From automation to employee loyalty: understanding the balance between robots and workforce stability

Purpose Grounded in the conservation of resources (COR) theory, this study aims to investigate how robot usage influences employees’ job insecurity, perceived organizational support, job satisfaction and turnover intention, while also considering the moderating effects of training and development opportunities. Design/methodology/approach Leveraging survey data from 343 employees across 20 robot-integrated hotels in Guangdong, China. This study pioneers a dual-method approach (partial least squares structural equation modeling [PLS-SEM] + fuzzy set qualitative comparative analysis [fsQCA]) to model turnover intention as both linear and configurational outcomes, revealing that robot usage alone explains only 32.8% of job insecurity, while combined factors (e.g. low perceived organizational support) drive 79.9% of turnover cases. Findings The findings reveal that the implementation of robots significantly heightens job insecurity, subsequently driving up turnover intentions. Conversely, job satisfaction and perceived organizational support effectively mitigate turnover intention and mediate the relationship between job insecurity and turnover intention. Notably, enhanced training and development opportunities alleviate the negative impacts of job insecurity on employee outcomes. In addition, fsQCA identifies six distinct configurations leading to high turnover intentions, offering actionable strategies for hotels to foster employee engagement and retention in the face of technological advancements. Originality/value This study combines PLS-SEM and fsQCA to address a key paradox in COR theory, how training can both reduce job insecurity and increase turnover. This dual-method approach highlights previously unexplored resource tradeoffs in automation literature. High robot usage leads to 32.8% job insecurity (PLS-SEM), but with low organizational support, it influences 79.9% of turnover instances (fsQCA).