Evaluation of the expediency of technology commercialization: a case of information technology and biotechnology
Abstract
The ability to timely and objectively evaluate the expediency of technology commercialising is a crucial step for R&D organisations. It is a game with business success, which could enable to operate technologies efficiently and prevent unproductive investments. Managers in power, involved in the technology commercialization cycle, create rules for the game and are the leading players. The research establishes specifics of different technological fields, which are essential for assessing the expediency of technology commercialization. The scientific literature of technology commercialization didn’t take into account the specifics of different technological fields. The study presents the first two phases of the expediency of commercialization of the information technologies and biotechnologies evaluation models: the development of elements collections and the establishment of the importance of elements. The proposed technique could be expanded to select the most suitable technology for sustainable management of commercialization and the rational use of resources. The results of the expert’s survey aimed at establishing the importance of the elements are compared, efforts are made to identify differences in the evaluation the expediency of technology commercialization for information technologies and biotechnologies. The MCDM method has applied the selection of which was established by the motive related to the goal of evaluation – to evaluate the expediency of technology commercialization for information technologies and biotechnologies.
Keyword : evaluation expediency of technology commercialization, commercial potential, information technology, biotechnology, the importance of the elements, fuzzy rating, MCDM, Eckenrode method
How to Cite
Zemlickienė, V., & Turskis, Z. (2020). Evaluation of the expediency of technology commercialization: a case of information technology and biotechnology. Technological and Economic Development of Economy, 26(1), 271-289. https://doi.org/10.3846/tede.2020.11918
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2018). An extended step-wise weight assessment ratio analysis with symmetric interval type-2 fuzzy sets for determining the subjective weights of criteria in multi-criteria decision-making problems. Symmetry, 10(4), 91. https://doi.org/10.3390/sym10040091
Kiškis, M., & Limba, T. (2016). Biotechnologijų MVĮ intelektinės nuosavybės strategijos (Monografija). Mykolas Romeris University.
Leberling, H. (1981). On finding compromise solution in multi-criteria problems using the fuzzy minoperator. Fuzzy Sets System, 6(2), 105–110. https://doi.org/10.1016/0165-0114(81)90019-1
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Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140(22), 1–55.
Maghsoodi, A. I., Maghsoodi, A. I., Poursoltan, P., Antucheviciene, J., & Turskis, Z. (2019). Dam construction material selection by implementing the integrated SWARA–CODAS approach with targetbased attributes. Archives of Civil and Mechanical Engineering, 19(4), 1194–1210. https://doi.org/10.1016/j.acme.2019.06.010
Mamzer, M. F., Sophie Duboisb, S., & Saoutc, Ch. (2018). How to strengthen the presence of patients in health technology assessments conducted by the health authorities? Therapie, 73, 95–105. https://doi.org/10.1016/j.therap.2017.11.004
Montagnier P., & Wurthmann A. (2011). Digital divide: From computer access to online activities – A micro data analysis (OECD Digital Economy Papers, No. 189). OECD Publishing.
