Algorithmic Control (AC), the use of algorithms to align worker behavior with organizational goals, is increasingly shifting from managing low-skill workers, for example in food delivery or on ride-hailing platforms, to controlling high-skill, knowledge-intensive professions. Previous research has mainly focused on workers’ relationships with single AC systems. However, we argue that given the complexity of real-world business environments, high-skill workers may navigate multiple conflicting AC systems. This is because traditional organizations implement AC into various existing processes with multiple, self-contained, and data-separated solutions. We argue that, in traditional organizations, multiple AC systems act as independent control agents, leading to Algorithmic Control Misalignment (ACM). To explore the phenomenon of ACM, we will conduct a vignette-based online experiment with finance students participating in an investment simulation game. In our study, we examine how ACM affects trading decisions, using Cognitive Dissonance Theory (CDT). We expect to discover behaviors of information avoidance, a mechanism to selectively prioritize one AC over another. While such behavior may resolve the finance students’ feelings of being in a decision conflict resulting from ACM, it likely results in suboptimal decision-making. Our study aspires to contribute to AC literature by highlighting how workers navigate conflicting AC systems in high-skill, conventional work. We aim to deliver primary practical insights for companies to recognize potential misalignment prior to implementing different AC systems into workers’ organizational settings.
