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ORIGINAL RESEARCH article
Front. Sustain. Food Syst.
Sec. Agricultural and Food Economics
Volume 9 - 2025 | doi: 10.3389/fsufs.2025.1547358
This article is part of the Research Topic Harnessing Digital Innovation for Sustainable Agricultural Development View all 13 articles
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Introduction: Digital transformation (DT) refers to the process of leveraging digital technologies to drive innovation in business models, thereby enabling enterprises to create greater value and deliver innovative solutions for efficient agricultural production.Using data from 211 listed agricultural companies in China from 2009 to 2022, this study investigates the impact and pathways through which DT influences financial performance (FP), employing a range of methodologies. To enhance text mining accuracy, the research incorporates natural language processing (NLP) and large language models (LLM).The findings indicate that DT within agricultural enterprises' production, purchasing, and sales departments significantly enhances FP. To address potential endogeneity concerns, robustness checks were conducted using propensity score matching (PSM), the Heckman two-stage model, and the two-stage least squares (2SLS) method. Mechanism analysis reveals that DT improves FP through three primary channels: reducing sales expenses, easing cost stickiness, and promoting breakthrough innovation. However, the positive effects of DT exhibit heterogeneity. These effects are more pronounced in non-state-owned enterprises, larger firms, and enterprises located in major grain-producing regions.This study validates the necessity for enterprises to use digital technology to improve financial performance in the digital age. By expanding the measurement methods for DT, the research provides valuable insights for enterprises seeking to leverage digital tools to optimize agricultural production efficiency.
Keywords: digital transformation, Natural Language Processing, Agricultural listed companies, Firm performance, technological innovation, Breakthrough innovation
Received: 18 Dec 2024; Accepted: 26 Mar 2025.
Copyright: © 2025 Yuan, Wu and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yang Shen, Institute of Quantitative Economics,Huaqiao University, Xiamen, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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