Research And Application Of Innovation Technology Adoption Decision And Diffusion

In modern society, development of science and technology is pretty fast, and the country owned with advanced technology will gain advantage over other countries. It has been widely accepted that as the main source of science and technology development, technology innovation is the initial power for sustainable developing of economics. To improve economical quality and efficiency, the state of high consumption and low production must be improved, it also relies on technology creation. As the subsequent process of general technology creation, innovation technology diffusion is an important topic subject to investigation. Only with diffusion, the substantial economical effect of a innovation technology can be embodied. Adopted by agent of terprise is the most common way for technology diffusion. Decision of whether, when and which innovation technology should be adopted is the key factors for successful adoption and competitive ability. To reach such goal, investigating dynamic factors affecting innovation technology diffusion, building multi-factors energy model of technology adoption, applying tools of BP learning algorithm, multi-agents, and ant colony algorithm in above investigations have very important theoretical and practical significance. The following topics are investigated:Related research works are taken as references. According to the complicate nonlinear and diverse feature of innovation technology diffusion, we adopted an approach as from theory to model, simulation and then practical case analysis, and proposed new concept of enterprise kinetic energy which reflects the dynamical factors affect enterprise's innovation technology adoption, then a multi-factors energy model is established with estimation of adoption efficiency, quantitative analysis is also carried out with BP learning algorithm along with the energy model. The proposed method helps to allocate weight for those factors influent adoption, and solves the problem of whether and when to adopt innovation technology. The evaluation system of innovation technology adoption is improved, a prototype system is also developed. Experiments on well known companies demonstrate the approach is efficient for estimation and analysis in optimization of adoption decision.With definition and assumption of competitive technology diffusion system, multi-agents modeling method is used in the diffusion analysis, and a dynamic evolution model of the diffusion system is established. Meanwhile, in competitive technology diffusion framework, the system balancing problem is investigated in detail with zero and positive diffusion velocity. With a large number of simulations, various energy impact factors and the effect of competition strength on diffusion process are analyzed. Taken the technology diffusion in China Mobil as an example, dynamic evolution model, multi-agents model and practical data are compared, and diffusion number of future mobile phone users is estimated. Results demonstrate the feasibility and efficiency of the dynamic evolution model in optimizing adoption of competitive innovation technology.In the research field of complementary innovation technology diffusion, the ACA algorithm is adopted. With the analog factor between shortest path search from cave to food in ant's behavior and effect maximization in innovation technology adoption, and that between information hormone concentration and the number of successful technology adoption, a new ants colony algorithm based innovation technology adoption method is designed. Complementary effect coefficient is introduced, and a dynamic evolution model of multiple complementary innovation technology diffusion system is established based on the rule of inter-promotion of complementary innovation technology. On basis of the proposed theory and technique, supporting software tool is developed and simulation analysis is carried out. As a demonstration, wireless association projector development is analyzed with the complementary transmission technique. The analysis proves the feasibility and effectiveness of the proposed dynamic evolution model in optimizing complementary innovation technology adoption.Based on the investigations, overall optimiazation is taken for biochip production technique adoption and multiple inspection techniques selection, relative software support tools are developed, and simulation analysis is conducted as well, all these demonstrates the correctness, practicability and efficiency of the proposed theory, approach and model.