2022/25 LEM Working Paper Series

Discovering pre-entry knowledge complexity with patent topic modeling and the post-entry growth of Italian firms

Marco Guerzoni, Massimiliano Nuccio and Federico Tamagni
pre-entry knowledge base; complexity; text analysis; patents; firm growth; post-entry performance.

  JEL Classifications
D22, O30
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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