大数据产业发展能力影响因素研究——基于fsQCA方法

Research on Influencing Factors of Big Data Industry Development Capability Based on fsQCA

摘要:

运用模糊集定性比较分析（fsQCA）方法，探讨多发性因素对大数据产业发展能力影响机制，研究条件变量与结果变量关系属性，以提出针对不同发展基础的大数据产业发展能力提升路径。结果表明：（1）R&D经费投入强度、每万人中研发人数、信息资源开发利用指数、信息产业规模水平四个条件变量构成大数据产业发展能力的必要非充分条件，是大数据产业发展不可或缺的因素；（2）信息技术创新能力和信息资源开发能力等八个条件变量构成大数据产业发展能力的七种前因条件构型，形成七种影响大数据产业发展能力的因素组合；（3）在经济发展水平相对落后的区域，充分开发利用自身优势资源，努力提升科技创新水平，也可实现后发赶超大力提升大数据产业发展能力。最后根据大数据产业发展能力七种前因条件构型，并结合各地区大数据产业基础条件，总结提出三条大数据产业发展能力提升路径，为各省提升大数据产业发展能力提供政策建议。

Abstract:

In order to propose the improving paths of big data industry development capability based on different development foundations, we made case study with big data development in 31 provinces and cities in china to discuss the relationship attributes between condition variables and the outcome variable, and then researched on the influence mechanism of multiple factors on the development ability of big data industry by fsQCA. And the result shows that: (1) R&D investment intensity, R&D per ten-thousand person, information resources development and utilization index, and information industry scale constitute the necessary but insufficient conditions ,which are also requisite for the development capability of the big data industry; (2)Other eight condition variables about information technology innovation capability and information resource development capability, constitute seven pre-conditional configurations of the development capability about the big data industry, forming seven combinations of factors; (3)If superior resources are exploited and utilized fully, and technological innovation are improved deeply in less-developed regions, they are also possible to catch up with the post and energetically enhance the development capacity of the big data industry. Finally, based on the seven configurations, combining with the basic conditions of the big data industry in each region, summarizing three major upgrade paths, providing policy proposals for the provinces to enhance the development of big data industry.