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dc.contributor.advisorGuaita Martínez, José Manuel
dc.contributor.advisorMartín Martín, José María 
dc.contributor.advisorOstos Rey, María Del Sol 
dc.contributor.advisorCastro Pardo, Mónica de
dc.contributor.authorGuaita Martínez, José Manuel
dc.contributor.authorMartín Martín, José María 
dc.contributor.authorOstos Rey, María Del Sol 
dc.contributor.authorCastro Pardo, Mónica de
dc.date.accessioned2020-11-17T12:20:11Z
dc.date.available2020-11-17T12:20:11Z
dc.date.issued2020-09
dc.identifier.citationGuaita Martínez, J. M., Martín Martín, J. M., Ostos Rey, M. S., & de Castro Pardo, M. (2020). Constructing Knowledge Economy Composite Indicators using an MCA-DEA approach. Economic Research-Ekonomska Istraživanja, 1-21. [https://doi.org/10.1080/1331677X.2020.1782765]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/64322
dc.description.abstractComposite indicators are a remarkably useful tool in policy analysis and public communication for assessing phenomena, such as Knowledge-Based Economy (KBE), that cannot be expressed by means of a simple indicator. The objective of this study is to propose and compare three MCA-DEA models from a “Benefit of Doubt” (BoD) approach in order to build KBE Composite Indicators. To show the effectiveness of the models, this paper proposes a case study of 36 European countries to assess the degree of development of KBE. The results revealed differences with respect to the optimal weights assigned to the sub-indicators, the discriminating power, the operability, and the participatory nature of the models. Model 1 yielded high scores for every country and low discriminating power. Model 2 favored the most efficient countries in terms of KBE and allows for the incorporation of expert knowledge, thereby giving flexibility to the process. Model 3 made it possible to construct composite indicators from an optimal balance approach and yielded low results overall. These results demonstrate the necessity to analyze the different choices for measuring KBE in order to determine which indicator is more suitable for each context.es_ES
dc.language.isoenges_ES
dc.publisherRoutledge Journalses_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectKnowledge economyes_ES
dc.subjectManagementes_ES
dc.subjectComposite indicatorses_ES
dc.subjectInnovationes_ES
dc.subjectGPes_ES
dc.subjectBoDes_ES
dc.titleConstructing Knowledge Economy Composite Indicators using an MCA-DEA approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1080/1331677X.2020.1782765
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España