Nakhaei, J., Bitarafan, M., Lale Arefi, S. & Kapliński, O. (2016). Model for rapid assessment of vulnerability of office buildings to blast using SWARA and SMART methods (a case study of Swiss Re Tower). Journal of Civil Engineering and Management, 22(6), 831–843. https://doi.org/10.3846/13923730.2016.1189457
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Turskis, Z., Dzitac, S., Stankiuviene, A., & Šukys, R. (2019a). A fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction SMEs. International Journal of Computers Communications & Control, 14(1), 90–106. https://doi.org/10.15837/ijccc.2019.1.3364
Turskis, Z., Goranin, N., Nurusheva, A., & Boranbayev, S. (2019b). Information security risk assessment in critical infrastructure: A hybrid MCDM approach. Informatica, 30(1), 187–211. https://doi.org/10.15388/Informatica.2019.203
Turskis, Z., Goranin, N., Nurusheva, A., & Boranbayev, S. (2019c). A fuzzy WASPAS-based approach to determine critical information infrastructures of EU sustainable development. Sustainability, 11(2), 424. https://doi.org/10.3390/su11020424
Turskis, Z., Lazauskas, M., & Zavadskas, E. K. (2012). Fuzzy multiple criteria assessment of construction site alternatives for non-hazardous waste incineration plant in Vilnius city, applying ARAS-F and AHP methods. Journal of Environmental Engineering and Landscape Management, 20(2), 110–120. https://doi.org/10.3846/16486897.2011.645827
Turskis, Z., Zavadskas, E. K., Antucheviciene, J., & Kosareva, N. (2015). A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. International Journal of Computers Communications & Control, 10(6), 113–128. https://doi.org/10.15837/ijccc.2015.6.2078
Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1–3), 229–241. https://doi.org/10.1016/S0165-0114(83)80082-7
Vechkinzova, E., Petrenko Y., Benčič S., Ulybyshev D., & Zhailauov, Y. (2019). Entrepreneurship and Sustainability Issues, 7(1), 498–509. https://doi.org/10.9770/jesi.2019.7.1(35)
Volpatti, L. R., & Yetisen, A. K. (2014). Commercialization of microfluidic devices. Trends in Biotechnology, 32(7), 347–350. https://doi.org/10.1016/j.tibtech.2014.04.010
Vu, Ch. H. T., Lee, H. G., Chan, Y. K., & Oh, H. M. (2018). Axenic cultures for microalgal biotechnology: establishment, assessment, maintenance, and applications. Biotechnology Advances, 36(2), 380–396. https://doi.org/10.1016/j.biotechadv.2017.12.018
Whinney, M. D. (1971). Christopher Wren. Praeger Publishers, New York.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3) 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zavadskas, E. K., Antucheviciene, J., Saparauskas, J., & Turskis, Z. (2013a). MCDM methods WASPAS and MULTIMOORA: Verification of robustness of methods when assessing alternative solutions. Economic Computation and Economic Cybernetics Studies and Research, 47(2), 5–20.
Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Kalibatas, D. (2009). An approach to multi-attribute assessment of indoor environment before and after refurbishment of dwellings. Journal of Environmental Engineering and Landscape Management, 17(1), 5–11. https://doi.org/10.3846/1648-6897.2009.17.5-11
Zavadskas, E. K., Turskis, Z., Volvačiovas, R., & Kildiene, S. (2013b). Multi-criteria assessment model of technologies. Studies in Informatics and Control, 22(4), 249–258. https://doi.org/10.24846/v22i4y201301
Zavadskas, E. K., & Vilutienė, T. (2006). A multiple criteria evaluation of multi-family apartment block’s maintenance contractors: I – Model for maintenance contractor evaluation and the determination of its selection criteria. Building and Environment, 41(5), 621–632, 2006. https://doi.org/10.1016/j.buildenv.2005.02.019
Zemlickienė, V. (2015). Assessment of the commercial potential of technologies (Doctoral Dissertation). Vilnius Gediminas Technical University, Lithuania.
Zemlickienė, V., Mačiulis, A., & Tvaronavičienė, M. (2017). Factors impacting the commercial potential of technologies: Expert approach. Technological and Economic Development of Economy, 23(2), 410–427. https://doi.org/10.3846/20294913.2016.1271061
Bagočius, V., Zavadskas, E. K., & Turskis, Z. (2014). Multi-person selection of the best wind turbine based on the multi-criteria integrated additive-multiplicative utility function. Journal of Civil Engineering and Management, 20(4), 590–599. https://doi.org/10.3846/13923730.2014.932836
Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis: An integrated approach. Kluwer Academic Publishers. https://doi.org/10.1007/978-1-4615-1495-4
