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Analyzing Phase-Specific Drivers of Digital Transformation in SMEs: A Machine Learning-Based Approach

Year: 2025

Type: conference-paper

Abstract: This study explores the evolving drivers of digital transformation (DT) in small and medium-sized enterprises (SMEs) by analyzing data from two phases: the shift from non-digitalization to initial digitalization, and from initial to advanced digitalization. Using a combination of Logit, linear regression and machine learning techniques, specifically XGBoost and SHAP value analysis, this research identifies key incentives in each phase. The external factors, such as the business environment and resource availability, are crucial in the early phase of digitalization. The internal factors, including digital organizational design and digital literacy are assuming an increasingly critical role in the second phase of DT. The findings offer insights into the challenges SMEs face during digital transformation journey, and underscore the importance of organizational restructuring and capacity-building for advancing digitalization.

DOI: 10.1145/3729706.3729773

Authors: Fan Zhang, Chongyu Liu, Haiting Meng, Gangqiang Du, Xiangyu Wang, Yanling Zhou

Institution: Beijing University of Chemical Technology

Language: English

Topic: Digital Innovation in Industries

Subfield: Management of Technology and Innovation

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Decent work and economic growth

Open Access status: gold

Funder: National Social Science Fund of China

Award: G4306893861