| Summary: | 碩士 === 中華大學 === 科技管理學系碩士班 === 101 === Abstract
The productivity gap among countries becomes obvious when we look into over a long period. There is a turning point of GDP per capita in 2003 among developed industrial Asia, especially between Korea and Taiwan. This paper examines what makes this turning point and investigates the driving factors of productivity. The innovation is considered as a key driver of productivity growth. Most researches use the econometric approach to investigate the relationship between productivity and innovation among firms by using CIS (Community Innovation Survey) panel data or similar data. The empirical studies may support the evidences to build up the econometric model such as CDM or its variations and find industry characteristics of innovation at macro-level of each country. By production function approach, it may find the correlation between productivity and individual innovation variable (product, process, organizational, marketing).But actually the innovation activities involve complex dynamic system with endogenous knowledge externalities and non-linear feedback within system. As resources limitation, the improvement of productivity by resource-based strategy with competition advantage can be simulated by dynamic system model. The simulation result can help the decision makers understand the micro-level system mechanism either under open-mode innovation or close-mode innovation environment and the impacts under different scenario. The System Dynamics is especially useful for observation of complex dynamic system over a long period. The analysis of productivity and innovation matches with the characteristics of complex system which are nonlinear and with complex feedback over time delay effect. The complex system dynamics analysis also needs the complementary methods to support the analysis with empirical data from fields to calibrate between theoretical model and practical situation. That’s why the integrated systematic model is used to investigate the productivity change and comparison between country and industry. The regression result of econometric CDM model and CIS empirical data can be used as boundary condition of system dynamic simulation with causal diagram of innovation process. The System Dynamics model is suitable for the analysis within single organization or agent. For more complex interactions and diffusions among agents, the agent-based model can be used to investigate the innovative activities including innovative business models inter firms or agents.This paper builds an integrated systematic model by combining the CDM model, System Dynamics, and agent-based model to explain the macro-level relationship and to find out the key factors of innovation activities to leverage the micro-level mechanism. Once the model is verified with historical data, then the model can be used as forecast by kinds of scenario.This research find out that the log RD expenditure per employee plays the dominant factor in log labor productivity regression for both manufacturing and service sectors. It implies that the R&;D expenditure affects the labor productivity output either directly or indirectly. It makes the gap enlarged between Korea and Taiwan since 2003. The manufacturing sector and servicer represents different characteristics. Service sector shows the indivisibility and positive effect significantly in statistics by combination of product, process, and organization. But product innovation only or combination with process and organization shows negative effect significantly in statistics. The manufacturing sector shows the divisibility and positive effect significantly in statistics by combination of process, and organization, but shows negative effect significantly to productivity with product innovation only in statistics. It shows the current advantage of manufacturing sector in OEM/ODM and the potential crisis with less effectiveness of product innovation.
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