The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
Big data and dynamic capabilities: a bibliometric analysis and systematic literature review
Open accessAbstract
Purpose: Recently, several manuscripts about the effects of big data on organizations used dynamic capabilities as their main theoretical approach. However, these manuscripts still lack systematization. Consequently, the purpose of this paper is to systematize the literature on big data and dynamic capabilities. Design/methodology/approach: A bibliometric analysis was performed on 170 manuscripts extracted from the Clarivate Analytics Web of Science Core Collection database. The bibliometric analysis was integrated with a literature review. Findings: The bibliometric analysis revealed four clusters of papers on big data and dynamic capabilities: big data and supply chain management, knowledge management, decision making, business process management and big data analytics. The systematic literature review helped to clarify each clusters’ content. Originality/value: To the authors’ best knowledge, minimal attention has been paid to systematizing the literature on big data and dynamic capabilities.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Software survey: VOSviewer, a computer program for bibliometric mapping
van Eck, N. J., & Waltman, L. (2010) · Scientometrics · doi:10.1007/s11192-009-0146-3 · Cited by 21,484
Tranfield, D., Denyer, D., & Smart, P. (2003) · British Journal of Management · doi:10.1111/1467-8551.00375 · Cited by 15,386
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses
Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008) · The FASEB Journal · doi:10.1096/fj.07-9492lsf · Cited by 5,039
Beyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226
Big Data : The Management Revolution
マカフィー, ア., ブリニョルフソン, エ., & 有賀, 裕. (2013) · Harvard business review · Cited by 3,545
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913 · cited 3×
“Moving from these premises, Chen et al. (2014) and Li et al. (2016) explored how machine learning or artificial intelligence can be integrated into BDA systems.”
BackgroundInfluentialBig data analytics and firm performance: Effects of dynamic capabilities
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.009 · Cited by 2,474
Critical analysis of Big Data challenges and analytical methods
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.001 · Cited by 2,066 · cited 2×
“Similarly, Sivarajah et al. (2017) assessed BDA systems capacity to adapt to different kinds of data and to the evolving environments and deduced that this may generate competitive advantages.”
BackgroundHow to improve firm performance using big data analytics capability and business strategy alignment?
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016) · International Journal of Production Economics · doi:10.1016/j.ijpe.2016.08.018 · Cited by 1,624
Research works printed but not linked: 3
- 2011, Big data, analytics and the path from insights to value, MIT Sloan Management Review, 52, 21
- 2011, Big Data: the Next Frontier for Innovation, Competition, and Productivity
- 2015, Trustworthy answers for top-k queries on uncertain big data in decision making, Information Sciences, 318, 73
Cited by
Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120766 · Cited by 571
Lee, I., & Mangalaraj, G. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6010017 · Cited by 176
Influencing models and determinants in big data analytics research: A bibliometric analysis
Aboelmaged, M., & Mouakket, S. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102234 · Cited by 53
Walking the talk: Making the SDGs core business–an integrated framework
de Almeida, F. P., van Tulder, R., & Rodrigues, S. B. (2023) · International Business and Sustainable Development Goals · doi:10.1108/s1745-886220230000017004 · Cited by 8
Metadata
- Authors
- Rialti, R., Marzi, G., Ciappei, C., & Busso, D.
- Venue
- Management Decision
- Publisher
- Emerald
- Published
- 2019-09-12
- Volume
- 57
- Issue
- 8
- Pages
- 2052-2068
- ISSN
- 0025-1747 (ISSN-L)
- OpenAlex
- W2909924818
- Cited by
- 319
- References
- 83
- FWCI
- 8.60
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.emerald.com/insight/site-policies (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Big data and dynamic capabilities: a bibliometric analysis and systematic literature review. https://app.cochat.ai/papers/10.1108/md-07-2018-0821
- The paper
- Rialti, R., Marzi, G., Ciappei, C., & Busso, D. (2019). Big data and dynamic capabilities: a bibliometric analysis and systematic literature review. Management Decision. https://doi.org/10.1108/md-07-2018-0821
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
