The use of algorithms to guide worker behavior, referred to as algorithmic control (AC), is increasingly prevalent in organizations. Despite its potential operational benefits, prior research indicates that workers often struggle with the opaque nature of such systems. Our research aims to explore how workers perceive, judge, and react to AC systems when exposed to two distinct facets of algorithmic transparency (AT): input and transformation AT.
Through an experimental study with 121 participants, we provide empirical evidence that increased transparency about the algorithm’s transformation process significantly enhances workers’ perceived AT, which in turn positively impacts workers’ judgments and, ultimately, their continuance intention and acceptance of an AC system. In doing so, we provide practical recommendations for organizations to mitigate the adverse effects associated with algorithmic control.

Attracting potential candidates is vital for employers amidst an increasing shortage of skilled workers. This paper explores how employers can change job seekers' misconceptions about job roles and company culture through digital employer branding. Using a design science research approach, we iteratively develop and evaluate a digital game that aids job seekers in unlearning misconceptions. By recognizing, examining, reflecting on, and discarding inaccurate beliefs, they can gain a more realistic understanding of how a job or a company actually is and thus can make more informed application decisions. From an employer's viewpoint, the game serves as a digital tool to enhance hiring strategies by reducing the adverse impacts of a mismatch. Drawing on insights from human resources, serious games, and unlearning, our study contributes to the development of playful digital employer branding tools to help them navigate challenges in the dynamic landscape of digital recruitment.

Algorithmic management (AM) is increasingly transferred to the traditional work context (TWC) and is applied to support the management of permanent workers. AM only partially replaces human managers here, but the core elements of AM remain similar. Hence, AM is implemented into pre-existing organizational structures to enhance processes and performance. AM in the platform-based context is already well-researched, its implications for the TWC from a managerial perspective remain unclear. To enhance our understanding, we conduct a quantitative study analyzing the utilization of AM at an international automotive supplier. Using linear mixed modeling, we examine a data set of 12743 error records and reveal that AM has performance advantages in the TWC as it reduces the error resolving time of workers. Furthermore, the impact of influencing factors such as workforce involvement, task complexity, time of work, and experience with AM are considered, evaluated, and discussed.