Mapping Data-Driven Governance in Sharing Economy Platforms: Algorithmic Management, Platform Control, and Value-Creation Mechanisms

Abstract

Research on sharing economy platforms has expanded rapidly, yet the literature remains fragmented across studies on platform business models, gig work, algorithmic management, trust, reputation systems, artificial intelligence, and data-driven value creation. This article addresses this fragmentation through a bibliometric and systematic review of 660 documents retrieved from Scopus and Web of Science covering the period from 2010 to May 2026. A PRISMA-based protocol guided identification, deduplication, screening, eligibility assessment, and final corpus construction. The analysis combined performance indicators, co-citation analysis, keyword co-occurrence mapping, country collaboration analysis, longitudinal thematic evolution, strategic diagrams, and systematic content coding using Bibliometrix/Biblioshiny 5.4.1, VOSviewer 1.6.21, and SciMAT 1.1.04. The results show a marked acceleration of the field after 2020 and identify major research clusters around algorithmic labour and platform control, algorithmic management, trust and reputation, and dynamic pricing. The systematic coding further indicates that algorithmic management, reputation systems, dynamic pricing, surveillance, matching, and AI-enabled mechanisms recur across governance and value-creation processes. The study develops an integrative framework that interprets these patterns through four connected elements: data inputs, algorithmic mechanisms, governance functions, and value outcomes. This framework provides managers and regulators with a basis for assessing transparency, accountability, participant autonomy, value distribution, and the legitimacy of platform governance.