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<p>In the digital era, improving supply chain performance (SCP) has become increasingly important for agricultural enterprises confronting global competition and market volatility. These organizations need to manage networks comprising multiple stakeholders, diverse information flows, and complex logistics processes, while addressing unique challenges in the agricultural sector. This study examines the interrelationships among four latent variables using partial least squares structural equation modeling (PLS-SEM): artificial intelligence (AI) applications, information sharing (IS), and supply chain resilience (SCR) as independent variables, with SCP as the dependent variable. Six hypotheses were tested using data collected from 151 agricultural enterprises in China through a structured questionnaire. This study contributes to the literature on supply chain management by examining how the triad of AI applications, IS, and SCR influences agricultural SCP. It provides insights into potential approaches for enterprises to implement AI-driven solutions in addressing sector-specific challenges like perishability management and multi-stakeholder coordination in volatile markets.</p>

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The impact of AI applications, information sharing, and supply chain resilience on agricultural supply chain performance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月27日

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  • Fengzhao Yin,
  • May Chiun Lo,
  • Abang Azlan Mohamad,
  • Kit Yeng Sin

摘要

In the digital era, improving supply chain performance (SCP) has become increasingly important for agricultural enterprises confronting global competition and market volatility. These organizations need to manage networks comprising multiple stakeholders, diverse information flows, and complex logistics processes, while addressing unique challenges in the agricultural sector. This study examines the interrelationships among four latent variables using partial least squares structural equation modeling (PLS-SEM): artificial intelligence (AI) applications, information sharing (IS), and supply chain resilience (SCR) as independent variables, with SCP as the dependent variable. Six hypotheses were tested using data collected from 151 agricultural enterprises in China through a structured questionnaire. This study contributes to the literature on supply chain management by examining how the triad of AI applications, IS, and SCR influences agricultural SCP. It provides insights into potential approaches for enterprises to implement AI-driven solutions in addressing sector-specific challenges like perishability management and multi-stakeholder coordination in volatile markets.