The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model
Open accessAbstract
Abstract Big data analytics (BDA) have the power to revolutionize traditional ways of doing business. Nevertheless, the impact of BDA capabilities on a firm's performance is still not fully understood. These capabilities relate to the flexibility of the BDA infrastructure and the skills of the management and the firm's personnel. Most scholars explored the phenomenon from either a theoretical standpoint or neglected intermediate factors, such as organizational traits. This article builds on the dynamic capabilities view to propose and empirically test a model exploring whether organizational ambidexterity and agility mediate the relationship between BDA capabilities and organizational performance. Using data from surveys of 259 managers of large European organizations, we tested a proposed model using bootstrapped moderated mediation analysis. We found that organizational BDA capabilities affect a firm's ambidexterity and agility, which, in turn, affect its performance. These results establish ambidexterity and agility as positive mediators in the relationship between organizational BDA capabilities and a firm's performance. Furthermore, the organizational resistance to the implementation of information management systems and the fit between the organization and these systems also moderated this relationship. Practical implications for managers are also discussed.
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Cited by
Benzidia, S., Makaoui, N., & Bentahar, O. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120557 · Cited by 703
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
“Despite BDA potential, however, there is relatively limited research that has empirically explored the antecedents of data-driven insights for enhancing decision-making quality (Rialti, Zollo, Ferraris, & Alon, 2019), and its impact on CE performance (Gupta et al., 2019).”
BackgroundUnveiling the impact of the adoption of digital technologies on firms’ innovation performance
Usai, A., Fiano, F., Messeni Petruzzelli, A., Paoloni, P., Farina Briamonte, M., & Orlando, B. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.04.035 · Cited by 553
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Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.03.028 · Cited by 270
Metadata
- Authors
- Rialti, R., Zollo, L., Ferraris, A., & Alon, I.
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 149
- Pages
- 119781
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W2982493052
- Cited by
- 420
- References
- 86
- FWCI
- 52.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
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- Rialti, R., Zollo, L., Ferraris, A., & Alon, I. (2019). Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2019.119781
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
