数字商业生态系统特征：数据控制和数据协调模式比较

数字平台重塑组织间关系形成数字商业生态系统，而数据要素分布模式会影响数字商业生态系统特征。文章选择浙江省和广东省分别作为数据控制模式和数据协调模式的代表，基于数字经济上市企业经营领域与授权发明专利数据，利用社会网络分析法，比较数据控制与数据协调两种模式在产业链整合、数字产业化和产业数字化融合以及数字商业生态系统网络结构上的差异，分析两种模式为提高经济效率所采纳的不同路径。研究发现：无论在何种模式下，民营企业通常选择创新活动频繁、创新风险较高的战略性新兴产业，而国有企业通常参与门槛较高的生产性服务业。数据控制模式是构建数字化营销渠道发挥消费互联网平台用户流量优势，数据协调模式是依托电子信息产业基础发挥工业互联网平台协同制造优势；数据控制模式是纵向一体化产业链整合方式，数据协调模式是水平型产业链整合方式。相较于数据控制模式，数据协调模式下的数字技术与传统产业融合度更高、在产业链上扩散速度更快；数据控制模式下组织网络呈现“垄断”结构，而数据协调模式呈现“区块链”结构；数据控制模式依托于单一平台很难做出全局最优决策部署，数据协调模式在不同领域构建工业互联网平台可能导致跨平台间的数据流动产生困难。消费者需求数据和工业数据高效匹配是两种模式共同演进方向，相较于讨论数据要素最优集中规模，畅通数字平台之间的数据要素流动渠道才是根本。

Characteristics of Digital Business Ecosystem: Comparison between “Data Control Mode” and “Data Coordination Mode”

As an important intermediary organization for transmitting data production factors, the digital platform integrates “piece data” across industries and fields into “block data”.Data production factors are dispersed or concentrated on different digital platforms, corresponding to the dispersion or concentration of the allocation right of production factors. When data production factors are concentrated on a digital platform, the configuration process of production factors is dominated by the single digital platform, which is called “Data Control Mode”. When data production factors are distributed on digital platforms of different industries, the digital platform coordinates the configuration process of production factors, which is called “Data Coordination Mode”. Digital platform reshapes the relationship between organizations to form a digital business ecosystem, and the distribution mode of data production factors affects the characteristics of digital business ecosystem. This article chooses Zhejiang Province and Guangdong Province as the representatives of “Data Control Mode” and “Data Coordination Mode”, respectively. Based on the business areas and authorized invention patents of listed companies in the digital economy, this article compares the differences between the two modes in industrial chain integration, digital industrialization and industrial digital integration, and the network structure of the digital business ecosystem, and analyzes the different paths adopted by the two modes to improve economic efficiency. The study finds that no matter what the structure is, private enterprises usually choose strategic emerging industries with frequent innovation activities and high innovation risks, while state-owned enterprises usually choose producer services with high entry barriers. “Data Control Mode” is to build a digital marketing channel to take advantage of the user traffic of the consumer Internet platform, and “Data Coordination Mode” is to rely on the foundation of the electronic information industry to give full play to the advantages of the industrial Internet platform for collaborative manufacturing. “Data Control Mode” is a vertically integrated industrial chain integration method. “Data Coordination Mode” is horizontal industrial chain integration method. Compared with “Data Control Mode”, digital technology under “Data Coordination Mode” has a higher degree of integration with traditional industries and spreads faster in the industrial chain. The digital business ecosystem organization network under “Data Control Mode” presents the characteristics of “monopoly”, while “Data Coordination Mode” presents the characteristics of “blockchain”. Relying on a single platform, “Data Control Mode” is difficult to make the overall optimal decision and deployment. In building Industrial Internet Platforms in various industries, “Data Coordination Mode” has led to barriers to data flow between platforms. Due to the low degree of differentiation of consumer demand data, data production factors are highly concentrated to improve economic efficiency through modular design. As industrial data condenses industry knowledge and experience, data production factors are distributed on different industry platforms to improve economic efficiency through modular production. Regardless of whether data production factors are concentrated on a single platform or scattered on different industry platforms, efficient matching of consumer demand data and industrial data is the co-evolution direction of the two modes. Compared with discussing the optimal concentration scale of data production factors, it is fundamental to unblock the flow channel of data factors between digital platforms.