Annals of Operations Research

, Volume 235, Issue 1, pp 177–201 | Cite as

When is it better to wait for a new version? Optimal replacement of an emerging technology under uncertainty

  • Michail ChronopoulosEmail author
  • Afzal Siddiqui


Firms that use an emerging technology often face uncertainty in both the arrival of new versions and the revenue that may be earned from their deployment. Via a sequential decision-making framework, we determine the value of the investment opportunity and the optimal replacement rule under three different strategies: compulsive, laggard, and leapfrog. In the first one, a firm invests sequentially in every version that becomes available, whereas in the second and third ones, it first waits for a new version to arrive and then either invests in the older or the newer version, respectively. We show that, under a compulsive strategy, technological uncertainty has a non-monotonic impact on the optimal investment decision. In fact, uncertainty regarding the availability of future versions may actually hasten investment. By comparing the relative values of the three strategies, we find that, under a low output price the compulsive strategy always dominates, whereas, at a high output price, the incentive to wait for a new version and adopt either a leapfrog or a laggard strategy increases as the rate of innovation increases. By contrast, high price uncertainty mitigates this effect, thereby increasing the relative attraction of a compulsive strategy.


Investment analysis Real options Emerging technologies Dynamic programming 


  1. Balcer, Y., & Lippman, S. (1984). Technological expectations and adoption of improved technology. Journal of Economic Theory, 34, 292–318.CrossRefGoogle Scholar
  2. Boomsma, T. K., Meade, N., & Fleten, S. E. (2012). Renewable energy investments under different support schemes: A real options approach. European Journal of Operational Research, 220(1), 225–237.CrossRefGoogle Scholar
  3. Chronopoulos, M., De Reyck, B., & Siddiqui, A. (2013). The value of capacity sizing under risk aversion and operational flexibility. IEEE Trasactions on Engineering Management, 60(2), 272–288.CrossRefGoogle Scholar
  4. Décamps, J. P., Mariotti, T., & Villeneuve, S. (2006). Irreversible investment in alternative projects. Economic Theory, 28, 425–448.CrossRefGoogle Scholar
  5. Dixit, A. K. (1993). Choosing among alternative discrete investment projects under uncertainty. Economics Letters, 41, 265–268.CrossRefGoogle Scholar
  6. Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton, NJ: Princeton University Press.Google Scholar
  7. Doraszelski, U. (2001). The net present value method versus the option value of waiting: A note on Farzin, Huisman and Kort (1998). Journal of Economic Dynamics and Control, 25, 1109–1115.CrossRefGoogle Scholar
  8. Farzin, Y., Huisman, K. J. M., & Kort, P. M. (1998). Optimal timing of technology adoption. Journal of Economic Dynamics and Control, 22, 779–799.CrossRefGoogle Scholar
  9. Financial Times. (2012a). Burden of obsolescence becomes more acute. 6 May.Google Scholar
  10. Financial Times. (2012b). Vestas a victim of its own Propaganda. 1 March.Google Scholar
  11. Franklin, S. L. (2014). Investment decisions in mobile telecommunications networks applying real options. Annals of Operations Research. doi: 10.1007/s10479-014-1672-9.
  12. Gollier, C., Proult, D., Thais, F., & Walgenwitz, G. (2005). Choice of nuclear power investments under price uncertainty: Valuing modularity. Energy Economics, 27, 667–685.CrossRefGoogle Scholar
  13. Grenadier, S. R., & Weiss, A. M. (1997). Investment in technological innovations: An option pricing approach. Journal of Financial Economics, 44, 397–416.CrossRefGoogle Scholar
  14. Huisman, K., & Kort, P. M. (2004). Strategic technology adoption taking into account future technological improvements: A real options approach. European Journal of Operational Research, 159, 705–728.CrossRefGoogle Scholar
  15. International Business Times. (2014). Apple iPhone 5s lacks consumer interest new report says. 21 May.Google Scholar
  16. Jensen, P., Morthorst, P., Skriver, S., Rasmussen, M., Larsen, H., Henrik Hansen, L., Nielsen, P., & Lemming, J. (2002). Økonomi for Vindmøller i Danmark, Annual report no. 1247, Technical University of Denmark, Denmark.Google Scholar
  17. Kauffman, R. J., & Li, X. (2005). Technology competition and optimal investment timing: A real options perspective. IEEE Transactions on Engineering Management, 52(1), 15–29.CrossRefGoogle Scholar
  18. Kort, P. M., Murto, P., & Grzegorz, P. (2010). Uncertainty and stepwise investment. European Journal of Operational Research, 202(1), 196–203.CrossRefGoogle Scholar
  19. MacGillivray, A., Jeffrey, H., Winskel, M., & Bryden, I. (2014). Innovation and cost reduction for marine renewable energy: A learning investment sensitivity analysis. Technological Forecasting and Social Change, 87, 108–124.CrossRefGoogle Scholar
  20. Majd, S., & Pindyck, R. S. (1987). Time to build, option value, and investment decisions. Journal of Financial Economics, 18, 7–27.CrossRefGoogle Scholar
  21. Malchow-Møller, N., & Thorsen, B. J. (2005). Repeated real options: Optimal investment behaviour and a good rule of thumb. Journal of Economic Dynamics and Control, 29, 1025–1041.CrossRefGoogle Scholar
  22. Mauritzen, J. (2014). Scrapping a wind turbine: Policy changes, scrapping incentives and why wind turbines in good locations get scrapped first. The Energy Journal, 35(2), 157–181.CrossRefGoogle Scholar
  23. Miltersen, R. K., & Schwartz, E. (2007). Real options with uncertainty maturity and competition. NBER working paper series.Google Scholar
  24. Siddiqui, A., & Fleten, S.-E. (2010). How to proceed with competing alternative energy technologies: A real options analysis. Energy Economics, 32, 817–830.CrossRefGoogle Scholar
  25. The Economist. (2009). Planned obsolescence. 23 March.Google Scholar
  26. Wind Power. (2012). Upgrade old turbines to their full potential. 1 December.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Business and Management ScienceNorwegian School of EconomicsBergenNorway
  2. 2.School of Computing Engineering and MathematicsUniversity of BrightonBrightonUK
  3. 3.Department of Statistical ScienceUniversity College LondonLondonUK
  4. 4.Department of Computer and Systems SciencesStockholm UniversityStockholmSweden

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