Digital government construction is a complex system project, and data sharing is its governance niche. Cross-sectoral data sharing is the core issue of improving governance capacity in the construction of digital governments.
数字政务建设是一项复杂的系统工程，数据共享是其治理利基。跨部门数据共享是数字政府建设中提升治理能力的核心问题。
1. Introduction
With the advent of the Fourth Industrial Revolution, the digital economy is experiencing an extraordinary boom, leading to a similar transformation of digital government in the area of government governance
[1]. In this context of development, the traditional discourse on production relations in the political economy is being further expanded, with data becoming a vital means of production
[2]. The widespread adoption and application of digital technology has resulted in organizational and managerial changes, particularly in government departments in which information technology changes have brought a gradual shift in the paradigm of administrative governance toward governance in the digital age
[3]. Compared to the deconstruction approach of new public management (NPM), which aims to create small, fragmented institutional governance, the digital government era focuses on reintegration and needs-based holism. This approach relies on digital technology to enhance collaborative governance across sectors, thereby re-governmentalizing and attempting to eliminate silos of public sector processes. This helps prevent administrative fragmentation dilemmas
[4]. In the progress of building a digital government, realizing the convergence and sharing of data elements across departments, regions and fields has become a core issue in enhancing the governance capacity of digital government.
To achieve integrated data management and construct a collaborative and open digital government management platform, data management departments have been established in different regions of China. These departments can be classified into three types: independent government departments with data management functions, established departments that have added data management functions, and new divisions that have incorporated data management functions under one of the original departments
[5]. A common challenge faced by different types of data management departments is the relationship with functional departments
[6][7][6,7]. On the one hand, the degree of informatization varies from department to department. The digital literacy and competence of public officials varies, as does the degree of standardization and differentiation of data in the sector. On the other hand, regarding sharing data, decision makers in a department assess the risks involved and are torn between active and passive sharing, or even nonsharing, as they see data as core assets for maintaining power
[8]. The priority of whether this sharing affects the core interests of the department is clearly higher for departmental heads than the overall performance of the digital government
[9].
Cross-department data sharing is a dynamic and complex game process containing many uncertain and unstable factors. In order to analyze in detail the strategic paths of the different subjects in this game process and the influence of relevant factors, this rpapesearchr constructed a tripartite evolutionary game model and introduced Gaussian white noise to simulate the random disturbance environment, and the changes and stability conditions of the data sharing game strategy between the data management departments and the different government functional departments are discussed. Furthermore, researcherswe used a numerical simulation to analyze the trajectory of the evolution of the strategy of the different subjects under the influence of multiple factors in a stochastic environment. ResearchersWe provide specific recommendations based on the findings of this study to promote smooth data sharing among different sectors with the expectation of advancing digital governance capabilities in the era of big data. From the existing studies, it can be seen that the role played by data management departments in data sharing and their influencing factors have received extensive focus, especially the relationship between data management departments and functional departments, which has also been somewhat elucidated from the qualitative research perspective. However, it should be emphasized that inter institutional relationships are not static. Especially in the complex environment of digital transformation, the behavioral performance and strategic choices of data management departments and functional departments change dynamically with the influence of different factors.
