Data-Driven Readiness Analytics for Blockchain Adoption in Public-Sector Organizations: Evidence from a TOE–SEM Modeling Approach

Authors

  • Yaojie Xu 1 https://orcid.org/0009-0001-2223-7573
    • Conceptualization
    • Formal Analysis
    • Investigation
    • Project Administration
    • Software
    • Writing – Original Draft Preparation
    • Writing – Review & Editing
    • Data Curation
    • Methodology
    • Validation
    • Visualization
  • Chi Xu 2,* https://orcid.org/0009-0008-6490-8292
    • Conceptualization
    • Formal Analysis
    • Methodology
    • Project Administration
    • Supervision
    • Validation
    • Writing – Review & Editing
  • Ying Hu 3 https://orcid.org/0009-0004-7779-6067
    • Data Curation
    • Investigation
    • Software
    • Visualization
    • Validation
    • Writing – Review & Editing
    • Resources
1 School of Smart Finance and Business, Hefei College of Finance & Economics, Hefei, China 230601
2 Hefei University of Economics, Hefei, China 230000
3 School of Modern Digital Creative Industry, Sanlian University, Hefei, China 230000
Corresponding author: 16778387@qq.com

DOI:

https://doi.org/10.63646/BUDX6461

Versions

Keywords:

Blockchain adoption; public-sector organizations; data-driven readiness analytics; TOE framework; structural equation modeling; digital government; institutional readiness

Abstract

Public-sector organizations increasingly view blockchain as a candidate infrastructure for trusted records, auditable workflows, and secure inter-agency information exchange. However, adoption decisions in government rarely depend on technological attractiveness alone. They are shaped by legacy-system compatibility, managerial authorization, financial readiness, regulatory legitimacy, data sensitivity, and perceived institutional risk. This study develops a data-driven readiness analytics framework for evaluating blockchain adoption in public-sector organizations through an integrated Technology-Organization-Environment (TOE) and structural equation modeling (SEM) approach. Unlike studies that treat adoption intentions as a single behavioral outcome, the proposed framework combines measurement validation, construct-level SEM, and readiness scoring to distinguish statistically supported drivers from weak or unsupported assumptions. A calibrated public-sector readiness dataset is used to demonstrate the analytical workflow across technology, organization, and environment dimensions. Results show that compatibility, trust, security, higher authority support, monetary resources, regulatory support, and inter-agency modernization pressure are the strongest predictors of blockchain readiness, while relative advantage, general IT resources, and partner pressure have weaker effects. The optimized SEM model reports acceptable fit and explains 64.7% of the variance in readiness intention. Readiness index results further reveal uneven preparedness across agency types, with regulatory and data-intensive agencies scoring higher than provincial service agencies. The findings provide a cautious but actionable basis for blockchain policy planning: government leaders should prioritize institutional fit, legal clarity, budget commitment, and cross-agency governance before moving from pilot design to implementation. The study contributes to business and data analytics research by transforming blockchain adoption analysis from descriptive perception assessment into a decision-oriented readiness analytics model.

How to Cite

Xu, Y., Xu, C., & Hu, Y. (2026). Data-Driven Readiness Analytics for Blockchain Adoption in Public-Sector Organizations: Evidence from a TOE–SEM Modeling Approach. Journal of Business and Data Analytics, 3(4), 81-105. https://doi.org/10.63646/BUDX6461 (Original work published 2025)

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