Abstract
Under a long lasting economic stagnation, since significant increase in R&D investment has become difficult, practical solution could be found in systems approach maximizing the effects of innovation as a system by making full utilization of potential resources of innovation. At the same time, under the increasing significance of information technology (IT) in an information society which emerged in the 1990s, functionality development has become crucial for stimulating a self-propagating nature of IT driven innovation.
Stimulated by these understandings and prompted by a concept of institutional innovation, this chapter attempts to analyze the interacting dynamism of innovation in a comprehensive and organic system. Theoretical analysis and empirical demonstration are attempted focusing on dynamism between learning and diffusion of technology taking Japan's PV development, which follows the similar trajectory of IT's functionality development, over the last quarter century.
The effects of functionality decrease on learning coefficient and consequent impacts on technology diffusion and its dynamic carrying capacity are analyzed. Fear of a vicious cycle between functionality decrease, deterioration of learning, stagnation of technology diffusion and its carrying capacity in long run is demonstrated. Thereby, the significance of institutional dynamism leading to a dynamic interaction between learning, diffusion and spillover of technology is identified.
Reprinted from Technovation 24, No. 8, C. Watanabe and B. Asgari, Impacts of Functionality Development on the Dynamism Between Learning and Diffusion of Technology, pages: 651–664, copyright (2004), with permission from Elsevier.
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Notes
- 1.
Given the production at time t, Y t , cumulative production at time t,Y* t , can be measured as follows:
$$Y_t^\ast=Y_{t-l_t}+(1-\rho)Y^\ast_{t-1}$$where l t, lead time between production and operation; and ρ, depreciation rate.
- 2.
This term is equivalent to −mt in (8.13). Given the small value of the power of exponent, the second term of (8.10′) can be approximated as follows:
$$\begin{array}{l}-\phi_2\left[{a (1-bt) + \frac{a_k}{1-b_k /b} \left(1-b_k\right)+a_h \left(1+b_ht^2\right)}\right] \\\quad =-\left[{\left({a+\frac{a_k}{1-b_k/b}+b_h}\right)\phi_2-\left({ab+\frac{a_k b_k}{1-b_k /b}}\right)\phi_2 t+a_h b_h \phi_2 t^2}\right] \\\quad \equiv -(\alpha_1-\beta_1\,t+\gamma_1\,t^2)\end{array}$$((A))While the second term of (8.14) can be approximated as follows:
$$-\beta\left[1-(l-mt)t\right]=-\beta(1-lt+mt^2)\equiv-(\alpha_2-\beta_2\,t+\gamma_2 t^2)$$((B))Under the condition within certain period, α1 = α2, β1 = β2 and γ1 = γ2, the structure of the additional term \(a_h{\rm e}^{b_h t^2}\) can satisfy the requirement of (A) and (B) are equivalent.
- 3.
These conditions can be satisfied and the requirements can be met after a certain period.
- 4.
Equation (8.11) suggests that under certain conditions, J(t) can be approximated as follows: \(\begin{array}{l}J(t)\approx\beta{\rm e}^{-\gamma t}(0<\beta,\ 0<\gamma\,{\ll}\,1) \\\ln\,J(t)=\ln\,\beta-\gamma t=(\ln\,\beta+1)-(1+\gamma t) \\\approx(1+\ln\beta)-{\rm e}^{\gamma t}=(1+\ln\beta)-\frac{\beta}{J(t)}\equiv\eta_1-\eta_2\frac{1}{J(t)}\end{array}\)
$$\ln\,J(t)=\eta_1-\eta_2\frac{1}{J(t)},$$((5.18(d)))where η1 and η2, coefficients.
Substituting \(\frac{1}{J(t)}\) in (5.18(d)) for \(\frac{1}{J(t)}\) in (5.18(c))
$$\ln\,W(t)=\left({\ln\,\varphi_2+a_h\cdot\frac{\eta_1}{\eta_2}} \right)+\left({1-\frac{a_h}{\eta_2}}\right)\ln\, J(t)+a_h\cdot b_h\frac{t^2}{J(t)},$$((5.18(e))) - 5.
In the process of IT diffusion, the number of users increases as time passes, which induces interaction with institutions leading to increasing potential users by increased value and function as the network externalities gain momentum. Thus, IT creates new demand in this development process and new functionality is formed which in turn enhances user interaction. Thus, the interactive self-propagating behavior continues [21].
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© 2009 International Institute for Applied Systems Analysis (IIASA), Laxenburg
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Watanabe, C. (2009). Impacts of Functionality Development on Dynamism between Learning and Diffusion of Technology. In: Managing Innovation in Japan. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89272-4_5
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