Quantifying the impact of inbound open innovation

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change Pub Date : 2024-10-21 DOI:10.1016/j.techfore.2024.123817

Nicole Cecchele Lago , Arthur Marcon , Jose Luis Duarte Ribeiro , Daniel de Abreu Pereira Uhr , Yasmin Olteanu , Klaus Fichter

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Abstract

Within the dynamic context of innovation ecosystems and their actors, this study quantifies the impact of engagement in inbound open innovation on startups. Using data from a 2021 German innovation survey, which included responses from 1512 startups, we examined how cooperation with actors from the innovation ecosystem influences startup innovation in terms of business models, processes, products/services, and technologies. We assessed these impacts using the Propensity Score Matching method with the Kernel Tricube estimator. Our results show that higher levels of inbound open innovation positively affect startups' business models, processes, and technological innovation, but the impact on their product and service innovation was not statistically significant in this particular context. These findings underscore the critical roles of other startups, companies, and research institutions in fostering startup innovation, as they can provide the resources and support that are so crucial to help them navigate the complexities of innovation development and commercialization. They also encourage startups to engage in collaborative activities and help them to make informed decisions about inbound open innovation. Our study offers valuable insights for policymakers, entrepreneurs, and researchers.

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量化入境开放式创新的影响

在创新生态系统及其参与者的动态背景下，本研究量化了参与对内开放式创新对初创企业的影响。利用 2021 年德国创新调查的数据（其中包括 1512 家初创企业的回复），我们研究了与创新生态系统参与者的合作如何在商业模式、流程、产品/服务和技术方面影响初创企业的创新。我们使用倾向得分匹配法和 Kernel Tricube 估计器评估了这些影响。我们的结果表明，较高水平的入境开放式创新会对初创企业的商业模式、流程和技术创新产生积极影响，但在这种特定情况下，对其产品和服务创新的影响在统计学上并不显著。这些发现强调了其他初创企业、公司和研究机构在促进初创企业创新方面的关键作用，因为它们可以提供资源和支持，而这些资源和支持对于帮助初创企业应对创新发展和商业化的复杂性至关重要。它们还鼓励初创企业参与合作活动，并帮助它们就入境开放式创新做出明智的决策。我们的研究为政策制定者、企业家和研究人员提供了宝贵的见解。

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来源期刊

Technological Forecasting and Social Change Multiple-

CiteScore

21.30

自引率

10.80%

发文量

813

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