Measuring Enterprise Digital Readiness in Manufacturing: Multi-Dimensional Index Construction, Structural Heterogeneity, and Longitudinal Spatiotemporal Evolution

Guilei Tan; Rozaini Rosli

doi:10.63313/EPP.9049

Authors

Guilei Tan School of Business and Management, Lincoln University College, Petaling Jaya, 47301, Malaysia Author
Rozaini Rosli School of Business and Management, Lincoln University College, Petaling Jaya, 47301, Malaysia Author

DOI:

https://doi.org/10.63313/EPP.9049

Keywords:

Digital Readiness Index, Entropy Weight Method, Technology Measurement, Structural Heterogeneity, Kernel Density Estimation, Manufacturing Modernization, Longitudinal Evaluation.

Abstract

Accurately quantifying enterprise digital capability is fundamental to evaluating industrial modernization, yet conventional empirical inquiries predominantly rely on single-dimensional proxy variables or unweighted keyword counts, failing to capture the multidimensional integration of digital assets. Moving beyond causal regression paradigms and reductionist metrics, this inquiry develops a comprehensive, objective Digital Readiness Index (DRI) for physical manufacturing enterprises using an information-theoretic Entropy Weight Method (EWM). Grounded in multi-source text-mining disclosures and corporate balance sheets across 41,756 firm-year observations of Chinese A-share listed manufacturing enterprises spanning 2000 to 2025, the evaluation framework integrates eight discrete operational indicators across three dimensions: Technical Depth (AI, Big Data, Cloud Computing, Blockchain, and Digital Applications), Intangible Capital Endowments, and Governance Oversight. Objective entropy weighting demonstrates that specialized frontier technologies, particularly Blockchain (w=37.81%), Artificial Intelligence (w=15.95%), Big Data Analytics (w=15.93%), and Cloud Computing (w=15.66%)—constitute the primary sources of informational divergence across manufacturing firms. Longitudinal trajectory evaluation reveals a sustained upward trajectory in mean digital readiness, accelerating markedly after the 2015 macroeconomic policy inflection point. Non-parametric Gaussian Kernel Density Estimation uncovers a distinct dynamic polarization pattern, characterized by a shifting rightward distribution and an elongating upper tail. Cross-sectional decomposition establishes substantial structural disparities: high-tech sectors such as Computers and Electronics exhibit the highest mean digital readiness (DRI=16.28), whereas chemical and pharmaceutical sectors display persistent digital inertia (DRI≈4.06). Furthermore, non-state-owned enterprises (Non-SOEs) systematically outperform state-owned enterprises (SOEs) across all asset scale tiers. These findings provide an objective measurement tool and benchmark for corporate technology auditing and industrial policy calibration.

References

[1] M. Savastano, C. Amendola, F. Bellini, and F. D’Ascenzo, “Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review,” Sustainability , vol. 11, no. 3, pp. 891–891, Feb. 2019, doi: 10.3390/su11030891.

[2] D. Chamrada, J. Chán, and M. Talíř, “Data-driven assessment of digital maturity in industrial enterprises: a multi-layer model for strategy and transformation readiness,” Journal of Entrepreneurship and Sustainability Issues , vol. 13, no. 3, pp. 262–278, Mar. 2026, doi: 10.9770/q5959857675.

[3] C. F. Rocha, C. O. Quandt, F. Deschamps, and G. Cruzara, “Digital transformation readiness in large manufacturing firms: a building block model proposition,” Journal of Manufacturing Technology Management , vol. 36, no. 1, pp. 45–68, Oct. 2024, doi: 10.1108/jmtm-12-2023-0544.

[4] L. Zou, L. Wan, H. Wu, J. Liu, and P. Gao, “Measuring Corporate Digital Transformation: Methodology, Indicators and Applications,” Sustainability, vol. 16, no. 10, pp. 4087–4087, May 2024, doi: 10.3390/su16104087.

[5] A. Jungherr, H. Schoen, O. Posegga, and P. Jürgens, “Digital Trace Data in the Study of Public Opinion,” Social Science Computer Review, vol. 35, no. 3, pp. 336–356, Feb. 2016, doi: 10.1177/0894439316631043.

[6] Brodny and M. Tutak, “Analyzing the Level of Digitalization among the Enterprises of the European Union Member States and Their Impact on Economic Growth,” Journal of Open Innovation Technology Market and Complexity , vol. 8, no. 2, pp. 70–70, Apr. 2022, doi: 10.3390/joitmc8020070.

[7] V. Basile, M. Tregua, and M. Giacalone, “A three-level view of readiness models: Statistical and managerial insights on industry 4.0,” Technology in Society , vol. 77, pp. 102528–102528, Mar. 2024, doi: 10.1016/j.techsoc.2024.102528.

[8] P. Tambe, L. M. Hitt, D. Rock, and E. Brynjolfsson, “Digital Capital and Superstar Firms,” Dec. 2020. doi: 10.3386/w28285.

[9] K. M. Kong, H. J. Huang, and A. Habib, “Board faultlines, financial reporting quality, and corporate innovation: evidence from China,” International Journal of Disclosure and Governance , Apr. 2026, doi: 10.1057/s41310-026-00374-y.

[10] L. Matthews, M. L. M. Heyden, and D. Zhou, “Paradoxical transparency? Capital market responses to exploration and exploitation disclosure,” Research Policy , vol. 51, no. 1, pp. 104396–104396, Oct. 2021, doi: 10.1016/j.respol.2021.104396.

[11] W. Su, L. Du, Y. Fan, and P. Wang, “Equity and efficiency of public hospitals’ health resource allocation in Guangdong Province, China,” International Journal for Equity in Health , vol. 21, no. 1, pp. 138–138, Sep. 2022, doi: 10.1186/s12939-022-01741-1.

[12] X. Zhang, P. Zhu, and Q. Ma, “Enhancing the Brand Competitiveness of Pujiang Kiwi: A Consumer-Centric Analysis,” in Advances in economics, business and management research/Advances in Economics, Business and Management Research , Atlantis Press, 2025, pp. 414–420. doi: 10.2991/978-94-6463-734-2_48.

[13] X. Gao, S. Dong, and C. Liu, “Corporate Digital Transformation and Stakeholder Green Engagement: A Stakeholder Stratification Perspective,” Corporate Social Responsibility and Environmental Management , Jun. 2026, doi: 10.1002/csr.70724.

[14] M. S. Khan, “Absorptive capacities and economic growth in low- and middle-income economies,” Structural Change and Economic Dynamics , vol. 62, pp. 156–188, Apr. 2022, doi: 10.1016/j.strueco.2022.03.015.

[15] P. R. Atluri, “Smart Factories in the Cloud: How Real-Time Data Pipelines Are Powering IoT-Driven Manufacturing,” Journal of Computer Science and Technology Studies , vol. 7, no. 5, pp. 417–430, Jun. 2025, doi: 10.32996/jcsts.2025.7.5.52.