Digital Transformation, Asymmetric Information and Debt Financing Costs for Small and Medium-sized Enterprises: Evidence from Machine Learning Text Analysis

Ziyue An ()
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Ziyue An: Beijing Union University

A chapter in Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025), 2026, pp 475-490 from Springer

Abstract: Abstract This study employs machine learning text analysis to construct a comprehensive Digital Transformation Index (DTI) encompassing three dimensions—technology application, strategic orientation, and organisational change—based on data from small and medium-sized enterprises listed on the Shanghai and Shenzhen A-share markets between 2019 and 2023. It empirically examines the impact of digital transformation on debt financing costs for SMEs and its underlying mechanisms. Findings reveal that digital transformation significantly reduces corporate debt financing costs, with each one-unit increase in DTI lowering debt financing costs by an average of 0.0098 percentage points. This effect is more pronounced in private enterprises and regions with advanced fintech ecosystems, indicating that ownership structure and regional fintech sophistication exert significant moderating effects. Mechanism analysis indicates that digital transformation primarily reduces financing costs through two parallel pathways: alleviating information asymmetry and enhancing analyst tracking capabilities. The mediating effect accounts for 65.2% of the total effect. This study provides theoretical and empirical evidence for understanding the financial empowerment effects of digital transformation, offering valuable insights for SME digital practices and financial institutions’ credit decision-making.

Keywords: Digital transformation; Debt financing costs; Small and medium-sized enterprises; Asymmetric information; Machine learning text analysis; Moderating effects (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-602-9_42

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