Trends in the use of multi-criteria decision-making methods in technology transfer processes (a critic review)

William A. Orjuela-Garzon, Santiago Quintero, Mauricio U. Maldonado

Abstract


The application of multi-criterion decision-making methods allows reducing the ambiguity, imprecision, uncertainty, and subjectivity in human-based judgments when processes of transfer and appropriation of technologies are developed. These types of MCDM are key for developing countries since the efficiency of the transfer process is vital to improve the productivity and competitiveness of companies and territories based on correct prioritization and selection of technologies, the definition of barriers and drivers, or the selection of the best provider, among others. In this sense, it is key to identify what is the evolution in the empirical use of this type of techniques for knowledge management and the reduction of competitive gaps. The objective of this review was to identify the current state-of-the-art of applications and use of methods for multi-criteria decision-making process in sectorial technology transfer, to establish trends, application areas, and future challenges. The review was conducted in the "SCOPUS" database between the years 2010 through 2021. The results showed three major research perspectives: a) Determination of technology-transfer strategies, b) Selection of appropriate technologies and c) Determination of barriers and drivers. The correct selection of transfer strategies and appropriate technologies can improve the efficiency of sectors such as agriculture, renewable energies, manufacturing, and construction that still refuse to introduce innovations, due to barriers such as acquisition and maintenance costs, complexity of use, ease of use. use and perceived utility.


Keywords


Technology transfer; Decision making; Trends; Uncertainty; Multi-criteria; Multi-attribute; Multi-objective

References


Aliakbari Nouri, F., S. Khalili Esbouei and J. Antucheviciene. 2015. A Hybrid MCDM Approach Based on Fuzzy ANP and Fuzzy TOPSIS for Technology Selection. Informatica, 26: 369-88.

Amirghodsi, S., A. Bonyadi Naeini and B. Roozbehani. 2020. An Integrated Shannon-PAF Method on Gray Numbers to Rank Technology Transfer Strategies. Engineering Management Journal, 32: 186-207.

Audretsch, D. B., E. E. Lehmann and M. Wright. 2012. Technology transfer in a global economy. The Journal of Technology Transfer, 39: 301-12.

Beyaz, H. F. and N. Yıldırım. 2019. A Multi-criteria Decision-Making Model for Digital Transformation in Manufacturing: A Case Study from Automotive Supplier Industry. Springer International Publishing. Place Published. pp.217-32.

Bosma, R., U. Kaymak, J. van den Berg, H. Udo and J. Verreth. 2010. Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam. Soft Computing, 15: 295-310.

Çakır, E. and Z. Ulukan. 2020. Fuzzy multi-objective decision making approach for nuclear power plant installation. Journal of Intelligent & Fuzzy Systems, 39: 6339-50.

Chansa, C. N. and A. Srijuntub. 2010. Using Analytic Hierarchy Process for innovative technology selection: a case study. International Journal of Innovation and Learning, 8: 279.

Chavosh Nejad, M., S. Mansour and A. Karamipour. 2021. An AHP-based multi-criteria model for assessment of the social sustainability of technology management process: A case study in banking industry. Technology in Society, 65: 101602.

Chehrehpak, M. 2012. Selecting of optimal methods for the technology transfer by using analytic hierarchy process (AHP). Indian Journal of Science and Technology, 5: 1-7.

Chen, H. and T. Ma. 2017. Optimizing systematic technology adoption with heterogeneous agents. European Journal of Operational Research, 257: 287-96.

Claire Erensal, Y. and Y. Esra Albayrak. 2008. Transferring appropriate manufacturing technologies for developing countries. Journal of Manufacturing Technology Management, 19: 158-71.

Dayo-Olupona, O., B. Genc and M. Onifade. 2020. Technology adoption in mining: A multi-criteria method to select emerging technology in surface mines. Resources Policy, 69: 101879.

Díaz-Díaz, N. L., I. Aguiar-Díaz and P. De Saá-Pérez. 2008. The effect of technological knowledge assets on performance: The innovative choice in Spanish firms. Research Policy, 37: 1515-29.

Dinmohammadi, A. and M. Shafiee. 2017. Determination of the Most Suitable Technology Transfer Strategy for Wind Turbines Using an Integrated AHP-TOPSIS Decision Model. Energies, 10: 642.

