A Hybrid Approach in Future-Oriented Technology Assessment

  • Ewa ChodakowskaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)


Technology Assessment has been a growing field of study for the few past decades. Intensive work on solving the problem of proper technology assessment has translated into the development, improvement or adjustment of the method and models used in technology evaluation projects. The article aims to present a new hybrid model that uses the Rough Sets approach and the DEA method to increase the objectivity in the selection of priority technologies in future-oriented technology assessment projects. Real-data application proved that this model: (i) reduces the number of considered assessment criteria by a few times without a significant change in technology rankings; (ii) gives individual objective weights to the criteria and allows highlighting the “strengths” of each technology; (iii) from the point of view of efficiency, considers the attractiveness of the development of each technology and the rationality of allocating resources required for the development; (iv) allows the inclusion of a possible contradiction among expert opinions.


Future-Oriented Technology Assessment Data Envelopment Analysis Rough Sets Model 



The research was conducted within project G/WIZ/5/2018 financed from National Science Centre funds (DEC 2018/02/X/ST8/02000).


  1. 1.
    Alinezhad, A., Makui, A., Kiani Mavi, R., Zohrehbandian, M.: An MCDM-DEA approach for technology selection. J. Industr. Eng. Int. 7(12), 32–38 (2011)Google Scholar
  2. 2.
    Amin, G.R., Emrouznejad, A.: A new DEA model for technology selection in the presence of ordinal data. Int. J. Adv. Manuf. Technol. 65, 1567–1572 (2013)CrossRefGoogle Scholar
  3. 3.
    Anderson, T.R., Daim, T.U., Kim, J.: Technology forecasting for wireless communication. Technovation 28(9), 602–614 (2008)CrossRefGoogle Scholar
  4. 4.
    Bai, C., Sarkis, J.: Improving green flexibility through advanced manufacturing technology investment: modeling the decision process. Int. J. Prod. Econ. 188, 86–104 (2017)CrossRefGoogle Scholar
  5. 5.
    Cagnin, C., Havas, A., Saritas, O.: Future-oriented technology analysis: its potential to address disruptive transformations. Technol. Forecast. Soc. Chang. 80(3), 379–385 (2013)CrossRefGoogle Scholar
  6. 6.
    Chan, F.T.S., Chan, H.K., Chan, M.H., Humphreys, P.K.: An integrated fuzzy approach for the selection of manufacturing technologies. Int. J. Adv. Manuf. Technol. 27, 747–758 (2006)CrossRefGoogle Scholar
  7. 7.
    Chan, F.T.S., Chan, M.H., Tang, N.K.H.: Evaluation methodologies for technology selection. J. Mater. Process. Technol. 107(1–3), 330–337 (2000)CrossRefGoogle Scholar
  8. 8.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision-making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Choi, M., Choi, H.-L., Yang, H.: Procedural characteristics of the 4th Korean technology foresight. Foresight 16(3), 198–209 (2014)CrossRefGoogle Scholar
  10. 10.
    Chuu, S.-J.: Selecting the advanced manufacturing technology using fuzzy multiple attributes group decision making with multiple fuzzy information. Comput. Ind. Eng. 57(3), 1033–1042 (2009)CrossRefGoogle Scholar
  11. 11.
    Ciflikli, C., Kahya-Ozyirmidokuz, E.: Enhancing product quality of a process. Ind. Manage. Data Syst. 112(8), 1181–1200 (2012)CrossRefGoogle Scholar
  12. 12.
    Cuhls, K., Kuwahara, T.: Outlook for Japanese and German Future Technology - Comparing Technology Forecast Surveys. Technology, Innovation and Policy. Physica-Verlag, Heidelberg (1994)CrossRefGoogle Scholar
  13. 13.
    Dimitras, A.I., Słowiński, R., Susmaga, R., Zopounidis, C.: Business failure prediction using rough sets. Eur. J. Oper. Res. 114(2), 263–280 (1999)zbMATHCrossRefGoogle Scholar
  14. 14.
    Fan, J.-L., Zhang, X., Zhang, J., Peng, S.: Efficiency evaluation of CO2 utilization technologies in China: a super-efficiency DEA analysis based on expert survey. J. CO2 Utilization 11, 54–62 (2015)CrossRefGoogle Scholar
  15. 15.
    Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI-1993) (1993)Google Scholar
  16. 16.
    Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8(1), 87–102 (1992)zbMATHGoogle Scholar
  17. 17.
