@article{zhang_bibliometric_2020,
	title = {A bibliometric review of a decade of research: {Big} data in business research – {Setting} a research agenda},
	issn = {0148-2963},
	shorttitle = {A bibliometric review of a decade of research},
	url = {http://www.sciencedirect.com/science/article/pii/S0148296320307475},
	doi = {10.1016/j.jbusres.2020.11.004},
	abstract = {The last several years have witnessed a surge of interest in artificial intelligence (AI). As the foundation of AI technologies, big data has attracted attention of researchers. Big data and data science have been recognized as new tools and methodologies for developing theories in business research (George, 2014). While several qualitative reviews have been conducted, there is still a lack of a quantitative and systematic review of big data in business research. Our review study fills this gap by depicting the development of big data in business research using bibliometric methods, such as publication counts and trends analysis, co-citation analysis, co-authorship analysis and keywords co-occurrence analysis. Based on the sample of 1366 primary focal articles and 55,718 secondary references, we visualize the landscape and evolution of big-data business research and capture the developmental trajectory and trends over time (between 2008 and 2018). Furthermore, based on our analyses, we provide several promising directions for future research. In doing so, we provide scholars with a systematic understanding of the development and panoramic roadmap of big data research in business.},
	language = {en},
	urldate = {2020-12-12},
	journal = {Journal of Business Research},
	author = {Zhang, Yucheng and Zhang, Meng and Li, Jing and Liu, Guangjian and Yang, Miles M. and Liu, Siqi},
	month = dec,
	year = {2020},
	keywords = {Bibliometric review, Big Data, Management and business, Scientific visualization},
}

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While several qualitative reviews have been conducted, there is still a lack of a quantitative and systematic review of big data in business research. Our review study fills this gap by depicting the development of big data in business research using bibliometric methods, such as publication counts and trends analysis, co-citation analysis, co-authorship analysis and keywords co-occurrence analysis. Based on the sample of 1366 primary focal articles and 55,718 secondary references, we visualize the landscape and evolution of big-data business research and capture the developmental trajectory and trends over time (between 2008 and 2018). Furthermore, based on our analyses, we provide several promising directions for future research. In doing so, we provide scholars with a systematic understanding of the development and panoramic roadmap of big data research in business.","language":"en","urldate":"2020-12-12","journal":"Journal of Business Research","author":[{"propositions":[],"lastnames":["Zhang"],"firstnames":["Yucheng"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Meng"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Jing"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Guangjian"],"suffixes":[]},{"propositions":[],"lastnames":["Yang"],"firstnames":["Miles","M."],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Siqi"],"suffixes":[]}],"month":"December","year":"2020","keywords":"Bibliometric review, Big Data, Management and business, Scientific visualization","bibtex":"@article{zhang_bibliometric_2020,\n\ttitle = {A bibliometric review of a decade of research: {Big} data in business research – {Setting} a research agenda},\n\tissn = {0148-2963},\n\tshorttitle = {A bibliometric review of a decade of research},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0148296320307475},\n\tdoi = {10.1016/j.jbusres.2020.11.004},\n\tabstract = {The last several years have witnessed a surge of interest in artificial intelligence (AI). As the foundation of AI technologies, big data has attracted attention of researchers. Big data and data science have been recognized as new tools and methodologies for developing theories in business research (George, 2014). While several qualitative reviews have been conducted, there is still a lack of a quantitative and systematic review of big data in business research. Our review study fills this gap by depicting the development of big data in business research using bibliometric methods, such as publication counts and trends analysis, co-citation analysis, co-authorship analysis and keywords co-occurrence analysis. Based on the sample of 1366 primary focal articles and 55,718 secondary references, we visualize the landscape and evolution of big-data business research and capture the developmental trajectory and trends over time (between 2008 and 2018). Furthermore, based on our analyses, we provide several promising directions for future research. In doing so, we provide scholars with a systematic understanding of the development and panoramic roadmap of big data research in business.},\n\tlanguage = {en},\n\turldate = {2020-12-12},\n\tjournal = {Journal of Business Research},\n\tauthor = {Zhang, Yucheng and Zhang, Meng and Li, Jing and Liu, Guangjian and Yang, Miles M. and Liu, Siqi},\n\tmonth = dec,\n\tyear = {2020},\n\tkeywords = {Bibliometric review, Big Data, Management and business, Scientific visualization},\n}\n\n","author_short":["Zhang, Y.","Zhang, M.","Li, J.","Liu, G.","Yang, M. 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