Erbay, H. and N. Yıldırım. 2018. Technology Selection for Digital Transformation: A Mixed Decision Making Model of AHP and QFD. Springer International Publishing. Place Published. pp.480-93.

Forrester, J. W. 2007. System dynamics—a personal view of the first fifty years. System Dynamics Review, 23: 345-58.

Gąbka, J. and G. Filcek. 2017. Multiple Criteria Decision Support System for Making the Best Manufacturing Technologies Choice and Assigning Contractors. Springer International Publishing. Place Published. pp.314-23.

Gossen, E., E. Abele and M. Rauscher. 2016. Multi-criterial Selection of Track and Trace Technologies for an Anti-counterfeiting Strategy. Procedia CIRP, 57: 73-78.

Gupta, A. K. and H. Goyal. 2021. Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach. Information Technology and Management, 22: 207-29.

Gupta, K. P., P. Bhaskar and S. Singh. 2017. Prioritization of factors influencing employee adoption of e-government using the analytic hierarchy process. Journal of Systems and Information Technology, 19: 116-37.

Gwo-Hshiung, T. and H. Jih-Jeng. 2011. Multiple Attribute Decision Making: Methods and Applications. Taylor & Francis. Place Published.

Heidary Dahooie, J., A. R. Qorbani and T. Daim. 2021. Providing a framework for selecting the appropriate method of technology acquisition considering uncertainty in hierarchical group decision-making: Case Study: Interactive television technology. Technological Forecasting and Social Change, 168: 120760.

Huang, C.-Y. 2012. The non-additive Choquet integration in the Fuzzy DNP framework for evaluating Co-dependent technology transfer models. IEEE. Place Published.

Huang, Y.-S., T.-L. Hsueh and G.-H. Zheng. 2013. Decisions on optimal adoption time for new technology. Computers & Industrial Engineering, 65: 388-94.

Hwang, C.-L. and K. Yoon. 1981. Methods for Multiple Attribute Decision Making. Springer Berlin Heidelberg. Place Published. pp.58-191.

Isgin, T., A. Bilgic, D. L. Forster and M. T. Batte. 2008. Using count data models to determine the factors affecting farmers’ quantity decisions of precision farming technology adoption. Computers and Electronics in Agriculture, 62: 231-42.

Ishikawa, A., M. Amagasa, T. Shiga, G. Tomizawa, R. Tatsuta and H. Mieno. 1993. The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets and Systems, 55: 241-53.

Jafarian, M. and S. E. Vahdat. 2012. A fuzzy multi-attribute approach to select the welding process at high pressure vessel manufacturing. Journal of Manufacturing Processes, 14: 250-56.

Khabiri, N., S. Rast and A. A. Senin. 2012. Identifying Main Influential Elements in Technology Transfer Process: A Conceptual Model. Procedia - Social and Behavioral Sciences, 40: 417-23.

Kim, W., S. K. Han, K. J. Oh, T. Y. Kim, H. Ahn and C. Song. 2010. The dual analytic hierarchy process to prioritize emerging technologies. Technological Forecasting and Social Change, 77: 566-77.

Komleh, A. A. and H. Fazlollahtabar. 2019. Stochastic Multi-Criteria Acceptability Analysis for Technology Transfer Evaluation: A Case Study in Construction Digging. International Journal of Mathematical, Engineering and Management Sciences, 4: 1031-39.

Krmac, E. and B. Djordjević. 2019. A Multi-Criteria Decision-Making Framework for the Evaluation of Train Control Information Systems, the Case of ERTMS. International Journal of Information Technology & Decision Making, 18: 209-39.

Kumar, S., S. Luthra and A. Haleem. 2015. Benchmarking supply chains by analyzing technology transfer critical barriers using AHP approach. Benchmarking: An International Journal, 22: 538-58.

Kumar, S., S. Luthra, A. Haleem, S. K. Mangla and D. Garg. 2015. Identification and evaluation of critical factors to technology transfer using AHP approach. International Strategic Management Review, 3: 24-42.

Lavoie, J. R. and T. Daim. 2020. Towards the assessment of technology transfer capabilities: An action research-enhanced HDM model. Technology in Society, 60: 101217.

Lee, A. H. I., W.-M. Wang and T.-Y. Lin. 2010. An evaluation framework for technology transfer of new equipment in high technology industry. Technological Forecasting and Social Change, 77: 135-50.

