The DOI matches its Crossref record; 104 of the 104 references it cites were found in a registry or an index.
Big Data for Creating and Capturing Value in the Digitalized Environment: Unpacking the Effects of Volume, Variety, and Veracity on Firm Performance*
Abstract
Despite significant academic and managerial interest in big data, there is a dearth of research on how big data impacts the long‐term firm performance. Reasons for this gap include a lack of objective indices to measure big data availability and its impact, and the tendency of studies to ignore the costs associated with collecting and analyzing big data, assuming that big data automatically delivers benefits to firms. Focusing on how firms create and capture value from big data about customers, we use the resource‐based view and three dimensions of big data (i.e., volume, variety, and veracity) to understand when the benefits outweigh the costs. Relying on the number of downloads of mobile device applications, we find that volume of big data has a negative effect on firm performance. This result suggests that the “bigness” of big data alone does not ensure value creation for a firm, and could even constitute a “dark side” of big data. Because big data variety—measured as the number of types of information taken per each application—moderates the negative effects of big data volume, simultaneous high values of volume and variety allow firms to create value that positively affects their performance. In addition, high levels of veracity (i.e., a high percentage of employees devoted to big data analysis), are linked to firms benefiting from big data via value capture. These findings shed light on the circumstances in which big data can be beneficial for firms, contributing to a better theoretical understanding of the opportunities and challenges and providing useful indications to managers.
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Cited by
Javed, A. R., Shahzad, F., Rehman, S. U., Zikria, Y. B., Razzak, I., Jalil, Z., & Xu, G. (2022) · Cities · doi:10.1016/j.cities.2022.103794 · Cited by 606
Algorithmic bias in data-driven innovation in the age of AI
Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D’Ambra, J., & Shen, K. N. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102387 · Cited by 440
Zheng, L. J., Zhang, J. Z., Wang, H., & Hong, J. F. L. (2025) · Annals of Operations Research · doi:10.1007/s10479-022-04800-6 · Cited by 42
Data Quality Management in Big Data: Strategies, Tools, and Educational Implications
Nguyen, T., Nguyen, H.-T., & Nguyen-Hoang, T.-A. (2025) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2025.105067 · Cited by 20
The evolving consumer IoT: A novel framework for marketing strategy based on assemblage theory
Hoffman, D. L., & Novak, T. P. (2025) · Journal of Product Innovation Management · doi:10.1111/jpim.12767 · Cited by 6
Metadata
- Authors
- Cappa, F., Oriani, R., Peruffo, E., & McCarthy, I.
- Venue
- Journal of Product Innovation Management
- Publisher
- Wiley
- Published
- 2020-08-29
- Volume
- 38
- Issue
- 1
- Pages
- 49-67
- ISSN
- 0737-6782 (ISSN-L)
- 1540-5885
- OpenAlex
- W3045741511
- Cited by
- 377
- References
- 104
- FWCI
- 50.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cappa, F., Oriani, R., Peruffo, E., & McCarthy, I. (2021). Big Data for Creating and Capturing Value in the Digitalized Environment: Unpacking the Effects of Volume, Variety, and Veracity on Firm Performance*. Journal of Product Innovation Management. https://doi.org/10.1111/jpim.12545
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