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A Hybrid Approach in Future-Oriented Technology Assessment

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Book cover Proceedings of the Future Technologies Conference (FTC) 2019 (FTC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1069))

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Abstract

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.

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References

  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. 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)

    Article  Google Scholar 

  3. Anderson, T.R., Daim, T.U., Kim, J.: Technology forecasting for wireless communication. Technovation 28(9), 602–614 (2008)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  8. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision-making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  9. Choi, M., Choi, H.-L., Yang, H.: Procedural characteristics of the 4th Korean technology foresight. Foresight 16(3), 198–209 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  11. Ciflikli, C., Kahya-Ozyirmidokuz, E.: Enhancing product quality of a process. Ind. Manage. Data Syst. 112(8), 1181–1200 (2012)

    Article  Google Scholar 

  12. Cuhls, K., Kuwahara, T.: Outlook for Japanese and German Future Technology - Comparing Technology Forecast Surveys. Technology, Innovation and Policy. Physica-Verlag, Heidelberg (1994)

    Book  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8(1), 87–102 (1992)

    MATH  Google Scholar 

  17. Förster, B.: Technology foresight for sustainable production in the German automotive supplier industry. Technol. Forecast. Soc. Chang. 92, 237–248 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  20. Halicka, K.: Innovative classification of methods of the future-oriented technology analysis. Technol. Econ. Dev. Econ. 22(4), 574–597 (2016)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  MATH  Google Scholar 

  26. Khouja, M.: The use of data envelopment analysis for technology selection. Comput. Ind. Eng. 28(1), 123–132 (1995)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  36. Liu, B.: Uncertain Theory: An Introduction to Its Axiomatic Foundation. Springer, Heidelberg (2004)

    Book  Google Scholar 

  37. Liu, Y., Sun, C., Xu, S.: Eco-efficiency assessment of water systems in China. Water Resour. Manage 27(14), 4927–4939 (2013)

    Article  Google Scholar 

  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. Luo, J.-L., Hu, Z.-H.: Risk paradigm and risk evaluation of farmers cooperatives’ technology innovation. Econ. Model. 44, 80–85 (2015)

    Article  Google Scholar 

  40. Magruk, A.: Concept of uncertainty in relation to the foresight research. Eng. Manage. Prod. Serv. 9(1), 46–55 (2017)

    Google Scholar 

  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. Nazarko, J., Magruk, A. (eds.): Kluczowe nanotechnologie w gospodarce Podlasia. Oficyna Wydawnicza Politechniki Białostockiej, Białystok (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  44. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177, 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  45. Pawlak, Z.: Rough sets. Int. J. Inf. Comput. Sci. 11, 344–356 (1982)

    Article  MATH  Google Scholar 

  46. Popper, R., Korte, W.: XTREME EUFORIA: Combining Foresight Methods, EU-US Seminar: New Technology Foresight, Forecasting & Assessment Methods, Sewilla (2004)

    Google Scholar 

  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. 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. Popper, R.: How are foresight methods selected? Foresight 10(6), 62–89 (2008)

    Article  Google Scholar 

  50. Porter, A.L.: Technology assessment. Impact Assess. 13(2), 135–151 (1995)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  53. Proskuryakova, L.: Energy technology foresight in emerging economies. Technol. Forecast. Soc. Chang. 119, 205–210 (2017)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  62. Tohidi, G., Valizadeh, P.: A non-radial rough DEA model. Int. J. Math. Model. Comput. 1(4), 257–261 (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Acknowledgment

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

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  1. Bialystok University of Technology, 45A, Wiejska Street, 15-351, Bialystok, Poland

    Ewa Chodakowska

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  1. Ewa Chodakowska
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Correspondence to Ewa Chodakowska .

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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Chodakowska, E. (2020). A Hybrid Approach in Future-Oriented Technology Assessment. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1069. Springer, Cham. https://doi.org/10.1007/978-3-030-32520-6_38

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