The composition of data economy: a bibliometric approach and TCCM framework of conceptual, intellectual and social structure

IF 2.1 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

Information Discovery and Delivery Pub Date : 2022-10-31 DOI:10.1108/idd-02-2022-0014

S. Olaleye, E. Mogaji, F. J. Agbo, D. Ukpabi, Akwasi Gyamerah

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引用次数: 2

Abstract

Purpose The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature. Design/methodology/approach The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus. Findings This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future. Research limitations/implications Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field. Practical implications The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies. Originality/value This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.

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数据经济的构成:文献计量学方法和概念、知识和社会结构的TCCM框架

数据经济主要依赖于监控资本主义的商业模式，使公司能够将其数据货币化。监控允许将私人的人类经验转化为可以在营销领域利用的行为数据。本研究旨在以已发表文献的定量文献计量分析的方法学视角来研究数据经济领域。设计/方法/方法文献计量学分析旨在揭示数据经济出现的趋势和时间表，其概念化，科学进步和主题协同作用，可以预测该领域的未来。2008年至2021年6月期间的591个数据被用于分析，使用web界面上的Biblioshiny应用程序和VOSviewer 1.6.16版本分析来自web of Science和Scopus的数据。本研究结合了可查找、可访问、可互操作和可重用(FAIR)数据和数据经济，通过阐明数据经济的概念、知识和社会结构，并证明数据相关性是公司和学术界现在和未来的关键战略资产，为大数据、信息发现和传递的文献做出了贡献。研究的局限性/意义本研究的发现为研究人员提供了一个踏脚石，他们可以通过采用定量和系统的回顾方法进行进一步的实证和纵向研究。此外，未来的研究可以将本研究的范围扩展到FAIR数据和数据经济之外，以检查理论等方面，并对新兴领域的几个现象给出合理的解释。实际意义研究人员可以将本研究的结果作为进一步实证和纵向研究的垫脚石。原创性/价值本研究证实了数据与社会的相关性，并揭示了未来需要弥补的一些差距。

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

Information Discovery and Delivery INFORMATION SCIENCE & LIBRARY SCIENCE-

CiteScore

5.40

自引率

4.80%

发文量

期刊介绍： Information Discovery and Delivery covers information discovery and access for digital information researchers. This includes educators, knowledge professionals in education and cultural organisations, knowledge managers in media, health care and government, as well as librarians. The journal publishes research and practice which explores the digital information supply chain ie transport, flows, tracking, exchange and sharing, including within and between libraries. It is also interested in digital information capture, packaging and storage by ‘collectors’ of all kinds. Information is widely defined, including but not limited to: Records, Documents, Learning objects, Visual and sound files, Data and metadata and , User-generated content.