EAGER: The Impact of Innovation Ecosystem Acceleration Training on Ecosystem Mapping, Stakeholder Engagement, and Regional Innovation Engine Choice

EAGER：创新生态系统加速培训对生态系统规划、利益相关者参与和区域创新引擎选择的影响

• 批准号: 2342005
• 负责人: Scott Stern
• 金额: $ 30万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2342005&HistoricalAwards=false
• 关键词: EAGER; Impact; Innovation; Ecosystem; Acceleration

Recent years have seen the establishment of a significant number of new Federal programs aimed at harnessing the potential of regional innovation “ecosystems,” including the NSF TIP Regional Innovation Engines (RIE) program. Relative to traditional research grants, place-based interventions are not directed toward a specific research project but rather aim to reshape interactions among researchers and other stakeholders within a given geographic location. The aim of an ecosystem Engine intervention is “wire” or “re-wire” an innovation ecosystem in order to enhance innovative productivity and impact. The choice, design elements, and ultimate performance of an innovation ecosystem is hypothesized to be a function of (a) the specific initial conditions of a given ecosystem, (b) the degree of engagement and commitment by specific stakeholders within that ecosystem and (c) potential interactions between initial conditions and stakeholder engagement. The research to be done in this project will take advantage of an innovation ecosystem training program in order to undertake an empirical analysis of these hypotheses. Despite significant academic and policy interest in the role of such federal interventions, there is to date no systematic empirical study of the process by which regional innovation stakeholders undertake innovation ecosystem analyses, engage innovation ecosystem stakeholders, or choose innovation ecosystem interventions. As such, the project has the potential to contribute to our theoretical understanding and policy analyses regarding the design of place-based innovation policy interventions. The research will take advantage of an opportunity to conduct training on innovation ecosystems as part of the NSF Regional Innovation Engines (“NSF Engines”) program for the program's Development Awardees. This training effort will provide the investigators the ability to (a) gather systematic and structured data about the innovation ecosystem assessments of the “as-is” ecosystem state undertaken by NSF RIE awardees, (b) document the participation (and level of engagement and commitment by different potential ecosystem stakeholders (e.g., universities, start-ups, local government, research laboratories, corporates, etc.), and (c) observe the proposed intervention that these Development Awardees ultimately submit as their future NSF Engines grant application. The ability to capture real-time data of the analysis and degree of engagement by more than 40 regional Type 1 awardees will allow for a direct evaluation of key hypotheses noted above, and provide insight into the role of ecosystem and stakeholder characteristics in shaping regional strategic Engine choice and performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，大量旨在利用区域创新“生态系统”潜力的新联邦计划相继出台，其中包括NSF TIP区域创新引擎（RIE）计划。 相对于传统的研究赠款，基于地点的干预措施不是针对特定的研究项目，而是旨在重塑研究人员和特定地理位置内其他利益攸关方之间的互动。 生态系统引擎干预的目的是“连接”或“重新连接”创新生态系统，以提高创新生产力和影响力。 创新生态系统的选择、设计要素和最终绩效被假设为（a）特定生态系统的具体初始条件，（B）该生态系统内特定利益相关者的参与和承诺程度，以及（c）初始条件和利益相关者参与之间的潜在相互作用的函数。 本项目的研究将利用创新生态系统培训计划，对这些假设进行实证分析。 尽管这种联邦干预的作用显着的学术和政策的兴趣，迄今为止，没有系统的实证研究的过程中，区域创新的利益相关者进行创新生态系统的分析，从事创新生态系统的利益相关者，或选择创新生态系统的干预措施。 因此，该项目有可能有助于我们对基于地方的创新政策干预措施的设计的理论理解和政策分析。该研究将利用机会对创新生态系统进行培训，作为NSF区域创新引擎（“NSF引擎”）计划的一部分，为该计划的发展获奖者提供培训。 这项培训工作将使调查人员能够（a）收集有关NSF RIE获奖者对“现状”生态系统状态进行的创新生态系统评估的系统化和结构化数据，（B）记录不同潜在生态系统利益相关者（例如，大学、初创企业、地方政府、研究实验室、企业等），以及（c）观察这些发展奖获得者最终作为其未来的NSF发动机资助申请提交的拟议干预。 能够捕获40多个区域1类获奖者的分析和参与程度的实时数据，将允许对上述关键假设进行直接评估，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。