2. The Advantages and Dilemmas of Cross-Department Data Sharing
Achieving openness and the sharing of data are the basis for efficient, agile and intelligent collaboration to deal with complex social issues in the current era of digital governance [10]. Government data sharing includes two meanings: that government departments seek data sharing from other government departments due to the need to perform their duties, and that government department data are open for sharing with the public. The cross-department data sharing studied in this research falls under the first meaning, i.e., the act of data sharing among government departments. Data sharing requires the building of systems or platforms among different departments and the harmonization of different business data standards, as well as the transformation of business processes to meet access to data sharing [11]. Unlike the traditional hierarchy of departments, cross-department data sharing can break down the information barriers among the different departments and can improve the efficiency of the information transfer, policy coordination and public service provision within the government. In the current era of data explosion, the information resources of government departments are also growing at an explosive pace. How to achieve coordination and stability in sharing data across departments has become the key to enhancing the government’s digital governance capabilities. A wealth of experience emerges from China’s digital government reforms. By analyzing the 在当前数字治理时代，实现数据开放、共享是高效、敏捷、智能协作应对复杂社会问题的基础[10]。政府数据共享包括两个含义：政府部门因履行职责的需要而寻求其他政府部门的数据共享，以及政府部门数据开放与公众共享。本文研究的跨部门数据共享属于第一个含义，即政府部门之间的数据共享行为。数据共享需要在不同部门之间建立系统或平台，协调不同的业务数据标准，以及业务流程的转换以满足数据共享的访问[11]。与传统的部门层级不同，跨部门数据共享可以打破不同部门之间的信息壁垒，提高政府内部信息传递、政策协调和公共服务提供的效率。在当前数据爆炸的时代，政府部门的信息资源也在以爆炸式的速度增长。如何在部门间实现数据共享的协调和稳定，成为提升政府数字治理能力的关键。中国的数字政府改革积累了丰富的经验。有学者通过分析浙江“
Visit Once一次走访”
reform in Zhejiang, some scholars found that enhancing data sharing among departments can reconcile the contradiction between the fragmentation of administrative functions and the integration of public services, and it can improve the level of business collaboration among government departments [12]. Through the use of information technology to promote data storage in the cloud and the syncing of government services, this new type of administrative approval has achieved a change in governance by allowing data transmission instead of the masses seeking out different departments [13].改革发现，加强部门间数据共享可以调和行政职能碎片化与公共服务一体化的矛盾，可以提高政府部门之间的业务协同水平[12]。通过使用信息技术促进数据存储在云中和政府服务的同步，这种新型的行政审批通过允许数据传输而不是群众寻找不同的部门来实现治理的改变[13]。
The government can improve the refinement and integration of its public services by improving the level of cross-department data sharing. On the one hand, cross-department data sharing within the government can enhance the accuracy of matching government public service supply with public demand. Cross-department data sharing has resulted in interdepartmental collaboration and functional integration, enhancing the government’s ability to respond quickly and accurately to citizens’ needs for governance [14][15]. On the other hand, data sharing can enable business integration and even parallel reinvention among different departments, enhancing the integration of government public services and reducing repetitive rule-based labor [16][17][18]. However, for a long time, the reality of inadequate government data sharing, such as 政府可以通过提高跨部门数据共享水平来完善公共服务。一方面，政府内部跨部门数据共享可以提高政府公共服务供给与公众需求匹配的准确性。跨部门数据共享实现了部门间的协作和职能整合，增强了政府快速准确地响应公民治理需求的能力[14,15]。另一方面，数据共享可以实现不同部门之间的业务整合甚至并行重塑，加强政府公共服务的整合，减少重复的基于规则的劳动[16,17,18]。然而，长期以来，政府数据共享不足的现实，如“
data chimneys数据烟囱”
, “data silos”, and “data barriers”, has always hindered cross-department business collaboration [19]. There are natural barriers to data sharing across sectors, industries, and hierarchies. The perception of data varies among different government departments, as does the perception of sectoral interests involving data. In addition, factors such as technical compatibility, the nonuniformity of the data structure, specialized operations and data security hinder the flow of data among government departments. The compartmentalization and weak correlation among business units also constrain the willingness of some departments to share data. In general, it can be simply summarized as technical factors, business factors, conceptual factors and management factors [20][21]. These problems arise due to the presence of a combination of factors, such as data-sharing inputs, data-sharing systems and unfavorable cross-department coordination [22]. Therefore, in further research, it is necessary to deeply explore the influence and effect of different factors on different subjects in the process of cross-department data sharing to provide theoretical support for analysis and to propose solutions.