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Ecological Innovation Decision Behavior of Enterprises in the Strategic Emerging Industrial Clusters Based on Cognitive Neuroscience | Li | NeuroQuantology


DOI: 10.14704/nq.2018.16.6.1589

Ecological Innovation Decision Behavior of Enterprises in the Strategic Emerging Industrial Clusters Based on Cognitive Neuroscience

Xing Li, Lin Liu

Abstract


For the decision makers of eco-innovation in the strategic emerging industrial clusters, the behavioral preference function is formed during their previous cognitive practice and learning process. The processes of Learning and memory can shape the structure and function of the brain. Meanwhile, the specific structure and function of the brain also affects and optimizes the learning behavior and cognitive process. Therefore, it is of great significance to grasp and explain the decision-making behavior of the enterprise's eco-innovation in the strategic emerging industrial clusters from the perspective of neuroscience. Firstly, we analyzed the influence of the mental model of the entrepreneurs in the strategic emerging industrial clusters on the choice of decision-making mode for company's eco-innovation. Secondly, we analyzed the process of decision of company's eco-innovation in the strategic emerging industrial clusters based on the mental model, and established an benefit model of eco-technology R&D, and analyzed the eco-innovation decision-making process of companies in the strategic emerging industrial clusters by simulation. The result shows that companies in the strategic emerging industrial clusters will decide whether to adopt eco-innovation behaviors according to their own mental model. At the same time, the knowledge accumulation obtained by the enterprises in the strategic emerging industrial clusters is inversely related to the average path length of the cluster eco-innovation network and is positively related to knowledge spillover benefits under the influence of the entrepreneurs’ mental model. The R&D revenue obtained by enterprises is inversely related to the average path length of the cluster eco-innovation network and is positively related to the average degree and aggregation coefficient of the cluster eco-innovation network.

Keywords


Cognitive Neuroscience, Strategic Emerging Industry, Eco-innovation, Decision-Making Behavior

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Supporting Agencies



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