Comparative Analysis of Innovation Districts to Set Up Performance Goals for Tec Innovation District
Abstract
Purpose: Innovation districts represent a way to create, foster, and manage innovation. Different regions apply their strategy according to the dominant stakeholder in the region, such as academia, industry, government, or entrepreneurs. This research aims to evaluate different innovation districts from a production system point of view to determine the output goals for a Tec Innovation District.
Methodology/Approach: Data Envelopment Analysis (DEA) was determined to be the best tool for this study; the variable returns to scale output-oriented model was used to determine the goals for the new district; also, the bootstrap method was employed to analyse the efficiency sensitivity in the sample of districts.
Findings: The average technical efficiency of the analysed innovation districts was 0.659, with the highest technical efficiency observed in the case of the Entrepreneurial type (0.831) and Industry Cluster (0.820) districts, whereas the Local government type registered the lowest technical efficiency (0.468).
Research Limitation/Implication: The projections for the Tec Innovation District’s output variables were obtained using a set of U.S. innovation districts due to the similarity of the studied region to the available group. The research allowed us to determine realistic outputs for the studied innovation district.
Originality/Value of paper: The study employs an original DEA for comparing innovation districts and performs a bootstrap to study the system’s robustness; within this research, the performance level of a new district was calculated to be within a specific efficiency level, according to their peers.
References
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Avilés-Sacoto, E.C., Avilés-Sacoto, S.V., Güemes-Castorena, D. and Cook, W.D., 2021. Environmental performance evaluation: A state-level DEA analysis. Socio-Economic Planning Sciences, [e-journal] 78, 101082. DOI: 10.1016/j.seps.2021.101082.
Avilés-Sacoto, S.V., Cook, W.D., Güemes-Castorena, D. and Zhu, J., 2020. Modelling Efficiency in Regional Innovation Systems: A Two-Stage Data Envelopment Analysis Problem with Shared Outputs within Groups of Decision-Making Units. European Journal of Operational Research, [e-journal] 287(2), pp.572-582. DOI: 10.1016/j.ejor.2020.04.052.
Aytekin, A., Ecer, F., Korucuk, S. and Karamaşa, Ç., 2022. Global innovation efficiency assessment of EU member and candidate countries via DEA-EATWIOS multi-criteria methodology. Technology in Society, [e-journal] 68, 101896. DOI: 10.1016/j.techsoc.2022.101896.
Broekel, T., Rogge, N. and Brenner, T., 2017. The innovation efficiency of German regions – a shared-input DEA approach. Review of Regional Research, [e-journal] 38, pp.77-109. DOI: 10.1007/s10037-017-0112-0.
Burke, J. and Gras, R., 2019. Atlas Innovation Districts. Aretian Urban Analytics and Design.
Charnes, A., Cooper, W. and Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, [e-journal] 2(6), pp.429-444. DOI: 10.1016/0377-2217(78)90138-8.
Christensen, C.M., Ojomo, E. and Dillion, K., 2019. The Prosperity Paradox: How Innovation Can Lift Nations Out of Poverty. New York: HarperCollins.
Data México, 2022. Indicadores económicos. [online] Gobierno de México. Available at:
Dénes, R.V., Kecskés, J., Koltai, T. and Dénes, Z., 2017. The Application of Data Envelopment Analysis in Healthcare Performance Evaluation of Rehabilitation Departments in Hungary. Quality Innovation Prosperity, [e-journal] 21(3), pp.127-142. DOI: 10.12776/QIP.V21I3.920.
Dzemydaitė, G., Dzemyda, I. and Galinienė, B., 2016. The Efficiency of Regional Innovation Systems in New Member States of the European Union: A Nonparametric DEA Approach. Economics and Business, [e-journal] 28(1), pp.83-89. DOI: 10.1515/eb-2016-0012.
Emrouznejad, A. and Yang, G.-L., 2018. A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, [e-journal] 61, pp.4-8. DOI: 10.1016/j.seps.2017.01.008.
Esmaeilpoorarabi, N., Yigitcanlar, T., Kamruzzaman, Md. And Guaralda, M., 2020. How does the public engage with innovation districts? Societal impact assessment of Australian innovation districts. Sustainable Cities and Society, [e-journal] 52, 101813. DOI: 10.1016/j.scs.2019.101813.
Etzkowitz, H. and Leydesdorff, L., 2000. The Dynamics of Innovation: From National Systems and ‘Mode 2’ to a Triple Helix of University–Industry–Government Relations. Research Policy, [e-journal] 29(2), pp.109–123. DOI: 10.1016/S0048-7333(99)00055-4.
Etzkowitz, H., 2003. Research groups as ‘quasi-firms’: the invention of the entrepreneurial university. Research Policy, [e-journal] 32(1), pp.109–121. DOI: 10.1016/S0048-7333(02)00009-4.
Fernández, J.A. and Alva, S., 2018. Un México posible: Una visión disruptiva para transformar a México. Monterrey: Debate.
Flegl, M. and Hernández Gress, E., 2023. A Two-Stage Data Envelopment Analysis Model for Investigating the Efficiency of the Public Security in Mexico. Decision Analytics Journal, [e-journal] 6, 100181. DOI: 10.1016/j.dajour.2023.100181.
