(1) Analysis of main indexes
Based on the analysis of supply chain data governance ecosystem perspective, the system governance system mainly consists of four indexes: data governance subject, governance technology, governance environment, and data and services. By analyzing the main indexes A and B, the index ranking of index centrality and cause degree is integrated to obtain the mean value of cause degree of A-level indexes (listed in the order of cause degree) as A2, A4, A1, A3, and the centrality degree as A3, A2, A1, A4, respectively. It can be seen that the cause factors of the first-level indexes are data governance technology and data and services; the result elements are data governance subject and data governance environment. The centrality of data governance technology as the cause factor ranks second, indicating that data governance technology dominates the governance system and has a high degree of influence on other indexes. The centrality of data governance environment is in the first place, and because it is the result element, it indicates that data governance environment also plays an important role in the governance system, but it is less stable and vulnerable to other indexes. The data governance subject as the governance subject of the supply chain data governance ecosystem is classified as the result element, which reflects that the current governance situation of the data governance subject is more and more passive, and the governance behavior needs to rely on data governance technology, data and services as support.
According to the comparison of the centrality of B-level indexes, technology and tools, internal environment, users and supply chain services are ranked high, among which the internal environment and users are more influenced, resulting in their centrality ranking among the top, that is, technology and tools and supply chain services are the key factors affecting the governance system, while internal environment and users are in a passive position in the system.
(2) Causal elements and centrality analysis
C-level indexes can more accurately and comprehensively assess the optimization effectiveness of supply chain data governance ecosystem. Based on the positive and negative comparison of C-level indexes, we can find that the supply chain data governance ecosystem index system consists of 13 cause elements and 11 result indexes, and the order of index centrality is C14, C9, C7, C24, C4, C10, C21, C12, C5, C19, C22, C8, C16, C15, C13, C23, etc. Combining the cause elements and centrality, it can be seen that C9, C24, C21, C12, C5, C22, C8 and C16 are the key impact indexes in the governance system, which can directly shape the governance effectiveness fluctuations and have a greater impact on other indexes.
Among them, data operation technical support has the greatest direct impact among all indexes, indicating that data technical support in the process of governance is a key bearing index for the effectiveness of governance optimization, and improving the level of data operation technology of supply chain enterprises and industrial Internet can guarantee the perfection of data governance infrastructure, effectively resist the problems of data circulation, storage, collection, exchange and sharing to upstream and downstream subjects of supply chain and industrial Internet platform through supply chain. It helps to enhance the intelligent construction of industrial Internet and strengthen the core competitiveness of enterprises, construct a good service and innovation environment for the information platform, and promote the sustainable development of supply chain data governance ecosystem.
(3) Resulting elements and centrality analysis
Combining the result factors and centrality, it can be found that the cause degree of C14, C7, C4, and C6 become negative and their are affected to a greater degree. This suggests that they are vulnerable to fluctuations in the governance system. Although their centrality is high, they are not counted in the screening of key indexes in this study due to the low ranking of the influence degree of the indexes.
C13, C15 and C23 are ranked in the middle in terms of centrality and reason and they are influenced by a greater degree. They are the easiest governance points and also belong to the key indexes. Data governance subjects should focus on these four indexes in the governance process. Therefore, in order to effectively improve the effectiveness of data governance optimization, it is necessary to collaborate with all value subjects in the governance system. Each subject should not only reasonably plan the data governance optimization system and improve data governance organization construction, but also open up the data flow of each business node and information system and enhance the core competitiveness of supply chain node enterprises and industrial Internet enterprises.
4.1.7. Cause-and-effect four-quadrant diagram analysis
Based on the data in Table 3, the inter-index causality diagram for supply chain data governance optimization is established by quadrant determination method with centrality as the x-axis and causality as the y-axis, and the values of each index are plotted one by one in the diagram (Fig. 1). Among them, since the median indicates the sample distribution based on the middle value, the set of values can be stratified equivalently. Therefore, the median x=4.41, which corresponds to the centrality of 24 indexes, is added to the graph as the internal auxiliary axis z. The purpose is to visualize the centrality and causality of indexes, so as to visually observe the distribution pattern of indexes and reasonably screen the key indexes.