Adaptive job recrafting of gig workers: Concept, measurement, and validation of its impact on job satisfaction

In the gig economy dominated by algorithmic control, online labor platforms tend to reduce work resources while increasing job demands, leading gig workers to face weakened agency and declining job satisfaction. This study integrates adaptive structuration theory and job crafting theory to propose the concept of adaptive job recrafting for gig workers who perceived algorithmic control, systematically exploring its connotations and impact mechanisms through a multi-stage mixed-methods research design. First, the grounded theory method is applied to construct a four-dimensional model of adaptive job recrafting comprising algorithm task debugging, socio-technical mutual construction, collaborative skill expansion, and identity cognition evolution. Subsequently, a 19-item measurement instrument was developed following rigorous scale development procedures, with reliability and validity confirmed through empirical testing. Finally, based on the Job Demands-Resources model, a longitudinal survey was conducted to empirically test the positive impact mechanism of adaptive job recrafting on job satisfaction, revealing the partial mediating role of work engagement and the moderating role of psychological capital. This study transcends the limitations of traditional job crafting theory by unveiling the adaptive mechanisms of human-technology interaction under algorithmic control. The practical implications provide evidence for platforms to optimize algorithmic design (such as reserving crafting spaces and incorporating performance evaluation) and for gig workers to construct systematic crafting strategies, helping to achieve a dynamic balance between control and empowerment.