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Multigeneration innovation diffusion: The impact of intergeneration time

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Abstract

This research focuses on the diffusion patterns of the adjacent generations of technology and its relation to the time that elapses between them (intergeneration time). The authors analyze 45 new technologies in 15 industries and find that the adoption curves systematically vary across generations from 2 years for dynamic random-access memory (DRAM) chips to more than 30 years for steelmaking. The longer the intergeneration time, the slower the adoption of the subsequent technology. Even though once the adoption begins imitation is greater for subsequent technologies, the slow initial innovation rate, driven by resistance to upgrading, retards adoption. The authors also demonstrate that predictions based on intergeneration time plus average patterns are more accurate than data-based predictions early in life cycles when such predictions are most crucial. Improved early predictions can provide advantages in terms of both making go versus no-go decisions and planning marketing and production.

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References

  • Bass, Frank M. 1969. “A New Product Growth Model for Consumer Durables.”Management Science 15(January): 215–227.

    Google Scholar 

  • Bayus, Barry L. 1992. “Have Diffusion Rates Been Accelerating Over Time?”Marketing Letters 3: 215–226.

    Article  Google Scholar 

  • — 1997. “Speed-to-Market and New Product Performance Trade-Off.”Journal of Product Innovation Management 14(November): 485–497.

    Article  Google Scholar 

  • Bewley, R. and D. G. Fiebig. 1988. “A Flexible Logistic Growth Model With Applications in Telecommunications.”International Journal of Forecasting 2: 177–192.

    Article  Google Scholar 

  • Blackman, A. W., Jr. 1971. “The Rate of Innovation in the Commercial Aircraft Jet Engine Market.”Technological Forecasting and Social Change 2: 341–352.

    Article  Google Scholar 

  • — 1972. “A Mathematical Model for Trend Forecasts.”Technological Forecasting and Social Change 3: 441–452.

    Article  Google Scholar 

  • Booz, Allen & Hamilton, Inc. 1982.New Products Management for the 1980s. New York: Booz, Allen & Hamilton, Inc.

    Google Scholar 

  • Datar, S., C. Jordan, S. Kekre, S. Rajiv, and K. Srinivasan. 1996. “New Product Development Structures: The Effect of Customer Overload on Post-Concept Time to Market.”Journal of Product Innovation Management 13(July): 325–333.

    Article  Google Scholar 

  • Demler, Frederic. R. 1980. “The Nature of Tin Substitution in the Beverage Container Industries.” Unpublished Ph. D. dissertation, Pennsylvania State University, University Park.

    Google Scholar 

  • Dixon, R. 1980. “Hybrid Corn Revisited”Econometrica 48: 1451–1461.

    Article  Google Scholar 

  • Fisher, J. C. and R. H. Pry. 1971 “A Simple Substitution Model for Technological Change.”Technological Forecasting and Social Change 2: 75–88.

    Article  Google Scholar 

  • Foster, R. N. 1986.Innovation: The Attacker's Advantage. New York: Summit Books.

    Google Scholar 

  • Gatignon, H. and T. S. Robertson. 1991. “Innovative Decision Processes.” InHandbook of Consumer Behavior. Eds. T. S. Robertson and H. H. Kassarijan. Englewood Cliffs, NJ: Prentice Hall, 316–348.

    Google Scholar 

  • Golder, Peter N. and G. J. Tellis. 1997. “Will It Ever Fly? Modeling the Takeoff of Really New Consumer Durables.”Marketing Science 16: 256–270.

    Google Scholar 

  • Griffin, Abbie. 1997. “PDMA Research on New Product Development Practices: Updating Trends and Benchmarking Best Practices.”Journal of Product Innovation Management 14(6): 429–458.

    Article  Google Scholar 

  • Islam, T. and N. Meade. 1997. “The Diffusion of Successive Generation of a Technology: A More General Model.”Technological Forecasting and Social Change 56(1): 49–60.

    Article  Google Scholar 

  • Jeuland, Abel P. 1994. “The Bass Model as a Tool to Uncover Empirical Generalizations in the Diffusion of Marketing.” Working paper, University of Chicago.

  • Johnston, Jack. 1972.Econometric Methods. New York: McGraw-Hill.

    Google Scholar 

  • Katz, M. and C. Shapiro. 1985. “Network Externalities, Competition, and Compatibility.”American Economic Review 75(3): 424–440.

    Google Scholar 

  • — and —. 1994. “Systems Competition and Network Effects.”Journal of Economic Perspectives 8(2): 93–115.

    Google Scholar 

  • Kim, N., D. R. Chang, and A. Shocker. 1999. “Modeling Inter-Category and Generational Dynamics for a Growing Information Technology Industry.”Management Science 46(April): 496–512.

    Google Scholar 

  • Kohli, R., D. Lehmann, and J. Pae. 1999. “The Extent and Impact of Incubation Time in New Product Diffusion.”Journal of Product Innovation Management 16: 134–144.

    Article  Google Scholar 

  • Kumar, V., J. Ganesh, and R. Echambadi. 1998. “Cross-National Diffusion Research: What Do We Know and How Certain Are We?”Journal of Product Innovation Management 15(May): 255–268.

    Article  Google Scholar 

  • Machnic, J. A. 1980. “Multilevel Versus Single-Level Substitution: The Case of the Beverage Can Market.”Technological Forecasting and Social Change 18: 141–149.

