Intelligent manufacturing and trade credit

Jie Yang, Juan Yu and Mengqi Bao

International Review of Financial Analysis, 2025, vol. 97, issue C

Abstract: While intelligent manufacturing has emerged as a pivotal strategy for industrial upgrading in emerging economies, its impact on firms' financial behavior, particularly trade credit decisions, remains unexplored. Prior research has focused primarily on how intelligent manufacturing affects operational efficiency and innovation performance, overlooking its potential role in reshaping supply chain financing patterns. This study addresses this gap by examining the causal relationship between intelligent manufacturing and firm trade credit dynamics. Employing a difference-in-differences (DID) approach with a comprehensive dataset of 2022 Chinese A-share manufacturing firms from 2010 to 2020, we document that intelligent manufacturing adopters significantly increase their trade credit provision while reducing trade credit financing from suppliers. Specifically, the ratio of trade credit provided to customers increases by 2.87% while trade credit received from suppliers decreases by 2.55%. Through extensive mechanism tests and heterogeneity analyses, we reveal that this effect operates primarily through the alleviation of financing constraints, with stronger impacts observed in non-SOEs, smaller firms, and those facing intense market competition. These findings illuminate how technological transformation influences supply chain financial relationships, offering novel insights for policymakers promoting industrial digitalization and firms managing supply chain credit relationships in the era of intelligent manufacturing.

Keywords: Intelligent manufacturing; Trade credit; Alternative financing theory; Supply chain finance; Digital transformation; Financial constraints; Artificial intelligence (search for similar items in EconPapers)
JEL-codes: G32 L60 M41 O33 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:97:y:2025:i:c:s1057521924007166

DOI: 10.1016/j.irfa.2024.103784

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