GenAI-enabled algorithmic control and gig workers' continuance work intention: evidence from ride-hailing drivers
Peng Wang, Congying Jiang, Xueyan Dong, Tianhui Gao, Lin ZhangPurpose
The purpose of this study is to examine how perceived GenAI-enabled algorithmic control influences ride-hailing drivers' continuance work intention and to unpack the underlying mechanisms from a double-edged sword perspective by testing the mediating role of challenge and hindrance stress and the moderating role of family motivation.
Design/methodology/approach
Drawing on the cognitive appraisal theory of stress and self-determination theory, this study develops and tests a theoretical model using online survey data from 514 ride-hailing drivers in China. The research model is tested using structural equation modeling (SEM).
Findings
The results showed that GenAI-enabled standardized guidance and GenAI-enabled behavioral constraints elicited both challenge and hindrance stress, whereas GenAI-enabled tracking evaluation elicited only hindrance stress. Hindrance stress increased drivers' withdrawal intention but had no significant effect on continuance intention, whereas challenge stress increased continuance intention but had no significant effect on withdrawal intention. Family motivation positively moderated the effects of both hindrance and challenge stress on continuance intention.
Practical implications
This study offers significant practical implications for ride-hailing platforms. Importantly, when deploying GenAI-enabled algorithms, platforms should strike a balance between standardization and humanization while continuing to promote efficiency-enhancing tools.
Originality/value
This research extends the GenAI literature by deepening the understanding of GenAI-enabled algorithmic control within the labor process. Furthermore, this study examines how family motivation moderates these relationships, offering a more nuanced view of ride-hailing drivers' work intentions in the digital age.