PUB - Publikationen an der Universität Bielefeld

Employers are increasingly using algorithmic directions to instruct employees. Algorithmic direction controls work through instructions and recommendations based on algorithms, either substituting or complementing traditional control mechanisms, such as formal input and output control or informal clan control. However, it is unclear how the combination of algorithmic direction and other control mechanisms influences employees’ perceived work autonomy. This empirical study analyses which combinations of algorithmic direction and other control mechanisms ensure employees continue to perceive work autonomy. A fuzzy-set qualitative comparative analysis was applied to a linked employer-employee dataset of 559 employees in large German workplaces. Results suggest that control configurations that combine algorithmic direction and informal clan control through personal interaction with supervisors or colleagues facilitate method autonomy, provided that organisations refrain from algorithmic monitoring and devaluation of prior skills. To ensure criteria autonomy, employees should also get to develop new skills and interact personally with both supervisors and colleagues.