The composition of data economy: a bibliometric approach and TCCM framework of conceptual, intellectual and social structure
| DOI | https://doi.org/10.1108/IDD-02-2022-0014 |
| Published date | 31 October 2022 |
| Date | 31 October 2022 |
| Pages | 223-240 |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
| Author | Sunday Adewale Olaleye,Emmanuel Mogaji,Friday Joseph Agbo,Dandison Ukpabi,Akwasi Gyamerah Adusei |
The composition of data economy: a
bibliometric approach and TCCM framework of
conceptual, intellectual and social structure
Sunday Adewale Olaleye
School of Business, JAMK University of Applied Sciences, Jyvaskyla, Finland
Emmanuel Mogaji
Department of Marketing, Events and Tourism, University of Greenwich, Greenwich, UK
Friday Joseph Agbo
School of Computing, University of Eastern Finland, Joensuu, Finland and School of Computing and Data Science,
Willamette University, Salem, Oregon, USA
Dandison Ukpabi
Jyväskylä School of Business and Economics, University of Jyväskylä, Jyvaskyla, Finland, and
Akwasi Gyamerah Adusei
Department of Industrial Engineering and Management, Oulun Yliopisto, Oulu, Finland
Abstract
Purpose –The data economy mainly relies on the surveillance capitalism b usiness 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 bibliometricanalysis 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 couldpredict the future of the field. A total of 591 data between 2008 and June 2021 were
used in the analysis with the Biblioshinyapp 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 emp irical 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.
Keywords Big data, Open data, Data privacy, Datafication, Data economy, FAIR data
Paper type Literature review
1. Introduction
Data refers to either textual or numeric units of information
presented using specific machine language systems that enable
interpretation by suitable technologies (Monino, 2016). The
volume of data is continuously increasing following the
proliferation of digital technologies, including smartph ones, web
services and social networks. The advancement of diver’s
technologies and diffusion of innovation has increas ed data
The current issue and full text archiveof this journal is available on Emerald
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Information Discovery and Delivery
51/2 (2023) 223–240
Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-02-2022-0014]
© Sunday Adewale Olaleye, Emmanuel Mogaji, Friday Joseph Agbo,
Dandison Ukpabi and Akwasi Gyamerah. Published by Emerald
Publishing Limited. This article is published under the Creative Commons
Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute,
translate and create derivative works of this article (for both commercial
and non-commercial purposes), subject to full attribution to the original
publication and authors. The full terms of this licence may be seen at
http://creativecommons.org/licences/by/4.0/legalcode
Erratum: It has come to the attention of the publisher that the article, “The
composition of data economy: a bibliometric approach and
TCCM framework of conceptual, intellectual, and social structure”by
Sunday Adewale Olaleye, Emmanuel Mogaji, Friday Joseph Agbo,
(continued on next page)
Received 14 February 2022
Revised 24 May 2022
10 July 2022
19 August 2022
16 September 2022
Accepted 1 October 2022
223
generation of big data in academia, industry and society. Data
economy as an offshoot of big data has created opportunities for
data partnership and better environment, cost and user-friendly
services. This development is clear visibility of hidden innovation,
as suggested by Edwards-Schachter and Wallace (2017).Thedata
economy is at the epicenter of society, from data generation, data
cleaning and data engineering to innovative products and services .
Through the data economy, ecosystems of the data-driven small,
medium and large companies stand to allay consumers’fear of
legal, privacy and security issues. Small, medium and large
companies can also create a sustainable data moat for competitive
advantage and to centralize their data assets with the intervention of
artificial intelligence and other emerging technology. One of the
earlier studies suggested prioritizing data product or service; it is
essential to identify the available opportunity, build the product or
design the service, evaluate the first two stages and iterate based on
data and the user feedback (Glassberg, 2018). To maximize the
data economy, the timely intervention of governments and societies
on the political, economic and social impacts of data-driven
artificial intelligence is crucial. This bibliometric study probed i nto
data economy, value and gaps for academia and the practicing
managers in a changing landscape of big data opportunity.
Therefore, the following research questions guide this study:
RQ1. How has data economy been conceptualized and
presented by scholars?
RQ2. What are the intellectual outputs and contributions of
scholars in data economy?
RQ3. What social synergy and collaborations exist in the
domain of the data economy?
