The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning from Samples of One or Fewer
Abstract
Organizations learn from experience. Sometimes, however, history is not generous with experience. We explore how organizations convert infrequent events into interpretations of history, and how they balance the need to achieve agreement on interpretations with the need to interpret history correctly. We ask what methods are used, what problems are involved, and what improvements might be made. Although the methods we observe are not guaranteed to lead to consistent agreement on interpretations, valid knowledge, improved organizational performance, or organizational survival, they provide possible insights into the possibilities for and problems of learning from fragments of history.
Overview
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
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Exploration and Exploitation in Organizational Learning
March, J. G. (1991) · Organization Science · doi:10.1287/orsc.2.1.71 · Cited by 21,942
Self-citationLord, C. G., Ross, L., & Lepper, M. R. (1979) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.37.11.2098 · Cited by 4,583
“Clinical versus Actuarial Judgment.”
Dawes, R. M., Faust, D., & Meehl, P. E. (1989) · Science · doi:10.1126/science.2648573 · Cited by 2,298
Cited by
Organizational learning: The contributing processes and the literatures
Huber, G. P. (1991) · Organization Science · doi:10.1287/orsc.2.1.88 · Cited by 8,072
Deliberate learning and the evolution of dynamic capabilities
Zollo, M., & Winter, S. G. (2002) · Organization Science · doi:10.1287/orsc.13.3.339.2780 · Cited by 6,963
Grant, R. M. (1996) · Organization Science · doi:10.1287/orsc.7.4.375 · Cited by 5,649
Organizational Response to Adversity: Fusing Crisis Management and Resilience Research Streams
Williams, T. A., Gruber, D. A., Sutcliffe, K. M., Shepherd, D. A., & Zhao, E. Y. (2017) · Academy of Management Annals · doi:10.5465/annals.2015.0134 · Cited by 1,945
The dark side of organizations: mistake, misconduct, and disaster.
Vaughan, D. (1999) · Annual Review of Sociology · doi:10.1146/annurev.soc.25.1.271 · Cited by 1,072
Levinthal, D., & Rerup, C. (2006) · Organization Science · doi:10.1287/orsc.1060.0197 · Cited by 744
A Behavioral Theory of the Firm—40 Years and Counting: Introduction and Impact
Argote, L., & Greve, H. R. (2007) · Organization Science · doi:10.1287/orsc.1070.0280 · Cited by 606
Competing with complementors: An empirical look at Amazon.com
Zhu, F., & Liu, Q. (2018) · Strategic Management Journal · doi:10.1002/smj.2932 · Cited by 546
The ‘‘what’’and ‘‘how’’of case study rigor: three strategies based on published work
Gibbert, M., & Ruigrok, W. (2010) · Organizational Research Methods · doi:10.1177/1094428109351319 · Cited by 506
Learning from Complexity: Effects of Prior Accidents and Incidents on Airlines' Learning
Haunschild, P. R., & Sullivan, B. N. (2002) · Administrative Science Quarterly · doi:10.2307/3094911 · Cited by 494
Kim, J.-Y. (., & Miner, A. S. (2007) · Academy of Management Journal · doi:10.5465/amj.2007.25529755 · Cited by 443
The Social Construction of Organizational Learning: Conceptual and Practical Issues in the Field
Nicolini, D., & Meznar, M. B. (1995) · Human Relations · doi:10.1177/001872679504800701 · Cited by 344
Linking knowledge management, organizational learning and memory
Antunes, H. D. J. G., & Pinheiro, P. G. (2020) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2019.04.002 · Cited by 342
Behavior change versus culture change: Divergent approaches to managing workplace safety
DeJoy, D. M. (2005) · Safety Science · doi:10.1016/j.ssci.2005.02.001 · Cited by 236
Practice makes perfect: entrepreneurial-experience curves and venture performance
Toft-Kehler, R., Wennberg, K., & Kim, P. H. (2014) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2013.07.001 · Cited by 216
Anthony, C. (2021) · Administrative Science Quarterly · doi:10.1177/00018392211016755 · Cited by 191
These Lives Will Not Be Lost in Vain: Organizational Learning from Disaster in U.S. Coal Mining
Madsen, P. M. (2009) · Organization Science · doi:10.1287/orsc.1080.0396 · Cited by 175
Lanham, H. J., McDaniel, R. R., Crabtree, B. F., Miller, W. L., Stange, K. C., Tallia, A. F., & Nutting, P. A. (2009) · The Joint Commission Journal on Quality and Patient Safety · doi:10.1016/s1553-7250(09)35064-3 · Cited by 170
Harnessing the Power of Self-Organization in an Online Community During Organizational Crisis1
Nan, N., & Lu, Y. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.4.09 · Cited by 107
A Case for Cases: Comparative Narratives in Sociological Explanation
Abell, P. (2009) · Sociological Methods & Research · doi:10.1177/0049124109339372 · Cited by 79
Varieties of entrepreneurial ecosystems: a comparative study of Tokyo and Bangalore
Kapturkiewicz, A. (2022) · Research Policy · doi:10.1016/j.respol.2021.104377 · Cited by 65
Soda, G., & Furnari, S. (2012) · Strategic Organization · doi:10.1177/1476127012452826 · Cited by 64
Metadata
- Authors
- March, J. G., Sproull, L. S., & Tamuz, M.
- Venue
- Organization Science
- Published
- 1991-02
- Volume
- 2
- Issue
- 1
- Pages
- 1-13
- ISSN
- 1047-7039 (ISSN-L)
- 1526-5455
- OpenAlex
- W2001606006
- Cited by
- 1,258
- References
- 4
- FWCI
- 21.68
- Type
- journal article
- Language
- en
- Open access
- closed
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.