组织间数据共享的问题:来自实践和研究挑战的发现

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Data & Knowledge Engineering Pub Date : 2024-01-10 DOI:10.1016/j.datak.2024.102280
Ilka Jussen , Frederik Möller , Julia Schweihoff , Anna Gieß , Giulia Giussani , Boris Otto
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引用次数: 0

摘要

数据共享在协助公司进行内部优化以及设计新产品和服务方面非常有效。虽然数据共享的好处似乎显而易见,但同时也伴随着各种担忧,包括害怕分享有价值的东西、不知道数据会发生什么变化,或者只是对短期和中期的好处缺乏了解。文章通过定性访谈研究和公共数据分析,对 13 个案例进行了研究,分析了组织间关系中的数据共享。鉴于 Gaia-X 和 Catena-X 等大型研究计划显示了组织间数据共享的重要性,我们探讨了这一过程中出现的问题,并提出了研究挑战。我们使用行动者网络理论(Actor-Network Theory)的理论视角来分析我们的数据,并将其构造与数据共享的概念联系起来。
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Issues in inter-organizational data sharing: Findings from practice and research challenges

Sharing data is highly potent in assisting companies in internal optimization and designing new products and services. While the benefits seem obvious, sharing data is accompanied by a spectrum of concerns ranging from fears of sharing something of value, unawareness of what will happen to the data, or simply a lack of understanding of the short- and mid-term benefits. The article analyzes data sharing in inter-organizational relationships by examining 13 cases in a qualitative interview study and through public data analysis. Given the importance of inter-organizational data sharing as indicated by large research initiatives such as Gaia-X and Catena-X, we explore issues arising in this process and formulate research challenges. We use the theoretical lens of Actor-Network Theory to analyze our data and entangle its constructs with concepts in data sharing.

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来源期刊
Data & Knowledge Engineering
Data & Knowledge Engineering 工程技术-计算机:人工智能
CiteScore
5.00
自引率
0.00%
发文量
66
审稿时长
6 months
期刊介绍: Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
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