2022/25 | LEM Working Paper Series | ||||||||||||||||
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Discovering pre-entry knowledge complexity with patent topic modeling and the post-entry growth of Italian firms |
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Marco Guerzoni, Massimiliano Nuccio and Federico Tamagni |
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Keywords | |||||||||||||||||
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pre-entry knowledge base; complexity; text analysis; patents;
firm growth; post-entry performance.
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JEL Classifications | |||||||||||||||||
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D22, O30
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Abstract | |||||||||||||||||
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Innovation studies have largely recognized the role of knowledge in
fostering innovation and growth of entrants. Previous literature has
focused on entrepreneurial and managerial capabilities and education and
knowledge incorporated in material and immaterial resources. We assume
that new firms need to possess different pieces of knowledge, but beyond
diversity, business performance relies also on knowledge
distinctiveness. In other words, the complexity of a knowledge base is
not simply the recombination of homogeneous pieces of knowledge but it
also depends on the specific nature of each of them. This paper develops
a new complexity indicator able to capture the complexity of the
knowledge base by applying a topic modeling approach to the analysis of
patent text. We explore the empirical relation between pre-entry
complexity of knowledge, as measured by our complexity index, and
post-entry growth performance of a sample of Italian firms entering the
market in 2009-2011, which we then follow over the period 2012-2021.
Baseline results show a significant and positive association between
knowledge complexity and growth, even after controlling for firm
characteristics and year, sector and region fixed-effects. Robustness
analysis reveal this positive effect is stronger in the medium-long run
while relatively weaker for innovative SMEs.
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