Buckley, J. J. (1984). The multiple judge, multiple criteria ranking problem: a fuzzy set approach. Fuzzy Sets and Systems, 13(1), 25–37. https://doi.org/10.1016/0165-0114(84)90024-1
Dubois, D., & Prade, H. (1978). Operations on fuzzy numbers. International Journal of Systems Science, 9(6), 613–626. https://doi.org/10.1080/00207727808941724
Dziallas, M., & Blind, K. (2019). Innovation indicators throughout the innovation process: An extensive literature analysis. Technovation. 80–81, 3–29. https://doi.org/10.1016/j.technovation.2018.05.005
Eckenrode, R. T. (1965). Weighting multiple criteria. Management Science, 12(3), 180–192. https://doi.org/10.1287/mnsc.12.3.180
European Commission. (2010). Communication from the Commission to the European Parliament, the council, the European Economic and social committee and the committee of the regions “A Digital Agenda for Europe”. COM(2010)245, May 2010. Brussels. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2010:0245:FIN:EN:PDF
Geldres-Weiss, V. V., Joaquín Monreal-Pérez, J., Tornavoi-Carvalho D., & Tello-Gamarra J. (2018). A new measure of international product innovation. Contemporary Economics, 12(4), 367–380. https://doi.org/10.2139/ssrn.3377276
Ghassemi, A., & Darvishpour, A. (2018). A novel approach for risk evaluation and risk response planning in a geothermal drilling project using DEMATEL and fuzzy ANP. Decision Science Letters, 7(3), 225–242. https://doi.org/10.5267/j.dsl.2017.10.001
Hashemkhani Zolfani, S., Zavadskas, E. K., & Turskis, Z. (2013). Design of products with both International and Local perspectives based on Yin-Yang balance theory and SWARA method. Economic Research-Ekonomska Istraživanja, 26(2), 153–166. https://doi.org/10.1080/1331677X.2013.11517613
International Telecommunication Union (ITU). (2012). Measuring the Information Society 2012. Geneva: ITU. http://www.itu.int/ITU-D/ict/publications/idi/material/2012/MIS2012_without_Annex_4.pdf
Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2018). An extended step-wise weight assessment ratio analysis with symmetric interval type-2 fuzzy sets for determining the subjective weights of criteria in multi-criteria decision-making problems. Symmetry, 10(4), 91. https://doi.org/10.3390/sym10040091
Kiškis, M., & Limba, T. (2016). Biotechnologijų MVĮ intelektinės nuosavybės strategijos (Monografija). Mykolas Romeris University.
Leberling, H. (1981). On finding compromise solution in multi-criteria problems using the fuzzy minoperator. Fuzzy Sets System, 6(2), 105–110. https://doi.org/10.1016/0165-0114(81)90019-1
Lechman, E. (2014). ICT diffusion trajectories and economic development: Empirical evidence for 46 developing countries. In H. Kaur & X. Tao (Eds.), ICTs and the millennium development goals: A United Nations perspective. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7439-6_2
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140(22), 1–55.
Maghsoodi, A. I., Maghsoodi, A. I., Poursoltan, P., Antucheviciene, J., & Turskis, Z. (2019). Dam construction material selection by implementing the integrated SWARA–CODAS approach with targetbased attributes. Archives of Civil and Mechanical Engineering, 19(4), 1194–1210. https://doi.org/10.1016/j.acme.2019.06.010
Mamzer, M. F., Sophie Duboisb, S., & Saoutc, Ch. (2018). How to strengthen the presence of patients in health technology assessments conducted by the health authorities? Therapie, 73, 95–105. https://doi.org/10.1016/j.therap.2017.11.004
Montagnier P., & Wurthmann A. (2011). Digital divide: From computer access to online activities – A micro data analysis (OECD Digital Economy Papers, No. 189). OECD Publishing.
Nakhaei, J., Bitarafan, M., Lale Arefi, S. & Kapliński, O. (2016). Model for rapid assessment of vulnerability of office buildings to blast using SWARA and SMART methods (a case study of Swiss Re Tower). Journal of Civil Engineering and Management, 22(6), 831–843. https://doi.org/10.3846/13923730.2016.1189457
Park, J. H., Kim, Y. B., & Kim, M. K. (2017). Investigating factors influencing the market success or failure of IT services in Korea. International Journal of Information Management, 37(1), 1418–1427. https://doi.org/10.1016/j.ijinfomgt.2016.10.004
Pérez, J. A. H., Galdes, C., Kunc, M. H., & Flores, A. (2019). New approach to the innovation process in emerging economies: The manufacturing sector case in Chile and Peru. Technovation, 79, 35–55. https://doi.org/10.1016/j.technovation.2018.02.012
Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resources allocation. McGraw.
Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process. RWS Publications.