、“数据孤岛”、“数据壁垒”，始终阻碍着跨部门业务协作[19]。跨部门、行业和层次结构的数据共享存在天然障碍。不同政府部门对数据的看法各不相同，对涉及数据的部门利益的看法也各不相同。此外，技术兼容性、数据结构不统一、专业化操作和数据安全等因素阻碍了政府部门之间的数据流动。业务部门之间的条块分割和弱相关性也限制了某些部门共享数据的意愿。一般可以简单概括为技术因素、业务因素、概念因素和管理因素[20,21]。这些问题的出现是由于存在多种因素，例如数据共享输入，数据共享系统和不利的跨部门协调[22]。因此，在进一步的研究中，有必要深入探讨跨部门数据共享过程中不同因素对不同学科的影响和效果，为分析提供理论支持，提出解决方案。
3. Factors Influencing Cross-Department Data Sharing影响跨部门数据共享的因素
In order to promote cross-department data sharing within the government, scholars from different disciplinary backgrounds have researched the issue of cross-department government data from different disciplinary paradigms, with two main disciplinary subdisciplines in general: public administration and intelligence [23][24]. Public administration scholars are more likely to study the data sharing mechanism based on government subjects and to focus on collaborative management among government departments from the organizational dimension. Cross-department data sharing needs to break down traditional administrative compartmentalization through building mechanisms [25]. A lack of trust among government departments is the cause of inactive data sharing [26]. This requires the development of appropriate systems to enhance positive incentives for cross-department data sharing, such as reward and punishment mechanisms. In the case of low levels of intersubjective trust and cooperation, top-down promotion by the central government is required [27]. The complex leadership mechanism of the 为了促进政府内部的跨部门数据共享，来自不同学科背景的学者研究了不同学科范式的跨部门政府数据问题，一般分为两个主要学科子学科：公共管理和情报[23,24]。公共行政学者更倾向于研究基于政府主体的数据共享机制，并从组织层面关注政府部门之间的协同管理。跨部门数据共享需要通过构建机制打破传统的行政区隔[25]。政府部门之间缺乏信任是数据共享不活跃的原因[26]。这需要开发适当的系统，以加强对跨部门数据共享的积极激励，例如奖惩机制。在主体间信任与合作水平较低的情况下，需要中央政府自上而下的推动[27]。“
line-block structure线块结构”
makes it difficult to form a centralized and unified operational mechanism and data interfacing model, resulting in “data chimneys” that are still standing [28]. The establishment of a dedicated big data management agency could be a good way to address this issue. This requires further realignment of the responsibilities and competencies to facilitate effective cross-department collaboration through the operation of a professional digital agency [21]. The study of intelligence is based on data as intelligence and focuses more on the technical aspects of government data platform construction, data sharing processes and the integration of data elements. From the perspective of the subject, enhanced training in data collection and processing, data infrastructure maintenance, and development can improve the technical capacity and digital literacy of government personnel. The cost of inhibiting government willingness to share can be reduced by harmonizing data interfaces, developing sharing specifications, and streamlining the data-sharing process. From a technical perspective, a citizen-centric model of distributed data sharing has been proposed. Distributed document exchange networks offer advantages, such as security, transparency, cost effectiveness and trust, which can better improve administrative efficiency and reduce bureaucratic procedures [29]. Some scholars have also proposed embedding blockchain technology into cross-department data governance by leveraging the decentralized and de-trusting features of blockchain technology, thereby improving the security and reliability of cross-department data sharing [30][31].
的复杂领导机制使得难以形成集中统一的运行机制和数据接口模型，导致“数据烟囱”依然存在[28]。建立一个专门的大数据管理机构可能是解决这个问题的好方法。这需要进一步调整职责和能力，通过专业数字机构的运作促进有效的跨部门协作[21]。智能化研究以数据即智能为基础，更侧重于政府数据平台建设、数据共享流程和数据要素整合的技术方面。从主题角度看，加强数据收集和处理、数据基础设施维护和开发方面的培训，可以提高政府人员的技术能力和数字素养。通过协调数据接口、制定共享规范和简化数据共享过程，可以降低抑制政府共享意愿的成本。从技术角度来看，已经提出了以公民为中心的分布式数据共享模型。分布式文档交换网络具有安全性、透明度、成本效益和信任等优势，可以更好地提高行政效率并减少官僚程序[29]。一些学者还提出，利用区块链技术的去中心化和去信任性特征，将区块链技术嵌入跨部门数据治理中，从而提高跨部门数据共享的安全性和可靠性[30,31]。
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