Stepping back to own: a moderated mediation model of blockchain governance, decentralized identity, and digital media sovereignty in the metaverse

1 Introduction

The incipient realization of the Metaverse represents a critical juncture in the evolution of digital communication. Envisioned as a persistent, shared, and interactive virtual continuum, the Metaverse integrates immersive technologies such as virtual reality (VR) and augmented reality (AR) with advanced 5G/6G wireless networks to facilitate real-time engagement in digital economies (Wang et al., 2023). However, this shift introduces complex challenges pertaining to data veracity, identity authentication, and the establishment of verifiable ownership in environments prone to infinite digital replication.

Blockchain technology, characterized by decentralization, transparency, and immutability, is emerging as an indispensable infrastructural layer to address these challenges (Mohamed and Imoize, 2024). Within the domain of digital media, blockchain instigates a fundamental redefinition of content models, shifting power dynamics from centralized entities to individual creators and communities. This transition is not merely technical but political. As the digital state evolves, centralized architectures like biometric identity regimes and algorithmic governance platforms are radically transforming the federal bargain between users and platforms (Ganaie et al., 2026). Without careful design, the Metaverse risks replicating the very surveillance capitalism it promises to transcend (Zuboff, 2019).

From the perspective of self-determination theory (Ryan and Deci, 2000), individuals and creators are not passive recipients of platform rules but are inherently motivated to regulate their engagement in pursuit of autonomy and creative fulfillment. Blockchain-based mechanisms—NFTs, smart contracts, and DIDs—represent a proactive strategy for reclaiming control over digital assets and identity. However, empirical research on how these mechanisms translate into reduced platform dependence and improved creator outcomes remains limited (Cao et al., 2026).

1.1 Research questions

This study addresses the following questions.

• RQ1: How do decentralized identity (DID) frameworks mediate the relationship between blockchain adoption and cross-platform interoperability?
• RQ2: To what extent do smart contract-based royalty mechanisms disrupt traditional intermediary-led media distribution models?
• RQ3: What are the primary socio-technical barriers—specifically regarding the digital divide and algorithmic governance—that moderate the adoption of blockchain-based media ecosystems?

2 Materials and methods

This research utilized a qualitative synthesis of contemporary literature (2023–2026) and a quantitative survey of digital media professionals to evaluate the impact of blockchain on digital media. The qualitative synthesis procedure is detailed in Supplementary Appendix A (search strategy, inclusion criteria, thematic analysis). The conceptual framework and moderation effects are visualized in Figures 1, 2.

2.1 Participants and procedure

Data were collected from 613 digital media professionals, including content creators, platform developers, and media executives, between January and March 2026. A mixed sampling strategy combined random and snowball sampling.

Inclusion criteria: Active digital media professional with ≥1 year experience using blockchain-based platforms (e.g., OpenSea, Rarible) or NFT marketplaces.

Recruitment channels: Professional networks (LinkedIn: n = 178), Discord blockchain communities (47 servers; random selection of every fifth active member; n = 312), and online media forums (n = 123).

Response rate: 34.2% (613 completed from 1,792 initial contacts). Non-response bias was assessed by comparing early vs. late responders on key variables; no significant differences emerged (all p > 0.10).

Geographic distribution: 41% North America, 28% Europe, 18% Asia-Pacific, 13% other (South America, Africa, Middle East).

Demographics: 382 men (62.3%), 231 women (37.7%); 68.4% held a bachelor’s degree or higher; mean age = 34.7 years (SD = 8.9).

Missing data: ≤3% missing per item; handled using multiple imputation (5 iterations).

Participants completed an anonymous online survey (average completion time: 18 min). Informed consent was obtained electronically. The study was approved by the Institutional Review Board of Umm Al Quwain University (protocol #2026–044). All procedures followed the ethical standards of the 1964 Helsinki Declaration and its later amendments.

2.2 Measures

All scales used a seven-point Likert-type response format (1 = strongly disagree, 7 = strongly agree).

Blockchain Adoption (BA): 12-item scale capturing use of NFTs, smart contracts, and DIDs (adapted from Nguyen et al., 2024). Sample item: “I regularly use smart contracts to automate royalty payments.” Cronbach’s α = 0.89.

Perceived Intermediary Dependence (PID): eight-item scale (adapted from Jiang and Balaji, 2021). Sample item: “My income depends heavily on platform recommendation algorithms.” α = 0.91.

Creator Monetization Autonomy (CMA): 10-item scale assessing perceived control over pricing, royalty streams, and secondary markets (Huynh-The et al., 2023). Sample item: “I can set my own terms for secondary sales of my work.” α = 0.87.

