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.