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Summary Khanna, R., Guler, I., & Nerkar, A. (2016). Fail Often, Fail Big, and Fail Fast? Learning from Small Failures and R&D Performance in the Pharmaceutical Industry. Academy of Management Journal, 59(2), 436–459. https://doi.org/10.5465/amj.2013.1109 $3.90   Add to cart
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  5. Strategic Entrepreneurship & Organizational Renewal

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Summary Khanna, R., Guler, I., & Nerkar, A. (2016). Fail Often, Fail Big, and Fail Fast? Learning from Small Failures and R&D Performance in the Pharmaceutical Industry. Academy of Management Journal, 59(2), 436–459. https://doi.org/10.5465/amj.2013.1109
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  • Course
  • Strategic Entrepreneurship & Organizational Renewal
  • Institution
  • Vrije Universiteit Amsterdam (VU)

Full summary of Khanna, R., Guler, I., & Nerkar, A. (2016). Fail Often, Fail Big, and Fail Fast? Learning from Small Failures and R&D Performance in the Pharmaceutical Industry. Academy of Management Journal, 59(2), 436–459.

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  • October 7, 2021
  • 3
  • 2021/2022
  • Summary

Subjects

  • fail big
  • fail fast
  • fail often
  • small failures
  • pharmaceutical industry

Written for

  • Vrije Universiteit Amsterdam (VU)
  • M.Sc. Business Administration - Strategy & Organization
  • Strategic Entrepreneurship & Organizational Renewal
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Khanna, Guler, Nerkar (2016) – Fail often, fail big, and fail fast? Learning from small
failures and R&D performance in the pharmaceutical industry
Summary

Under what conditions do firms learn from their failed innovation attempts (in Pharma)?

 3 conditions are investigated: number of failures, importance of failures and timing of
failures (early/late)
 2 dependent variables: output of R&D (no of patents) and quality of R&D outputs (citations
of patents)

Findings:

 The higher the number of failures, the more important these failures to the firm and the
earlier these failures occur, the lower the amount of R&D outputs and the higher the quality
of these outputs

Why:

 Quality increases because:
o Higher number of failures = firms gain a better sense of causal relationships
o Higher importance of failures = decision-makers will pay attention to these failures
o Earlier occurrence of failures = easier to pinpoint the factor causing the failure

 Output decreases because:
o Learning from failures is a multi-level process. Patent decontinuation (=failure) is
decided at the firm level, however, idea generation happens at the individual
scientist level. When patents are discontinued, there is a time lag in feedback to the
individual level, causing the scientist to develop fewer ideas from failures



Findings in detail – Impact of failures on quality of R&D output

The quality of R&D output is positively influenced by 3 factors:

1) A high number of failures
2) A high importance (within the firm) of the failed projects
3) An early detection of failure (in the R&D process)



The more failures, the higher the quality of R&D outputs because:

 Failure leads to feedback to make improvements
 Failures initiates the search for the cause of failures
 Failures are associated with deliberate learning process (inductively coming up with
innovative ideas)
 Failure = rejection of hypotheses, so the next hypothesis can be supported
 Failure stimulates distant search outside of knowledge corridors, resulting in more possible
solutions, from which the best one can be chosen

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