Every Coin Has Two Sides: Enterprise Digital Transformation and Symbolic Innovation

IF 4.6 3区管理学 Q1 BUSINESS

IEEE Transactions on Engineering Management Pub Date : 2024-11-12 DOI:10.1109/TEM.2024.3496494

Shiyi Tang;Liangliang Zhang;Yong Xu

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引用次数: 0

Abstract

Despite extensive research on the benefits of enterprise digital transformation (EDT) to innovation, information about its dark side is scarce. Drawing on the resource dependence theory (RDT) and using samples of Chinese listed enterprises from 2010 to 2019, we adopt the double-fixed-effects model, mediation effect model, and moderation effect model to explore how EDT affects symbolic innovation (SI), as well as discussing the moderating roles of government research and development (R&D) subsidies, economic policy uncertainty (EPU), and media coverage (MC). We found that EDT induces SI by increasing executive performance pressure (Pressure) and managerial myopia (Myopia). Such effects are weakened by R&D and EPU but strengthened by MC. Enterprises with higher digital levels and lower substantive innovation levels are more inclined to engage in SI. Our conclusions are still robust after using a series of robustness tests. This study not only sheds light on the role of EDT in stimulating SI, thereby enriching the RDT, but also contributes to optimize decision-making by both business owners and governments.

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任何事物都有两面性：企业数字化转型与符号创新

尽管对企业数字化转型（EDT）对创新的好处进行了广泛的研究，但有关其阴暗面的信息却很少。基于资源依赖理论（RDT），以2010 - 2019年中国上市企业为样本，采用双固定效应模型、中介效应模型和调节效应模型，探讨EDT对符号创新的影响，并探讨政府研发补贴、经济政策不确定性和媒体报道对符号创新的调节作用。我们发现，EDT通过增加高管绩效压力（pressure）和管理近视（myopia）来诱导SI。R&D和EPU的作用减弱，MC的作用增强。数字化水平越高、实质性创新水平越低的企业越倾向于从事科学创新。经过一系列稳健性检验，我们的结论仍然是稳健性的。本研究不仅揭示了EDT在刺激SI中的作用，从而丰富了RDT，而且有助于企业所有者和政府优化决策。

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来源期刊

IEEE Transactions on Engineering Management 管理科学-工程：工业

CiteScore

10.30

自引率

19.00%

发文量

604

审稿时长

5.3 months

期刊介绍： Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.