Digital Literacy (DL): 14-item Digital Literacy Scale (Nguyen, 2022). Sample item: “I know how to verify the authenticity of a smart contract before interacting with it.” α = 0.90.

Algorithmic Governance Overload (AGO): seven-item scale adapted from Maier et al. (2015). Items were modified to replace “social support” with “algorithmic decision-making and DAO voting tasks.” Sample item: “Keeping up with DAO proposals and voting feels mentally exhausting.” Confirmatory factor analysis (CFA) showed factor loadings 0.73–0.86, CR = 0.89, AVE = 0.63. α = 0.88.

2.3 Theoretical framework: socio-technical systems (STS)

To move beyond technical description, this paper analyzes data through the lens of socio-technical systems theory. This framework allows us to evaluate the Metaverse not just as a collection of code (blockchain/NFTs) but as a complex interaction between technical infrastructure and social equity (Nkuna, 2026).

2.4 Data analysis

Structural equation modeling (PLS-SEM) was employed using SmartPLS 4. PLS-SEM was chosen because the model includes both formative (blockchain adoption as a composite of NFTs/smart contracts/DIDs) and reflective constructs, and because prediction was a key objective (Stone-Geisser Q² values are reported). Moderation and chain mediation analyses were conducted using PROCESS macro (Model 84) for SPSS, with 5,000 bootstrap resamples. PLS-SEM and PROCESS converged on the same directional conclusions.

Measurement model assessment (Table 1) included: composite reliability (CR: 0.86–0.93), average variance extracted (AVE: 0.62–0.75), HTMT ratios (all <0.85), factor loadings (0.71–0.89, all p < 0.001), and variance inflation factors (VIF: 1.28–2.94, below 3.5). Discriminant validity was established via Fornell-Larcker and HTMT criteria. Common method variance was assessed using Harman’s single-factor test (first factor explained 28.7% of variance) and a marker variable analysis (social desirability, uncorrelated with focal constructs).

TABLE 1. Measurement model results.

N = 613. CR, Composite Reliability; AVE, Average Variance Extracted. Diagonal values (in parentheses) are square roots of AVE., Off-diagonal values are inter-construct correlations. All HTMT, ratios <0.85, supporting discriminant validity. All factor loadings significant at p < .001 (0.71–0.89). VIF, range = 1.28–2.94*.

3 Results

3.1 Descriptive statistics and correlations

Blockchain adoption was moderately correlated with lower perceived intermediary dependence (r = −0.33, p < 0.01) and higher creator monetization autonomy (r = 0.45, p < 0.01). Digital literacy was positively associated with blockchain adoption (r = 0.41, p < 0.01). Algorithmic governance overload was negatively associated with creator autonomy (r = −0.29, p < 0.01). See Table 2 for full descriptive statistics and correlations.

TABLE 2. Descriptive statistics and bivariate correlations.

N = 613. M = mean; SD, standard deviation. *p < .05; *p < .01 (two-tailed).

3.2 Path analysis

The structural model demonstrated acceptable fit (χ²/df = 2.08, RMSEA = 0.04, CFI = 0.92). Blockchain adoption was negatively associated with perceived intermediary dependence (β = −0.41, p < 0.001) and positively associated with creator monetization autonomy (β = 0.38, p < 0.001). Perceived intermediary dependence fully mediated the relationship between blockchain adoption and creator autonomy (indirect effect = 0.21, 95% CI [0.14, 0.29]).

3.3 Sequential mediation

A significant chain mediation effect was identified (Table 3): blockchain adoption → lower intermediary dependence → higher digital literacy → greater creator autonomy (β = 0.09, 95% CI [0.04, 0.15]). This specification was theoretically justified by treating digital literacy as a dynamic, platform-contingent capability that develops when intermediary control is reduced (see Discussion). A post hoc sensitivity analysis testing the reverse order (literacy → intermediary dependence → autonomy) yielded significantly worse model fit (Δχ² = 14.3, p < 0.001), supporting our specified direction.

TABLE 3. Direct, indirect, and total effects (chain mediation model).

N = 613. BA, blockchain adoption; PID, perceived intermediary dependence; DL, digital literacy; CMA, Creator Monetization Autonomy. Bootstrap resamples = 5,000. CI, confidence interval (bias-corrected); LL, lower limit; UL, upper limit.*.

3.4 Moderating role of algorithmic governance overload

Algorithmic governance overload significantly moderated the relationship between blockchain adoption and creator autonomy (β = −0.11, p < 0.01). At low levels of governance overload, blockchain adoption strongly predicted higher autonomy. At high levels of overload, this association was non-significant, indicating that complex DAO structures and smart contract management can paradoxically undermine the benefits of decentralization. This moderation effect was interpreted through the lens of surveillance federalism (Ganaie et al., 2026), wherein users perceive “decentralization in name only” when a small set of validators or DAO whales retain effective control.

