What is the business value of your data? A multi-perspective empirical study on monetary valuation factors and methods for data governance

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frank Bodendorf, Jörg Franke
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引用次数: 0

Abstract

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.

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
近年来,数字化大大提高了数据的重要性,使数据成为我们这个时代创造价值不可或缺的资源。目前还缺乏对数据进行货币估值的理论,也缺乏切实可行的方法和技术,因此在商业管理原则方面对数据的管理还不够充分。在这种情况下,本研究的目的是设计理论根深蒂固的原则,多维的概念方法,以数据作为资产的货币估值。我们借鉴了动态能力理论,作为资源理论和价值理论的进一步发展。为此,本研究首先进行定性的实地研究,然后进行定量的调查研究。在定性的实地研究中,文献分析法被用来解释不同的维度。采用结构方程模型对定量研究中收集的经验数据进行分析。结果表明,数据值确定是一个多维层次结构,由三个主要维度组成。这三个维度分别是利益导向、成本导向和质量导向。研究结果还证实,影响组织行为的制度压力(强制性、规范性、模仿性)导致组织更倾向于适应货币数据价值的确定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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