Purpose - As algorithmic management expands within traditional hospitality workplaces, understanding its impact on employees becomes essential. This study investigates how employees’ perceptions of algorithmic management influence their resistance to these systems, well-being, and turnover intentions.

Design/methodology/approach - A mixed-methods approach, combining qualitative interviews (Study 1), quantitative surveys to validate and test the model (Study 2) and a between-subjects online experiment (Study 3), was employed to explore these dynamics under varying conditions of algorithmic control, managerial involvement, and algorithmic opacity.

Findings - The results indicate that while algorithmic enhancements positively influence well-being, they do not diminish resistance to algorithmic management, which is primarily driven by algorithmic restraints. Resistance negatively impacts well-being, which in turn drives turnover intentions. Furthermore, a non-linear relationship between enhancements and restraints emerges, indicating an optimal point at which enhancements are maximised. Human managers play a critical moderating role by providing emotional support and facilitating transparency in algorithmic decision-making.

Originality – This study identifies resistance as a mediator of employee well-being, and the results indicate that technology acceptance and resistance can co-exist. It provides an employee-centred perspective on attitudes towards technology in hospitality, thus offering a novel view compared to prevailing consumer-focused technology acceptance models.