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Choosing effective dates from multiple optima in Technology Forecasting using Data Envelopment Analysis (TFDEA)

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  • Lim, Dong-Joon
  • Anderson, Timothy R.
  • Inman, Oliver Lane

Abstract

Technology Forecasting using Data Envelopment Analysis (TFDEA) provides an effective means to forecast technological capability over time without the burden of fixed a priori weighting schemes. However, there are situations where result reproduction can be a challenge as first pointed out in a previous Technological Forecasting and Social Change article [11]. When using a commonly used extension of TFDEA, there are circumstances where multiple optimal solutions can complicate analysis. This paper addresses this issue through extending the TFDEA model in a manner consistent with common Data Envelopment Analysis (DEA) techniques. The extension is then demonstrated using datasets from previous publications on fighter jet and commercial airplane technology where the issue of multiple optima has been observed. The results indicate that traditional TFDEA may generate varying forecasts depending on the software used, which can be dealt with by introducing a secondary objective function. Therefore, researchers should explicitly state which secondary objective function they are using for the TFDEA applications.

Suggested Citation

  • Lim, Dong-Joon & Anderson, Timothy R. & Inman, Oliver Lane, 2014. "Choosing effective dates from multiple optima in Technology Forecasting using Data Envelopment Analysis (TFDEA)," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 91-97.
    • Handle: RePEc:eee:tefoso:v:88:y:2014:i:c:p:91-97
    DOI: 10.1016/j.techfore.2014.06.003
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    References listed on IDEAS

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    1. Cooper, William W. & Ruiz, Jose L. & Sirvent, Inmaculada, 2007. "Choosing weights from alternative optimal solutions of dual multiplier models in DEA," European Journal of Operational Research, Elsevier, vol. 180(1), pages 443-458, July.
    2. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    3. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, January.
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    Cited by:

    1. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    2. Lim, Dong-Joon, 2016. "Inverse DEA with frontier changes for new product target setting," European Journal of Operational Research, Elsevier, vol. 254(2), pages 510-516.
    3. Lu, Louis Y.Y. & Hsieh, Chih-Hung & Liu, John S., 2016. "Development trajectory and research themes of foresight," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 347-356.
    4. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science Technology, Faculty of Management, vol. 31, pages 41-59.
    5. Harrison, Gillian & Thiel, Christian, 2017. "An exploratory policy analysis of electric vehicle sales competition and sensitivity to infrastructure in Europe," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 165-178.
    6. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    7. Lim, Dong-Joon & Anderson, Timothy R. & Shott, Tom, 2015. "Technological forecasting of supercomputer development: The March to Exascale computing," Omega, Elsevier, vol. 51(C), pages 128-135.
    8. An, Qingxian & Tao, Xiangyang & Xiong, Beibei & Chen, Xiaohong, 2022. "Frontier-based incentive mechanisms for allocating common revenues or fixed costs," European Journal of Operational Research, Elsevier, vol. 302(1), pages 294-308.
    9. Emrouznejad, Ali & Rostami-Tabar, Bahman & Petridis, Konstantinos, 2016. "A novel ranking procedure for forecasting approaches using Data Envelopment Analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 235-243.

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