Abstract
In the present study, a new framework was established for studying the evolution of strategic alliance innovation system. Based on data from 514 questionnaires, knowledge absorption capacity, knowledge potential, and knowledge transfer capacity were extracted to constitute variables for strategic alliance innovation system, and incentive mechanism was taken as a control variable for system evolution. A dynamic system evolution model was established based on the correlations between variables, for the purpose of analyzing correlations between knowledge absorption capacity, knowledge potential, knowledge transfer capacity and their evolution trends under incentives of different intensities. The following findings were obtained: Incentive mechanism can promote the evolution of strategic alliance innovation system, and strong incentives can shorten the cycle of system evolution. In the evolution of strategic alliance innovation system, knowledge absorption capacity is a core variable dominating system evolution and is positively correlated with knowledge transfer capacity. The gap between knowledge sources and recipients gradually narrows, and there is a critical value of knowledge potential for improving knowledge absorption capacity and knowledge transfer capacity. Under the premise of enhanced knowledge absorption capacity, the evolution of the innovation system is more stable when knowledge transfer capacity and knowledge potential reach a relatively balance state.
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This article has been partly funded by the National Natural Science Foundation of China (IDs: 71602041, 71602042); National Social Science Key Project of China (ID: 14AGL004); Natural Science Foundation of Heilongjiang Province of China (ID: QC2017082); Social Science Foundation of Ministry of Education of China (ID: 14YJC630142); Fundamental Research Funds for the Central Universities of China (ID: HEUCF170904).
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Zhao, J., Dong, L. & Xi, X. Research on the strategic alliance innovation system evolution mechanism: the perspective of knowledge flow. Cluster Comput 22 (Suppl 4), 9209–9227 (2019). https://doi.org/10.1007/s10586-018-2113-3
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DOI: https://doi.org/10.1007/s10586-018-2113-3