Female-Accommodating Algorithmic Management Practices

This dataset was generated through a two-part empirical investigation of Female-Accommodating Algorithmic Management Practices (FAAMP) in the gig economy. Study 1 comprises semi-structured interviews with female app-based food delivery workers in China. These interviews examined participants’ lived experiences, expectations, and emotional responses—such as felt gratitude and perceived stigma—regarding gender-sensitive algorithmic management. Thematic analysis of this qualitative data inductively yielded a six-dimensional FAAMP framework.Building on these insights, Study 2 employed a 2×2 video-based experimental design to test the effects of FAAMP and Autonomy-Supportive Implementation Style (ASIS) on app-based food delivery workers. The experiment assessed psychological empowerment as the outcome variable, with gratitude and stigma as dual mediators. The dataset includes anonymized interview transcripts and experimental data collected through validated survey measures.