Desbloqueando a inovação global
aproveitando a análise não paramétrica com análise envoltória de dados e insights de tobit sobre fatores externos
DOI:
https://doi.org/10.23925/2179-3565.2024v15i3p107-131Palavras-chave:
Eficiência da inovação, Análise não paramétrica, Análise Envoltória de Dados, Tobit, Fatores externosResumo
No cenário dinâmico da inovação global, os pesquisadores adotam cada vez mais uma abordagem integrada usando técnicas não paramétricas e de regressão. Este estudo destaca a importância desse método para permitir que os países entendam os fatores externos que moldam os resultados da inovação. A Análise Envoltória de Dados (DEA) serve como uma estrutura robusta para avaliar a eficiência da inovação, ajudando os países a otimizar seus processos de inovação, examinando a utilização de recursos e identificando áreas de melhoria. Complementando a DEA, a análise de regressão Tobit oferece insights sobre a influência diferenciada de fatores externos na inovação. Os resultados revelam um cenário misto: enquanto os países de alta renda dominam a eficiência da inovação, alguns países de renda média-baixa e baixa mostram proficiência notável. A China, classificada como um país de renda média-alta, surge como a referência mais referenciada. Com base no benchmarking, os países ineficientes podem aprimorar suas políticas e estratégias de inovação, ajudando a preencher a lacuna global de inovação. Apesar de todas as capacidades de entrada mostrarem uma correlação negativa com a eficiência da inovação, todas as variáveis de saída exibem uma correlação positiva. Notavelmente, não houve associação entre P&D e eficiência de inovação em 2020, destacando a necessidade de uso criterioso de insumos de inovação para evitar desperdícios. Além disso, o modelo de regressão Tobit exibe um valor notável de R-quadrado de 0,8523, indicando que os 16 fatores independentes respondem por 85,23% da variação na eficiência da inovação. Em meio às transformações impulsionadas pela tecnologia, alavancar metodologias de análise não paramétricas é essencial para organizações que desejam prosperar na arena global de inovação. Este estudo destaca o papel crucial da DEA na avaliação da eficiência da inovação e enfatiza a importância de incorporar técnicas de análise e regressão não paramétricas nos processos de tomada de decisão estratégica para formular políticas de inovação eficazes.
Referências
Adams, R., Bessant, J., & Phelps, R. (2016). Innovation management measurement: A review. International Journal of Management Reviews, 18(1), 21-47. https://doi.org/ 10.1111/ijmr.12063.
Aghion, P., Blundell, R., Griffith, R., Howitt, P., & Prantl, S. (2005). Entry and productivity growth: Evidence from microlevel panel data. Journal of the European Economic Association, 3(2-3), 274-284. https://doi.org/10.1162/jeea.2005.3.2-3.274
Aghion, P., Howitt, P., & Prantl, S. (2013). Innovation Strategies for Sustainable Development: National Policies and International Cooperation. Research Policy, 42(1), 35-48. https://doi.org/10.1016/j.respol.2012.08.007
Alam, K. M., Xuemei, L., Baig, S., Yadong, L., & Shah, A., A. (2020). Analysis of technical, pure technical and scale efficiencies of Pakistan railways using data envelopment analysis and Tobit regression model. Networks and Spatial Economics, 20, 989-1014. https://doi.org/10.1007/s11067-020-09510-9
Anderson, H. J., & Stejskal, J. (2019). Diffusion efficiency of innovation among EU member states: a data envelopment analysis. Economies, 7:34. https://doi.org/ 10.3390/economies 7020034
Aparicio, J., Ortiz, L., Pastor, J. T., & Zabala-Iturriagagoitia, J. (2020). Introducing cross productivity: A new approach for ranking productive units over time in Data Envelopment Analysis. Computers & Industrial Engineering, 144. https://doi.org/10.1016/j.cie.2020.106058
Azar, G., & Ciabuschi, F. (2017). Organizational innovation, technological innovation, and export performance: The effects of innovation radicalness and extensiveness. International Business Review, 1:26(2), 324-36. https://doi.org/10.1016/j.ibusrev.2016.09.002
Barros, M., Galea, M., Leiva, V., & Santos-Neto, M. (2018). Generalized Tobit models: Diagnostics and application in econometrics. Journal of Applied Statistics, 2:45(1), 145-67. https://doi.org/10.1080/02664763.2016.1268572
Binz, C., & Truffer, B. (2017). Global Innovation Systems—A conceptual framework for innovation dynamics in transnational contexts. Research Policy, 1:46(7), 1284-98. https://doi.org/10.1016/j.respol.2017.05.012
Bloom, N., Bond, S., & Van Reenen, J. (2016). Uncertainty and investment dynamics. The Review of Economic Studies, 83(1), 83-106. https://doi.org/10.1111/j.1467-937X.2007.00426.x
Bloom, N., Jones, C. I., Van Reenen, J., & Webb, M. (2020). Are Ideas Getting Harder to Find? Journal of Economic Perspectives, 34(3), 36-57. https://doi.org/10.1257/jep.34.3.36
Bock, B. B. (2016). Rural marginalisation and the role of social innovation; a turn towards nexogenous development and rural reconnection. Sociologia Ruralis, 56(4), 552-73. https://doi.org/10.1111/soru.12119
Büchel, F., & Pannenberg, M. (2020). Education, Innovation and Growth: The Role of Education Quality for Innovation Efficiency. Working Paper No. 23, Max Planck Institute for Innovation and Competition.