SearchDataCenter. (2018). Information technology (IT). Definition. https://searchdatacenter.techtarget.com/definition/IT
Silvestre, B. S., & Ţîrcă, D. M. (2019). Innovations for sustainable development: Moving toward a sustainable future. Journal of Cleaner Production, 208, 325-332. https://doi.org/10.1016/j.jclepro.2018.09.244
Turskis, Z., Dzitac, S., Stankiuviene, A., & Šukys, R. (2019a). A fuzzy group decision-making model for determining the most influential persons in the sustainable prevention of accidents in the construction SMEs. International Journal of Computers Communications & Control, 14(1), 90–106. https://doi.org/10.15837/ijccc.2019.1.3364
Turskis, Z., Goranin, N., Nurusheva, A., & Boranbayev, S. (2019b). Information security risk assessment in critical infrastructure: A hybrid MCDM approach. Informatica, 30(1), 187–211. https://doi.org/10.15388/Informatica.2019.203
Turskis, Z., Goranin, N., Nurusheva, A., & Boranbayev, S. (2019c). A fuzzy WASPAS-based approach to determine critical information infrastructures of EU sustainable development. Sustainability, 11(2), 424. https://doi.org/10.3390/su11020424
Turskis, Z., Lazauskas, M., & Zavadskas, E. K. (2012). Fuzzy multiple criteria assessment of construction site alternatives for non-hazardous waste incineration plant in Vilnius city, applying ARAS-F and AHP methods. Journal of Environmental Engineering and Landscape Management, 20(2), 110–120. https://doi.org/10.3846/16486897.2011.645827
Turskis, Z., Zavadskas, E. K., Antucheviciene, J., & Kosareva, N. (2015). A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. International Journal of Computers Communications & Control, 10(6), 113–128. https://doi.org/10.15837/ijccc.2015.6.2078
Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1–3), 229–241. https://doi.org/10.1016/S0165-0114(83)80082-7
Vechkinzova, E., Petrenko Y., Benčič S., Ulybyshev D., & Zhailauov, Y. (2019). Entrepreneurship and Sustainability Issues, 7(1), 498–509. https://doi.org/10.9770/jesi.2019.7.1(35)
Volpatti, L. R., & Yetisen, A. K. (2014). Commercialization of microfluidic devices. Trends in Biotechnology, 32(7), 347–350. https://doi.org/10.1016/j.tibtech.2014.04.010
Vu, Ch. H. T., Lee, H. G., Chan, Y. K., & Oh, H. M. (2018). Axenic cultures for microalgal biotechnology: establishment, assessment, maintenance, and applications. Biotechnology Advances, 36(2), 380–396. https://doi.org/10.1016/j.biotechadv.2017.12.018
Whinney, M. D. (1971). Christopher Wren. Praeger Publishers, New York.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3) 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zavadskas, E. K., Antucheviciene, J., Saparauskas, J., & Turskis, Z. (2013a). MCDM methods WASPAS and MULTIMOORA: Verification of robustness of methods when assessing alternative solutions. Economic Computation and Economic Cybernetics Studies and Research, 47(2), 5–20.
Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Kalibatas, D. (2009). An approach to multi-attribute assessment of indoor environment before and after refurbishment of dwellings. Journal of Environmental Engineering and Landscape Management, 17(1), 5–11. https://doi.org/10.3846/1648-6897.2009.17.5-11
Zavadskas, E. K., Turskis, Z., Volvačiovas, R., & Kildiene, S. (2013b). Multi-criteria assessment model of technologies. Studies in Informatics and Control, 22(4), 249–258. https://doi.org/10.24846/v22i4y201301
Zavadskas, E. K., & Vilutienė, T. (2006). A multiple criteria evaluation of multi-family apartment block’s maintenance contractors: I – Model for maintenance contractor evaluation and the determination of its selection criteria. Building and Environment, 41(5), 621–632, 2006. https://doi.org/10.1016/j.buildenv.2005.02.019
Zemlickienė, V. (2015). Assessment of the commercial potential of technologies (Doctoral Dissertation). Vilnius Gediminas Technical University, Lithuania.
Zemlickienė, V., Mačiulis, A., & Tvaronavičienė, M. (2017). Factors impacting the commercial potential of technologies: Expert approach. Technological and Economic Development of Economy, 23(2), 410–427. https://doi.org/10.3846/20294913.2016.1271061