4 Discussion

4.1 Blockchain as a coping mechanism for platform dependency

The finding that blockchain adoption is associated with lower perceived intermediary dependence aligns with self-determination theory: intentional disengagement from centralized platforms functions as an autonomous behavioral choice that restores a sense of control over creative labor (Cao et al., 2026). This pattern is particularly relevant for digital creators whose income and reputation are deeply embedded in platform-specific ecosystems. In an environment characterized by opaque algorithmic curation and sudden policy changes, blockchain-based ownership is not merely a technical feature but an intentional effort toward economic self-regulation.

4.2 Surveillance federalism and the paradox of decentralization

A critical risk identified in this study is the potential for what Ganaie et al. (2026) term surveillance federalism: the reconfiguration of intergovernmental and platform-user relations through technologically mediated centralized controls. While blockchain promises decentralization, in practice, many platforms retain governance authority over smart contract execution and dispute resolution. This creates a hybrid model where users experience the illusion of decentralization while remaining subject to algorithmic governance. Our moderation results—showing that governance overload negates the benefits of blockchain—directly support this concern. Empirically, high AGO scores reflect the perception that effective control rests with a small set of validators or DAO whales, consistent with surveillance federalism.

4.3 Governance portfolios for system resilience

The resilience of digital media ecosystems requires a diversified “governance portfolio” that balances on-chain efficiency (computation) with off-chain deliberative processes (human judgment). Attempts to centralize all governance into algorithmic logic are likely to create brittle systems that fail under novel or stressful conditions. Drawing on principles from polycentric governance and distributed systems design, a robust Metaverse should avoid mono-centralization and instead maintain multiple, overlapping governance mechanisms that can adapt to changing user needs and regulatory environments.

4.4 The digital divide and digital literacy as a dynamic capability

Our findings also suggest that digital literacy—which is unevenly distributed—moderates the benefits of blockchain adoption. However, contrary to traditional models that treat literacy as a static antecedent, our chain mediation revealed that reduced intermediary dependence enables the development of digital skills. This aligns with situated learning theory: creators previously occupied with navigating opaque platform algorithms gain cognitive bandwidth to learn blockchain-specific skills (e.g., smart contract deployment, wallet security). This finding also supports Nkuna’s (2026) observation that South African social movements representing the poorest communities are digitally excluded not only from internet access but also from the skills required to leverage social media for advocacy.

4.5 Practical implications

Based on our findings, we propose the following design principles to mitigate governance overload while preserving decentralization benefits.

• DAO delegation interfaces that default to vote-whitelisting (reducing daily cognitive load) with optional opt-in for active governance.
• Smart contract templates with pre-audited “beginner” vs. “advanced” settings to accommodate varying digital literacy levels.
• Liquid democracy mechanisms allowing routine governance to be offloaded to trusted delegates, with recall rights preserved.
• Periodic “governance detox” periods (e.g., 1 week per quarter of automated delegation) to reduce chronic cognitive strain.
• Transparency dashboards showing concentration of validator/whale voting power to counter the illusion of decentralization.

4.6 Limitations

Several limitations should be noted. First, the cross-sectional design precludes causal inference. Longitudinal studies are needed to examine how blockchain adoption influences creator outcomes over time. Second, the sample was predominantly male (62.3%) and highly educated (68.4% bachelor’s+), limiting generalizability to broader creator communities, particularly in Global South contexts where blockchain adoption faces different barriers (mobile-first access, data costs, regulatory uncertainty). Third, the study focused on perceived measures rather than objective blockchain transaction data; future research should integrate on-chain metrics (e.g., actual royalty payments, DAO voting frequency) with survey responses. Fourth, despite statistical remedies for common method variance, self-report bias cannot be fully ruled out. Fifth, the chain mediation directionality, while theoretically justified and empirically supported via model comparison, should be tested in experimental or longitudinal designs.

5 Conclusion

Blockchain technology provides the architectural scaffolding required to protect digital media ownership and ensure creator sovereignty. However, technology alone is not a panacea. The future of the Metaverse depends on a commitment to data sovereignty and a move away from the algorithmic paradigm of citizens as data (Ganaie et al., 2026). By recognizing the importance of governance portfolios, we can build digital media ecosystems that are both technologically robust and democratically resilient. Future research must focus on establishing ethical standards for decentralized governance that prioritize human agency over computational rationality, and on testing the proposed design principles in real-world DAO and NFT platform settings.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbloc.2026.1871552/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Syed Badruddoja, California State University, Sacramento, United States

Reviewed by: Archana Nanade, SVKM’S NMIMS University, India

Musawer Hakimi, Osmania University, India