A Taxonomy of Digital Technology Configurations in Manufacturing Enterprises: An Unsupervised Profiling and Longitudinal Trajectory Analysis

Guilei Tan; Rozaini Rosli

doi:10.63313/EBM.9222

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/EBM.9222

Keywords:

Digital Taxonomy, Technology Configurations, Unsupervised Clustering, Manufacturing Archetypes, Digital Maturity, Longitudinal Evolution

Abstract

Enterprise digital transformation in physical manufacturing is characterized by substantial heterogeneity, yet prevailing research predominantly aggregates diverse digital tools into a single composite index, obscuring distinct technological configurations and adoption pathways. Departing from traditional linear regression models and causal hypothesis testing, this inquiry develops an empirical taxonomy of enterprise digital technology configurations through an unsupervised machine learning approach. Utilizing multidimensional text-mining disclosures across 44,103 firm-year observations from Chinese A-share listed manufacturing enterprises spanning 2000 to 2025, we evaluate the joint adoption patterns across five core technological pillars: Artificial Intelligence (AI), Big Data Analytics (BDA), Cloud Computing (CC), Blockchain Technology (BD), and Advanced Digital Technology Applications (ADT). Unsupervised K-Means clustering identifies four distinct industrial archetypes: Traditional Laggards (64.40%), Application-Centric Followers (20.71%), Cloud and Algorithmic Integrators (11.56%), and Full-Stack Frontier Pioneers (3.33%). Cross-sectional comparative evaluations demonstrate significant structural divergences across these archetypes in terms of enterprise scale, capital leverage, board independence, and state ownership. Furthermore, longitudinal trajectory mapping reveals a decisive macroeconomic transition after 2015, marked by a progressive contraction of traditional laggards and a rapid expansion of cloud-integrated and application-focused configurations. These findings advance organizational configuration theory, providing industrial practitioners and policymakers with a clear taxonomic benchmark to calibrate digital investment portfolios.

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