3. Construction of index system
3.1. Selection of indexes
Supply chain data governance optimization is in line with the core idea of information ecology theory. It is based on improving the efficiency of supply chain data governance optimization, the data governance subjects establish interdependence (Jr et al, 2016). They form a benign interaction of multiple participants and build a data governance model of shared governance, thus ensuring the continuous circulation, sharing and application of data resources and services relying on technical power in a good governance environment. Therefore, constructing a supply chain data governance ecosystem index system oriented to the constituent elements of information ecology is conducive to expanding the thinking of index selection for supply chain data governance optimization and further promoting theoretical innovation of supply chain data governance optimization research. Based on this, this paper selects indexes of supply chain data governance optimization from four dimensions: data governance subject, data governance technology, data and service, and data governance environment.
3.2. Content of the index system
3.2.1. Data governance subjects
Based on the relevant stakeholders associated with supply chain data governance and relevant authoritative reports, the data governance subjects are divided into three secondary indexes: government departments, enterprises and users. The tertiary indexes that affect the optimization level of supply chain data governance are refined based on secondary indicatiors.
As an external source force driving the data governance process, the government plays an important role in the digital transformation of manufacturing industry by participating, promoting as well as regulating. The State Council’s emphasizes that scientific deployment of digital government policies, innovative government governance concepts and the construction of government information systems are powerful supports for advancing the construction of the national governance system and the modernization of governance capacity (Kaur & Nand, 2021). The government’s willingness to govern data is a prerequisite for the government to effectively develop and implement data governance strategies, and its willingness to govern has a significant positive impact on data governance outcomes.
Users as one of the value subjects of supply chain data governance, supply chain data governance is a means to achieve user satisfaction. In recent years, the supply chain has focused more on demand chain management and implemented supply chain reorganization around user needs. Users’ demand for data use increases, and their participation in product purchase and data governance increases, which in turn drives the data governance process.
Supply chain node enterprises, operation service enterprises and industrial Internet platform enterprises are all value subjects of the governance system. The core competitiveness of enterprises is an important index to assess the current development status of the supply chain industry and the investment risk of key enterprises, while the targeted planning of governance objectives in the digital transformation of industrial enterprises is still a must for enterprises to promote the governance process.
In view of this, the secondary indexes under the data governance subject dimension are further decomposed into encouraging government policies, government data governance willingness, government service platform construction, supply chain data governance innovation concept, public participation degree, meeting user needs, enterprise core competitiveness, and effective planning of governance objectives.
3.2.2. Data governance technologies
The development of governance technology plays a core guarantee role for supply chain data governance optimization, which is an important basis for achieving sustainable development of the governance ecosystem. Supply chain data governance optimization is closely related to factors such as data operation capability, talent availability, and information platform infrastructure. Therefore, it can be further subdivided into two secondary indexes: technology and talent, and infrastructure.
The development of next-generation information technology stimulates the urgent demand of governance system at the technical level, and the upstream and downstream subjects in the supply chain need the governance side to provide the level of data operation integrating emerging technologies such as Internet of Things, artificial intelligence and blockchain (Bhattacharyya, Mandke & Wood, 2021). Therefore, data operation ability is the key element to judge the efficiency of data governance optimization, and data governance talents with high-level operation ability can improve the efficiency of data collection and enhance the ability of independent analysis and data resources mining.
The orderly operation of the information platform can effectively solve the problems of numerous data calibers, uneven quality and data security risks, thus improving the efficiency of data governance optimization. The development and intelligent construction of infrastructure as the premise of big data integration and open sharing of industrial Internet platform, the higher the degree of its perfection, the more it can improve the efficiency of supply chain data governance optimization.
In view of this, technical support for data operation, information department personnel availability, degree of information platform development, and intelligent construction of industrial Internet are selected as the three-level indexes under the technical dimension of data governance.
3.2.3. Data and services
Data is the core element of the ecological composition of the Industrial Internet. Products as value carriers, their value-added processes interact with enterprise entity networks and resource networks in the form of data or data services (Dubey et al, 2017). The processes reflect industrial entities and integrate product value streams under resource loading, and they are precisely mapped, flowed and applied at different stages and levels. Therefore, data and services are selected as indexes for data governance optimization, respectively.
Based on the whole life cycle of the data flow process, the data governance content is divided into data management modules at the levels of metadata management, data quality, data standards, data storage and collection, and data security (Yang et al, 2022). The degree of data security risk depends on the amount of false data flow between the industrial Internet platform and the whole chain of the supply chain as well as the amount of users’ personal privacy data access. As a key part of data governance, supply chain service is based on cost reduction and efficiency improvement by providing data planning, process control and financing support to the data demand side, and realizing the co-creation of emerging business models and enterprise organization models. However, with the increase of data redundancy in the service process, the demand for effective data in the supply chain is increasing, and the data ease of use and data sharing ability will form positive feedback to the supply chain market resources and affect the data circulation rate upstream and downstream of the supply chain.
In view of this, based on the current status of data governance, the three-level indexes under the data and service dimension are identified as metadata management, data quality, data specification, data storage and collection, data security and risk level, data service and innovation, data ease of use, and data sharing capability.
3.2.4. Data governance environment
The internal and external environments in the supply chain data governance ecosystem circulate with each other, forming a closed-loop circulation path with logistics, capital flow and information flow(Rla et al, 2020). Based on the complexity of the supply chain data governance ecosystem, the data governance environment is divided into two secondary indexes: the governance micro-environment and the governance macro-environment.
In the governance micro-environment, the financial demand brought by the cost of data governance stimulates the governance subject to generate financial investment in the process of data governance, and the financial investment will increase the importance of the relevant governance organization to the mechanism of professional staffing, and the professionalism of its staffing reflects the level of organization construction to a certain extent. The organizational construction of data governance includes organizational structure, departmental responsibilities, staffing, job responsibilities, competency requirements, performance management, etc (Pant, Dutta & Sarmah, 2021). It can open up the data flow between each business node and information system, and improve the efficiency of data governance optimization within the organization and between organizations.
In the macro environment of governance, proper supply chain market competition is a favorable supply chain value-added strategy that helps enhance the data governance capability of supply chain enterprises and effectively promote the data governance process. In addition, supply chain synergy can establish the synergy mechanism of node enterprises through governance strategy, data sharing, benefits and risks, and improve the data governance of the whole supply chain.
In view of this, four three-level indexes are selected in the data governance environment dimension: organization construction level, capital investment, supply chain market competition, and supply chain synergy capability. Based on the above sorting and summarizing of the index dimensions and the subordinate indexes, the supply chain data governance ecosystem index system is constructed as shown in Table 1.