The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Editorial—Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research
Abstract
We address key questions related to the explosion of interest in the emerging fields of big data, analytics, and data science. We discuss the novelty of the fields and whether the underlying questions are fundamentally different, the strengths that the information systems (IS) community brings to this discourse, interesting research questions for IS scholars, the role of predictive and explanatory modeling, and how research in this emerging area should be evaluated for contribution and significance.
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Citations
Cites
The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523 · cited 2×
“One, the scathing review of Google flu trends (Lazer et al. 2014) (that uses search terms to predict the incidence of flu) with respect to its accuracy as compared with estimates produced by the Centers for Disease Control and Prevention, and two, the ethical debate ignited by experiments conducted…”
MethodDistinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Aral, S., Muchnik, L., & Sundararajan, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0908800106 · Cited by 1,207
Identifying influential and susceptible members of social networks
Aral, S., & Walker, D. (2012) · Science · doi:10.1126/science.1215842 · Cited by 1,047
Lucas, H. C., Agarwal, R., Clemons, E. K., El Sawy, O. A., & Weber, B. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.2.03 · Cited by 358
Self-citationGraph cluster randomization: network exposure to multiple universes
Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487695 · Cited by 183
Cited by
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Critical analysis of Big Data challenges and analytical methods
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.001 · Cited by 2,066
“Akerkar (2014) and Zicari (2014) refer veracity to as coping with the biases, doubts, imprecision, fabrications, messiness and misplaced evidence in the data.”
BackgroundHow to improve firm performance using big data analytics capability and business strategy alignment?
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016) · International Journal of Production Economics · doi:10.1016/j.ijpe.2016.08.018 · Cited by 1,624
Big Data Analytics in Operations Management
Choi, T., Wallace, S. W., & Wang, Y. (2018) · Production and Operations Management · doi:10.1111/poms.12838 · Cited by 1,027
Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021) · International Journal of Production Research · doi:10.1080/00207543.2019.1582820 · Cited by 958
Metadata
- Authors
- Agarwal, R., & Dhar, V.
- Venue
- Information Systems Research
- Published
- 2014-09
- Volume
- 25
- Issue
- 3
- Pages
- 443-448
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2160327429
- Cited by
- 610
- References
- 5
- 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.
