Geographical influences, media attention and enterprise digital transformation

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change Pub Date : 2024-11-07 DOI:10.1016/j.techfore.2024.123853

Teng Wang , Xiaoyang Zhao , Xiaotong Li

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Abstract

We investigate how geographic factors influence enterprise digital transformation (EDT) by applying resource dependence theory and the attention-based view. Analyzing spatial and attribute data from Chinese firms between 2011 and 2020, our study reveals three key findings: (1) proximity to major cities positively affects EDT; (2) local enterprise digitalization density (LEDD) mediates the relationship between city proximity and EDT; and (3) media attention has an inverted U-shaped moderating effect on both the relationship between LEDD and EDT, and on LEDD’s mediation effect. Our heterogeneity analysis shows that compared to non-provincial dual-core cities, proximity to major cities has stronger influence on enterprise digital transformation in provincial capitals and first-tier cities. Additionally, the impact of proximity to major cities on EDT is significantly stronger for non-state-owned enterprises and technology-intensive enterprises. Our research broadens the understanding of the spatial dimensions of digital transformation, offering a nuanced theoretical framework that highlights the interplays between geographic and cognitive factors in shaping corporate digital strategy. These insights have valuable implications for policymakers and business leaders seeking to promote enterprise digital transformation.

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地域影响、媒体关注和企业数字化转型

我们运用资源依赖理论和注意力观点研究了地理因素如何影响企业数字化转型（EDT）。通过分析2011年至2020年间中国企业的空间数据和属性数据，我们的研究揭示了三个关键结论：（1）靠近大城市对企业数字化转型有积极影响；（2）本地企业数字化密度（LEDD）在城市距离与企业数字化转型之间起到中介作用；（3）媒体关注度对LEDD与企业数字化转型之间的关系以及LEDD的中介作用都有倒U型的调节作用。我们的异质性分析表明，与非省会双核城市相比，靠近大城市对省会和一线城市企业数字化转型的影响更大。此外，对于非国有企业和技术密集型企业来说，靠近大城市对 EDT 的影响明显更大。我们的研究拓宽了人们对数字化转型空间维度的理解，提供了一个细致入微的理论框架，强调了地理因素和认知因素在塑造企业数字化战略中的相互作用。这些见解对寻求促进企业数字化转型的政策制定者和企业领导者具有重要意义。

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来源期刊

Technological Forecasting and Social Change Multiple-

CiteScore

21.30

自引率

10.80%

发文量

813

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