The Rapid Adoption of Data-Driven Decision-Making
Open accessAbstract
We provide a systematic empirical study of the diffusion and adoption patterns of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for a large representative sample of manufacturing plants, we find that DDD rates nearly tripled (11%-30%) between 2005 and 2010. This rapid diffusion, along with results from a companion paper, are consistent with case-based evidence that DDD tends to be productivity-enhancing. Yet certain plants are significantly more likely to adopt than others. Key correlates of adoption are size, presence of potential complements such as information technology and educated workers, and firm learning.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Technological advances and access to a multitude of data enable organizations to make evidence-based decisions rather than relying solely on speculation and intuition.
“Technological advances and access to a multitude of data enable organizations to make evidence-based decisions rather than relying solely on speculation and intuition (Brynjolfsson & McElheran, 2016).”
From Data-Driven Decision-Making through Real-time Student Progress Monitoring: Academic Administrators Perspectives (2025) · cites this paper as Brynjolfsson & McElheran, 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 13
- Bartel, Ann, Casey Ichniowski, and Kathryn Shaw. 2007. "How Does Information Tech- nology Affect Productivity? Plant-Level Comparisons of Product Innovation, Process Improvement, and Worker Skills." Quarterly Journal of Economics 122 (4): 1721-58.
- Blader, Steven, Claudine Gartenberg, Rebecca Henderson, and Andrea Prat. 2015. "The Real Effects of Relational Contracts." American Economic Review 105 (5): 452-56.
- Bresnahan, Timothy F., Erik Brynjolfsson, and Lorin M. Hitt. 2002. "Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence." Quar- terly Journal of Economics 117 (1): 339-76.
- Brynjolfsson, Erik, and Lorin M. Hitt. 2000. "Beyond Computation: Information Tech- nology, Organizational Transformation and Business Performance." Journal of Economic Perspectives 14 (4): 23-48.
- Brynjolfsson, Erik, and Paul Milgrom. 2013. "Complementarity in Organizations." In The Handbook of Organizational Econom- ics, edited by Robert Gibbons and John Rob- erts, 11-55. Princeton: Princeton University Press.
- Dunne, Timothy, Lucia Foster, John Haltiwanger, and Kenneth R. Troske. 2004. "Wage and Productivity Dispersion in United States Manufacturing: The Role of Computer Investment." Journal of Labor Economics 22 (2): 397-429.
- Geroski, Paul A. 2000. "Models of Technology Diffusion." Research Policy 29 (4-5): 603-25.
- Ichniowski, Casey, Kathryn Shaw, and Giovanna Prennushi. 1997. "The Effects of Human Resource Management Practices on Productiv- ity: A Study of Steel Finishing Lines." Ameri- can Economic Review 87 (3): 291-313.
- McAfee, Andrew, and Erik Brynjolfsson. 2012. "Big Data: The Management Revolution." Harvard Business Review 26 September.
- McElheran, Kristina. 2015. "Do Market Lead- ers Lead in Business Process Innovation? The Case(s) of E-Business Adoption." Manage- ment Science 61 (6): 1197-1216.
- Milgrom, Paul, and John Roberts. 1990. "The Economics of Modern Manufacturing: Tech- nology, Strategy, and Organization." American Economic Review 80 (3): 511-28.
- Porter, Theodore M. 1996. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton: Princeton University Press.
- Syverson, Chad. 2011. "What Determines Pro- ductivity?" Journal of Economic Literature 49 (2): 326-65.
Cited by
Sorted by citations
Goldfarb, A., & Tucker, C. (2019) · Journal of Economic Literature · doi:10.1257/jel.20171452 · Cited by 2,744 · see citing works
Artificial intelligence, firm growth, and product innovation
Babina, T., Fedyk, A., He, A., & Hodson, J. (2024) · Journal of Financial Economics · doi:10.1016/j.jfineco.2023.103745 · Cited by 1,460 · see citing works
Augmenting the algorithm: Emerging human-in-the-loop work configurations
Grønsund, T., & Aanestad, M. (2020) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2020.101614 · Cited by 364 · see citing works
“The capabilities of utilizing data has in itself become a significant strategic asset and an object of strategy formulation (DalleMule and Davenport, 2017; Iansiti and Lakhani, 2020), and data-driven decision-making is increasingly implemented (Brynjolfsson and McElheran, 2016).”
BackgroundKinkel, S., Baumgartner, M., & Cherubini, E. (2022) · Technovation · doi:10.1016/j.technovation.2021.102375 · Cited by 316 · see citing works
Unlocking the power of big data in new product development
Zhan, Y., Tan, K. H., Li, Y., & Tse, Y. K. (2018) · Annals of Operations Research · doi:10.1007/s10479-016-2379-x · Cited by 173 · see citing works
Neirotti, P., Pesce, D., & Battaglia, D. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121088 · Cited by 39 · see citing works
The international empirics of management.
Scur, D., Ohlmacher, S., Van Reenen, J., Bennedsen, M., Bloom, N., Choudhary, A., Foster, L., Groenewegen, J., Grover, A., Hardeman, S., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412205121 · Cited by 4 · see citing works
Secreto, P. V., Ofrin, D., & Tabo, E. (2025) · International Journal in Information Technology in Governance, Education and Business · doi:10.32664/ijitgeb.v7i1.147 · Cited by 2 · see citing works
“Technological advances and access to a multitude of data enable organizations to make evidence-based decisions rather than relying solely on speculation and intuition (Brynjolfsson & McElheran, 2016).”
- Authors
- Brynjolfsson, E., & McElheran, K.
- Affiliations
- Boston University (US)
- University of Toronto (CA)
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2016-05-01
- Volume
- 106
- Issue
- 5
- Pages
- 133-139
- ISSN
- 0002-8282 (ISSN-L)
- OpenAlex
- W2360467051
- Cited by
- 843
- References
- 0
- Citations by year
- 2026: 220 · 2025: 219 · 2024: 108 · 2023: 80 · 2022: 79 · 2021: 51 · 2020: 32 · 2019: 24 · 2018: 21 · 2017: 6 · 2016: 2 · 2015: 1
- FWCI
- 31.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Economic Review (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Firm Innovation and Growth
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Keywords
- data-driven decision making, technology adoption, diffusion of innovations, factories, productivity, firm size, information technology, worker education, firm learning, complementarity
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
