Algorithm-based job flexibility paradox and job insecurity among gig workers: the full mediation of work-family conflict

Purpose When flexibility is viewed as a core resource that attracts workers to engage in gig work, algorithmic observational monitoring increasingly introduces blur and uncertainty for gig work flexibility, forming the algorithm-based job flexibility paradox. Based on conservation of resources (COR) theory, we investigate how algorithmic observational monitoring, platform job flexibility and their interplay in this paradox impact workers’ platform job insecurity by the mediation of work-family conflict. Design/methodology/approach We collected two-wave time-lagged surveys from 314 gig workers in China. Then, we employed the partial least squares structural equation modeling (PLS-SEM) to examine the moderated-mediation model. Findings Via the full mediation of work-family conflict, platform job flexibility and algorithmic observational monitoring decrease and increase platform job insecurity, respectively. When maximizing the usage of platform job flexibility, the above indirect negative relationship is weakened. Practical implications Digital labor platforms should help workers reduce job insecurity by redesigning algorithmic observational monitoring mechanisms that balance the conflict between work and family. Originality/value From the COR perspective, we systematically incorporated the respective and interplay dynamics of resource gain (platform job flexibility) and resource loss (algorithmic observational monitoring) into the internal resource conflict between work and family, revealing the formation, impact and mitigation process of the algorithm-based job flexibility paradox in the gig economy.