Enhancing Work Engagement in the Gig Economy: Evidence from Platform Workers

Work engagement is pivotal for service quality and the long-term viability of platform businesses, yet its micro-level drivers remain insufficiently understood in algorithmically managed gig work. Drawing on self-regulation, social exchange, organizational justice, and algorithmic governance perspectives, this study develops an integrative framework linking workers’ self-management, perceived organizational support, organizational justice, and perceived algorithmic control to work engagement. We surveyed 292 platform-based gig workers in China using an online questionnaire. Hierarchical regressions and robustness checks using structural equation models show that all four antecedents are positively associated with engagement; when considered jointly, perceived algorithmic control, organizational support, and organizational justice remain significant, whereas the incremental association of self-management becomes weaker. Facet-level analyses further indicate that self-improvement is the key self-management mechanism; supervisor, coworker, and climate support all contribute; distributive, procedural, and interactional justice are all positively associated; and the algorithmic process and outcome control matter more than perceived task discretion. The findings highlight actionable levers for social sustainability and decent work in the platform economy, including strengthening developmental opportunities, institutionalizing fair and contestable governance, and improving the transparency and predictability of algorithmic decisions.