Show me the Money: Federal R&amp;D Support for Academic Chemistry, 1990–2009

Rosenbloom, Joshua L.; Ginther, Donna K.

doi:10.1016/j.respol.2017.06.008

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…In addition, the number of R&D personnel also has a significant impact on innovation outputs. Rosenbloom [15] studied the relationship between the federal R&D funding received by industry-academia research groups in the United States and the number of postdoctoral personnel employed, and found a significant positive correlation between the two, which means the larger number of postdoctoral researchers leads to more research funding and thus increased innovation output. The internal culture or environment of an enterprise, including organizational structure, working culture and ad-ministrative processes, will affect its innovation.…”

Section: Enterprise Factor Inputmentioning

confidence: 99%

Influence of Enterprise’s Factor Inputs and Co-Opetition Relationships to Its Innovation Output

Shi

Gao

et al. 2023

Sustainability

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In the context of economic globalization, innovation has become a major drive for the sustainable development of enterprises, which emphasizes the importance of studying the influencing factors of enterprise innovation output. The purpose of this study is to clarify the influence mechanism of different indicators on enterprise innovation output, and then provide relevant suggestions for improving enterprise innovation ability. This paper takes 562 enterprises in Chaoyang Sub-park and Fengtai Sub-park of Zhongguancun in Beijing within the time span between 2015 and 2016 as the research objects, and constructs a comprehensive indicator system of influencing factors for enterprise innovation output from the perspective of enterprise co-opetition relationship, factor input and environmental factors. A quantitative model of innovation output and influencing factors was built and then solved, via spike-and-slab sparse function and stepwise regression, aiming at analyzing the influence of different indicators on enterprise innovation output. In addition, this paper also classifies enterprises according to their innovation level and explores the influence of indicators on different types of enterprises. The innovation of this study lies in the modeling of competition and cooperation between enterprises and the establishment of a relatively comprehensive indicator system of influencing factors for enterprise innovation output. The results show that the degree of technological collaboration between enterprises, the level of financing and the degree of capital and labor input in innovation activities have significant positive effects on enterprise innovation output. On the contrary, product and service competition, as well as capital competition, tends to have a negative impact, which urges enterprises to pay heed to the intensity of competition faced by products and services as well as the diversity of financing sources and investment targets to reduce the negative impacts. In addition, enterprises with different levels of innovation should take customized measures in terms of factor input and co-opetition relationships, in that some indicators such as network structure indicators negatively influence the innovation output of enterprises with lower levels of innovation, but has positive impacts on those with higher levels of innovation.

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Section: Enterprise Factor Inputmentioning

confidence: 99%

Influence of Enterprise’s Factor Inputs and Co-Opetition Relationships to Its Innovation Output

Shi

Gao

et al. 2023

Sustainability

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“…The core independent variables are government support (GST) and its quadratic term (GST2). As mentioned above, fiscal expenditure is the most basic way for local governments to support regional innovative activities [21]. It is reasonable to use government fiscal expenditure to reflect government support for university-enterprise knowledge flow [33].…”

Section: Dependent Variablesmentioning

confidence: 99%

“…We divided university-enterprise knowledge flow into dual stages, namely, knowledge creation and knowledge transfer, which further promote the research progress of university-enterprise cooperation in the field of knowledge management. Second, while many studies have shown that government support has a significant impact on university innovation performance or enterprise innovation ability [16,21], there is no unified answer as to whether local government behavior can play a role in university-enterprise cooperation. We found heterogeneous effects of government support on university-enterprise knowledge creation and knowledge transfer.…”

Section: Introductionmentioning

confidence: 99%

Effects of Local Government Behavior on University–Enterprise Knowledge Flow: Evidence from China

Zhang

Wang

2022

Sustainability

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Government financial investment has been increasingly adopted as a policy support to stimulate university–enterprise cooperation, however, empirical research from the perspective of knowledge flow remains limited. We reason that school–enterprise knowledge flow can be divided into dual stages, namely, knowledge creation and knowledge transfer, and this dual efficiency can be measured with the super-efficiency DEA model. The results show that the average value of knowledge creation efficiency (KCE) is higher than knowledge transfer efficiency (KTE). We adopt the Dynamic Generalized Spatial Model method to study the effect of government support on knowledge flow dual efficiency, and the regression results show that government support has a nonlinear effect on KCE while having a positive impact on KTE. We find that intergovernmental competition has a moderating influence on the relationship between government support and dual efficiency. Fiscal transparency can enhance the moderating effect of intergovernmental competition.

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The influence of shared R&D-project innovativeness perceptions on university-industry collaboration performance

2020

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University-industry collaboration (UIC) partners' different organizational backgrounds entail socialization with distinct thought worlds, management approaches, and therefore diverging goals, which may lead to differences in their perceptions of the R&D challenge. These differences can hamper relationship quality and may cause coordination challenges. Using dyadic survey data of 164 UIC project managers of 82 projects, we analyze the consequences of partners' shared R&D-project innovativeness perceptions for project performance. We consider the planning intensity and the collaboration in the planning process as antecedents to partners' shared perceptions. We find that partners' shared R&D-project innovativeness perceptions increase project performance. We also show that collaborative planning enhances shared perception. Moreover, we find support that planning intensity (i.e., formal control) and collaborative planning (i.e., social control) complement each other. Hence, if both partners are involved in a collaborative and intensive planning process, they will more likely exhibit a shared R&D project innovativeness perception and, therefore, increase UIC project performance. This will help to manage their R&D activities to create a sense of ownership and to prevent inefficient project deviations.

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Show me the Money: Federal R&D Support for Academic Chemistry, 1990–2009

Cited by 6 publications

References 19 publications

Influence of Enterprise’s Factor Inputs and Co-Opetition Relationships to Its Innovation Output

Influence of Enterprise’s Factor Inputs and Co-Opetition Relationships to Its Innovation Output

Effects of Local Government Behavior on University–Enterprise Knowledge Flow: Evidence from China

The influence of shared R&D-project innovativeness perceptions on university-industry collaboration performance

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