Lee, H., S. Lee and Y. Park. 2009. Selection of technology acquisition mode using the analytic network process. Mathematical and Computer Modelling, 49: 1274-82.

Lee, S., W. Kim, Y. M. Kim and K. J. Oh. 2012. Using AHP to determine intangible priority factors for technology transfer adoption. Expert Systems with Applications, 39: 6388-95.

Lee, Y.-C. and C. Chou. 2016. Technology Evaluation and Selection of 3DIC Integration Using a Three-Stage Fuzzy MCDM. Sustainability, 8: 114.

Ma, D., C.-C. Chang and S.-W. Hung. 2013. The selection of technology for late-starters: A case study of the energy-smart photovoltaic industry. Economic Modelling, 35: 10-20.

Magliocca, N. R. 2020. Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus. Land, 9: 519.

Mohammadi, N., J. Heidary Dahooie and M. Khajevand. 2021. A hybrid approach for identifying and prioritizing critical success factors in technology transfer projects (case study: diesel locomotive manufacturing). Journal of Engineering, Design and Technology, ahead-of-print.

Mustafa Kamal, M. and M. Alsudairi. 2009. Investigating the importance of factors influencing integration technologies adoption in local government authorities. Transforming Government: People, Process and Policy, 3: 302-31.

Mutingi, M. 2013a. Adoption of Renewable Energy Technologies: A Fuzzy System Dynamics Perspective. Springer New York. Place Published. pp.175-96.

———. 2013b. Understanding the dynamics of the adoption of renewable energy technologies: A system dynamics approach. Decision Science Letters, 2: 109-18.

———. 2014. System dynamics of information technology adoption in a complex environment. International Journal of Industrial and Systems Engineering, 17: 78.

Mutingi, M. and S. Matope. 2013. System dynamics of renewable energy technology adoption. IEEE. Place Published.

Naicker, P. and G. A. Thopil. 2019. A framework for sustainable utility scale renewable energy selection in South Africa. Journal of Cleaner Production, 224: 637-50.

Nilashi, M., H. Ahmadi, A. Ahani, R. Ravangard and O. b. Ibrahim. 2016. Determining the importance of Hospital Information System adoption factors using Fuzzy Analytic Network Process (ANP). Technological Forecasting and Social Change, 111: 244-64.

Onar, S. C., B. Oztaysi, İ. Otay and C. Kahraman. 2015. Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy, 90: 274-85.

Orjuela-Garzon, W., S. Quintero, D. P. Giraldo, L. Lotero and C. Nieto-Londoño. 2021. A Theoretical Framework for Analysing Technology Transfer Processes Using Agent-Based Modelling: A Case Study on Massive Technology Adoption (AMTEC) Program on Rice Production. Sustainability, 13: 11143.

Orjuela, W. A., W. A. Araque Echeverry and R. A. Cabrera Pedraza. 2020. Identificación de tecnologías y métodos para la detección temprana del Huanglongbing (HLB) a través de cienciometría en artículos científicos y patentes. Ciencia & Tecnología Agropecuaria, 21.

Osabutey, E. L. C. and Z. Jin. 2016. Factors influencing technology and knowledge transfer: Configurational recipes for Sub-Saharan Africa. Journal of Business Research, 69: 5390-95.

Öztürk, N., H. Tozan and Ö. Vayvay. 2020. A New Decision Model Approach for Health Technology Assessment and A Case Study for Dialysis Alternatives in Turkey. International journal of environmental research and public health, 17: 3608.

Pinto, M. M. A., J. L. Kovaleski, R. T. Yoshino and R. N. Pagani. 2019. Knowledge and Technology Transfer Influencing the Process of Innovation in Green Supply Chain Management: A Multicriteria Model Based on the DEMATEL Method. Sustainability, 11: 3485.

Raj, A., J. A. Kumar and P. Bansal. 2020. A multicriteria decision making approach to study barriers to the adoption of autonomous vehicles. Transportation Research Part A: Policy and Practice, 133: 122-37.

Robinson, S. A. and V. Rai. 2015. Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach. Applied Energy, 151: 273-84.

Saaty, T. L. 1990. The Analytic Hierarchy Process: Planning, Priority Setting, Resources Allocation. McGraw-Hill. Place Published.

