coherence.