Technological innovation diffusion: the proliferation of substitution models and easing the user's dilemma

The proliferation, assumptions, motivation, and behavior of various substitution models of the technological diffusion process are explored. The underlying notion is that such an understanding helps the model user to choose the most appropriate model for the situation. The authors discuss the development, motivation, and assumptions of various deterministic and binary substitution models and compare them on the basis of their three mathematical characteristics. It is shown that the study of the interrelationships between the models is useful in narrowing the choice. The behavior of the models is studied through an illustration of diffusion of innovative oxygen-steel technology in Spain and in Japan. >

[1]  Uma Kumar,et al.  Innovation diffusion: Some new technological substitution models , 1992 .

[2]  M. Bundgaard-Nielsen The international diffusion of new technology , 1976 .

[3]  A.Wade Blackman,et al.  A mathematical model for trend forecasts , 1971 .

[4]  Chaim Fershtman,et al.  Advertising, Pricing and Stability in Oligopolistic Markets for New Products , 1983 .

[5]  F. Bass The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations , 1980 .

[6]  Abel P. Jeuland Parsimonious Models of Diffusion of Innovation: Part A: Derivations and Comparisons , 1981 .

[7]  D. Horsky,et al.  Advertising and the Diffusion of New Products , 1983 .

[8]  Bruce R. Robinson,et al.  Dynamic Price Models for New-Product Planning , 1975 .

[9]  Christos H. Skiadas Two simple models for the early and middle stage prediction of innovation diffusion , 1987, IEEE Transactions on Engineering Management.

[10]  Abel P. Jeuland,et al.  Experience Curves and Dynamic Demand Models: Implications for Optimal Pricing Strategies , 1981 .

[11]  Robert U. Ayres,et al.  The future of technological forecasting , 1989 .

[12]  Frederick E. Smith,et al.  Population Dynamics in Daphnia magna and a New Model for Population Growth , 1963 .

[13]  Robert A. Peterson,et al.  Innovation Diffusion in a Dynamic Potential Adopter Population , 1978 .

[14]  Christos H. Skiadas,et al.  Two generalized rational models for forecasting innovation diffusion , 1985 .

[15]  Eitan Muller,et al.  A nonsymmetric responding logistic model for forecasting technological substitution , 1981 .

[16]  L. Bertalanffy Quantitative Laws in Metabolism and Growth , 1957 .

[17]  Donald N. Merino,et al.  Development of a technological S-curve for tire cord textiles , 1990 .

[18]  Joseph P. Martino,et al.  The effect of errors in estimating the upper limit of a growth curve , 1972 .

[19]  Kazimierz Z. Poznański,et al.  International diffusion of steel technologies: Time-lag and the speed of diffusion , 1983 .

[20]  Vijay Mahajan,et al.  A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance , 1983 .

[21]  E. Muller,et al.  Models of New Product Diffusion Through Advertising and Word-of-Mouth , 1978 .

[22]  S. Kalish Monopolist Pricing with Dynamic Demand and Production Cost , 1983 .

[23]  M. N. Sharif,et al.  Binomial innovation diffusion models with dynamic potential adopter population , 1981 .

[24]  D. Clarke,et al.  A Simulation Analysis of Alternative Pricing Strategies for Dynamic Environments , 1984 .

[25]  E. Mansfield TECHNICAL CHANGE AND THE RATE OF IMITATION , 1961 .

[26]  Ambar G. Rao,et al.  Bayesian Estimation and Control of Detailing Effort in a Repeat Purchase Diffusion Environment , 1981 .

[27]  J. Eliashberg,et al.  The Impact of Competitive Entry in a Developing Market Upon Dynamic Pricing Strategies , 1986 .

[28]  M. Sharif,et al.  A generalized model for forecasting technological substitution , 1976 .

[29]  F. Bass A new product growth model for consumer durables , 1976 .

[30]  A.Wade Blackman,et al.  The rate of innovation in the commercial aircraft jet engine market , 1971 .

[31]  J. C. Fisher,et al.  A simple substitution model of technological change , 1971 .

[32]  C. Skiadas Innovation diffusion models expressing asymmetry and/or positively or negatively influencing forces , 1986 .