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Understanding the diffusion process for technology-intensive products
ABSTRACTS
lized for normalizing the noncentral F distribution to estimate the noncentrality parameter h = Np*/( 1 - p*) using N observations, K predictor variables, R, the coefficient of determination, and Z, the standard normal deviate. This estimate, L, is then used to calculate the p* and rjz and their confidence intervals. Random-Predictors Model: a quadratic formula is developed as a function of L, N, and K. The roots of this quadratic expression are then utilized to establish the upper and lower confidence limits for the random predictors case. Finally, comparisons are made between the proposed models and the models presented by Cattin in order to demonstrate the efficiency of this approach.
Understanding the Diffusion Process for Technology-Intensive Products, Adil T. Talaysum, Research Management (July-August 1985), pp. 22-26. (CMC)
Diffusion refers to the spread of a new technology within the universe of potential adopters. Technology management has placed inadequate emphasis on managing the diffusion of new technologies. Planning for diffusion is an integral part of new products management, and should extend well beyond the initial marketing of the product. The classical diffusion model presents an S-shaped adoption curve, based on an assumed relationship between the number of potential users who have adopted it relative to the total number of potential users and those yet to adopt it. But this model has some inherent limitations. First, it is very difficult, in advance, to estimate the value of the innovation to various users. Experience is usually inadequate prior to marketing. Second, the model treats the innovation as univariant, whereas managers know that once the new product is introduced, competitors are likely to develop more sophisticated and/or less costly versions. Third, the mathematics of the function requires that we know the ceiling of the adoption percentage, again something that is rarely forecastable in advance. An alternative framework for diffusion planning is
J PROD INNOV MANAG 1986:4:292-306
299
the contribution of this article. The author urges that diffusion be conceived as a series of changes in the array of prospective users as affected by improvements in the operating characteristics of the technology, the emergence of new areas of application, changes in economic appeal of the technology, and changes in needs, resources, government controls, incentives, etc. Thus the ultimate diffusion curve for the technology may be conceived as the aggregation of a series of smaller diffusion curves (spread out over time). Each smaller curve represents adoption in a subpopulation, or in one use or user category. The shape of this cumulative diffusion curve is determined by the expansion in the universe of prospective uses/users and by the speed of their individual adoptions. Each of these subpopulation curves is S-shaped, and so is the overall curve, though with irregularity that comes with summation of the diverse subgroups. The diffusion of the CT scanner in the hospital industry is given as an example of this process. There were four subpopulations:
A small group of very large hospitals. These hospitals were the leading research centers in the nation and were potential adopters from the time of the technology’s first availability.
The large nonresearch hospitals requiring significant clinical utility. With large patient bases, they entered the universe subsequently, after improvements with respect to clinical applications were made to the initial technology.
The medium to large hospitals with small patient bases. These hospitals awaited significant extensions of the clinical utility of CT scanners before they could enter the universe of potential adopters.
Medium-scale hospitals that could not become potential adopters until the development of relatively inexpensive scanners and mobile scanners.
New products managers should plan to implement appropriate modifications to the technology in order to effect an orderly and profitable entry of subpopulations into the total group of potential adopters.
lized for normalizing the noncentral F distribution to estimate the noncentrality parameter h = Np*/( 1 - p*) using N observations, K predictor variables, R, the coefficient of determination, and Z, the standard normal deviate. This estimate, L, is then used to calculate the p* and rjz and their confidence intervals. Random-Predictors Model: a quadratic formula is developed as a function of L, N, and K. The roots of this quadratic expression are then utilized to establish the upper and lower confidence limits for the random predictors case. Finally, comparisons are made between the proposed models and the models presented by Cattin in order to demonstrate the efficiency of this approach.
Understanding the Diffusion Process for Technology-Intensive Products, Adil T. Talaysum, Research Management (July-August 1985), pp. 22-26. (CMC)
Diffusion refers to the spread of a new technology within the universe of potential adopters. Technology management has placed inadequate emphasis on managing the diffusion of new technologies. Planning for diffusion is an integral part of new products management, and should extend well beyond the initial marketing of the product. The classical diffusion model presents an S-shaped adoption curve, based on an assumed relationship between the number of potential users who have adopted it relative to the total number of potential users and those yet to adopt it. But this model has some inherent limitations. First, it is very difficult, in advance, to estimate the value of the innovation to various users. Experience is usually inadequate prior to marketing. Second, the model treats the innovation as univariant, whereas managers know that once the new product is introduced, competitors are likely to develop more sophisticated and/or less costly versions. Third, the mathematics of the function requires that we know the ceiling of the adoption percentage, again something that is rarely forecastable in advance. An alternative framework for diffusion planning is
J PROD INNOV MANAG 1986:4:292-306
299
the contribution of this article. The author urges that diffusion be conceived as a series of changes in the array of prospective users as affected by improvements in the operating characteristics of the technology, the emergence of new areas of application, changes in economic appeal of the technology, and changes in needs, resources, government controls, incentives, etc. Thus the ultimate diffusion curve for the technology may be conceived as the aggregation of a series of smaller diffusion curves (spread out over time). Each smaller curve represents adoption in a subpopulation, or in one use or user category. The shape of this cumulative diffusion curve is determined by the expansion in the universe of prospective uses/users and by the speed of their individual adoptions. Each of these subpopulation curves is S-shaped, and so is the overall curve, though with irregularity that comes with summation of the diverse subgroups. The diffusion of the CT scanner in the hospital industry is given as an example of this process. There were four subpopulations:
A small group of very large hospitals. These hospitals were the leading research centers in the nation and were potential adopters from the time of the technology’s first availability.
The large nonresearch hospitals requiring significant clinical utility. With large patient bases, they entered the universe subsequently, after improvements with respect to clinical applications were made to the initial technology.
The medium to large hospitals with small patient bases. These hospitals awaited significant extensions of the clinical utility of CT scanners before they could enter the universe of potential adopters.
Medium-scale hospitals that could not become potential adopters until the development of relatively inexpensive scanners and mobile scanners.
New products managers should plan to implement appropriate modifications to the technology in order to effect an orderly and profitable entry of subpopulations into the total group of potential adopters.