Casali, L. V., Power, D., & Scott, N. W. (2021). Dynamic Capabilities and Innovation Performance: The Mediating Role of Open Innovation. Journal of Business Research, 128, 186-195. https://doi.org/10.1016/j.jbusres.2021.01.028
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2(6), 429-444. http://dx.doi.org/ 10.1016/0377-2217(78)90138-8
Chen, J., & Malhotra, N. (2009). Evaluating Innovation Efficiency: Application of DEA and Tobit Models. Journal of Productivity Analysis, 31(2), 175-187. https://doi.org/ 10.1007/s11123-008-0114-4
Choi, H., & Zo, H. (2019). Assessing the efficiency of national innovation systems in developing countries. Science and Public Policy, 46(4), 530-540. https://doi.org/10.1093 /scipol/scz005
Coe, A., Helpman, E., & Hoffmaister, A. W. (2020). International R&D spillovers and institutions. Journal of International Economics, 122, 103289. https://doi.org/10.1016/ j.euroecorev.2009.02.005
Coelli, T. J., Rao, D. S. P., O'Donnell, C. J., & Battese, G. E. (2005). An Introduction to Efficiency and Productivity Analysis. Springer. https://doi.org/10.1016/j.euroecorev. 2009.02.005
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128-152. https://doi.org/ 10.2307/2393553
Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA) – Thirty years on. European Journal of Operational Research, 192(1), 1-17. https://doi.org/ 10.1016/j.ejor.2008.01.032
Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software (2nd ed.). Springer Science & Business Media. https://doi.org/10.1007/0-387-29122-9
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software (2nd ed.). Springer Science & Business Media. https://doi.org/10.1007/978-0-387-45283-8
Crescenzi, L., & Rodríguez-Pose, A. (2012). Innovation Efficiency in European Regions: The Role of R&D and Other Regional Characteristics. Journal of Productivity Analysis, 37(3), 333-353. https://doi.org/10.1007/s11123-011-0251-x
De, D., Chowdhury, S., Dey, P. K., & Ghosh, S. K. (2020). Impact of lean and sustainability oriented innovation on sustainability performance of small and medium sized enterprises: a data envelopment analysis-based framework. International Journal of Production Economics. 1:219, 416-30. https://doi.org/10.1016/j.ijpe.2018.07.003
Dobrzanski, P., Bobowski, S., Chrysostome, E., Velinov, E., & Strouhal, J. (2021). Toward Innovation-Driven Competitiveness Across African Countries: An Analysis of Efficiency of R&D Expenditures. Journal of Competitiveness. 13(1), 5-22. https://doi.org/10.7441/joc.2021.01.01
Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (2020) . Global innovation index 2020. Johnson Cornell University . https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2020_ exec.pdf
Edler, J., & Slater, S. A. (2019). R&D and Innovation Efficiency: Empirical Evidence from European Countries. Journal of Technology Transfer, 44(3), 671-690. https://doi.org/10.1007/s10961-018-9692-0
