Innovative efficiency in the service sector: the case of Uruguay
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.3945Keywords:
innovation, technical efficiency, Innovation Activities Survey, Data Envelopment AnalysisAbstract
This study analyse the technical efficiency in terms of innovation of the firms in the service sector in the Uruguayan economy. Therefore, we use data from the 2010-2012 Innovation Activities Survey of the National Agency for Research and Innovation (ANII). In a first stage, we estimate the level of efficiency of each company through a Data Envelopment Analysis model, incorporating the inputs and outputs related to the innovative activity of the firms. The results reveal that, despite the effort made in terms of investment in innovation, the results are still very poor. On average, the companies analysed could increase their innovation production by 44.5% given the resources invested. Subsequently, we explore the relationship between innovative efficiency and several characteristics of the firms. In this sense, we found that medium-sized firms have lower efficiency than small and large companies. The results can be useful for both those responsible for the area of innovation of the firms, as well as to the institutions that provide support in the field of innovation (such as the ANII), since they provide substantive evidence to improve the orientation of the innovative strategy to be followed by firms in the service sector.
Downloads
References
Aboal, D., & Garda, P. (2016). Technological and non-technological innovation and productivity in services vis-à-vis manufacturing sectors. Economics of Innovation and New Technology, 25(5), 435-454.
Aboal, D., Garda, P., Lanzilotta, B., & Perera, M. (2015a). Does Innovation Destroy Employment in the Services Sector? Evidence from a Developing Country. Emerging Markets Finance and Trade, 51(3), 558-577.
Aboal, D., Angelelli, P., Crespi, G., López, A., Vairo, M., & Pereschi, F. (2015b). Innovación en Uruguay: diagnóstico y propuestas de políticas. Documento de Trabajo, 11. Recuperado de https://scielo.conicyt.cl/scielo.php?script=sci_nlinks&ref=4728301&pid=S0718-2724201700030000900001&lng=es
Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425-455.
Álvarez, R., & Crespi, G. (2003). Determinants of technical efficiency in small firms. Small Business Economics, 20(3), 233-244.
ANII (2015). V Encuesta de Actividades de Innovación en la Industria manufacturera y Servicios Seleccionados 2010-2012. Colección Indicadores y Estudios nº 9. Montevideo, Uruguay, 2015.
Bae, Y., & Chang, H. (2012). Efficiency and effectiveness between open and closed innovation: empirical evidence in South Korean manufacturers. Technology Analysis & Strategic Management, 24(10), 967-980.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
Cassoni, A., & Ramada-Sarasola, M. (2010). Innovation, R&D investment and productivity: Uruguayan manufacturing firms. Available at SSRN: https://ssrn.com/abstract=1818742.
Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Charnes, A., Cooper, W.W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: an application of data envelopment analysis to program follow through. Management Science, 27(6), 668-697.
Chataway, J., Hanlin, R., & Kaplinsky, R. (2014). Inclusive innovation: an architecture for policy development. Innovation and Development, 4(1), 33-54.
Coelli, T.J., Rao, D.S.P., O'Donnell, C. J., & Battese, G.E. (2005). An introduction to efficiency and productivity analysis. New York: Springer Science & Business Media. DOI: 10.1007/b136381.
Crépon, B., Duguet, E., & Mairessec, J. (1998). Research, Innovation And Productivity: An Econometric Analysis At The Firm Level. Economics of Innovation and New Technology, 7(2), 115-158.
Crespi, G., & Zuñiga, P. (2012). Innovation and productivity: evidence from six Latin American countries. World Development, 40(2), 273-290.
Díaz-Balteiro, L., Herruzo, A.C., Martínez, M., & González-Pachón, J. (2006). An analysis of productive efficiency and innovation activity using DEA: An application to Spain's wood-based industry. Forest Policy and Economics, 8(7), 762-773.
Dutrénit, G., De Fuentes, C., Santiago, F., Torres, A., & Gras, N. (2013). Innovation and productivity in the service sector: The case of Mexico. Discussion Paper, num. IDB-DP-293, Washington. Recuperado de https://publications.iadb.org/publications/english/document/Innovation-and-Productivity-in-the-Service-Sector-The-Case-of-Mexico.pdf
Emrouznejad, A., & Yang, G.L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences, 61, 4-8.
Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
Gijbels, I., Mammen, E., Park, B.U., & Simar, L. (1999). On estimation of monotone and concave frontier functions. Journal of the American Statistical Association, 94(445), 220-228.
Griliches, Z. (1973). Research expenditures and growth accounting. In Science and technology in economic growth (pp. 59-95). UK: Palgrave Macmillan.
Khoshnevis, P., & Teirlinck, P. (2017). Performance evaluation of R&D active firms. Socio-Economic Planning Sciences, 61, 16-28.
Kneip, A., Park, B.U., & Simar, L. (1998). A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econometric Theory, 14(6), 783-793.
Kneip, A., Simar, L., & Wilson, P.W. (2008). Asymptotics and consistent bootstraps for DEA estimators in nonparametric frontier models. Econometric Theory, 24(6), 1663-1697.
Kneip, A., Simar, L., & Wilson, P.W. (2011). A computationally efficient, consistent bootstrap for inference with non-parametric DEA estimators. Computational Economics, 38(4), 483-515.
Lundvall, B.Å. (Ed.). (2010). National systems of innovation: Toward a theory of innovation and interactive learning (Vol. 2). London: Anthem press.
Malerba, F. (2002). Sectoral systems of innovation and production. Research Policy, 31(2), 247-264.
Malerba, F. (2003). Sectoral systems and innovation and technology policy. Revista Brasileira de Inovação, 2(2), 329-375.
Malerba, F. (Ed.). (2004). Sectoral systems of innovation: concepts, issues and analyses of six major sectors in Europe. United Kingdom: Cambridge University Press.
Malerba, F., & Mani, S. (Eds.). (2009). Sectoral systems of innovation and production in developing countries: actors, structure and evolution. Northampton, USA: Edward Elgar Publishing.
Malerba, F., & Nelson, R. (2011). Learning and catching up in different sectoral systems: evidence from six industries. Industrial and corporate change, 20(6), 1645-1675.
Malerba, F., & Nelson, R. (Eds.). (2012). Economic development as a learning process: Variation across sectoral systems. Northampton, USA: Edward Elgar Publishing.
Terleckyj, N.E. (1974). Effects of R&D on the productivity growth of industries: an exploratory study (No. 140). Washington, DC: National Planning Association.
Wang, E.C., & Huang, W. (2007). Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36(2), 260-273.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Journal of Quantitative Methods for Economics and Business Administration
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.
