The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Exploring the path to big data analytics success in healthcare
Open accessAbstract
Although big data analytics have tremendous benefits for healthcare organizations, extant research has paid insufficient attention to the exploration of its business value. In order to bridge this knowledge gap, this study proposes a big data analytics-enabled business value model in which we use the resource-based theory (RBT) and capability building view to explain how big data analytics capabilities can be developed and what potential benefits can be obtained by these capabilities in the health care industries. Using this model, we investigate 109 case descriptions, covering 63 healthcare organizations to explore the causal relationships between the big data analytics capabilities and business value and the path-to-value chains for big data analytics success. Our findings provide new insights to healthcare practitioners on how to constitute big data analytics capabilities for business transformation and offer an empirical basis that can stimulate a more detailed investigation of big data analytics implementation.
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Cites
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Self-citationBig Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
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Cited by
An integrated big data analytics-enabled transformation model: application to health care
Wang, Y., Kung, L., Wang, W. Y. C., & Cegielski, C. G. (2018) · Information & Management · doi:10.1016/j.im.2017.04.001 · Cited by 508
Self-citationConcurrence of big data analytics and healthcare: a systematic review
Mehta, N., & Pandit, A. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.03.013 · Cited by 494
Medical 4.0 technologies for healthcare: Features, capabilities, and applications
Haleem, A., Javaid, M., Pratap Singh, R., & Suman, R. (2022) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2022.04.001 · Cited by 468
Sivarajah, U., Irani, Z., Gupta, S., & Mahroof, K. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.04.005 · Cited by 332
Mikalef, P., & Krogstie, J. (2020) · European Journal of Information Systems · doi:10.1080/0960085x.2020.1740618 · Cited by 331
Metadata
- Authors
- Wang, Y., & Hajli, N.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 70
- Pages
- 287-299
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W2510113590
- Cited by
- 429
- References
- 12
- FWCI
- 62.35
- Type
- journal article
- Language
- en
- Open access
- green
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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.
