Platforms

This article explores the implications of digital labor intermediation platforms in paid domestic work (PDW) in Chile, a sector historically marked by informality and vulnerability. Drawing on a qualitative study conducted with members of the Federation of Domestic Workers’ Unions of Chile (FESINTRACAP), we analyze the narratives of workers who engage with digital platforms to access employment. We propose that these platforms, while expanding job search opportunities, reproduce and exacerbate precarious working conditions by weakening employment relationships, increasing surveillance through rating systems, and reinforcing structural inequalities such as gender, class, and migratory status. Using a grounded theory approach, we identify six thematic categories: (1) Access and Technological Transition, (2) Recruitment and Labor Matching Modalities, (3) Procedures and Technological Requirements, (4) Use of Ratings and Reputation, (5) Perceptions of Autonomy vs. Dependency, and (6) Lack of Regulation and Legal Guarantees. Our findings suggest that digital intermediation reconfigures labor relations under a neoliberal logic of individual responsibility while failing to provide institutional protections. We argue that the digitalization of labor intermediation in PDW deepens the sector’s historical patterns of invisibility and exclusion, highlighting the urgent need for regulatory frameworks that address the specificities of this type of employment.

Green development serves as the foundation for high-quality development. As one of the most commonly used macroeconomic regulation policies, taxation is a crucial component of the modernization of the national governance system and governance capacity, playing an irreplaceable role in accelerating the comprehensive green transformation of economic and social development. Based on panel data from 30 provinces (municipalities, autonomous regions) from 2011 to 2021, this paper empirically analyzes the impact of green taxation on green transformation. The study finds that green taxation can significantly promote urban green transformation, and there is significant regional heterogeneity in the impact of green taxation on urban green transformation. Mechanism tests further reveal that green taxation influences the scale of urban investment platform debt, thereby driving urban green transformation.

The increasing complexity of managing maintenance activities across geographically dispersed hospitality facilities necessitates advanced digital solutions capable of effectively balancing operational costs and service quality. This study addresses this challenge by designing and validating an intelligent Prescriptive Maintenance module, leveraging advanced Reinforcement Learning (RL) techniques within a Digital Twin (DT) infrastructure, specifically tailored for luxury hospitality networks characterized by high standards and demanding operational constraints. The proposed framework is based on an RL agent trained through Proximal Policy Optimization (PPO), which allows the system to dynamically prescribe preventive and corrective maintenance interventions. By adopting such an AI-driven approach, platforms are the enablers to minimize service disruptions, optimize operational efficiency, and proactively manage resources in dynamic and extended operational contexts. Experimental validation highlights the potential of the developed solution to significantly enhance resource allocation strategies and operational planning compared to traditional preventive approaches, particularly under varying resource availability conditions. By providing a comprehensive and generalizable representation model of maintenance management, this study delivers valuable insights for both researchers and industry practitioners aiming to leverage digital transformation and AI for sustainable and resilient hospitality operations.