The double-edged sword effects of perceived algorithmic control on platform workers’ service performance

Abstract Algorithmic control has been reflected in online labor platform management, but there is a lack of empirical research on how platform algorithmic control affects platform workers’ service performance. To address this gap, drawing upon the transactional theory of stress and regulatory focus theory, this study sheds light on how perceived algorithmic control affects the platform workers’ service performance. Data collected from 286 platform workers was used for empirical study. Findings indicate: (1) perceived algorithmic control indirectly positively affects service performance through job crafting; (2) perceived algorithmic control indirectly negatively affects service performance through withdrawal behavior; (3) the indirect effect of perceived algorithmic control on service performance via job crafting is stronger when there is a high promotion focus and weaker in the case of high prevention focus; and (4) the indirect effect of perceived algorithmic control on service performance via withdrawal behavior is weaker in situations of high promotion focus and stronger in those of high prevention focus. The theoretical and practical implications are also discussed in this work.