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<p>As data emerge as a key asset in the digital economy, AI applications have become a crucial strategy for enterprises seeking to enhance value creation and competitive positioning. However, existing literature lacks direct empirical analysis of how data elements influence AI adoption. This study addresses this gap by introducing innovative measures for both data elements and AI applications, and by providing a comprehensive analysis of their relationship. We construct a regional data element index encompassing government data, enterprise data, data markets, and data policies. AI applications are measured using the entropy weight method across four dimensions: AI attention, investment, R&amp;D, and automation, offering a robust view of enterprise-level AI utilization. Drawing on panel data from non-financial firms in the Shanghai and Shenzhen A-share markets (2007–2019), the results show that regional data development significantly promotes AI applications. Further analysis reveals heterogeneous impacts across executive backgrounds, value chain stages, firm sizes, industry technology attributes, data resource abundance, and regional economic conditions. Spatial spillover effects are also identified, where data advancements in one city positively influence AI applications in neighboring cities. This study deepens the understanding of how data elements drive AI adoption and provides practical insights for enterprises and policymakers to optimize data-driven AI strategies.</p>

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Impact of data element development on the application of artificial intelligence in enterprises

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Fang Tang,
  • Longpeng Zhang

摘要

As data emerge as a key asset in the digital economy, AI applications have become a crucial strategy for enterprises seeking to enhance value creation and competitive positioning. However, existing literature lacks direct empirical analysis of how data elements influence AI adoption. This study addresses this gap by introducing innovative measures for both data elements and AI applications, and by providing a comprehensive analysis of their relationship. We construct a regional data element index encompassing government data, enterprise data, data markets, and data policies. AI applications are measured using the entropy weight method across four dimensions: AI attention, investment, R&D, and automation, offering a robust view of enterprise-level AI utilization. Drawing on panel data from non-financial firms in the Shanghai and Shenzhen A-share markets (2007–2019), the results show that regional data development significantly promotes AI applications. Further analysis reveals heterogeneous impacts across executive backgrounds, value chain stages, firm sizes, industry technology attributes, data resource abundance, and regional economic conditions. Spatial spillover effects are also identified, where data advancements in one city positively influence AI applications in neighboring cities. This study deepens the understanding of how data elements drive AI adoption and provides practical insights for enterprises and policymakers to optimize data-driven AI strategies.