Human vs Algorithmic Leadership: Justice, Commitment, and Job Satisfaction

Abstract

Platform-mediated work increasingly transfers managerial functions from interpersonal supervision to algorithmic coordination. This study examines whether human-supervised and algorithmically mediated work settings activate different organizational-justice pathways to commitment and job satisfaction. A two-wave panel of 321 workers compared conventional settings with riders and pony-express workers. Structural equation modelling and multigroup analyses distinguished procedural/distributive justice (Justice1) from interpersonal/informational justice (Justice2), controlling for region of origin. In human-supervised settings, Justice2 predicted affective commitment (β = .42, p < .001), whereas in platform-mediated settings Justice1 predicted continuance commitment (β = .39, p < .01). Job satisfaction showed lower variability in the platform-mediated group, with Justice1 contributing to satisfaction stability (β = .35, p < .05). Region of origin did not materially affect these relationships. The findings suggest that the mode through which managerial authority is enacted changes the salience of justice mechanisms: human supervision foregrounds relational fairness, whereas algorithmically mediated coordination foregrounds procedural predictability.