Big Data Analytics and Firm Performance: A Systematic Review
dc.date.accessioned | 2019-09-10T13:37:53Z | |
dc.date.available | 2019-09-10T13:37:53Z | |
dc.date.issued | 2019-07-01 | |
dc.identifier | doi:10.17170/kobra-20190910680 | |
dc.identifier.uri | http://hdl.handle.net/123456789/11310 | |
dc.description.sponsorship | Gefördert durch den Publikationsfonds der Universität Kassel | |
dc.language.iso | eng | |
dc.rights | Urheberrechtlich geschützt | |
dc.rights.uri | https://rightsstatements.org/page/InC/1.0/ | |
dc.subject | big data analytics | eng |
dc.subject | business analytics | eng |
dc.subject | firm performance | eng |
dc.subject | technology adoption | eng |
dc.subject | systematic review | eng |
dc.subject.ddc | 330 | |
dc.title | Big Data Analytics and Firm Performance: A Systematic Review | eng |
dc.type | Aufsatz | |
dcterms.abstract | The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance. | eng |
dcterms.accessRights | open access | |
dcterms.creator | Maroufkhani, Parisa | |
dcterms.creator | Wagner, Ralf | |
dcterms.creator | Ismail, Wan Khairuzzaman Wan | |
dcterms.creator | Baroto, Mas Bambang | |
dcterms.creator | Nourani, Mohammad | |
dc.relation.doi | doi:10.3390/info10070226 | |
dc.type.version | publishedVersion | |
dcterms.source.identifier | ISSN 2078-2489 | |
dcterms.source.issue | Issue 7 | |
dcterms.source.journal | Information | eng |
dcterms.source.pageinfo | 226 | |
dcterms.source.volume | Volume 10 |
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