Sadr, S. M. K., M. B. Johns, F. A. Memon, A. P. Duncan, J. Gordon, R. Gibson, H. J. F. Chang, M. S. Morley, D. Savic and D. Butler. 2020. Development and Application of a Multi-Objective-Optimization and Multi-Criteria-Based Decision Support Tool for Selecting Optimal Water Treatment Technologies in India. Water, 12: 2836.

Santos, F. A. and R. Garcia. 2010. Decision process model to the health technology incorporation. IEEE. Place Published.

Schreier, M. 2012. Qualitative content analysis in practice. Sage publications.

Schreinemachers, P. and T. Berger. 2011. An agent-based simulation model of human–environment interactions in agricultural systems. Environmental Modelling & Software, 26: 845-59.

Secundo, G., C. De Beer and G. Passiante. 2016. Measuring university technology transfer efficiency: a maturity level approach. Measuring Business Excellence, 20: 42-54.

Servati, Y. 2017. Robust technology transfer policy making using scenario based fuzzy topsis ‒ a case study of iran’s gas industry. Applied Ecology and Environmental Research, 15: 593-610.

Shen, Y.-C., S.-H. Chang, G. T. R. Lin and H.-C. Yu. 2010. A hybrid selection model for emerging technology. Technological Forecasting and Social Change, 77: 151-66.

Talaei, A., M. S. Ahadi and S. Maghsoudy. 2014. Climate friendly technology transfer in the energy sector: A case study of Iran. Energy Policy, 64: 349-63.

Tektas, B. and S. Gozlu. 2008. General packet radio service (GPRS) technology transfer: A case study to evaluate transferors. IEEE. Place Published.

Thampi, A. and B. Rao. 2015. Application of Multi-criteria Decision Making Tools for Technology Choice in Treatment and Disposal of Municipal Solid Waste for Local Self Government Bodies—A Case Study of Kerala, India. The Journal of Solid Waste Technology and Management, 41: 84-95.

Trivedi, V., A. Chauhan and A. Trivedi. 2021. Analysing consumers' smartphone adoption decisions using qualitative dimensions: a multi-criteria decision approach. International Journal of Technology Marketing, 15: 48.

Vera-Montenegro, L., A. Baviera-Puig and J.-M. Garcia-Alvarez-Coque. 2014. AHP choice in cocoa post-harvest technology for small-scale farmers. Spanish Journal of Agricultural Research, 12: 542.

Vera-Montenegroa, L., A. Baviera-Puig and J.-M. Garcia-Alvarez-Coque. 2014. Selection of cocoa post-harvest technology using fuzzy logic. FUZZY ECONOMIC REVIEW, 19.

Vrana, I. and S. Aly. 2010. Assessing candidate industrial technologies utilising hierarchical fuzzy decision making systems. International Journal of Industrial and Systems Engineering, 6: 187.

Wen-Hsiang, L. and T. Chien-Tzu. 2008. Analyzing influence factors of technology transfer using fuzzy set theory. IEEE. Place Published.

Winebrake, J. J. 1992. A study of technology-transfer mechanisms for federally funded R&D. The Journal of Technology Transfer, 17: 54-61.

Yadegaridehkordi, E., M. Nilashi, L. Shuib, S. Asadi and O. Ibrahim. 2019. Development of a SaaS Adoption Decision-Making Model Using a New Hybrid MCDM Approach. International Journal of Information Technology & Decision Making, 18: 1845-74.

Yoon, B., C. Lee and J. Hwang. 2011. A framework for impact analysis of the international transfer of marine technology in a climate change era: an input-output analysis and analytic hierarchy process approach. Asian Journal of Technology Innovation, 19: 1-19.

Yu, O. Y., S. D. Guikema, J. L. Briaud and D. Burnett. 2011. Quantitative decision tools for system selection in environmentally friendly drilling. Civil Engineering and Environmental Systems, 28: 185-208.

Yung-Hsiang, H. 2012. Applying the fuzzy analytic network process to the selection of an advanced integrated circuit (IC) packaging process development project. International Journal of the Physical Sciences, 7.

Zadeh, L. A. 1965. Fuzzy sets. Information and Control, 8: 338-53.


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DOI: 10.33687/ijae.009.03.3686

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