    Förster, B.: Technology foresight for sustainable production in the German automotive supplier industry. Technol. Forecast. Soc. Chang. 92, 237–248 (2015)CrossRefGoogle Scholar
  18. 18.
    Gao, Y., Zhang, X., Wu, L., Yin, S., Lu, J.: Resource basis, ecosystem and growth of grain family farm in China: based on rough set theory and hierarchical linear model. Agric. Syst. 154, 157–167 (2017)CrossRefGoogle Scholar
  19. 19.
    Górny, Z., Kluska-Nawarecka, S., Wilk-Kołodziejczyk, D., Regulski, K.: Methodology for the construction of a rule-based knowledge base enabling the selection of appropriate bronze heat treatment parameters using rough sets. Arch. Metall. Mater. 60(1), 309–312 (2015)CrossRefGoogle Scholar
  20. 20.
    Halicka, K.: Innovative classification of methods of the future-oriented technology analysis. Technol. Econ. Dev. Econ. 22(4), 574–597 (2016)CrossRefGoogle Scholar
  21. 21.
    He, Y., Pang, Y., Zhang, Q., Jiao, Z., Chen, Q.: Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory. Renewable Energy 122, 643–653 (2018)CrossRefGoogle Scholar
  22. 22.
    van Hemert, P., Nijkamp, P.: Knowledge investments, business R&D and innovativeness of countries: a qualitative meta-analytic comparison. Technol. Forecast. Soc. Chang. 77(3), 369–384 (2010)CrossRefGoogle Scholar
  23. 23.
    Inman, O.L., Anderson, T.R., Harmon, R.R.: Predicting U.S. jet fighter aircraft introductions from 1944 to 1982: a dogfight between regression and TFDEA. Technol. Forecast. Soc. Chang. 73, 1178–1187 (2006)CrossRefGoogle Scholar
  24. 24.
    Jian, L., Liu, S., Liu, Y.: The selection of regional key technology based on the hybrid model of grey fixed clustering and variable precision rough set. In: ISTASC 2010 Proceedings of the 10th WSEAS International Conference on Systems Theory and Scientific Computation, pp. 54–59 (2010)Google Scholar
  25. 25.
    Karsak, E.E., Ahiska, S.S.: Practical common weight multicriteria decision-making approach with an improved discriminating power for technology selection. Int. J. Prod. Res. 43(8), 1537–1554 (2005)zbMATHCrossRefGoogle Scholar
  26. 26.
    Khouja, M.: The use of data envelopment analysis for technology selection. Comput. Ind. Eng. 28(1), 123–132 (1995)CrossRefGoogle Scholar
  27. 27.
    Kwon, D.S., Cho, J.H., Sohn, S.Y.: Comparison of technology efficiency for CO2 emissions reduction among European countries based on DEA with decomposed factors. J. Clean. Prod. 151, 109–120 (2017)CrossRefGoogle Scholar
  28. 28.
    Lai, X., Liu, J.X., Georgiev, G.: Low carbon technology integration innovation assessment index review based on rough set theory - an evidence from construction industry in China. J. Clean. Prod. 126, 88–96 (2016)CrossRefGoogle Scholar
  29. 29.
    Lamb, A., Anderson, T.R., Daim, T.U.: Difficulties in R&D target-setting addressed through technology forecasting using data envelopment analysis. In: Technology Management for Global Economic Growth, PICMET, pp. 1–9 (2010)Google Scholar
  30. 30.
    Lee, C., Lee, H., Seol, H., Park, Y.: Evaluation of new service concepts using rough set theory and group analytic hierarchy process. Expert Syst. Appl. 39, 3404–3412 (2012)CrossRefGoogle Scholar
  31. 31.
    Lee, H., Lee, C., Seol, H., Park, Y.: On the R&D priority setting in technology foresight: a DEA and ANP approach. Int. J. Innov. Technol. Manage. 5(2), 201–219 (2008)CrossRefGoogle Scholar
  32. 32.
    Lee, S.K., Mogi, G., Hui, K.S.: A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: in the case of energy technologies against high oil prices. Renew. Sustain. Energy Rev. 21, 347–355 (2013)CrossRefGoogle Scholar
  33. 33.
    Li, N., Chen, K., Kou, M.: Technology foresight in China: academic studies, governmental practices and policy applications. Technol. Forecast. Soc. Chang. 119, 246–255 (2017)CrossRefGoogle Scholar
  34. 34.
