Forecasting Capabilities in Blockchain Data Networks: Trends from Bibliometric Analysis - Evy Nurhayati Sri Hardini; Rizki Oktavianto (Universitas Muhammadiyah Sidoarjo, 2025)
General Background: Blockchain technology has gained significant global attention due to its potential to enhance transparency, security, and efficiency in various domains, including business forecasting. Specific Background: The integration of blockchain into forecasting mechanisms can improve supply chain efficiency, inventory management, and market demand prediction. Knowledge Gap: Despite its potential, limited research has systematically examined blockchain's role in forecasting capabilities, particularly through a bibliometric analysis approach. Aims: This study employs R Studio and VOSviewer to analyze bibliometric data from Scopus, aiming to identify trends, influential publications, and research gaps in blockchain-based forecasting. Methods: A systematic bibliometric analysis was conducted on 287 relevant articles published between 2015 and 2023, focusing on citation networks, keyword co-occurrence, and thematic clustering. Results: The findings indicate that forecasting is a dominant research theme, with China contributing the most publications. Key studies highlight blockchain's role in cryptocurrency prediction, supply chain management, and decentralized finance. Novelty: This research provides the first comprehensive bibliometric mapping of blockchain-based forecasting, revealing emerging trends and future directions. Implications: The study informs businesses, policymakers, and researchers on leveraging blockchain for predictive analytics, offering insights for enhancing decision-making in finance, trade, and supply chain management.