The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Looking Forward and Looking Backward: Cognitive and Experiential Search
Abstract
We used computer simulations to examine the role and interrelationship between search processes that are forward-looking, based on actors' cognitive map of action-outcome linkages, and those that are backward-looking, or experience based. Cognition was modeled as a simple, low-dimensional representation of a more complex, higher dimensional fitness landscape. Results show that, although crude, these representations still act as a powerful guide to initial search efforts and usefully constrain the direction of subsequent experiential search. Changing a cognitive representation itself can act as an important mode of adaptation, effectively resulting in the sequential allocation of attention to different facets of the environment. This virtue of shifting cognitive representation, however, may be offset by the loss of tacit knowledge associated with the prior cognition.
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
Organizational Learning: A Theory of Action Perspective
Argyris, C., & Schön, D. A. (1997) · Reis · doi:10.2307/40183951 · Cited by 11,113 · cited 1×
“As a result, behavior is often driven by simplified representations based on implicit theories of the world (Kelley, 1971; Argyris and Schon, 1978).”
BackgroundExploring internal stickiness: Impediments to the transfer of best practice within the firm
Szulanski, G. (1996) · Strategic Management Journal · doi:10.1002/smj.4250171105 · Cited by 7,839
Bounded rationality and organizational learning
Simon, H. A. (1991) · Organization Science · doi:10.1287/orsc.2.1.125 · Cited by 3,028
Cited by
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
Organizing and the process of sensemaking
Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (2005) · Organization Science · doi:10.1287/orsc.1050.0133 · Cited by 6,943
Misery Loves Companies: Rethinking Social Initiatives by Business
Margolis, J. D., & Walsh, J. P. (2003) · Administrative Science Quarterly · doi:10.2307/3556659 · Cited by 5,130
A set-theoretic approach to organizational configurations
Fiss, P. C. (2007) · Academy of Management Review · doi:10.5465/amr.2007.26586092 · Cited by 2,225
Ontologies, socio-technical transitions (to sustainability), and the multi-level perspective
Geels, F. W. (2010) · Research Policy · doi:10.1016/j.respol.2010.01.022 · Cited by 1,718
Toward a Theory of Paradox: A Dynamic equilibrium Model of Organizing
Smith, W. K., & Lewis, M. W. (2011) · Academy of Management Review · doi:10.5465/amr.2009.0223 · Cited by 1,631
A knowledge-based theory of the firm—The problem-solving perspective
Nickerson, J. A., & Zenger, T. R. (2004) · Organization Science · doi:10.1287/orsc.1040.0093 · Cited by 1,342
Innovation objectives, knowledge sources, and the benefits of breadth
Leiponen, A., & Helfat, C. E. (2010) · Strategic Management Journal · doi:10.1002/smj.807 · Cited by 1,341
Technology as a complex adaptive system: evidence from patent data
Fleming, L., & Sorenson, O. (2001) · Research Policy · doi:10.1016/s0048-7333(00)00135-9 · Cited by 1,315
Business model innovation through trial-and-error learning
Sosna, M., Trevinyo-Rodríguez, R. N., & Velamuri, S. R. (2010) · Long Range Planning · doi:10.1016/j.lrp.2010.02.003 · Cited by 1,054
Balancing Search and Stability: Interdependencies Among Elements of Organizational Design
Rivkin, J. W., & Siggelkow, N. (2003) · Management Science · doi:10.1287/mnsc.49.3.290.12740 · Cited by 900
Eggers, J. P., & Kaplan, S. (2009) · Organization Science · doi:10.1287/orsc.1080.0401 · Cited by 774
Levinthal, D., & Rerup, C. (2006) · Organization Science · doi:10.1287/orsc.1060.0197 · Cited by 744
Self-citationThe Network Structure of Exploration and Exploitation
Lazer, D., & Friedman, A. (2007) · Administrative Science Quarterly · doi:10.2189/asqu.52.4.667 · Cited by 696
Siggelkow, N., & Levinthal, D. A. (2003) · Organization Science · doi:10.1287/orsc.14.6.650.24870 · Cited by 953
Self-citationA 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
The role of organizational learning in the opportunity-recognition process
Lumpkin, G. T., & Lichtenstein, B. B. (2005) · Entrepreneurship Theory and Practice · doi:10.1111/j.1540-6520.2005.00093.x · Cited by 590
Unlocking the hidden value of concepts: A cognitive approach to business model innovation
