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ISSN 1003-2053　CN 11-1805/G3
科学学研究

科学学研究 ›› 2023, Vol. 41 ›› Issue (6): 1106-1120.

• 技术创新与制度创新 • 上一篇    下一篇

大数据驱动创新过程提高数字创新绩效的路径

张海丽1,2,王宇凡2,Michael Song3   

  1. 1.
    2. 西安工业大学
    3. 西安工业大学经济管理学院
  • 收稿日期:2022-03-21 修回日期:2022-07-19 出版日期:2023-06-15 发布日期:2023-06-13
  • 通讯作者: 王宇凡
  • 基金资助:
    国家自然科学基金青年项目;陕西省科技厅软科学研究计划一般项目

A Cross-National Comparative Study on the Relationship Between Big Data-Driven Innovation Process and Digital Innovation Performance

  • Received:2022-03-21 Revised:2022-07-19 Online:2023-06-15 Published:2023-06-13

摘要： 企业数字创新进程不断加速，研究表明，大数据驱动创新过程有利于提高创新绩效，但不同阶段以及不同水平的大数据驱动创新过程与创新绩效的作用机理仍有待研究，数字创新理论及具有中国特色数字创新理论亟待发展。 本文探究大数据驱动创新过程通过产品创新度影响创新绩效的路径与机理，收集594个中国、475个美国和507个英国数字创新项目，采用多元回归、层次回归与“pick-a-point”方法，实证分析大数据驱动创新过程对产品创新度的倒U型影响，检验大数据驱动运营对产品创新度与创新绩效关系的调节作用。研究结果表明，在中国，大数据驱动商业分析、产品设计与商业化对产品创新度产生倒U型影响，即双元驱动商业分析、产品设计与商业化最能提升产品创新度。调节检验结果表明，随经验驱动运营向大数据驱动运营转变，产品创新度对创新绩效由U型影响变为正向影响。当产品创新度很低或很高时，大数据驱动运营抑制产品创新度对创新绩效的积极影响，而当产品创新度处于中间水平时，大数据驱动运营促进产品创新度对创新绩效的积极影响。跨国比较还发现，美国和英国具有类似的研究结论。 通过理论分析与实证检验，研究丰富了数字创新理论，推进了具有中国特色的数字创新理论发展，揭示了大数据驱动创新过程、产品创新度与创新绩效的效应路径与作用机理，发展了大数据驱动运营相关理论，拓展了影响产品创新度与创新绩效关系的情境因素，为企业数字创新实施提供了新的管理见解和策略启示。

Abstract: Firms are accelerating the digital innovation. Previous studies have shown that big data-driven innovation process has a positive affect innovation performance. Yet, few extant studies have examined how different stages and different levels of big data-driven innovation process affect innovation performance. This study develops a research model to examine how big data-driven innovation process affect innovation performance through the mediating effects of innovativeness and how big data-driven operation moderate the relationship between innovativeness and innovation performance. To test the proposed theoretical model, this study collected data from 594 China, 475 U.S., and 507 United Kingdom digital innovation projects and analyzed the data using regression analysis methods. The results show that: (1) ambidexterity-driven product design and big data- driven product testing have a significant positive impact on innovativeness in China, U.S., and United Kingdom. (2) changing from experience-driven operation to big data-driven operation, the effect of innovativeness on innovation performance changes from U-shaped to positive; (3) in China, when innovativeness is very low or very high, big data-driven operation has a negative moderating effect on the relationship between innovativeness and innovation performance; when innovativeness is at middle level, big data-driven operation plays a positive moderating role. In U.S. when innovativeness is very low, big data-driven operation plays a negative moderating role; with the improvement of innovativeness, big data-driven operation plays a positive moderating role. In United Kingdom, big data-driven operation always plays a positive moderating role. This study makes several theoretical contributions to the literature on digital innovation. This study extends big data-driven innovation process and innovation performance model and enriches the theoretical research on digital innovation. This study also develops big data-driven operation and tests the moderating effect of big data-driven operation. Finally, this study promotes the development of theoretical research on digital innovation in China.