An extended model for measuring the technology transfer potentials at the industrial level

Technology contributes to the development of society and economy of the nation through the invention, diffusion, transfer, and application of new knowledge. In the emerging global economy of the 21st century, technology is a key to sustainable economic prosperity. Transfer of technology is the key element for the industrialization, growth, and economic development of the countries. The knowledge transferring capabilities of the transferor, adaptation and assimilation capabilities of the transferee are important to a large extent on the success of any transfer of technology. The quantitative or mathematical modeling has not been significantly utilized in analyzing the technology transfer process. Some of the well known quantitative studies on technology transfer have been done by Haq (1979), Suckchareonpong (1979), Baruch Raz, Gerald Steinberg, and Andrew Ruina (1983), Baruch Raz and Isak Assa (1988), Liu (1993), Bhargava (1995), Jayaraman, Truong and Agrawal (1998), Truong (2002). It is possible that more contribution to the knowledge relating to technology transfer can be made by studying the process of technology transfer using quantitative methods. The main focus of this study is to develop an extended quantitative model incorporating time, technological level and a dynamic potential technological distance for measuring the technology transfer potentials that exist between a transferor and a transferee at the industrial level. In this study, the technological level of a country, called ‘technology index’, is computed using various variables and employing the factor analysis method. The factor analysis is used to determine the factor loadings of the variables for formulating the technology index at the industrial level. By using the logistic growth pattern, the technology indexes data are then applied to develop its technology index function. By using the technology index function, the technology level is determined by way of an index. As the indices are derived through factor analysis, it is likely that the index will represent the level of a country more accurately if number of variables is considered for its formulation. It is accepted in the literature on Technology Transfer (Haq 1979, Sharif-Haq 1981, Sukchareonpong 1979, Jayaraman-Truong-Agrawal 1996) that the main factor governing the technological transfer process, the transfer rate, at any time is proportional to the current level of assimilation of such technology of the transferee and the level remaining to be achieved by the transferee in the long run. However, there could be many other important factors influencing the transfer rate such as the technological gap between transferor and transferee, the potential technological distance between the transferor and transferee, the geographical distance between locations etc. In this study, an extended mathematical model is developed for measuring technology transfer potentials. It is hypothesised that the rate of assimilation of a particular technology of a transferee at a certain time, , is proportional to: (a) the existing level of assimilation of such technology of the transferee, (b) the level remaining to be achieved by the transferee in the long run, and (c) a technology transfer function that incorporates the relative technological gap (potential technological distance) between the transferor and transferee. Using the technology transfer model developed, the level of assimilation that the transferee can achieve with the selected transferor during the period of technology transfer is measured. The boundary conditions for technology transfer model are then verified. The boundary conditions are based on the fact that when the time tends to minus infinity, the assimilation level of the transferee would be equal to zero, and when time tends to plus infinity, the assimilation level of the transferee will be equal to the maximum level that can be achieved in the long run by the transferee as shown in the model. The time-level technology transfer models (Haq-Shariff model, Sukchareonpong model, and Jayaraman-Truong-Agrawal model), and the models of technological change (namely, Blackman’s model, Fisher-Pry model, Mansfield’s model, and Bhargava model) are shown to be the derived cases of the dynamic model developed in this study. To demonstrate the applicability of the developed model, case studies of technology transfer in automobile industry, electronics industry, and computing industry in selected member countries such as Korea, Japan, China, Singapore, Malaysia, UK, Germany, USA, Brazil, and France are presented. In this study, only a few countries are selected due to the limitation on the availability and reliability of the data. As this study is on developing an extended model for measuring the technology transfer potentials that exist between a transferor and a transferee at the industrial level, the variables that influence and reflect the performance of the given industry of various countries under study are identified and collected for the past years. The variables used in this study are broadly categorized into three groups, namely, (i) variables relevant to national technology climate conditions, (ii) variables reflecting manufacturing technology climate conditions, and (iii) variables pertinent to the specific industry technology climate conditions in a country. In this study, the indicators that influence and reflect the performance of