BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Autor
Nunes Paulo Maçãs (Beira Interior University and CEFAGE Research Centre, Portugal), Serrasqueiro Zélia (Beira Interior University and CEFAGE Research Centre, Portugal), de Matos António Fernandes (Beira Interior University, Portugal)
Tytuł
Determinants of Investment in Fixed Assets and in Intangible Assets for Hightech Firms
Źródło
Journal of International Studies, 2017, vol. 10, nr 1, s. 173-179, tab., bibliogr. 13 poz.
Słowa kluczowe
Wiedzochłonne usługi biznesowe, Środki trwałe, Zasoby niematerialne, Inwestycje, Dane panelowe
Knowledge Intensive Business Services (KIBS), Fixed assets, Intangible assets, Investment, Panel data
Uwagi
Klasyfikacja JEL: C23, G32, L26
summ.
Abstrakt
Based on a sample of 141 Portuguese high-tech firms for the period 2004- 2012 and using GMM system (1998) and LSDVC (2005) dynamic estimators, this paper studies whether the determinants of high-tech firms' investment in fixed assets are identical to the determinants of their investment in intangible assets. The multiple empirical evidence obtained allows us to conclude that the determinants of their investment in fixed assets are considerably different from those of their investment in intangible assets. Debt is a determinant stimulating investment in fixed assets, with age being a determinant restricting such investment. Size, age, internal finance and GDP are determinants stimulating investment in intangible assets, whereas debt and interest rates restrict such investment. These results let us make important suggestions for the owners/managers of high-tech firms, and also for policy-makers. (original abstract)
Pełny tekst
Pokaż
Bibliografia
Pokaż
  1. Al-Najjar, B., & Elgammal, M. M. (2013). Innovation and credit ratings, does it matter? UK evidence. Applied Economics Letters, 20(5), 428-431.
  2. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The review of economic studies, 58(2), 277-297.
  3. Audretsch, D. B. (1995). Innovation and industry evolution. Mit Press.
  4. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of econometrics, 87(1), 115-143.
  5. Bruno, G. S. (2005). Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models. Economics Letters, 87(3), 361-366.
  6. Coleman, S., & Robb, A. (2009). A comparison of new firm financing by gender: evidence from the Kauffman Firm Survey data. Small Business Economics, 33(4), 397-411.
  7. Colombo, M. G., Croce, A., & Guerini, M. (2014). Does informal risk capital relax the financial constraints of hightech entrepreneurial ventures?. Applied Economics Letters, 21(5), 335-339.
  8. Dilling-Hansen, M., & Smith, V. (2014). R&D, export and productivity: testing the Bustos model on Danish data. Applied Economics Letters, 21(11), 733-737.
  9. Gujarati, D. N., & Porter, D. C. (2010). Essentials of Econometrics. 4th ed. New York`: McGraw - Hill International.
  10. Henrekson, M., & Johansson, D. (2010). Gazelles as job creators: a survey and interpretation of the evidence. Small Business Economics, 35(2), 227-244.
  11. Hussinger, K. & Pacher, S. (2015). Information Ambiguity and Firm Value. Applied Economics Letters, 22, 843-847.
  12. Máñez, J. A., Rochina-Barrachina, M. E., Sanchis-Llopis, A., & Sanchis-Llopis, J. A. (2015). The determinants of R&D persistence in SMEs. Small Business Economics, 44(3), 505-528.
  13. Muscettola, M. (2015). Difficulties for small firms to invest in research prerogatives. An empirical analysis of a sample of Italian firms. Applied Economics, 47(15), 1495-1510.
Cytowane przez
Pokaż
ISSN
2071-8330
Język
eng
URI / DOI
http://dx.doi.org/10.14254/2071-8330.2017/10-1/12
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu