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A Theoretical and Empirical Comparison of Innovation Diffusion Models Applying Data from the Software Industry

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Abstract

This paper presents a theoretical and empirical comparison of quantitative innovation diffusion models with data from the software industry. Looking at some of the most widely used aggregate models, such as the generalised Bass (Mark Sci 13:203–223, 1994) or the Kalish (Manag Sci 31:1569–1585, 1985) model, the inclusion of decision variables is compared to scientific groundings from innovation marketing, focusing on the analysis of their mathematical equations. Eligible attributes such as the reduction and the carry-through effect are addressed and performance simulations of the mapping function are run, using commonly observed pricing strategies. Adoption and marketing data of two innovative software products are applied to calibrate the models and to evaluate their forecasting precision by comparing the results with true data within a period of 10 months after initial release. It becomes evident that the predominant S-shaped models are not always suitable. A market analysis shows the complexity of market structures and the involved requirements for these models. Critical branch-related aspects, such as the reputation of the company, online distribution channels, and piracy are discussed and disclose future research spots in the estimation of diffusion shapes of innovative products.

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References

  1. Bass FM (1969) A new product growth for model consumer durables. Manag Sci 15:215–227

    Article  Google Scholar 

  2. Bass FM (1980) The relationship between diffusion rates, experience curves, and demand elasticities for consumer durable technological innovation. J Bus 53:551–567

    Article  Google Scholar 

  3. Bass FM, Jain DP, Krishnan TV (2000) Modeling the marketing-mix influence in new product diffusion. In: Mahajan V, Muller E, Wind Y (eds) New-product diffusion. Kluwer, Boston, pp 99–123

    Google Scholar 

  4. Bass FM, Krishnan TV, Jain DC (1994) Why the bass model fits without decision variables. Mark Sci 13:203–223, summer

    Article  Google Scholar 

  5. Bottomley PA, Fildes R (1998) The role of prices in models of innovation diffusion. J Forecast 17:539–555

    Article  Google Scholar 

  6. Chow GC (1967) Technological change and the demand of computers. Am Econ Rev 57:1117–1130

    Google Scholar 

  7. Dockner E, Jorgensen S (1988) Optimal pricing strategies for new products in dynamic oligopolies. Mark Sci 7:315–334, autumn

    Article  Google Scholar 

  8. Gatignon H, Robertson TS (1991) Innovative decision processes. In: Kassarjian H, Robertson T (eds) Handbook of consumer behavior theory and research. Prentice-Hall, Englewood Cliffs, pp 316–348

    Google Scholar 

  9. Givon M, Mahajan V, Muller E (1995) Software piracy: estimation of lost sales and the impact on software diffusion. J Mark 59:29–37

    Article  Google Scholar 

  10. Horsky D, Simon LS (1983) Advertising and the diffusion of new products. J Mark Sci 2:1–17, winter

    Article  Google Scholar 

  11. Jain D, Mahajan V, Muller E (1991) Innovation diffusion in the presence of supply restrictions. Mark Sci 10:83–90, winter

    Article  Google Scholar 

  12. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:261–293

    Article  Google Scholar 

  13. Kalish S (1985) A new product adoption model with price, advertising, and uncertainty. Manag Sci 31:1569–1585

    Article  Google Scholar 

  14. Kalish S, Sen SK (1986) Diffusion models and the marketing mix for single products. In: Mahajan V, Wind Y (eds) Innovation diffusion models of new product acceptance. Ballinger, Cambridge, pp 87–115

    Google Scholar 

  15. Kamakura WA, Balasubramanian SK (1987) Long-term forecasting with innovation diffusion models: the impact of replacement purchases. J Forecast 6:1–19

    Article  Google Scholar 

  16. Kamakura WA, Balasubramanian SK (1988) Long-term view of the diffusion of durables. Int J Res Market 5:1–13

    Article  Google Scholar 

  17. Liftin T (2000) Adoptionsfaktoren—empirische analyse am Beispiel eines innovativen telekommunikationsdienstes. Deutscher Universitäts, Wiesbaden

    Google Scholar 

  18. Lilien G, Rangaswamy A, Van den Bulte C (1999) Diffusion models: managerial applications and software. Working Paper, University of Pennsylvania, version 20.05.1999

  19. Mahajan V, Muller E (1979) Innovation diffusion and new product growth models in marketing. J Mark 43:55–68, autumn

    Article  Google Scholar 

  20. Mahajan V, Muller E, Bass FM (1990) New product diffusion models in marketing: a review and directions for research. J Mark 54:1–26

    Article  Google Scholar 

  21. Mahajan V, Muller E, Bass FM (1995) Diffusion of new products: empirical generalizations and managerial uses. J Mark Sci 14:79–88

    Article  Google Scholar 

  22. Mahajan V, Peterson RA (1978) Innovation diffusion in a dynamic potential adopter population. Manag Sci 15:1589–1597

    Article  Google Scholar 

  23. Putsis WP Jr, Srinivasan V (2000) Estimation techniques for macro diffusion models. In: Mahajan V, Muller E, Wind Y (eds) New-product diffusion. Kluwer, Boston, pp 263–294

    Google Scholar 

  24. Raman K, Chatterjee R (1995) Optimal monopolist pricing under demand uncertainty in dynamic markets. Manag Sci 41:144–162

    Article  Google Scholar 

  25. Rao RC, Bass FM (1985) Competition, strategy, and price dynamics: a theoretical and empirical investigation. J Mark Res 22:283–296

    Article  Google Scholar 

  26. Rogers EM (1995) Diffusion of innovations, 4th edn. The Free Press, New York

    Google Scholar 

  27. Selnes F (1993) An examination of the effect of product performance on brand reputation, satisfaction, and loyalty. Eur J Mark 27:19–35

    Article  Google Scholar 

  28. Simon H (1982) ADPULS: an advertising model with wearout and pulsation. J Mark Res 19:352–363

    Article  Google Scholar 

  29. Simon H (1992) Preismanagement, 2nd edn. Gabler, Wiesbaden

    Google Scholar 

  30. Srinivasan V, Mason CH (1986) Nonlinear least squares estimation of new product diffusion models. Mark Sci 5:169–178

    Article  Google Scholar 

  31. Teng J-T, Thompson GL (1983) Oligopoly models for optimal advertising. Manag Sci 29:1087–1101

    Article  Google Scholar 

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  1. Deutsche Telekom Laboratories, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587, Berlin, Germany

    Martin Hewing

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  1. Martin Hewing
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Correspondence to Martin Hewing.

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Hewing, M. A Theoretical and Empirical Comparison of Innovation Diffusion Models Applying Data from the Software Industry. J Knowl Econ 3, 125–141 (2012). https://doi.org/10.1007/s13132-011-0073-4

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  • DOI: https://doi.org/10.1007/s13132-011-0073-4

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