The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Marginality and Problem-Solving Effectiveness in Broadcast Search
Open accessAbstract
We examine who the winners are in science problem-solving contests characterized by open broadcast of problem information, self-selection of external solvers to discrete problems from the laboratories of large research and development intensive companies, and blind review of solution submissions. Analyzing a unique data set of 166 science challenges involving over 12,000 scientists revealed that technical and social marginality, being a source of different perspectives and heuristics, plays an important role in explaining individual success in problem solving. The provision of a winning solution was positively related to increasing distance between the solver's field of technical expertise and the focal field of the problem. Female solvers—known to be in the “outer circle” of the scientific establishment—performed significantly better than men in developing successful solutions. Our findings contribute to the emerging literature on open and distributed innovation by demonstrating the value of openness, at least narrowly defined by disclosing problems, in removing barriers to entry to nonobvious individuals. We also contribute to the knowledge-based theory of the firm by showing the effectiveness of a market mechanism to draw out knowledge from diverse external sources to solve internal problems.
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
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Sample Selection Bias as a Specification Error
Heckman, J. J. (1979) · Econometrica · doi:10.2307/1912352 · Cited by 28,779
Exploration and Exploitation in Organizational Learning
March, J. G. (1991) · Organization Science · doi:10.1287/orsc.2.1.71 · Cited by 21,942 · cited 2×
“While a bulk of the problems firms face may be solved through a combination of internal experienced-based local (March 1991), cognitive (Gavetti and Levinthal 2000) and analogical (Gavetti et al. 2005) search processes, there may still remain some problems that for a variety of reasons (lack of…”
BackgroundA Dynamic Theory of Organizational Knowledge Creation
Nonaka, I. (1994) · Organization Science · doi:10.1287/orsc.5.1.14 · Cited by 17,809 · cited 1×
“An emerging perspective on the knowledge-based theory of the firm has argued that problem-solving effectiveness is key to superior organizational performance (Nickerson and Zenger 2004; Nonaka 1994; Nonaka and von Krogh, 2009).”
BackgroundLaursen, K., & Salter, A. (2006) · Strategic Management Journal · doi:10.1002/smj.507 · Cited by 6,324
Do Women Shy Away From Competition? Do Men Compete Too Much?
Niederle, M., & Vesterlund, L. (2007) · The Quarterly Journal of Economics · doi:10.1162/qjec.122.3.1067 · Cited by 3,186
Duncker, K. (1945) · Psychological Monographs · doi:10.1037/h0093599 · Cited by 2,938
Groups of diverse problem solvers can outperform groups of high-ability problem solvers
Hong, L., & Page, S. E. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403723101 · Cited by 2,103
Aging, Obsolescence, and Organizational Innovation
Sørensen, J. B., & Stuart, T. E. (2000) · Administrative Science Quarterly · doi:10.2307/2666980 · Cited by 1,862
Mechanization in problem solving: The effect of Einstellung.
Luchins, A. S. (1942) · Psychological Monographs · doi:10.1037/h0093502 · Cited by 1,143
Cited by
Gender Differences in Preferences
Croson, R., & Gneezy, U. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.2.448 · Cited by 5,694
Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis
Boudreau, K. J., Lacetera, N., & Lakhani, K. R. (2011) · Management Science · doi:10.1287/mnsc.1110.1322 · Cited by 830
Self-citationLivingstone, S., & Smith, P. K. (2014) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12197 · Cited by 590
Chua, R. Y. J., Roth, Y., & Lemoine, J.-F. (2015) · Administrative Science Quarterly · doi:10.1177/0001839214563595 · Cited by 433
Antons, D., & Piller, F. T. (2015) · Academy of Management Perspectives · doi:10.5465/amp.2013.0091 · Cited by 289
Metadata
- Authors
- Jeppesen, L. B., & Lakhani, K. R.
- Venue
- Organization Science
- Published
- 2010-10
- Volume
- 21
- Issue
- 5
- Pages
- 1016-1033
- ISSN
- 1047-7039 (ISSN-L)
- 1526-5455
- OpenAlex
- W2161343059
- Cited by
- 1,178
- References
- 10
- FWCI
- 137.32
- Type
- journal article
- 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.
