Hybrid Visualization for Deep Insight into Knowledge Retention in Firms

Sáiz Bárcena, Lourdes; Manzanedo, Miguel A.; Pérez Pulido, Arturo; Herrero Cosío, Álvaro; Corchado Rodríguez, Emilio Santiago

Título

dc.contributor.author	Sáiz Bárcena, Lourdes
dc.contributor.author	Manzanedo, Miguel A.
dc.contributor.author	Pérez Pulido, Arturo
dc.contributor.author	Herrero Cosío, Álvaro
dc.contributor.author	Corchado Rodríguez, Emilio Santiago
dc.date.accessioned	2017-09-06T09:14:05Z
dc.date.available	2017-09-06T09:14:05Z
dc.date.issued	2013
dc.identifier.citation	Hybrid Artificial Intelligent Systems Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 8073, pp. 280-293.
dc.identifier.isbn	978-3-642-40845-8 (Print) / 978-3-642-40846-5 (Online)
dc.identifier.issn	0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.uri	http://hdl.handle.net/10366/134839
dc.description.abstract	Neural projection models are applied in this study to the analysis of Human Resources (HR) from a Knowledge Management (KM) standpoint. More precisely, data projections are combined with the glyph metaphor to analyse KM data and to gain deeper insight into patterns of knowledge retention. Following a preliminary study, the retention of specialized employees in hi-tech companies is investigated, by applying the configurational approach of Strategic HR Management. The combination of these two aforementioned techniques generates meaningful conclusions and the proposal is validated by means of an empirical study on a real case study related to the Spanish hi-tech sector.
dc.format.mimetype	application/pdf
dc.language.iso	en
dc.publisher	Springer Science + Business Media
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject	Computer Science
dc.title	Hybrid Visualization for Deep Insight into Knowledge Retention in Firms
dc.type	info:eu-repo/semantics/conferenceObject
dc.rights.accessRights	info:eu-repo/semantics/openAccess