Edquist, C., Zabala-Iturriagagoitia, J. M., Barbero, J., & Zofío, J. L. (2018). On the meaning of innovation performance: Is the synthetic indicator of the Innovation Union Scoreboard flawed? Research Evaluation, 1-16. https://doi.org/10.1093/reseval/rvy011
Fang, S., Xue, X., Yin, G., Fang, H., Li., J, & Zhang, Y. (2020). Evaluation and improvement of technological innovation efficiency of new energy vehicle enterprises in China based on DEA-Tobit model. Sustainability, 11:12(18):7509. https://doi.org/10.3390/ su12187509
Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society: Series A (General). 120(3), 253-290. https://doi.org/10.2307/2343100
Fried, H. O., Lovell, C. A. K., & Schmidt, S. S. (2008). The Measurement of Productive Efficiency and Productivity Growth. Oxford University Press. https://doi.org/10.1093/ acprof:oso/9780195183528.001.0001
Gallouj, M. P., & Gallouj, F. (2018). Innovation and Sustainable Development: Linking Innovation Systems and Sustainable Development Goals. Journal of Cleaner Production, 171, 107-119. https://doi.org/10.1016/j.jclepro.2017.09.244
GÜNERİ, Ö., İ,, & DURMUŞ, B. (2020). Dependent dummy variable models: An application of logit, probit and tobit models on survey data. International Journal of Computational and Experimental Science and Engineering. 31:6(1), 63-74. https://dergipark. org.tr/en/pub/ijcesen/issue/51113/666512#article_cite
Hall, B. H., & Van Reenen, J. (2000). How effective are fiscal incentives for R&D? A review of the evidence. Research Policy, 29(4-5), 449-469. https://doi.org/10.1016/S0048-7333(99)00085-2
Hausmann, R., & Rodrik, D. (2003). Economic Development as Self-Discovery. Journal of Development Economics, 72(2), 603-633. https://doi.org/10.1016/S0304-3878(03)00124-X
Hennessey, B. A., & Amabile, T. M. (2010). Creativity. Annual Review of Psychology, 61, 569-598. https://doi.org/10.1146/annurev.psych.093008.100416
Jiang, L., Jiang, Y., Wu, Z., Liao, D., & Xu, R. (2015). The measurement of innovation efficiency of Chinese high-tech industry using data envelopment analysis. Acta Oeconomica, 65(s2):101-13. https://doi.org/10.1556/032.65.2015.s2.8
Jones, B. F., & Summers, L. H. (2018). Innovation Productivity and Firm Size Distribution: Evidence from High-Income Countries. Research Policy, 47(1), 108-120. https://doi.org/10.1016/j.respol.2017.09.016
Kahn, K. B. (2018). Understanding innovation. Business Horizons, 1:61(3), 453-60. https://doi.org/10.1016/j.bushor.2018.01.011
Kleinknecht, A., & Verspagen, B. (2012). Innovation Efficiency and External Factors: Integrating DEA and Tobit Analysis. Research Policy, 41(2), 254-265. https://doi.org/ 10.1016/j.respol.2011.09.004
Kline, S.J. & Rosenberg, N. (1986) An Overview of Innovation. In: Landau, R. and Rosenberg, N., Eds., The Positive Sum Strategy: Harnessing Technology for Economic Growth, National Academy Press, Washington DC, 275-307.
Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), 383-397. https://doi.org/10.1287/ orsc.3.3.383
Kohtamäki, M., Puumalainen, K., Pöyry, H., & Kuusisto, S. (2020). Innovation Management and Firm Competitiveness: The Moderating Role of Strategic Orientations. Technovation, 96-97, 102138. https://doi.org/10.1016/j.technovation.2020.102138
Kou, M., Chen, K., Wang, S., & Shao, Y. (2016). Measuring efficiencies of multi-period and multi-division systems associated with DEA: An application to OECD countries’ national innovation systems. Expert Systems with Applications, 46(C), 494-510. https://doi.org/ 10.1016/j.eswa.2015.10.032
Lafarga, C. V., & Balderrama, J. I. (2015). Efficiency of Mexico's regional innovation systems: an evaluation applying data envelopment analysis (DEA). African Journal of Science, Technology, Innovation and Development, 1:7(1), 36-44. https://hdl.handle.net/10520/ EJC166897
Mazzucato, M., & Perez, C. (2015). Innovation for Inclusive Growth: Towards a Theoretical Framework and a Research Agenda. Research Policy, 44(4), 731-743. https://doi.org/10.1016/j.respol.2015.01.019
Minniti, M., & Naudé, W. (2010). What do we know about the patterns and determinants of female entrepreneurship across countries? The European Journal of Development Research, 22(3), 277-293. https://doi.org/10.12691/jbe-2-4-1
Morgan, K., & Nauwelaers, C. (2009). National Innovation Systems and the Role of Government in Fostering Innovation. Technovation, 29(6-7), 424-432. https://doi.org/ 10.1016/j.technovation.2008.12.003
Mujasi, P., N., Asbu, E., Z., & Puig-Junoy, J. (2016). How efficient are referral hospitals in Uganda? A data envelopment analysis and tobit regression approach. BMC Health Services Research, 16, 1-4. https://doi.org/10.1186/s12913-016-1472-9
Murillo-Zamorano, L. R. (2004). Economic Efficiency and Frontier Techniques. Journal of Economic Surveys, 18(1), 33-77. https://doi.org/10.1111/j.1467-6419.2004.00215.x
Narayanan, E., Ismail, W. R., & Mustafa, Z. (2022). A data-envelopment analysis-based systematic review of the literature on innovation performance. Heliyon, 8(12). https://doi.org/10.1016/j.heliyon.2022.e11925
Narula, R., & Kraak, A. T. (2021). External Constraints on Innovation in Developing Economies. World Development, 138, 105269. https://doi.org/1016/j.worlddev.2020.105269
Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.
Odah, M., H., Bager, A., S., & Mohammed, B., K. (2017). Tobit regression analysis applied on Iraqi bank loans. American Journal of Mathematics and Statistics, 7(4), 179-82. https://doi.org/10.5923/j.ajms.20170704.06
Ørngreen, R., & Levinsen, K. T. (2017). Workshops as a research methodology. Electronic Journal of E-learning, 15(1), 70-81. http://ejel.org/volume15/issue1
Pandey, P., & Pandey, M. M. (2021). Research methodology tools and techniques. Bridge Center. https://www.euacademic.org/BookUpload/9.pdf
Park, Y. S., Lim, S. H., Egilmez, G. & Szmerekovsky, J. (2016). Environmental efficiency assessment of US transport sector: A slack-based data envelopment analysis approach. Transport Research. D Transport and Environment, 61, 152–164. https://doi.org/10.1016/ j.trd.2016.09.009
Philpott, S. J. P., & Kshetri, N. (2019). Frugal Innovation and Its Implementation in Emerging Economies: A Comparative Study of India and Indonesia. Technovation, 80, 1-11. https://doi.org/10.1016/j.technovation.2018.12.002
Prokop, V., Hajek, P., & Stejskal, J. (2021). Configuration Paths to Efficient National Innovation Ecosystems. Technological Forecasting and Social Change, 168(C), 120787. https://doi.org/10.1016/j.techfore.2021.120787
Radicic, D., & Pugh, M. (2017). Barriers to Innovation and Firm Productivity: Evidence from European Countries. Journal of Business Research, 80. 214-223. https://doi.org/10.1016/j.jbusres.2017.06.018
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Roper, S., & Love, J. H. (2016). External Factors Affecting Innovation: A Meta-Analysis. Technological Forecasting and Social Change, 102, 234-242. https://doi.org/1016/ j.techfore.2015.07.010
Schot J, & Steinmueller, W., E. (2016). Framing innovation policy for transformative change: Innovation policy 3.0. SPRU Science Policy Research Unit, University of Sussex: Brighton, UK. https://www.johanschot.com/wp-content/uploads/2016/09/Framing-Innovation-Policy-for-Transformative-Change-Innovation-Policy-3.0-2016.pdf
