A2 Refereed review article in a scientific journal
Big data analytics and enterprises: a bibliometric synthesis of the literature
Authors: Sayantan Khanra, Amandeep Dhir, Matti Mäntymäki
Publisher: Taylor & Francis
Publication year: 2020
Journal: Enterprise Information Systems
Journal name in source: ENTERPRISE INFORMATION SYSTEMS
Journal acronym: ENTERP INF SYST-UK
Volume: 14
Issue: 6
Number of pages: 32
ISSN: 1751-7575
eISSN: 1751-7583
DOI: https://doi.org/10.1080/17517575.2020.1734241(external)
Self-archived copy’s web address: https://lutpub.lut.fi/bitstream/10024/160875/1/khanra_et_al_big_data_analytics_am.pdf(external)
Abstract
Understanding the developmental trajectories of big data analytics in the corporate context is highly relevant for information systems research and practice. This study presents a comprehensive bibliometric analysis of applications of big data analytics in enterprises. The sample for this study contained a total of 1727 articles from the Scopus database. The sample was analyzed with techniques such as bibliographic coupling, citation analysis, co-word analysis, and co-authorship analysis. Findings from the co-citation analysis identified four major thematic areas in the extant literature. The evolution of these thematic areas was documented with dynamic co-citation analysis.
Understanding the developmental trajectories of big data analytics in the corporate context is highly relevant for information systems research and practice. This study presents a comprehensive bibliometric analysis of applications of big data analytics in enterprises. The sample for this study contained a total of 1727 articles from the Scopus database. The sample was analyzed with techniques such as bibliographic coupling, citation analysis, co-word analysis, and co-authorship analysis. Findings from the co-citation analysis identified four major thematic areas in the extant literature. The evolution of these thematic areas was documented with dynamic co-citation analysis.
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