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RIIIT. Revista internacional de investigación e innovación tecnológica

versión On-line ISSN 2007-9753


SILVA-BLANCAS, V.H.  y  ROMERO-GONZALEZ, R.M.. Methodology for retrieving data model as from cognitive variables. RIIIT. Rev. int. investig. innov. tecnol. [online]. 2018, vol.6, n.35. ISSN 2007-9753.

The objective of this paper is to design a methodology that leads to produce data models arising from the cognitive variables that are obtained from the epistemological interpretation of political discourses originating both in the public and private environments. In such a way that these models can be translated into tools with technological value and consequently economic. As a base document of analysis, the Altenational Nation Project of the political party Movimiento de Regeneración Nacional (Morena), of Mexico, has been used, of whose fifty guidelines the knowledge variables that enclose, or cognitive variables, have been obtained, according to Lamarti (2015), are epistemic correspondence, knowledge, which represents the shared property between cognitive domains: one as an origin and another as destiny. Work was complemented in a documentary way with a grounded qualitative research methodology, by consulting the careers catalog of the INEGI (National Institute of Statistics and Geography) of Mexico, and the correspondence of the subgroups with each of the classified cognitive variables was obtained, then four universities academic programs, and two big companies and one PYME (small and medium enterprises), over their national academic offer and linked to the corresponding cognitive variable. The result was a data model wich serve as tool for retrieve efficiency ratio between careers that form part of the intellectual capital of the companies and the universities' academic offer over each cognitive variable, verifying the efficiency of the designed methodology by adding the amount of careers obtained from the sample among those coinciding with the subgroups assigned to the cognitive variables.

Palabras llave : Academic efficiency; competitiveness; data modeling; intellectual capital; knowledge value.

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