Martins, L. L., Rindova, V. P., & Greenbaum, B. E. (2015) · Strategic Entrepreneurship Journal · doi:10.1002/sej.1191 · Cited by 581
New games, new rules: big data and the changing context of strategy
Constantiou, I. D., & Kallinikos, J. (2015) · Journal of Information Technology · doi:10.1057/jit.2014.17 · Cited by 526
The Lean Startup Framework: Closing the Academic–Practitioner Divide
Shepherd, D. A., & Gruber, M. (2021) · Entrepreneurship Theory and Practice · doi:10.1177/1042258719899415 · Cited by 459
Pre-Entry Knowledge, Learning, and the Survival of New Firms
Dencker, J. C., Gruber, M., & Shah, S. K. (2009) · Organization Science · doi:10.1287/orsc.1080.0387 · Cited by 369
Benner, M. J., & Tushman, M. L. (2015) · Academy of Management Review · doi:10.5465/amr.2015.0042 · Cited by 343
Management challenges in a new time
Barkema, H. G., Baum, J. A. C., & Mannix, E. A. (2002) · Academy of Management Journal · doi:10.2307/3069322 · Cited by 307
Learning while (re)configuring: Business model innovation processes in established firms
Berends, H., Smits, A., Reymen, I., & Podoynitsyna, K. (2016) · Strategic Organization · doi:10.1177/1476127016632758 · Cited by 274
Dynamic Capabilities in Public Organizations: A literature review and research agenda
Piening, E. P. (2013) · Public Management Review · doi:10.1080/14719037.2012.708358 · Cited by 270
Sarta, A., Durand, R., & Vergne, J.-P. (2021) · Journal of Management · doi:10.1177/0149206320929088 · Cited by 264
Hebert, E. A., & Dugas, M. J. (2019) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2018.07.007 · Cited by 165
Hierarchical Structure and Search in Complex Organizations
Mihm, J., Loch, C. H., Wilkinson, D., & Huberman, B. A. (2010) · Management Science · doi:10.1287/mnsc.1100.1148 · Cited by 154
Reconsideration of the Winner-Take-All Hypothesis: Complex Networks and Local Bias
Lee, E., Lee, J., & Lee, J. (2006) · Management Science · doi:10.1287/mnsc.1060.0571 · Cited by 157
Theory Is All You Need: AI, Human Cognition, and Causal Reasoning
(2024) · doi:10.1287/stsc.2024.0189 · Cited by 151
Economic Opportunity and Evolution: Beyond Landscapes and Bounded Rationality
Felin, T., Kauffman, S., Koppl, R., & Longo, G. (2014) · Strategic Entrepreneurship Journal · doi:10.1002/sej.1184 · Cited by 140
Bockstedt, J., Druehl, C., & Mishra, A. (2016) · Production and Operations Management · doi:10.1111/poms.12552 · Cited by 111
Coordinated Exploration for Grand Challenges: The Role of Advocacy Groups in Search Consortia
Olsen, A. Ø., Sofka, W., & Grimpe, C. (2016) · Academy of Management Journal · doi:10.5465/amj.2015.0730 · Cited by 110
NK model as a representation of innovative search
Ganco, M. (2017) · Research Policy · doi:10.1016/j.respol.2017.08.009 · Cited by 83
How Do Franchise Contracts Evolve? A Study of Three German SMEs*
Cochet, O., & Garg, V. K. (2008) · Journal of Small Business Management · doi:10.1111/j.1540-627x.2007.00236.x · Cited by 72
Revisiting the multinational enterprise in global production networks
Fuller, C., & Phelps, N. A. (2018) · Journal of Economic Geography · doi:10.1093/jeg/lbx024 · Cited by 65
Search on Rugged Landscapes: An Experimental Study
Billinger, S., Stieglitz, N., & Schumacher, T. R. (2010) · doi:10.2139/ssrn.1711743 · Cited by 54
“Preparing for business model change: The “pre-stage” finding”
Cavalcante, S. A. (2014) · Journal of Management & Governance · doi:10.1007/s10997-012-9232-7 · Cited by 48
The Crowdless Future? Generative AI and Creative Problem Solving
Boussioux, L., N. Lane, J., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2023) · doi:10.2139/ssrn.4533642 · Cited by 343
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
Disruptive evolution: harnessing functional excess, experimentation, and science as tool
Felin, T., & Kauffman, S. (2023) · Industrial and Corporate Change · doi:10.1093/icc/dtad057 · Cited by 11
Yan, J., Liu, R., & Zhang, G. (2018) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.3854 · Cited by 5
Metadata
- Authors
- Gavetti, G., & Levinthal, D.
- Venue
- Administrative Science Quarterly
- Publisher
- JSTOR
- Published
- 2000-03
- Volume
- 45
- Issue
- 1
- Pages
- 113-137
- ISSN
- 0001-8392 (ISSN-L)
- 1930-3815
- OpenAlex
- W2113812196
- Cited by
- 2,050
- References
- 3
- FWCI
- 12.16
- 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.