Measuring the interdisciplinarity of Big Data research: a longitudinal study

Pages681-696
Published date10 September 2018
Date10 September 2018
DOIhttps://doi.org/10.1108/OIR-12-2016-0361
AuthorJiming Hu,Yin Zhang
Subject MatterLibrary & information science,Information behaviour & retrieval,Collection building & management,Bibliometrics,Databases,Information & knowledge management,Information & communications technology,Internet,Records management & preservation,Document management
Measuring the interdisciplinarity
of Big Data research:
a longitudinal study
Jiming Hu
Department of Information Management, Wuhan University, Wuhan, China, and
Yin Zhang
Department of Library and Information Science, Kent State University,
Kent, Ohio, USA
Abstract
Purpose The purpose of this paper is to measure the degree of interdisciplinary collaboration in Big
Data research based on the co-occurrences of subject categories using Stirlings diversity index and
specialization index.
Design/methodology/approach Interdisciplinarity was measured utilizing the descriptive statistics of
disciplines, network indicators showing relationships between disciplines and within individual disciplines,
interdisciplinary communities, Stirlings diversity index and specialization index, and a strategic diagram
revealing the development status and trends of discipline communities.
Findings Comprehensively considering all results, the degree of interdisciplinarity of Big Data research is
increasing over time, particularly, after 2013. There is a high level of interdisciplinarity in Big Data research
involving a large number of disciplines, but it is unbalanced in distribution. The interdisciplinary
collaborations are not intensive on the whole; most disciplines are aggregated into a few distinct communities
with computer science, business and economics, mathematics, and biotechnology and applied microbiologyas
the core. Four major discipline communities in Big Data research represent different directions with different
development statuses and trends. Community 1, with computer science as the core, is the most mature and
central to the whole interdisciplinary network. Accounting for all network indicators, computer science,
engineering, business and economics, social sciences, and mathematics are the most important disciplines in
Big Data research.
Originality/value This study deepens our understanding of the degree and trend of interdisciplinary
collaboration in Big Data research through a longitudinal study and quantitative measures based on two
indexes. It has practical implications to study and reveal the interdisciplinary phenomenon and
characteristics of related developments of a specific research area, or to conductcomparative studies between
different research areas.
Keywords Network analysis, Indicators, Big Data research, Measures, Interdisciplinarity
Paper type Research paper
Introduction
In the information age, Big Data holds great value to research and industry (Fang et al.,
2015), and also generates numerous opportunities and challenges (Marx, 2013). These
challenges require a vast variety of theories, methods, techniques, and policy to address
problems in capture, storage, curation, and analysis (Chen and Zhang, 2014). Big Data
optimizes various applications in a large number of fields, including industry, agriculture,
business and economics, traffic, transportation, medical care, families, law, public
administration, etc. (Savitz, 2012; Bardi et al., 2014; Ekbia et al., 2015).
Online Information Review
Vol. 42 No. 5, 2018
pp. 681-696
© Emerald PublishingLimited
1468-4527
DOI 10.1108/OIR-12-2016-0361
Received 29 December 2016
Revised 8 May 2017
30 September 2017
10 January 2018
Accepted 8 February 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1468-4527.htm
This study is supported by Ministry of Education of China (MOE) World-class Discipline Library,
Information, and Data Science,National Social Science Key Fund of China (NSSFC) (No. 15ZDC025),
China Postdoctoral Science Foundation Special Funded Project (No. 2016T90736), National Natural
Science Foundation of China Funded Project (No. 71303178) and Kent State University 2014
Postdoctoral Program for the Smart Big Data project.
681
Interdisciplinarity
of Big Data
research
Big Data, as an emerging field of research and practice, involves many disciplines and
contexts. It is challenging to define Big Data given its evolving and interdisciplinary nature.
Overall, major characteristics of Big Data include being huge in volume, high in velocity,
diverse in variety, exhaustive in scope, fine-grained in resolution, uniquely indexical in
identification, relational in nature, flexible in holding the traits of extensionality, and
scalability (Kitchin, 2014). Furthermore, Big Data requires innovative techniques and
technologies to perform.
Big Data research is a multidisciplinary and interdisciplinary research (IDR) field that
requires collaborations from a great diversity of disciplines (Chen et al., 2014; Hilbert, 2016).
As reflected in retrieved articles related to Big Data research from the Web of Science (WoS)
Core Collection, they span a large number of disciplines as represented by the subject
categories (SCs) assigned by WoS; and the number of disciplines is increasing over time.
More importantly, a majority of these papers have two or more SCs. It shows that Big Data
research integrates different sciences and technologies into one field in a way that is
characterized as interdisciplinary (Gandomi and Haider, 2015; Singh et al., 2015). All of these
observations provide evidence that Big Data research is regarded as an IDR (Wagner et al.,
2011) field. However, the degree of IDR in Big Data, named as interdisciplinarity (Tomov
and Mutafov, 1996; Morillo et al., 2003; Leydesdorff and Rafols, 2011) has not yet been
studied or measured. It is important to study interdisciplinarity in Big Data research to
understand the development of collaboration between disciplines.
This study aims to address the lack of study on revealing interdisciplinarity in Big Data
research, utilizing SCs as the unit of analysis to achieve the following research purposes.
First, using social network analysis, based on disciplinesco-occurrence data, this paper
attempts to discover the structure and patterns of interdisciplinary collaboration, and to
detect collaboration communities and trends over time. Second, relying on existing
measurement indexes of interdisciplinarity (e.g. Stirling, 2007; Porter et al., 2007; Rafols and
Meyer, 2010, Leydesdorff and Goldstone, 2014), this paper utilizes Stirlings diversity index
(Stirling, 2007) and specialization index (Porter et al., 2007, 2008; Porter and Rafols, 2009) to
ascertain the interdisciplinarity of Big Data research, revealing the degree of
interdisciplinarity, supplemented by network indicators to describe status and trends.
Literature review
Background and status of Big Data research and development
Big Data is a research frontier (Manyika et al., 2011) with significant growth in research
articles in recent years (Akoka et al., 2015). It is revolutionizing business, scientific research,
and public administration (Chen and Zhang, 2014), with researchers from a variety of
disciplines paying attention to the ability of Big Data to accelerate their research and
applications (Wu et al., 2015). The focus of most Big Data research is mainly on data
generation, data acquisition, data storage, and data analysis (Chen et al., 2014). The
explosive growth rate of large data generates numerous challenges regarding technological
innovation (Al-Jarrah et al., 2015; Acharjya and Ahmed, 2016), security and management
(Liang et al., 2015), and even society and ethics (Smith et al., 2015; Mittelstadt and Floridi,
2016). More importantly, Big Data also brings new opportunities (Michael and Miller, 2013)
for discovering new values in all kinds of fields (Emani et al., 2015) and making valuable and
accurate predictions and decisions (Chen et al., 2012; Suresh, 2016).
Because Big Data research is interdisciplinary, researchers with different disciplinary
backgrounds share their respective approaches to solve issues and promote applications
(Fang et al., 2015). Computer science, engineering, and mathematics provide theories,
methods and tools to capture and analyze the large-scale data in almost all fields, and make
valuable contributions in other disciplines or fields, such as social science (e.g. Olmedilla
et al., 2016), business and economics (e.g. Einav and Levin, 2014), and health care
682
OIR
42,5

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