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MCDA in knowledge-based economies: Methodological developments and real world applications

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  • Carayannis, Elias G.
  • Ferreira, João J.M.
  • Jalali, Marjan S.
  • Ferreira, Fernando A.F.

Abstract

The importance of knowledge in creating value, driving productivity and promoting economic growth has long been recognized. Accompanying this recognition of the central role of knowledge in today's economies has been an added focus on information technology, learning and the accelerated pace of technical and scientific advance that results therefrom. Closely connected to these developments has been the advent of big data; and as information becomes available at greater volumes and higher speed, the focus is shifting from quantity to the quality of the information collected and the manner in which it is used. In this respect, Multiple Criteria Decision Analysis (MCDA) techniques constitute valuable tools for structuring and evaluating complex decision situations, and can allow for more informed, potentially better decisions. MCDA techniques are able to build on the knowledge of expert participants in a given field, and produce assessment systems based on values and experience. Constructivist in nature, this approach has grown exponentially over the past few decades, causing a change in the decision-making arena in general, and in the field of decision support systems (DSS) in particular. The objective of this special issue is to bring together recent developments and methodological contributions within MCDA, with the challenges which characterize the knowledge-based economy, as they pertain to the themes of technological forecasting and social change.

Suggested Citation

  • Carayannis, Elias G. & Ferreira, João J.M. & Jalali, Marjan S. & Ferreira, Fernando A.F., 2018. "MCDA in knowledge-based economies: Methodological developments and real world applications," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 1-3.
    • Handle: RePEc:eee:tefoso:v:131:y:2018:i:c:p:1-3
    DOI: 10.1016/j.techfore.2018.01.028
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    References listed on IDEAS

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    Cited by:

    1. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
    2. Murcia, Nathanaëlle N.S. & Ferreira, Fernando A.F. & Ferreira, João J.M., 2022. "Enhancing strategic management using a “quantified VRIO”: Adding value with the MCDA approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Qun Zhao & Pei-Hsuan Tsai & Jin-Long Wang, 2019. "Improving Financial Service Innovation Strategies for Enhancing China’s Banking Industry Competitive Advantage during the Fintech Revolution: A Hybrid MCDM Model," Sustainability, MDPI, vol. 11(5), pages 1-29, March.
    4. Liu, Chih-Hsing Sam & Chou, Sheng-Fang & Lin, Jun-You, 2021. "Implementation and evaluation of tourism industry: Evidentiary case study of night market development in Taiwan," Evaluation and Program Planning, Elsevier, vol. 89(C).
    5. Elena Širá & Roman Vavrek & Ivana Kravčáková Vozárová & Rastislav Kotulič, 2020. "Knowledge Economy Indicators and Their Impact on the Sustainable Competitiveness of the EU Countries," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    6. R. N. Ossei-Bremang & F. Kemausuor, 2021. "A decision support system for the selection of sustainable biomass resources for bioenergy production," Environment Systems and Decisions, Springer, vol. 41(3), pages 437-454, September.
    7. Beriro, Darren & Nathanail, Judith & Salazar, Juan & Kingdon, Andrew & Marchant, Andrew & Richardson, Steve & Gillet, Andy & Rautenberg, Svea & Hammond, Ellis & Beardmore, John & Moore, Terry & Angus,, 2022. "A decision support system to assess the feasibility of onshore renewable energy infrastructure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

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