A Review of Digital Employment Platforms for Daily Wage Workers
Year: 2025
Type: article
Abstract: The rapid expansion of the gig economy has transformed employment structures, offering flexible work opportunities through digital job-matching platforms. While these platforms have successfully catered to skilled professionals and freelancers, daily wage workers continue to face significant barriers to employment due to digital illiteracy, financial insecurity, and the lack of structured hiring mechanisms. This review critically examines existing digital employment platforms, identifying key limitations such as wage instability, algorithmic job allocation biases, and the absence of trust-building mechanisms. Furthermore, the paper explores the role of artificial intelligence (AI) and real-time job-matching technologies in bridging employment gaps for daily wage workers. It highlights the need for mobile-friendly, location-based platforms that offer transparent wage structures, financial planning tools, and improved worker-employer trust mechanisms. Finally, this study presents future research directions aimed at developing inclusive, fair, and sustainable digital employment ecosystems for unskilled and semi-skilled workers in the gig economy. Keywords— Gig economy, K-Nearest Neighbours (KNN), AI-driven job matching, IVR (Interactive Voice Response), USSD (Unstructured Supplementary Service Data).
Author: Linsa Mathew, Ardra Sajeevan, Anand Babu, Ashish Jacob Reni
Institution: Amal Jyothi College of Engineering
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W6949588282
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