{"title":"数据的商业价值是什么?货币估值因素与数据治理方法的多视角实证研究","authors":"Frank Bodendorf, Jörg Franke","doi":"10.1016/j.datak.2023.102242","DOIUrl":null,"url":null,"abstract":"<div><p><span>Digitalization has greatly increased the importance of data in recent years, making data an indispensable resource for value creation in our time. There is currently still a lack of theories as well as practicable methods and techniques for the monetary valuation of data, and data is therefore not yet sufficiently managed in terms of business management principles. In this context, this research is intended to design theory ingrained principles for a multidimensional conceptual approach to the monetary valuation of data as assets. We draw on the theory of dynamic capabilities as a further development of resource theory as well as value theory. To this end, the research conducts a qualitative field study followed by a quantitative survey study. Literature analysis is used to explain different dimensions in the qualitative field study. Structural equation modeling is used to analyze empirical data collected in the quantitative study. The results show that data value determination is a multidimensional and hierarchical construct consisting of three primary dimensions. These are the benefit-oriented, cost-oriented, and quality-oriented dimensions. The results also confirm that institutional pressures (coercive, normative, mimetic) that influence </span>organizational behaviors lead to a greater intention for organizations to adapt a monetary data value determination.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"149 ","pages":"Article 102242"},"PeriodicalIF":2.7000,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What is the business value of your data? A multi-perspective empirical study on monetary valuation factors and methods for data governance\",\"authors\":\"Frank Bodendorf, Jörg Franke\",\"doi\":\"10.1016/j.datak.2023.102242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>Digitalization has greatly increased the importance of data in recent years, making data an indispensable resource for value creation in our time. There is currently still a lack of theories as well as practicable methods and techniques for the monetary valuation of data, and data is therefore not yet sufficiently managed in terms of business management principles. In this context, this research is intended to design theory ingrained principles for a multidimensional conceptual approach to the monetary valuation of data as assets. We draw on the theory of dynamic capabilities as a further development of resource theory as well as value theory. To this end, the research conducts a qualitative field study followed by a quantitative survey study. Literature analysis is used to explain different dimensions in the qualitative field study. Structural equation modeling is used to analyze empirical data collected in the quantitative study. The results show that data value determination is a multidimensional and hierarchical construct consisting of three primary dimensions. These are the benefit-oriented, cost-oriented, and quality-oriented dimensions. The results also confirm that institutional pressures (coercive, normative, mimetic) that influence </span>organizational behaviors lead to a greater intention for organizations to adapt a monetary data value determination.</p></div>\",\"PeriodicalId\":55184,\"journal\":{\"name\":\"Data & Knowledge Engineering\",\"volume\":\"149 \",\"pages\":\"Article 102242\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2023-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data & Knowledge Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169023X23001027\",\"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/S0169023X23001027","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}
What is the business value of your data? A multi-perspective empirical study on monetary valuation factors and methods for data governance
Digitalization has greatly increased the importance of data in recent years, making data an indispensable resource for value creation in our time. There is currently still a lack of theories as well as practicable methods and techniques for the monetary valuation of data, and data is therefore not yet sufficiently managed in terms of business management principles. In this context, this research is intended to design theory ingrained principles for a multidimensional conceptual approach to the monetary valuation of data as assets. We draw on the theory of dynamic capabilities as a further development of resource theory as well as value theory. To this end, the research conducts a qualitative field study followed by a quantitative survey study. Literature analysis is used to explain different dimensions in the qualitative field study. Structural equation modeling is used to analyze empirical data collected in the quantitative study. The results show that data value determination is a multidimensional and hierarchical construct consisting of three primary dimensions. These are the benefit-oriented, cost-oriented, and quality-oriented dimensions. The results also confirm that institutional pressures (coercive, normative, mimetic) that influence organizational behaviors lead to a greater intention for organizations to adapt a monetary data value determination.
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.