    Li, S., Wu, C., Zhang, H.: Key technology analysis of implementing lean production. In: IEEE 16th International Conference on Industrial Engineering and Engineering Management, vol. 1–2, pp. 1993–1996 (2009)Google Scholar
  35. 35.
    Liang, X., van Dijk, M.P.: Identification of decisive factors determining the continued use of rainwater harvesting systems for agriculture irrigation in Beijing. Water 8(1), 7 (2016)CrossRefGoogle Scholar
  36. 36.
    Liu, B.: Uncertain Theory: An Introduction to Its Axiomatic Foundation. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  37. 37.
    Liu, Y., Sun, C., Xu, S.: Eco-efficiency assessment of water systems in China. Water Resour. Manage 27(14), 4927–4939 (2013)CrossRefGoogle Scholar
  38. 38.
    Lu, W.-G., Huang, L.-C., Wang, J.-W.: The new technology evaluation based on rough-set theory. In: PICMET 2007 - 2007 Portland International Conference on Management of Engineering & Technology, pp. 883–886 (2007)Google Scholar
  39. 39.
    Luo, J.-L., Hu, Z.-H.: Risk paradigm and risk evaluation of farmers cooperatives’ technology innovation. Econ. Model. 44, 80–85 (2015)CrossRefGoogle Scholar
  40. 40.
    Magruk, A.: Concept of uncertainty in relation to the foresight research. Eng. Manage. Prod. Serv. 9(1), 46–55 (2017)Google Scholar
  41. 41.
    Miles, I., Keenan, M.: Overview of methods used in foresight. The Technology Foresight for Organisers Training Course, United Nations Industrial Development Organisation, Ankara (2003)Google Scholar
  42. 42.
    Nazarko, J., Magruk, A. (eds.): Kluczowe nanotechnologie w gospodarce Podlasia. Oficyna Wydawnicza Politechniki Białostockiej, Białystok (2013)Google Scholar
  43. 43.
    Nazarko, Ł.: Future-oriented technology assessment. In: 7th International Conference on Engineering, Project, and Production Management, Procedia Engineering, vol. 182, pp. 504–509 (2017)CrossRefGoogle Scholar
  44. 44.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177, 3–27 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  45. 45.
    Pawlak, Z.: Rough sets. Int. J. Inf. Comput. Sci. 11, 344–356 (1982)zbMATHCrossRefGoogle Scholar
  46. 46.
    Popper, R., Korte, W.: XTREME EUFORIA: Combining Foresight Methods, EU-US Seminar: New Technology Foresight, Forecasting & Assessment Methods, Sewilla (2004)Google Scholar
  47. 47.
    Popper, R., Popper, M., Velasco, G.: Towards a more responsible sustainable innovation assessment and management culture in Europe. Eng. Manage. Prod. Serv. 9(4), 7–20 (2017)Google Scholar
  48. 48.
    Popper, R.: Foresight methodology. In: Georghiou, L., Harper, J.C., Keenan, M., Miles, I., Popper, R. (eds.) The Handbook of Technology Foresight. Concepts and Practice. Edward Elgar Publishing Limited, Northampton (2008)Google Scholar
  49. 49.
    Popper, R.: How are foresight methods selected? Foresight 10(6), 62–89 (2008)CrossRefGoogle Scholar
  50. 50.
    Porter, A.L.: Technology assessment. Impact Assess. 13(2), 135–151 (1995)CrossRefGoogle Scholar
  51. 51.
    Predki, B., Słowiński, R., Stefanowski, J., Susmaga, R., Wilk, S.: ROSE - software implementation of the rough set theory. In: Polkowski, L., Skowron, A. (eds.) Rough Sets and Current Trends in Computing. Lecture Notes in Artificial Intelligence, vol. 1424, pp. 605–608. Springer-Verlag, Heidelberg (1998)CrossRefGoogle Scholar
  52. 52.
    Predki, B., Wilk, S.: Rough set based data exploration using ROSE system. In: Ras, Z.W., Skowron, A. (eds.) Foundations of Intelligent Systems. Lecture Notes in Artificial Intelligence, vol. 1609, pp. 172–180. Springer-Verlag, Heidelberg (1999)CrossRefGoogle Scholar
  53. 53.
    Proskuryakova, L.: Energy technology foresight in emerging economies. Technol. Forecast. Soc. Chang. 119, 205–210 (2017)CrossRefGoogle Scholar
  54. 54.
