Risk Control for Knowledge Transfer in the Big Data Environment

Firms need to continuously carry out product innovation to survive in dynamic market. In the big data environment, most firms, especially internet firms, realize new product innovation by taking imitation innovation as a stepping stone leading to independent innovation. Knowledge transfer, one of the main methods that firms acquire knowledge from external environment for imitation innovation, is a complex process of multiple knowledge transfer among different organizations and subject to various risks. Thus, it is necessary to understand knowledge transfer risks in the big data environment and help firms to carry out effective knowledge transfer in the process of new product innovation. Based on the influence factors of knowledge transfer risks and development process of innovation, a theoretical framework for risk control of knowledge transfer in the big data environment is proposed and a risk control model of knowledge transfer is presented. The model can be used to determine the maximum profit of a new product, the optimal time of knowledge transfer, and the update time of independent innovation knowledge in the big data environment. The results of simulation experiments are in line with the actual economic situation, and the model is valid.