LEARNING FROM SMALL FAILURES: ROLE OF RELATEDNESS, FAMILIARITY, AND STRUCTURE OF KNOWLEDGE BASE

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  • March 19, 2019
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  • Khanna, Rajat
    • Affiliation: Kenan-Flagler Business School
Abstract
  • Does the approach that firms adopt in their search for solutions influence their ability to learn from failures? Does the structure of a firm's knowledge base affect learning from failures? Recent research has begun to explore the learning from small failures in experimentation. However, failures may differ in terms of learning opportunities they provide, and presence of learning opportunities may not result in better performance. In this dissertation, I focus on small failures in experimentation. I examine two aspects of search behavior. First, I explore relatedness of failures in a firm's knowledge base and examine how failures of varying degree of relatedness can lead to heterogeneous learning outcomes. Second, I investigate whether familiarity of knowledge and knowledge elements associated with failures has an effect on learning from these failures. Finally, presence of learning opportunities may not always result in increased performance, and therefore examination of factors that moderate a firm's ability to implement learning from failures is important. I argue that decomposability of a firm's knowledge base plays a crucial role in facilitating learning from failures. With the help of data on patents and their expiration for 76 pharmaceutical firms, I show that relatedness of failures has positive effect on a firm's R&D performance, but beyond a certain point increase in relatedness hurts the R&D performance. Also, failed experiments that use more familiar knowledge and knowledge elements have negative effect on a firm's R&D performance. Finally, decomposability of a firm's knowledge base moderates the above relationships such that nearly decomposable knowledge base facilitates the learning more than fully decomposable or integrated knowledge bases. By studying the role of different characteristics of small failures in learning and structure of a firm's knowledge base in incorporating that learning, this dissertation increases our understanding of mechanisms underlying R&D processes in pharmaceutical firms.
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  • In Copyright
Advisor
  • O'Neill, Hugh
  • Bettis, Richard Allan
  • Nerkar, Atul
  • Rockart, Scott
  • Guler, Isin
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2015
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  • Chapel Hill, NC
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  • There are no restrictions to this item.
Date uploaded
  • June 23, 2015

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