The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Innovation and Learning: The Two Faces of R & D
Cohen, W. M., & Levinthal, D. A. (1989) · The Economic Journal · doi:10.2307/2233763 · Cited by 7,530
Organization design: an information processing view
Galbraith, J. R. (1974) · Interfaces · doi:10.1287/inte.4.3.28 · Cited by 2,451
Investment in Enterprise Resource Planning: Business Impact and Productivity Measures
Hitt, L. M., Wu, D. J., & Zhou, X. (2002) · Journal of Management Information Systems · doi:10.1080/07421222.2002.11045716 · Cited by 877 · cited 1×
“Thirty Second International Conference on Information Systems, Shanghai 2011 3 Closely related to these studies is the emerging literature on the value of enterprise systems, that have shown that investments in ERP (Hitt et al. 2002; Anderson et al. 2003) and combinations of ERP systems with other complementary enterp…”
BackgroundSelf-citationThree-Way Complementarities: Performance Pay, Human Resource Analytics, and Information Technology
Aral, S., Brynjolfsson, E., & Wu, L. (2012) · Management Science · doi:10.1287/mnsc.1110.1460 · Cited by 365
Self-citation
Cited by
Data science and its relationship to big data and data-driven decision making
Provost, F., & Fawcett, T. (2013) · Big Data · doi:10.1089/big.2013.1508 · Cited by 1,568
The Rapid Adoption of Data-Driven Decision-Making
Brynjolfsson, E., & McElheran, K. (2016) · American Economic Review · doi:10.1257/aer.p20161016 · Cited by 843
Self-citationBenzidia, S., Makaoui, N., & Bentahar, O. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120557 · Cited by 703
Big data for development: a review of promises and challenges
Hilbert, M. (2016) · Development Policy Review · doi:10.1111/dpr.12142 · Cited by 679
Data Analytics, Innovation, and Firm Productivity
Wu, L., Hitt, L., & Lou, B. (2020) · Management Science · doi:10.1287/mnsc.2018.3281 · Cited by 368
Self-citationTutorial: Big Data Analytics: Concepts, Technologies, and Applications
Watson, H. J. (2014) · Communications of the Association for Information Systems · doi:10.17705/1cais.03465 · Cited by 340
A survey on data‐efficient algorithms in big data era
Adadi, A. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00419-9 · Cited by 331
Shamim, S., Zeng, J., Khan, Z., & Zia, N. U. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120315 · Cited by 243
On the safety of machine learning: Cyber-physical systems, decision sciences, and data products
Varshney, K. R., & Alemzadeh, H. (2017) · Big Data · doi:10.1089/big.2016.0051 · Cited by 211
“Man y emerging application domains are now shifting to data-driv en decision making due to greater capture of information digit ally and the desire to be more scientific rather than relying on (fallible) gut instinct [22].”
BackgroundRetailing and retailing research in the age of big data analytics
Dekimpe, M. G. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2019.09.001 · Cited by 202
Ashaari, M. A., Singh, K. S. D., Abbasi, G. A., Amran, A., & Liebana-Cabanillas, F. J. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121119 · Cited by 175
Ghasemaghaei, M. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.102055 · Cited by 135
Business analytics adoption process: An innovation diffusion perspective
Nam, D., Lee, J., & Lee, H. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.07.017 · Cited by 115
Artificial Intelligence Tool Penetration in Business: Adoption, Challenges and Fears
Schlögl, S., Postulka, C., Bernsteiner, R., & Ploder, C. (2019) · Communications in Computer and Information Science · doi:10.1007/978-3-030-21451-7_22 · Cited by 52
Neirotti, P., Pesce, D., & Battaglia, D. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121088 · Cited by 39
How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program
Choi, S., Kim, N., Kim, J., & Kang, H. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3893835 · Cited by 30
The nexus between data analytics and firm performance
Gul, R., & Ellahi, N. (2021) · Cogent Business & Management · doi:10.1080/23311975.2021.1923360 · Cited by 20
Metadata
- Authors
- Brynjolfsson, E., Hitt, L. M., & Kim, H. H.
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2011
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W1921302649
- Cited by
- 791
- References
- 4
- Type
- posted content
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
