An Explanation of the Inverted-U Relationship between Profitability and Innovation

KRČÁL, Ondřej. An Explanation of the Inverted-U Relationship between Profitability and Innovation. Online. Brno: Masaryk University, 2014, 156 s. ISBN 978-80-210-7498-9. Dostupné z: https://dx.doi.org/10.5817/CZ.MUNI.M210-7423-2014.

Základní údaje

• Originální název: An Explanation of the Inverted-U Relationship between Profitability and Innovation
• Název anglicky: An Explanation of the Inverted-U Relationship between Profitability and Innovation
• Autoři: KRČÁL, Ondřej.
• Vydání: Brno, 156 s. 2014.
• Nakladatel: Masaryk University

Další údaje

• Typ výsledku: Odborná kniha
• Utajení: není předmětem státního či obchodního tajemství
• Forma vydání: elektronická verze "online"
• WWW: Munishop Flexibooks
• ISBN: 978-80-210-7498-9
• Doi: 10.5817/CZ.MUNI.M210-7423-2014
• Klíčová slova česky: inovace konkurence ekonomická efektivnost modelování simulace predikční modely monografie
• Klíčová slova anglicky: innovations competition economic efficiency modeling simulation predictive models monograph
• Bibliografie: 101 záznamů

Anotace

In this book I introduce two models of innovation that explain the inverted-U relationship between profitability and innovation, and the findings of Aghion et al. (2005) and Hashmi (2005) related to the relationship between profitability and the dispersion of productivity in the industry. The basic model provides a simple and general explanation of the empirical findings. In the basic model firms choose R&D expenditures that maximize their expected profits under the assumption that R&D expenditures of firms might be constrained by the size of their profits. The prospect-theory model provides a more specific explanation of the empirical findings, which includes a behavioral model of managerial decision-making. Managers in the model choose R&D expenditures according to the preferences represented by the prospect-theory value function. For specific sets of parameter values, both models generate predictions that correspond to the empirical findings of Aghion et al. (2005) and Hashmi (2005). Finally, I show that both models generate realistic predictions for a wider range of parameter combinations around the specific parameter values.