Guan, J. and Chen, K., 2012. Modeling the relative efficiency of national innovation systems. Research Policy, [e-journal] 41(1), pp.102-115. DOI: 10.1016/j.respol.2011.07.001.
Halásková, R., Mikušová Meričková, B. and Halásková, M., 2022. Efficiency of Public and Private Service Delivery: The Case of Secondary Education. Journal on Efficiency and Responsibility in Education and Science, [e-journal] 15(1), pp.33-46. DOI: 10.7160/eriesj.2022.150104.
Hall, P., 1986. On the number of bootstrap simulations required to construct a confidence interval. The Annals of Statistics, [e-journal] 14, pp.1453-1462.
Hoffecker, E. and Rubenstein, M.W., 2019. Understanding Innovation Ecosystems: A Framework for Joint Analysis and Action. [online] MITD-Lab. Available at:
Hosseinzadeh, M.M., Ortobelli Lozza, S., Hosseinzadeh Lotfi, F. and Moriggia, V., 2023. Portfolio optimization with asset preselection using data envelopment analysis. Central European Journal of Operations Research, [e-journal] 31, pp.287-310. DOI: 10.1007/s10100-022-00808-2.
Kaihua, C. and Mingting, K., 2014. Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure. The Annals of Regional Science, [e-journal] 52, pp.627-657. DOI: 10.1007/s00168-014-0604-6.
Lu, W.-M., Kweh, Q.L. and Huang, C.-L., 2014. Intellectual capital and national innovation systems performance. Knowledge-Based Systems, [e-journal] 71, pp.201-210. DOI: 10.1016/j.knosys.2014.08.001.
Medina, C., 2020. Distrito de Innovación, la transformación de la zona sur en la CDMX. [online] Conecta. Available at:
Narayanan, E., Ismail, W.R.B. and Mustafa, Z.B., 2022. A data-envelopment analysis-based systematic review of the literature on innovation performance. Heliyon, [e-journal] 8(12), e11925. DOI: 10.1016/j.heliyon.2022.e11925.
Pan, D., Hong, W. and Kong, F., 2020. Efficiency evaluation of urban wastewater treatment: Evidence from 113 cities in the Yangtze River Economic Belt of China. Journal of Environmental Management, [e-journal] 270, 110940. DOI: 10.1016/j.jenvman.2020.110940.
QS Top Universities, 2022. QS Latin America University Rankings 2023,” Discover the top universities in Latin America with the QS Latin America University Rankings 2023. [online] QS Quacquarelli Symonds Limited. Available at:
Rudskaya, I., Kryzhko, D., Shvediani, A. and Missler-Beh, M., 2022. Regional Open Innovation Systems in a Transition Economy: A Two-Stage DEA Model to Estimate Effectiveness. Journal of Open Innovation: Technology, Market, and Complexity, [e-journal] 8(1), 41. DOI: 10.3390/joitmc8010041.
Simar, L. and Wilson, P.W., 1998. Sensitivity analysis of efficiency scores: How to bootstrap in non-parametric frontier models. Management Science, [e-journal] 44(1), pp.49-61. DOI: 10.1287/mnsc.44.1.49.
Solís, A., 2021. En el marco del Día Mundial del Urbanismo, José Antonio Torre, líder de urbanismo en el Tec, habla de la transparencia, conexión y flexibilidad, pilares del proyecto Distrito Tec. [online] Conecta. Available at:
The Global Institute on Innovation Districts, 2022. The global network of innovation districts. [online] The Global Institute On Innovation Districts. Available at:
Toloo, M., Keshavarz, E. and Hatami-Marbini, A., 2021. Selecting data envelopment analysis models: A data-driven application to EU countries. Omega, [e-journal] 101, 102248. DOI: 10.1016/j.omega.2020.102248.
Tripp, S., 2002. New Empirical Evidence: How One Innovation District Is Advancing the Regional Economy. The Global Institute of Innovation Districts, [online] 12 March. Available at:
Tziogkidis, P., 2012. Bootstrap DEA and hypothesis testing. Cardiff Economics. [Working Papers, No. E2012/18] Cardiff: Cardiff University, Cardiff Business School. Available at:
Valdez Lafarga, C. and León 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, [e-journal] 7(1), pp.36-44. DOI: 10.1080/20421338.2014.979652.
Wei, D., 2019. Evaluation of Regional Innovation Efficiency in China Based on Three-Stage DEA Model. In: FEBM (Fourth International Conference on Economic and Business Management), Proceedings of the Fourth International Conference on Economic and Business Management. Sanya, China, 19-21 October 2019. Dordrecht, Netherlands: Atlantis Press. pp.45-49. DOI: 10.2991/febm-19.2019.20.
Authors
Alarcón-Martínez, J. E., Güemes-Castorena, D., & Flegl, M. (2023). Comparative Analysis of Innovation Districts to Set Up Performance Goals for Tec Innovation District. Quality Innovation Prosperity, 27(2), 158–176. https://doi.org/10.12776/qip.v27i2.1873
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