The Restructuring of Commercial Credit under Digital Transformation: From Relational Contracts to Algorithmic Trust and Network Power

Abstract

This paper aims to transcend the functionalist research paradigm currently prevalent in academia, which focuses on the economic consequences of corporate digital transformation. Instead, it constructs a purely theoretical framework for the structural restructuring of commercial credit. While existing research primarily concentrates on the positive impact of digitalization as an efficiency-enhancing tool on the scale of commercial credit financing, this paper argues that digital transformation is not merely an "enhancer" of commercial credit but a "reconstructor" of its intrinsic logic. This reconstruction unfolds along two intertwined axes. First, a paradigm shift in the foundation of trust: the basis for granting credit is moving from "interpersonal trust" and "relational contracts," rooted in long-term social interaction, to an impersonal "system trust" driven by data and executed by code, giving rise to a new form of trust-"algorithmic trust." Second, a re-centralization of power structures: power within the supply chain ecosystem has not been decentralized but is instead concentrating, shifting from traditional core enterprises that control production and financial capital to a new type of digital intermediary-the platform operator-that controls data, algorithms, and network connectivity. To systematically explain this transition, this paper integrates relational contract theory, Luhmann's system trust theory, and Castells' network power theory. It deeply analyzes the construction mechanisms and inherent risks of algorithmic trust (such as the "black box" problem and data bias) and deconstructs how platforms become new power centers by mastering "programming power" and "switching power," which may ultimately lead to "data monopolies." The paper concludes that we are witnessing a fundamental shift in commercial credit from a "relational paradigm" to an "algorithmic-network paradigm." This poses profound challenges to financial theory, business practice, and public governance, calling for a shift in research and regulatory focus toward issues of fairness and stability in the age of algorithmic governance.