Navigating Co-Evolution: A Multi-Level Analysis of Socio-Technical Transitions in NFT Ecosystems

Abstract

Non-fungible tokens (NFTs) have emerged as a transformative innovation within blockchain ecosystems, enabling decentralized ownership, governance, and community formation. However, their sustainability remains uncertain due to reduced community engagement, waning public interest, and declining market valuations. This thesis investigates the socio-technical transitions of NFT ecosystems to address these challenges using the multi-level perspective (MLP) framework, integrating insights from human-computer interaction (HCI), computer-supported cooperative work (CSCW), and visual analytics (VA).

Employing a mixed-methods approach, this thesis explores the sustainability of NFT innovation across three socio-technical levels: At the niche level, it explores how collective intelligence emerges through stakeholder collaboration and algorithmic coordination in early-stage NFT communities. Content analysis of 776 social media posts, 223 survey responses, and 22 interviews identifies hybrid governance mechanisms that combine decentralized participation with intermediary facilitation, supporting bottom-up innovation.

At the regime level, it examines how dominant cultural values, particularly Confucian traditions in our case, shape trust-building within Chinese NFT communities. Qualitative analysis of WeChat group discussions and 21 stakeholder interviews reveals culturally embedded guidelines for establishing trust within blockchain’s “distrust infrastructures.” At the landscape level, it investigates how broader socio-economic forces—such as market volatility, project migration, and social media trends—reshape the rise, competition, and substitution of NFT projects. This analysis is supported by the Minimal Substitution (MS) model and NFTracer, an interactive visual analytics system that maps substitutive relationships between NFT projects using multi-attribute-aware node-link graphs and mechanism-based simulations.

This thesis contributes by (1) empirically characterizing collaboration, trust, and migration across MLP levels; (2) developing tools to support stakeholder needs; and (3) extending MLP to algorithm-driven blockchain environments. These contributions provide a comprehensive understanding of NFT ecosystems and actionable strategies for sustainable development.

Date of Award	2025
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Huamin QU (Supervisor) & Yang WANG (Supervisor)