Modeling the Self-Propagating Growth Function of Science and Technological Diffusion for Selected Asian Countries

In the transition to a knowledge-based economy, many Asian emerging countries attempt to raise the national investment to develop their science and technological capacity. Science and technological production can be the result of self-propagating growth in the process of diffusion. By theoretical analysis and empirical demonstration, this paper attempts to analyze science and technology by investigating the self-propagating behavior of science and technological diffusion for South Korea, Malaysia and Japan. The dynamics of the self-propagating behavior in these selected countries are examined through the logistic growth function with a dynamic carrying capacity. Evidence suggests that this function is particularly relevant for an advanced country like Japan.

1. Introduction

In recent decades, scientific resources and production play an important role for technological and economic development. Areas of modern technology, such as biotechnology, semiconductor, and nanotechnology, are closely related to scientific production [1]. The word ‘science’ is often used in the literature to represent basic research activities or research carried out by non-industrial institutions such as universities and public research laboratories. Publications that report theoretical works and research findings are the main channel for documentation and dissemination of scientific findings to further the development of science [1], [2]. The word ‘technology’ refers to applied research activities or research carried out by industries and private organizations [1], [2]. According to Grupp [1], [2] and Stankiewics [3], technology represents the body of knowledge about techniques, and it is characterized by conception and development of products which are capable of diffusing services and have a market demand. Scientific publications (papers) and patents as the indicators for codified-type of knowledge are considered the most revealed indicators for science and technology respectively. Many studies [1], [2], [4], [5] used the statistics for papers and patents as the indicators for analyzing the relationship between science and technology¹

According to Grupp [2], direct quantitative measures of knowledge transfer between science and technology is impossible. Indicators of science and technology are often indirectly observed and measured as underlying the indicators are different aspect of interests and innovation [5], [6].

. With economic and technological changes, their works suggest stronger connections and higher interactions between science, technology and economic growth. This positive interaction thus creates a virtuous cycle in the diffusion of science and technology, and self-propagating functions are therefore useful for understanding the behavior of these variables.