The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective
Abstract
Abstract This study draws on the sense-seize-transform view of dynamic capabilities as the theoretical lens for examining the role of BI&A in organizations. It views BI&A as the sensing and seizing components of dynamic capabilities that contribute to firm performance by enabling business process change. Findings confirm a positive relationship between BI&A and performance, mediated by business process change capabilities. This study answers the call for a theoretically grounded examination of the relationship between BI&A and firm performance by highlighting the significance of the BI&A seizing capabilities, and the importance of business process change in translating BI&A output into improved performance.
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Cites
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Cited by
Heredia, J., Castillo-Vergara, M., Geldes, C., Carbajal Gamarra, F. M., Flores, A., & Heredia, W. (2022) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2022.100171 · Cited by 414
Mikalef, P., & Krogstie, J. (2020) · European Journal of Information Systems · doi:10.1080/0960085x.2020.1740618 · Cited by 331
Alzghoul, A., Khaddam, A. A., Abousweilem, F., Irtaimeh, H. J., & Alshaar, Q. (2024) · Information Development · doi:10.1177/02666669221108438 · Cited by 87
A longitudinal study of the actual value of big data and analytics: The role of industry environment
Zhu, S., Dong, T., & Luo, X. (. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102389 · Cited by 47
Metadata
- Authors
- Torres, R., Sidorova, A., & Jones, M. C.
- Venue
- Information & Management
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 55
- Issue
- 7
- Pages
- 822-839
- ISSN
- 0378-7206 (ISSN-L)
- OpenAlex
- W2796188668
- Cited by
- 265
- References
- 108
- FWCI
- 31.72
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
