A Population-Growth Model for Multiple Generations of Technology Products

Published Online:https://doi.org/10.1287/msom.2013.0430

References

  • Amemiya T. Advanced Econometrics (1985) (Harvard University Press, Cambridge, MA) Google Scholar
  • Bass FM. A new product growth model for consumer durables. Management Sci. (1969) 15(5):215–227LinkGoogle Scholar
  • Bass FM. The relationship between diffusion rates, experience curves, and demand elasticities for consumer durable technological innovations. J. Bus. (1980) 53(3):S51–S67CrossrefGoogle Scholar
  • Bass FM, Krishnan TV, Jain DC. Why the bass model fits without decision variables. Marketing Sci. (1994) 13(3):203–223LinkGoogle Scholar
  • Bayus BL, Kim N, Shocker AD, Mahajan V, Muller E, Wind Y. Growth models for multiproduct interactions: Current status and new directions. New-Product Diffusion Models (2000) (Kluwer Academic Publishers, New York) 141–163Google Scholar
  • Bertsekas DP. Nonlinear Programming (2003) (Athena Scientific, Belmont, MA) Google Scholar
  • Danaher PJ, Hardie BGS, Putsis WP. Marketing-mix variables and the diffusion of successive generations of a technological innovation. J. Marketing Res. (2001) 38(4):501–514CrossrefGoogle Scholar
  • Debo LG, Toktay LB, Van Wassenhove LN. Joint life-cycle dynamics of new and remanufactured products. Production Oper. Management (2006) 15(4):498–513CrossrefGoogle Scholar
  • Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc., Series B Method. (1977) 39(1):1–38Google Scholar
  • Fisher JC, Pry RH. A simple substitution model of technological change. Tech. Forecasting Soc. Change (1971) 3:75–88CrossrefGoogle Scholar
  • Gordon BR. A dynamic model of consumer replacement cycles in the PC processor industry. Marketing Sci. (2009) 28(5):846–867LinkGoogle Scholar
  • Gowrisankaran G, Rysman M. Dynamics of consumer demand for new durable consumer goods. (2009) . NBER Working Paper 14737, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Greene WH. Econometric Analysis (2003) (Prentice Hall, Upper Saddle River, NJ) Google Scholar
  • Ho T-H, Savin S, Terwiesch C. Managing demand and sales dynamics in new product diffusion under supply constraint. Management Sci. (2002) 48(2):187–206LinkGoogle Scholar
  • Jain DC, Rao RC. Effect of price on the demand for durables: Modeling, estimation, and findings. J. Bus. Econom. Statist. (1990) 8(2):163–170CrossrefGoogle Scholar
  • Jun DB, Park YS. A choice-based diffusion model for multiple generations of products. Tech. Forecasting Soc. Change (1999) 61(1):45–58CrossrefGoogle Scholar
  • Kalish S. A new-product adoption model with price, advertising, and uncertainty. Management Sci. (1985) 31(12):1569–1585LinkGoogle Scholar
  • Kamakura W, Balasubramanian S. Long-term view of the diffusion of durables: A study of the role of price and adoption influence process via tests of nested models. Internat. J. Res. Marketing (1988) 5(1):1–13CrossrefGoogle Scholar
  • Kempf KG, Erhun F, Hertzler EF, Rosenberg TR, Peng C. Optimizing capital investment decisions at Intel Corporation. Interfaces (2013) 43(1):62–78LinkGoogle Scholar
  • Kim N, Chang D, Shocker A. Modeling inter-category dynamics for a growing information technology industry. Management Sci. (2000) 46(4):496–512LinkGoogle Scholar
  • Kumar S, Swaminathan JM. Diffusion of innovations under supply constraints. Oper. Res. (2003) 51(6):866–879LinkGoogle Scholar
  • Libai B, Muller E, Peres R. The influence of within-brand and cross-brand word of mouth on the growth of competitive markets. J. Marketing (2009) 73(2):19–34CrossrefGoogle Scholar
  • Mahajan V, Muller E. Timing, diffusion, and substitution of successive generations of technological innovations: The IBM mainframe case. Tech. Forecasting Soc. Change (1996) 51(2):109–132CrossrefGoogle Scholar
  • Meade N, Islam T. Modelling and forecasting the diffusion of innovation—A 25-year review. Internat. J. Forecasting (2006) 22(3):519–545CrossrefGoogle Scholar
  • Melnikov O. Demand for differentiated durable products: The case of the U.S. computer printer industry. (2001) . Mimeo, Yale University, New Haven, CTGoogle Scholar
  • Murray JD. Mathematical Biology: An Introduction (2002) (Springer-Verlag, New York) CrossrefGoogle Scholar
  • Newey W, West K. A simple positive semi-definite, heteroscedasticity and correlation consistent covariance matrix. Econometrica (1988) 55(3):703–708CrossrefGoogle Scholar
  • Norton JA, Bass FM. A diffusion theory model of adoption and substitution for successive generations of high-technology products. Management Sci. (1987) 33(9):1069–1086LinkGoogle Scholar
  • Peng C, Erhun F, Hertzler EF, Kempf KG. Capacity planning in the semiconductor industry: Dual-mode procurement with options. Manufacturing Service Oper. Management (2012) 14(2):170–185LinkGoogle Scholar
  • Peres R, Muller E, Mahajan V. Innovation diffusion and new product growth models: A critical review and research directions. Internat. J. Res. Marketing (2010) 27(2):91–106CrossrefGoogle Scholar
  • Pielou E. Mathematical Ecology (1977) (Wiley, New York) Google Scholar
  • Rothenberg TJ. Identification in parametric models. Econometrica (1971) 39(3):577–591CrossrefGoogle Scholar
  • Savin S, Terwiesch C. Optimal product launch times in a duopoly: Balancing life-cycle revenues with product cost. Oper. Res. (2005) 53(1):26–47LinkGoogle Scholar
  • Shenoy SR, Daniel A. Intel architecture and silicon cadence: The catalyst for industry innovation. Tech. at Intel Magazine (2006) October):1–7Google Scholar
  • Song I, Chintagunta P. A micromodel of new product adoption with heterogeneous and forward-looking consumers: Application to the digital camera category. Quant. Marketing Econom. (2003) 1(4):371–407CrossrefGoogle Scholar
  • SPEC SPEC benchmarks. (2010) . http://www.spec.org/benchmarks.htmlGoogle Scholar
  • Tuma N, Hannan M. Social Dynamics: Models and Methods (1984) (Academic Press, New York) Google Scholar
  • White H. A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica (1980) 48(4):817–838CrossrefGoogle Scholar
  • Wu SD, Kempf K, Atan M, Aytac B, Shirodkar S, Mishra A. Improving new-product forecasting at Intel Corporation. Interfaces (2010) 40(5):385–396LinkGoogle Scholar
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