Jaime Eduardo Alarcón-Martínez, D. Güemes-Castorena, M. Flegl
{"title":"创新区绩效目标设定的比较分析","authors":"Jaime Eduardo Alarcón-Martínez, D. Güemes-Castorena, M. Flegl","doi":"10.12776/qip.v27i2.1873","DOIUrl":null,"url":null,"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.\nMethodology/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.\nFindings: 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).\nResearch 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.\nOriginality/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.","PeriodicalId":44057,"journal":{"name":"Quality Innovation Prosperity-Kvalita Inovacia Prosperita","volume":"4 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative Analysis of Innovation Districts to Set Up Performance Goals for Tec Innovation District\",\"authors\":\"Jaime Eduardo Alarcón-Martínez, D. Güemes-Castorena, M. 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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.\\nMethodology/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.\\nFindings: 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).\\nResearch 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. 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Comparative Analysis of Innovation Districts to Set Up Performance Goals for Tec Innovation District
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.