Knowledge flows between universities and industry: the impact of distance, technological compatibility, and the ability to diffuse knowledge

Mukherji, Nivedita; Silberman, Jonathan

doi:10.1007/s10961-019-09770-9

Cited by 19 publications

(8 citation statements)

References 29 publications

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“…According to Aurora Big Data, users of knowledge payment apps are mainly between 20 and 24 years old. Among the users of Dedao app, 47.6% are aged 20-24, 53.1% are Zhihu, and 50.2% are Fenda [80], which is just the age range of university students and postgraduates, so a part of the student group was chosen as the survey users in this study. This study's survey mainly used WeChat, QQ, and online communities to distribute questionnaires and finally received 523 questionnaires.…”

Section: Data Collectionmentioning

confidence: 99%

A Study on the Factors Influencing Users’ Online Knowledge Paying-Behavior Based on the UTAUT Model

Chen

Yao

et al. 2021

JTAER

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With the explosive growth of information and the increase of people’s fragmented time, the knowledge payment industry’s market size is growing. However, the heterogeneity between online knowledge payment behavior and traditional consumption gradually comes to the fore. It is of great practical significance to analyze the factors influencing users’ online knowledge payment behavior and clarify users’ online knowledge payment mechanism. Based on UTAUT theory, this study uses statistics, structural equation modeling, and mediating effect analysis to construct a theoretical model of the influencing factors of users’ payment behavior of knowledge payment platform from the user level, knowledge-provider level, and platform level. The findings show that content quality, peer influence, KOL influence, perceived interaction, effort expectation, and perceived trust significantly affect users’ willingness-to-pay and have an indirect effect on users’ paying behavior through their willingness-to-pay. Perceived cost, perceived interaction, content quality, peer influence, performance expectation, and effort expectation directly and significantly affect user paying behavior. By regulating the above elements, the improvement of customer attraction ability of online knowledge platforms can be realized.

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Section: Data Collectionmentioning

confidence: 99%

A Study on the Factors Influencing Users’ Online Knowledge Paying-Behavior Based on the UTAUT Model

Chen

Yao

et al. 2021

JTAER

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“…Indeed, digital technologies facilitate the generation/transference of knowledge and the introduction of new business models or operational processes (He, 2019 ; Sahut et al, 2019 ). Knowledge transfer benefits from remote communication technologies, as it has been shown that physical distance between N-Helix collaborators is an essential element to consider for knowledge transfer and innovation (Mukherji & Silberman, 2021 ). During the COVID-19 pandemic, the insufficient resources/investments in health infrastructures to respond to the population’s needs have motivated the emergence of apps created by collaborating partnerships (Friedman et al, 2020 ).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Digital social entrepreneurship: the N-Helix response to stakeholders’ COVID-19 needs

et al. 2021

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This study explores the emergence of a new entrepreneurship phenomenon (digital social entrepreneurship) as a result of the collaboration among many agents (N-Helix), given the government’s limited capacity to respond to the stakeholders’ needs satisfaction related to an exogenous event (e.g., the COVID-19 pandemic). Our theory development is based on three ongoing academic debates related to (a) the unrepresentativeness of the stakeholder theory in entrepreneurship research; (b) the emergence of digital social entrepreneurship (DSE) as a bridge between stakeholders’ needs, socio-economic actors, and digital-social initiatives; and (c) the role of N-Helix collaborations to facilitate the emergence of global knowledge-intensive initiatives and the rapid adoptions of open innovations. Our results support our assumptions about the positive mediation effect of DSE in the relationship between N-Helix collaborations and stakeholders’ satisfaction. Notably, results show how pandemic has intensified these relationships and how DSE in N-Helix collaborations can generate social impacts globally. Some implications for policy-makers have emerged from our results that should be considered during/post-COVID-19 pandemic.

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“…However, it should be pointed out that the influence of geographical distance on the external technology introduction of high-tech industry cannot be ignored. Mukherji [21] showed that knowledge flow has localization effect. Being in the same province can greatly promote knowledge spillover between universities and enterprises, but once they are not in the same province, distance has insignificant effect on knowledge spillover.…”

Section: Theoretical Framework and Research Hypothesis 21 Theoretical Framework Of Knowledge Potential Differencementioning

confidence: 99%

Research on innovation Mode Selection of high-tech Industry from the perspective of knowledge potential difference

2021

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This paper puts the supply and demand of external technology into the scope of knowledge potential difference and discusses the choice of innovation mode in high-tech industry from the perspective of knowledge potential difference. And based on high technology industry in 2009-2016 provincial panel data, the relative difference between the human capital of universities and the human capital of high-tech industries is used to measure the regional knowledge potential difference. The threshold regression method is used to empirically test the nonlinear effect of innovation mode on innovation performance of high-tech industry under different knowledge potential differences and identify the “Inverted U” mechanism of knowledge potential difference on external technology transfer of high-tech industry. The results show that there are two thresholds for knowledge potential difference in Chinese provinces, and with the increase of knowledge potential difference, the efficiency of independent R&D in high-tech industries decreases. When the knowledge potential difference is moderate, the innovation mode of domestic technology transformation can significantly promote the high-tech industry. According to the two threshold values, the knowledge potential difference of each jurisdiction presents typical three-stage regular changes. The conclusion of this paper provides theoretical support for the innovation mode selection of high-tech industry.

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Knowledge flows between universities and industry: the impact of distance, technological compatibility, and the ability to diffuse knowledge

Cited by 19 publications

References 29 publications

A Study on the Factors Influencing Users’ Online Knowledge Paying-Behavior Based on the UTAUT Model

A Study on the Factors Influencing Users’ Online Knowledge Paying-Behavior Based on the UTAUT Model

Digital social entrepreneurship: the N-Helix response to stakeholders’ COVID-19 needs

Research on innovation Mode Selection of high-tech Industry from the perspective of knowledge potential difference

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