Abstract
How are limited resources efficiently allocated among different innovation populations? The performances of different innovation populations are quite different with either synergy or competition between them. If the innovation population is kept under an appropriate scale, full use can be made of the allocated resources. The maximization of the development and performance for a certain scale of innovation population is a typical multichoice development problem. Therefore, the scale optimization of the innovation population should be analyzed. According to the population dynamics, a resource constraint model for the growth of innovation population is developed, and the growth of innovation population under resource constraints is in equilibrium accordingly. With the help of a multichoice goal programming model, the scale optimization of innovation population performance can be obtained. The results of the resource constraint model and multichoice goal programming model are used to determine the optimal scale of the innovation population. From the panel data of the innovation population in Jiangsu Province from 2000 to 2017, we have found that R&D investment was the main innovation resource variable and that patent number was the main innovation output variable. Based on these data, the scale optimization of the innovation population under resource constraints can be calculated. The results of the study show that, in the observation period, the enterprise innovation population is often in the appropriate scale state. The scale development of enterprise innovation population is often more suitable for innovation ecosystem than that of scientific research institutions. According to these results, the government can provide appropriate guiding policies and incentives for different innovation populations. The innovative population can adjust its own development strategy and plan in time accordingly.
Highlights
In real economic activities, the essence of enterprises’ innovation behavior is to seek differences. e competitive advantages and the improvement of production efficiency brought by enterprises through innovation are the roots of realizing economic growth and sustainable change
A variety of IPs are linked together to form various communities in the Discrete Dynamics in Nature and Society innovation ecosystem. e symbiosis and interaction of IPs contribute to the evolution of the innovation ecosystem. e heterogeneity among various IPs is reflected in the innovation resource input [2], innovation output, and innovation interaction mechanism [3]
How do resource constraints affect relationships between IPs? What is the appropriate interaction mechanism between IPs? ese are the main questions in the study that should be answered while constructing an appropriate enterprise innovation ecosystem to effectively improve innovation behavior and competitiveness
Summary
Received 16 June 2021; Revised September 2021; Accepted September 2021; Published 4 October 2021. With the help of a multichoice goal programming model, the scale optimization of innovation population performance can be obtained. E results of the resource constraint model and multichoice goal programming model are used to determine the optimal scale of the innovation population. From the panel data of the innovation population in Jiangsu Province from 2000 to 2017, we have found that R&D investment was the main innovation resource variable and that patent number was the main innovation output variable Based on these data, the scale optimization of the innovation population under resource constraints can be calculated. E scale development of enterprise innovation population is often more suitable for innovation ecosystem than that of scientific research institutions According to these results, the government can provide appropriate guiding policies and incentives for different innovation populations. The government can provide appropriate guiding policies and incentives for different innovation populations. e innovative population can adjust its own development strategy and plan in time
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