Theory‐based learning and experimentation: How strategists can systematically generate knowledge at the edge between the known and the unknown
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DOI: 10.1002/smj.3381
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Cited by:
- Sanasi, Silvia & Ghezzi, Antonio & Cavallo, Angelo, 2023. "What happens after market validation? Experimentation for scaling in technology-based startups," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
- Armand Hatchuel, 2023. "The quest for non-Bayesian decision-making heuristics : towards a logic of imagination," Post-Print hal-04300547, HAL.
- Priyadarshini Das & Amer A. Hijazi & Duncan W. Maxwell & Robert C. Moehler, 2023. "Can Business Models Facilitate Strategic Transformation in Construction Firms? A Systematic Review and Research Agenda," Sustainability, MDPI, vol. 15(17), pages 1-20, August.
- Zellweger, Thomas & Zenger, Todd, 2022. "Entrepreneurs as scientists, Bayesian inference, and belief revision," Journal of Business Venturing Insights, Elsevier, vol. 18(C).
- Felipe A. Csaszar & Harsh Ketkar & Hyunjin Kim, 2024. "Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors," Papers 2408.08811, arXiv.org.
- Chengbin, Wang & Hongbin, Wang & Min, Dai & Yongyan, Fang, 2022. "Lean Startup Approaches(LSAs): Convergence, Integration and Improvement," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
- Ehrig, Timo & Foss, Nicolai J., 2022. "Why we need normative theories of entrepreneurial learning that go beyond Bayesianism," Journal of Business Venturing Insights, Elsevier, vol. 18(C).
- Timo Ehrig & Todd Zenger, 2024. "Competing with Theories: Using Awareness and Confidence to secure Resources and Rents," Working Papers TIMES² 2024-078, ULB -- Universite Libre de Bruxelles.
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