WORKWAVE 2.0: A Trust-Centric Micro-GIG Economy Connector Using Behavioural Analytics

Abstract

The hyper-extension of hyperlocal micro-gig platforms reconstituted the access to household and personal services, but the majority of them have not passed the trust-safety-service reliability trilemma. They employ most of them with the use of the static rating mechanism that fails to characterize the consistency of behaviour and accountability in real-time. A micro-gig economy connector called WORKWAVE 2.0, proposed in this paper, is a trust-based micro-gig economy connector that dynamically assesses service providers with behavioural analytics and community-tested interactions. In this case, a mixed trust assessment system is suggested, which implements the history of fulfilling tasks, punctuality, customer feedback, and patterns of interaction. It is the graph based trust propagation model that gives the trust the ability to develop automatically by means of confirmed user worker relationship, and it is also the GPS based location tracking that improves the transparency and also permits the ability to check on the punctuality. The fact that anomaly-aware trust adjustment decreases the chance of presenting unreliable or dishonest behaviour even further. It has been experimentally tested that the proposed method enhances trust and service reliability and the accuracy of gig matching as compared to traditional rating-based methods. WORKWAVE 2.0 offers a scalable and explainable model of the development of far safer and reliable hyperlocal gig platforms.