Abstract
PurposeThe 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 Stirling’s diversity index and specialization index.Design/methodology/approachInterdisciplinarity was measured utilizing the descriptive statistics of disciplines, network indicators showing relationships between disciplines and within individual disciplines, interdisciplinary communities, Stirling’s diversity index and specialization index, and a strategic diagram revealing the development status and trends of discipline communities.FindingsComprehensively 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 microbiology as 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/valueThis 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 conduct comparative studies between different research areas.
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