Academic work on sharing-economy platforms has surged since 2020 and clustered around algorithmic control, management, reputation systems and dynamic pricing; the authors offer a concise data→algorithm→governance→value framework to help managers and regulators evaluate transparency, accountability and value distribution.

Main Finding

A bibliometric and systematic review of 660 articles (Scopus & Web of Science, 2010–May 2026) finds that research on sharing-economy platforms has accelerated sharply after 2020 and coalesced around a small number of dominant themes—especially algorithmic labour/platform control, algorithmic management, trust and reputation systems, and dynamic pricing. Across these areas, algorithmic mechanisms (e.g., matching, surveillance, dynamic pricing, reputation algorithms) repeatedly appear as core elements linking data inputs to governance functions and value outcomes. The authors propose an integrative four-part framework (data inputs → algorithmic mechanisms → governance functions → value outcomes) to interpret platform dynamics and to guide managerial and regulatory assessment of transparency, accountability, participant autonomy, value distribution, and governance legitimacy.

Key Points

• Corpus: 660 documents from Scopus and Web of Science, covering 2010–May 2026.
• Rapid growth: field shows marked acceleration after 2020.
• Major research clusters identified:
  • Algorithmic labour and platform control
  • Algorithmic management
  • Trust and reputation systems
  • Dynamic (algorithmic) pricing
• Recurring mechanisms across governance and value-creation: algorithmic management, reputation systems, dynamic pricing, surveillance, matching, other AI-enabled mechanisms.
• Integrative framework: four connected elements — data inputs, algorithmic mechanisms, governance functions, value outcomes — to analyze platform governance and impacts.
• Practical focus: framework aids managers and regulators in evaluating transparency, accountability, autonomy, value distribution, and legitimacy.

Data & Methods

• Search & corpus construction:
  • Sources: Scopus and Web of Science.
  • Timeframe: 2010 to May 2026.
  • Final corpus size: 660 documents.
  • Screening protocol: PRISMA-based workflow (identification, deduplication, screening, eligibility assessment, final inclusion).
• Bibliometric and systematic analyses:
  • Performance indicators (publication and citation trends).
  • Co-citation analysis to reveal intellectual structure.
  • Keyword co-occurrence mapping to identify topical clusters.
  • Country collaboration analysis to map geographic research patterns.
  • Longitudinal thematic evolution and strategic diagrams to track theme dynamics over time.
  • Systematic content coding to extract recurring mechanisms and governance links.
• Software/tools:
  • Bibliometrix / Biblioshiny 5.4.1
  • VOSviewer 1.6.21
  • SciMAT 1.1.04

Implications for AI Economics

• Conceptual integration: The proposed data→algorithm→governance→value framework supplies a parsimonious lens for economists studying platform markets, highlighting how data flows and algorithmic design translate into governance choices and economic outcomes (prices, wages, surplus distribution).
• Research priorities:
  • Measure how algorithmic mechanisms redistribute value between platforms, workers, and consumers (dynamic pricing, matching, commission structures).
  • Quantify effects of algorithmic management and surveillance on worker behavior, productivity, labor supply, and bargaining power.
  • Study reputation and trust algorithms’ influence on market efficiency, entry barriers, and competition.
  • Expand causal and microdata studies—particularly post-2020 developments—and cross-country comparative work to capture regulatory and institutional heterogeneity.
• Policy and regulation:
  • Regulators should target transparency, accountability, and auditability of platform algorithms (e.g., disclosure requirements, algorithmic impact assessments).
  • Consider metrics and standards to evaluate participant autonomy and fair value distribution (wage impacts, consumer surplus, platform rents).
  • Governance interventions (e.g., limits on surveillance, constraints on dynamic pricing, minimum standards for reputation systems) can be evaluated using the framework’s mapping from inputs→mechanisms→outcomes.
• Methodological implications:
  • Interdisciplinary approaches (economics, computer science, law, management) and access to platform-level data are crucial to move from descriptive bibliometric patterns to causal, policy-relevant findings.
  • Standardized outcome measures and replication datasets would help consolidate the fragmented literature the review documents.

If you’d like, I can: - Extract a short list of the most-cited papers identified in the review. - Map concrete policy levers onto the four-part framework with examples (e.g., algorithmic audits → accountability → reduced information asymmetry).