The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
Does big data enhance firm innovation competency? The mediating role of data-driven insights
Abstract
Abstract Grounded in gestalt insight learning theory and organizational learning theory, we collected data from 280 middle and top-level managers to investigate the impact of each big data characteristic (i.e., data volume, data velocity, data variety, and data veracity) on firm innovation competency (i.e., exploitation competency and exploration competency), mediated through data-driven insight generation (i.e., descriptive insight, predictive insight, and prescriptive insight). Findings show that while data velocity, variety, and veracity enhance data-driven insight generation, data volume does not impact it. Additionally, results of the post hoc analysis indicate that while descriptive and predictive insights improve innovation competency, prescriptive insight does not affect it. These results provide interesting and unique theoretical and practical insights.
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Cited by
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.107599 · Cited by 889
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
“By means of BDA, firms can improve internal processes, operations, and organizational efficiency, allowing them to identify opportunities from different kinds of data (Rialti et al., 2019) that could be used for decision-making (Ghasemaghaei, 2019).”
BackgroundMethodResultInfluentialArtificial intelligence and industrial innovation: Evidence from German firm-level data
Rammer, C., Fernández, G. P., & Czarnitzki, D. (2022) · Research Policy · doi:10.1016/j.respol.2022.104555 · Cited by 489
Artificial intelligence and firm-level productivity
Czarnitzki, D., Fernández, G. P., & Rammer, C. (2023) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2023.05.008 · Cited by 438
Tang, M., Liu, Y., Hu, F., & Wu, B. (2023) · Energy Economics · doi:10.1016/j.eneco.2023.107135 · Cited by 312
Metadata
- Authors
- Ghasemaghaei, M., & Calic, G.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 104
- Pages
- 69-84
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W2958784708
- Cited by
- 331
- References
- 102
- FWCI
- 28.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- McMaster University (20008720)
- Social Sciences and Humanities Research Council of Canada (20006922)
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- Ghasemaghaei, M., & Calic, G. (2019). Does big data enhance firm innovation competency? The mediating role of data-driven insights. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2019.07.006
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