    Saen, R.F.: Technology selection in the presence of imprecise data, weight restrictions, and nondiscretionary factors. Int. J. Adv. Manuf. Technol. 41(7–8), 827–838 (2009)CrossRefGoogle Scholar
  55. 55.
    Shabani, A., Saen, R.F., Torabipour, S.M.R.: A new data envelopment analysis (DEA) model to select eco-efficient technologies in the presence of undesirable outputs. Clean Technol. Environ. Policy 16(3), 513–525 (2014)CrossRefGoogle Scholar
  56. 56.
    Sharma, S., Dua, A., Singh, M., Kumar, N., Prakash, S.: Fuzzy rough set based energy management system for self-sustainable smart city. Renew. Sustain. Energy Rev. 82, 3633–3644 (2018)CrossRefGoogle Scholar
  57. 57.
    Shiau, T.-A., Chuen-Yu, J.-K.: Developing an indicator system for measuring the social sustainability of offshore wind power farms. Sustainability 8(5), 470 (2016)CrossRefGoogle Scholar
  58. 58.
    Shiraz, R.K., Charles, V., Jalalzadeh, L.: Fuzzy rough DEA model: a possibility and expected value approaches. Expert Syst. Appl. 41(2), 434–444 (2014)CrossRefGoogle Scholar
  59. 59.
    Shiraz, R.K., Fukuyama, H., Tavana, M., Di Caprio, D.: An integrated data envelopment analysis and free disposal hull framework for cost-efficiency measurement using rough sets. Appl. Soft Comput. 46, 204–219 (2016)CrossRefGoogle Scholar
  60. 60.
    Shuai, J.J., Li, H.L.: Using rough set and worst practice DEA in business failure prediction. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds.) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC. Lecture Notes in Computer Science, vol. 3642, pp. 503–510. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  61. 61.
    Sueyoshi, T., Goto, M.: Environmental assessment for corporate sustainability by resource utilization and technology innovation: DEA radial measurement on Japanese industrial sectors. Energy Econ. 46, 295–307 (2014)CrossRefGoogle Scholar
  62. 62.
    Tohidi, G., Valizadeh, P.: A non-radial rough DEA model. Int. J. Math. Model. Comput. 1(4), 257–261 (2011)Google Scholar
  63. 63.
    Tran, T.A., Daim, T.: A taxonomic review of methods and tools applied in technology assessment. Technol. Forecast. Soc. Chang. 75(9), 1396–1405 (2008)CrossRefGoogle Scholar
  64. 64.
    Tsai, Y.-H., Lai, W.-H., Chang, P.-L., Watada, J.: Dilemma of behavioral uncertainty of R&D alliance in Taiwan machinery industry. In: IEEE International Conference on Fuzzy Systems, vol. 1–3, pp. 439–1444 (2009)Google Scholar
  65. 65.
    Wang, C.-H., Chin, Y.-C., Tzeng, G.-H.: Mining the R&D innovation performance processes for high-tech firms based on rough set theory. Technovation 30(7–8), 447–458 (2010)CrossRefGoogle Scholar
  66. 66.
    Wang, X., Jia, F., Wang, Y.: Evaluation of clean coal technologies in China: based on rough set theory. Energy Environ. 26(6–7), 985–995 (2015)CrossRefGoogle Scholar
  67. 67.
    Wu, H.-Y., Lin, H.-Y.: A hybrid approach to develop an analytical model for enhancing the service quality of e learning. Comput. Educ. 58(4), 1318–1338 (2012)CrossRefGoogle Scholar
  68. 68.
    Xu, J., Li, B., Wu, D.: Rough data envelopment analysis and its application to supply chain performance evaluation. Int. J. Prod. Econ. 122(2), 628–638 (2009)CrossRefGoogle Scholar
  69. 69.
    Yu, P., Lee, J.H.: A hybrid approach using two-level SOM and combined AHP rating and AHP/DEA-AR method for selecting optimal promising emerging technology. Expert Syst. Appl. 40, 300–314 (2013)CrossRefGoogle Scholar
  70. 70.
    Zeng, X.T., Huang, G.H., Yang, X.L., Wang, X., Fu, H., Li, Y.P., Li, Z.: A developed fuzzy-stochastic optimization for coordinating human activity and eco-environmental protection in a regional wetland ecosystem under uncertainties. Ecol. Eng. 97, 207–230 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Bialystok University of TechnologyBialystokPoland

Personalised recommendations