The post-pandemic economic landscape has witnessed a rapid expansion of the gig economy, particularly within the tourism, hospitality, and digital service sectors. However, this growth is increasingly threatened by "quiet quitting"—a phenomenon where workers psychologically disengage and perform only the bare minimum to retain their platform status. This paper explores the drivers of quiet quitting among decentralized workforces, moving beyond traditional employment models to examine algorithmically managed gig workers. Utilizing a mixed-methods approach involving 450 gig workers and 25 qualitative interviews across Southeast Asia's digital travel and service platforms, the study identifies emotional exhaustion, algorithmic opacity, and the violation of psychological contracts as primary catalysts for silent attrition. Drawing upon Psychological Contract Theory and the Job Demands-Resources (JD-R) model, we propose an innovative Human Resource Management (HRM) framework tailored for the gig economy. The recommended strategies include algorithmic empathy, micro-incentivization, gamified dynamic recognition, and proactive community well-being support. These interventions aim to transform transactional gig relationships into sustainable, engaged partnerships, providing actionable insights for platform managers and HR practitioners seeking to bolster retention and service quality in the modern digital economy.
Keywords: Quiet Quitting, Gig Economy, Human Resource Management, Employee Retention, Tourism Innovation, Algorithmic Management, Post-Pandemic Workforce.