the given industry such as Research and Development (R&D) Expenses per economically active population, Output per employee in the manufacturing sector, Value added per employee of the manufacturing sector, Output per employee of the specific industry, and Value added per employee of the specific industry, are considered for formulating the technology index at the industrial level. The national level technology climate variables and the manufacturing sectoral level technology climate variables are assumed to have direct influence on the growth of the specific industry. The Value added per employee in the specific industry is considered as the technology assimilation parameter at the industrial level. For international comparison of monetary values, the US dollar is considered as a standard currency in this study. However, the exchange rate does not reflect the real value of each currency unit. To overcome the above problem of the exchange rate, purchasing power parity (PPP) is used in this study. In the case study of technology transfer in Automobile industry in selected member countries such as Korea, Japan, China, Singapore, Malaysia, UK, Germany, USA, Brazil, and France, the historical data of the value added per employee (US$ in PPP terms) and the predicted values are used to fit the technology transfer phenomenon in that industry. In terms of technology transfer in Automobile industry, the model developed in this study explains the variation in the prediction to the extent of 99.97% for Japan, 99.94% for Malaysia, 98.76% for Singapore, 93.63% for France, 91.26% for Brazil, 90.71% for UK, 90.60% for Korea, 90.51% for Germany, and 83.15% for China. It is found that the technology transfer model developed in this study provides a very good fit in all the above transfer situations. The fitness of the model is quite significant for countries such as Japan, Singapore, Malaysia, Germany and France at the 0.01 level where as it is significant for countries such as Korea, China, UK, and Brazil at the 0.05 level. In the case study of technology transfer in Electronics industry in selected member countries such as Korea, Japan, China, Singapore, Malaysia, UK, Germany, USA, Brazil, and France, the historical data of the value added per employee (US$ in PPP terms) and the predicted values are used to fit the technology transfer phenomenon in that industry. In terms of technology transfer in Electronics industry, the model developed in this study explains the variation in the prediction to the extent of 99.30% for Germany, 98.20% for UK, 97.97% for France, 96.20% for Singapore, 95.44% for China, 90.59% for Brazil, 89.96% for Japan, 88.40% for Malaysia, and 76.87% for Korea. It is found that the technology transfer model developed in this study provides a very good fit in all the above transfer situations. The fitness of the model is quite significant for countries such as China, Singapore, UK, Germany, and France at the 0.01 level where as it is significant for countries such as Korea, Japan, Malaysia and Brazil at the 0.05 level. In the case study of technology transfer in Computing industry in selected member countries such as Korea, Japan, China, Singapore, Malaysia, UK, Germany, USA, Brazil, and France, the historical data of the value added per employee (US$ in PPP terms) and the predicted values are used to fit the technology transfer phenomenon in that industry. In terms of technology transfer in Computing industry, the model developed in this study explains the variation in the prediction to the extent of 96.42% for Singapore, 96.40% for Brazil, 95.59% for UK, 94.07% for Malaysia, 87.28% for France, 87.00% for China, 72.07% for Japan, and 69.40% for Korea. From the above results, it can be seen that the technology transfer model developed in this study provides a very good fit in most of the above transfer situations. The fitness of the model is quite significant for countries such as Singapore, Malaysia, UK, Germany, Brazil, and France at the 0.01 level where as it is significant for country China, Korea and Japan at the 0.05 level. Based on the results obtained from the case studies of technology transfer in automobile industry, electronics industry, and computing industry for Korea, Japan, China, Singapore, Malaysia, UK, Germany, USA, Brazil, and France, it is concluded that the hypothesis used in this research that the rate of assimilation of a particular technology of a transferee at a certain time, t , is proportional to: (a) the existing level of assimilation of such technology of the transferee, (b) the level remaining to be achieved by the transferee in the long run, and (c) a technology transfer function that incorporates the relative technological gap (potential technological distance) between the transferor and transferee, is accepted at the significance level of 0.05. The fitness of the technology transfer model is found to be very satisfactory in all the three case studies done. The case studies indicate that the model can provide an effective means for measuring the transfer potentials that exist between a transferor and a transferee. Since the model predicts the level of assimilation that a transferee can achieve with a given transferor in the long run, it is possible for this dynamic model to be used as a decision-making tool by countries in determining the optimum partner for most effective technology transfer. Finally, based on the outcome of the research undertaken, conclusions and the recommendations for further studies are presente