    Article  Google Scholar 

  • Mahajan, V., C. H. Mason, and V. Srinivasan. 1986. “An Evaluation of Estimation Procedures for New Product Diffusion Models.” InInnovation Diffusion Models of New Product Acceptance. Eds. Vijay Mahajan and Yoram Wind. Cambridge, MA: Balliger.

    Google Scholar 

  • — and E. Muller. 1996. “Timing, Diffusion, and Substitution of Successive Generations of Technological Innovations: The IBM Mainframe Case”.Technological Forecasting and Social Change 51 (February): 109–132.

    Article  Google Scholar 

  • Mansfield, E. 1961. “Technical Change and the Rate of Imitation.”Econometrica 29(October): 741–766.

    Article  Google Scholar 

  • Merino, D. N. 1990. “Development of a Technological S-Curve for Tire Cord Textiles”.Technological Forecasting and Social Change 37: 275–291.

    Article  Google Scholar 

  • Moore, William L. 1994. “Radical Innovations: What Can Be Learned From the Past?” InAnd Now for Something Completely Different: “Really” New Products. Conference Summary. Marketing Science Institute, Cambridge, MA.

    Google Scholar 

  • Norton, John A. and F. Bass. 1987. “A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products”.Management Science 33(September): 1069–1086.

    Google Scholar 

  • — and —. 1992. “Evolution of Technological Generations: The Law of Capture.”Sloan Management Review 33(Winter): 66–77.

    Google Scholar 

  • Shanklin, W. and J. Ryans. 1987.Essentials of Marketing High Technology. Lexington, MA: D. C. Health.

    Google Scholar 

  • Sharif, M. N. and C. Kabir. 1976. “System Dynamics Modeling for Forecasting Multilevel Technological Substitution”.Technological Forecasting and Social Change 9: 89–112.

    Article  Google Scholar 

  • —,— and K. Ramanathan. 1982. “Polynomial Diffusion Models.”Technological Forecasting and Social Change 21: 301–323.

    Article  Google Scholar 

  • Silk, A. J. and G. L. Urban. 1978. “Pre-Test Market Evaluation of New Packaged Goods: A Model and Measurement Methodology.”Journal of Marketing Research 15: 171–191.

    Article  Google Scholar 

  • Silverman, B. G. 1981. “Market Penetration Model: Multimarket, Multitechnology, Multiatribute Technological Forecasting.”Technological Forecasting and Social Change 20: 215–233.

    Article  Google Scholar 

  • Speece, M. W. and D. L. MacLachlan. 1992. “Forecasting Fluid Milk Package Type With a Multigeneration New Product Diffusion Model.”IEEE Transactions on Engineering Management 39 (March): 169–175.

    Article  Google Scholar 

  • Srinivasan, V. and C. Mason. 1986. “Nonlinear Least Squares Estimation of New Product Diffusion Models.”Marketing Science 5(Spring): 169–178.

    Google Scholar 

  • Stern, M. O., R. U. Ayres, and A. Shapanka. 1975. “A Model for Forecasting the Substitution of One Technology for Another”.Technological Forecasting and Social Change 7: 57–79.

    Article  Google Scholar 

  • Sultan, Fareena, J. U. Farley, and D. Lehmann. 1990. “A Meta-Analysis of Applications of Diffusion Models.”Journal of Marketing Research 27(February): 70–77.

    Article  Google Scholar 

  • Wilson, L. O. and J. A. Norton. 1989. “Optimal Entry Timing for a Product Line Extension”.Marketing Science 8(Winter): 1–17.

    Article  Google Scholar 

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Authors and Affiliations

  1. Hong Kong Polytechnic University, Hong Kong

    Jae H. Pae

  2. Columbia University, Columbia, USA

    Donald R. Lehmann

Authors
  1. Jae H. Pae
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  2. Donald R. Lehmann
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Additional information

Jae H. Pae (Ph.D., Columbia University) is an assistant professor of marketing at Hong Kong Polytechnic University. He received his B.A. in anthropology from Seoul National University, Korea. His current research interests include adoption of innovation, new product forecasting, and marketing strategy. He has published in several journals, including theJournal of Product Innovation Management, theJournal of Business Research, and theJournal of Advertising Research.

Donald R. Lehmann (Ph.D., Purdue University) is George E. Warren Professor of Business at Columbia University Graduate School of Business. He has a B.S. degree in mathematics from Union College, Schenectady, New York, and an M.S.I.A. and Ph.D. from the Krannert School of Purdue University. His research interests include modeling individual choice and decision making, understanding group and interdependent decisions, meta-analysis, and the introduction and adoption of innovations. He has taught courses in marketing, management, and statistics at Columbia and has also taught at Cornell, Dartmouth, and New York University. He has published in and served on the editorial boards of theJournal of Consumer Research, theJournal of Marketing, theJournal of Marketing Research, Management Science, andMarketing Science and was founding editor ofMarketing Letters. In addition to numerous journal articles, he has published four books:Marketing Research and Analysis, Analysis for Marketing Planning, Product Management, andMeta Analysis in Marketing. He has served as executive director of the Marketing Science Institute and as president of the Association for Consumer Research.

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Pae, J.H., Lehmann, D.R. Multigeneration innovation diffusion: The impact of intergeneration time. J. of the Acad. Mark. Sci. 31, 36–45 (2003). https://doi.org/10.1177/0092070302238601

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