Ilka Jussen , Frederik Möller , Julia Schweihoff , Anna Gieß , Giulia Giussani , Boris Otto
{"title":"组织间数据共享的问题:来自实践和研究挑战的发现","authors":"Ilka Jussen , Frederik Möller , Julia Schweihoff , Anna Gieß , Giulia Giussani , Boris Otto","doi":"10.1016/j.datak.2024.102280","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"150 ","pages":"Article 102280"},"PeriodicalIF":2.7000,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000041/pdfft?md5=8cca34784bb0ed03de222b7dc6fbfc47&pid=1-s2.0-S0169023X24000041-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Issues in inter-organizational data sharing: Findings from practice and research challenges\",\"authors\":\"Ilka Jussen , Frederik Möller , Julia Schweihoff , Anna Gieß , Giulia Giussani , Boris Otto\",\"doi\":\"10.1016/j.datak.2024.102280\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":55184,\"journal\":{\"name\":\"Data & Knowledge Engineering\",\"volume\":\"150 \",\"pages\":\"Article 102280\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0169023X24000041/pdfft?md5=8cca34784bb0ed03de222b7dc6fbfc47&pid=1-s2.0-S0169023X24000041-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data & Knowledge Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169023X24000041\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data & Knowledge Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169023X24000041","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.