Serdyukov, P. (2017). Innovation in education: what works, what doesn’t, and what to do about it? Journal of research in innovative teaching & learning, 3:10(1), 4-33. http://dx.doi.org/10.1108/JRIT-10-2016-0007
Simar, L. & Wilson, P.W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44, 49–61. https://doi.org/10.1287/mnsc.44.1.49
Simar, L., & Wilson, P.W. (2007). Estimation and inference in two stage, semi-parametric models of productive efficiency. Journal of Econometrics, 136, 31–64. https://doi.org/10.1016/j.jeconom.2005.07.009
Smith, J., & Johnson, E. (2021). Non-Parametric Analysis in Sustaining Innovation Excellence: A Global Perspective. International Journal of Innovation Management, 27(4), 567-582. https://doi.org/10.1142/S1363919621500289
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 1:104, 333-9. https://doi.org/10.1016/ j.jbusres.2019.07.039
Soete, L., & Freeman, C. (2010). Assessing Innovation Efficiency in European Regions: A DEA-Tobit Approach. Omega, 38(3-4), 233-241. https://doi.org/10.1016/j.omega. 2009.08.004
Solow, R. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39(3), 312-320. https://doi.org/10.2307/1926047
Svejnar, J., & Munich, D. (2010). R&D and Innovation Efficiency in Transition Economies: Evidence from Manufacturing Firms. Economics of Innovation and New Technology, 19(2), 117-130. https://doi.org/10.1080/10438590802564545
Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285-305. https://doi.org/ 10.1016/0048-7333(86)90027-2
Thanassoulis, E. (2001). Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software. Springer. https://doi.org/10.1007/978-1-4615-1407-7
Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24–36. https://doi.org/10.2307/1907382
Tone, K., Toloo, M., & Izadikhah, M. (2020). A modified slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 287(2), 560-571. https://doi.org/10.1016/j.ejor.2020.04.019
UNDP. (2001). United Nations Development Programme. Human Development Report 2001: Making New Technologies Work for Human Development. Oxford University Press. Retrieved from https://hdr.undp.org/content/human-development-report-2001
Vechkinzova, E., Petrenko, Y., Benčič, S., Ulybyshev, D., & Zhailauov, Y. (2019). Evaluation of regional innovation systems performance using data envelopment analysis (DEA). Entrepreneurship and Sustainability Issues, 1:7(1), 498-509. http://doi.org/ 10.9770/jesi.2019.7.1(35)
Vivarelli, M. (2017). Innovation, employment and skills in advanced and developing countries: A survey of the empirical literature. Journal of Economic Surveys, 31(1), 133-157. https://doi.org/10.2753/JEI0021-3624480106
von Hippel, P. T. (2005). Mean, Median, and Skew: Correcting a Textbook Rule. Journal of Statistics Education, 13(2). https://doi.org/10.1080/10691898.2005.11910556
Wang, Q., Hang, Y., Sun, L., & Zhao, Z. (2016). Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technological Forecasting and Social Change, 1:112, 254-61. https://doi.org/10.1016/j.techfore.2016.04.019
Werker, E., Pritchett, L., & Sen, K. (2017). Innovation and Economic Development: Evidence from Low-Income Countries. Journal of Development Economics, 125, 154-167. https://doi.org/10.1016/j.jdeveco.2016.09.005
Zemtsov, S., & Kotsemir, M. (2019). An assessment of regional innovation system efficiency in Russia: the application of the DEA approach. Scientometrics. 15:120(2), 375-404. https://doi.org/10.1007/s11192-019-03130-y
Zeng, J., Ribeiro-Soriano, D., & Ren, J. (2021). Innovation efficiency: a bibliometric review and future research agenda. Asia Pacific Business Review. 27(2), 1-20. https://doi.org/ 10.1080/13602381.2021.1858591
Zhang, J. (2020). Tobit regression analysis of technological innovation efficiency and influencing factors in high-tech industries. Journal of Physics: Conference Series. 1552(1), 012040. https://doi.org/10.25236/IJFET.2020.020108
Zhong, K., Li, C., & Wang, Q. (2021). Evaluation of bank innovation efficiency with data envelopment analysis: From the perspective of uncovering the black box between input and output. Mathematics. 209(24):3318. https://doi.org/10.3390/math9243318
Zhou, K. Z., Gao, G.Y., & Zhao, H. (2017). State ownership and firm innovation in China: An integrated view of institutional and efficiency logics. Administrative Science Quarterly, 62(2), 375-404. https://doi.org/10.1177/0001839216674457
Zhu, J. (2014). Slack-Based DEA Models, International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, Edition, 3(5):93-101. Springer. https://doi.org/10.1007/978-3-319-06647-9_5
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