Big Data in Business: A Bibliometric Analysis of Relevant Literature
| dc.contributor.author | Nobanee, Haitham | |
| dc.date.accessioned | 2024-07-03T12:29:28Z | |
| dc.date.available | 2024-07-03T12:29:28Z | |
| dc.date.issued | 2020-12 | |
| dc.description | Big Data is a fast-growing field that describes and analyses huge amounts of structured, semi-structured, or unstructured data that comes from various sources that are vast and complex. | |
| dc.description.abstract | This special issue was open for submissions in the field of big data in business. Accordingly, this special issue includes five contributions to the fields of business process innovation in the big data era, unstructured big data analytical methods in firms, online analytical processing (OLAP) approach for business intelligence in big data, geospatial insights for retail recommendation using similarity measures, and big data and operational changes through interactive data visualization. A bibliometric approach is used to visualize and highlight the exciting literature on big data followed by highlighting the contribution of this special issue. Keywords Big Data, Business, Bibliometric analysis, Business process innovation, Business intelligence | en |
| dc.identifier.citation | Gupta, R., Nair, K., Mishra, M., Ibrahim, B., & Bhardwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4(1), 100232. | en |
| dc.identifier.doi | https://doi.org/10.1089/big.2020.29042.edi | |
| dc.identifier.uri | https://dspace.adu.ac.ae/handle/1/5945 | |
| dc.language.iso | en | |
| dc.publisher | Mary Ann Liebert Inc. | |
| dc.title | Big Data in Business: A Bibliometric Analysis of Relevant Literature | |
| dc.type | Other |