This study contributes to the literature on data economy in
multiple ways and presents implications for educators, academic
researchers, managers and policymakers. For educators, our
analysis provides current teaching materials on essential areas of
data economy, providing pedagogical insight relevant to enhancing
students’teaching and learning experiences. Our study provides a
comprehensive overview of the current state of research on the
data economy for academic researchers. We reviewed 591 articles
exploring how data economy has been conceptualized and
presented by scholars. Our analysis suggested that scientificoutput
about data economy has remained on the rising curve, suggesting
that the emerging field has the potential to grow significantly on an
annual basis.
Based on the theories, contexts, characteristics and methodology
(TCCM) framework, our review findsthatdynamiccapability
theory has been a dominant theoretical underpinning in the data
economy research stream. Data managers gain insights from our
comprehensive business model that harmonizes the ethical, legal,
technology and societal issues. This proposed business model will
proffer solutions to the existing teething problems of the data
economy. For policymakers,we posited that the timely intervention
of governments and societies on the political, economic and social
impacts of data-driven artificialin telligence is crucial; we, therefore,
provided insight into developing data-driven strategies that make
the stakeholders proactive. Based on the insights gained from our
literature review, we develop an agenda for future research,
outlining topics and potential research questions based on the
TCCM framework. We suggest future research to expand on the
intersection of sharing, platform and data economy. In addition,
researchers can test existing theories and show a plausible
explanation for their investigationof the data economy.
2. Review of the literature
There are numerous sources of data, and companies, financial
institutions and health service providers generate large amounts of
data through their interactions with suppliers, customers and
employees. Data is a crucial factor in production that
complements physical capital and labor (Opher et al., 2016). It is
nondepletable, and its increased use increases its value. As an
asset, its value can deplete over time, as the data becomes less
relevant, and its value depends on its unique characteristics. Data
is also regarded as a nonrival asset, as multiple users can use it
simultaneously (Agata, 2020). However, it is not automatically
labeled as a public good because the data owners reserve the right
to exclude individuals from using it, further increasing its value.
According to Nobre and Tavares (2017), data can be produced
and stored at low costs and households, businesses and individuals
constitute the major producers and consumers of data.
2.1 FAIR data
FAIR data refers to findable, accessible, interoperable and reusable
data (Dunning et al., 2017). The characteristics of findable,
accessible, interoperable and reusable data (FAIR) data must
adhere to the FAIR principles, which are used to determine the
levels of compliance. This data is assigned a globally unique and
persistent identifierandissimpletoexecute.Suitableexamplesof
the persistent identifier include the digital object identifier,
HANDLE (a unique and persistent identifierfor Internet resources)
and uniform resource name systems (Dunning et al.,2017). FAIR
data is also characterized by several other facets, including being
indexedorregisteredinasearchableresource,anditmustbe
accompanied by a description comprising differentattributes.
According to Tanhua et al. (2019), FAIR data enables effective
data management through the collaboration of various activities,
including quality assurance and control, observations, metadata
and data assembly and data publication. Effective data
management aims to enhance local and interoperable data
discovery access and secures archiving, resulting in long-term
preservation. FAIR data is becoming a crucial tool for enabling
Dandison Ukpabi and Akwasi Gyamerah Adusei published in Information
Discovery and Delivery omitted the first affiliation of Friday Joseph Agbo,
and the last name of the author Akwasi Gyamerah Adusei. Friday Joseph
Agbo’saffiliations are: School of Computing, University of Eastern
Finland, Joensuu, Finland and School of Computing and Data Science,
Willamette University, Salem, Oregon, USA. The author previously listed
as Akwasi Gyamerah should be referred to as Akwasi Gyamerah Adusei.
This error was introduced in the editorial process and has now been
corrected in the online version. The publisher sincerely apologises for this
error and for any inconvenience caused. This article should now be cited as
Olaleye, S.A., Mogaji, E., Agbo, F.J., Ukpabi, D. and Adusei, A.G.
(2022), “The composition of data economy: a bibliometric approach and
TCCM framework of conceptual, intellectual and social structure”,
Information Discovery and Delivery, Vol. ahead-of-print No. ahead-of-print.
https://doi.org/10.1108/IDD-02-2022-0014.
This work was supported by the Foundation for Economic Education
(Liikesivistysrahasto) [grant numbers: 16–9388, 18–10407].
TCCM framework
Sunday Adewale Olaleye et al.
Information Discovery and Delivery
Volume 51 · Number 2 · 2023 · 223–240
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