Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model
This study presents a comparative analysis of the management of innovation among family and non-family companies of the Graphic Communication Industry in Colombia. For which a questionnaire was applied in order to know the divergences in the innovation process carried out by these two types of organ...
- Autores:
-
Gaitan, Mercedes
Santander Abril, Jairo Enrique
Viloria Silva, Amelec Jesus
MOJICA HERAZO, JULIO CESAR
Hernandez Malpica, Pedro Emilio
Martinez Ventura, Jairo Luis
Hernández Fernández, Lissette
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_f744
- Fecha de publicación:
- 2018
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/1831
- Acceso en línea:
- https://hdl.handle.net/11323/1831
http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-319-93803-5_48.
https://repositorio.cuc.edu.co/
- Palabra clave:
- Bayesian inference
Family and non-family business
Innovation management
Logistic model
MCMC
- Rights
- openAccess
- License
- Atribución – No comercial – Compartir igual
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|
dc.title.eng.fl_str_mv |
Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model |
title |
Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model |
spellingShingle |
Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model Bayesian inference Family and non-family business Innovation management Logistic model MCMC |
title_short |
Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model |
title_full |
Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model |
title_fullStr |
Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model |
title_full_unstemmed |
Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model |
title_sort |
Company family, innovation and colombian graphic industry: a bayesian estimation of a logistical model |
dc.creator.fl_str_mv |
Gaitan, Mercedes Santander Abril, Jairo Enrique Viloria Silva, Amelec Jesus MOJICA HERAZO, JULIO CESAR Hernandez Malpica, Pedro Emilio Martinez Ventura, Jairo Luis Hernández Fernández, Lissette |
dc.contributor.author.spa.fl_str_mv |
Gaitan, Mercedes Santander Abril, Jairo Enrique Viloria Silva, Amelec Jesus MOJICA HERAZO, JULIO CESAR Hernandez Malpica, Pedro Emilio Martinez Ventura, Jairo Luis Hernández Fernández, Lissette |
dc.subject.eng.fl_str_mv |
Bayesian inference Family and non-family business Innovation management Logistic model MCMC |
topic |
Bayesian inference Family and non-family business Innovation management Logistic model MCMC |
description |
This study presents a comparative analysis of the management of innovation among family and non-family companies of the Graphic Communication Industry in Colombia. For which a questionnaire was applied in order to know the divergences in the innovation process carried out by these two types of organizations. From this, the methodology of Generalized Linear Models (MLG) was used and the Bayesian inference was used on the parameters of the model, analyzing the effect of the family business, the products that commercialize on the management of innovation in goods observed as a product tangible Obtaining in this way, the identification of some characteristics of innovation management and divergences with non-family companies, among them: a tendency towards the type of preferred innovation, the different sources and objectives to innovate, and the factors that hinder its process of innovation. |
publishDate |
2018 |
dc.date.accessioned.none.fl_str_mv |
2018-11-26T14:01:24Z |
dc.date.available.none.fl_str_mv |
2018-11-26T14:01:24Z |
dc.date.issued.none.fl_str_mv |
2018 |
dc.type.spa.fl_str_mv |
Documento de Conferencia |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_c94f |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_f744 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/EC |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_f744 |
status_str |
acceptedVersion |
dc.identifier.isbn.spa.fl_str_mv |
978-331993802-8 |
dc.identifier.issn.spa.fl_str_mv |
03029743 |
dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/1831 |
dc.identifier.doi.spa.fl_str_mv |
http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-319-93803-5_48. |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
identifier_str_mv |
978-331993802-8 03029743 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
url |
https://hdl.handle.net/11323/1831 http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-319-93803-5_48. https://repositorio.cuc.edu.co/ |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.rights.spa.fl_str_mv |
Atribución – No comercial – Compartir igual |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
Atribución – No comercial – Compartir igual http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.publisher.spa.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
institution |
Corporación Universidad de la Costa |
bitstream.url.fl_str_mv |
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Gaitan, MercedesSantander Abril, Jairo EnriqueViloria Silva, Amelec JesusMOJICA HERAZO, JULIO CESARHernandez Malpica, Pedro EmilioMartinez Ventura, Jairo LuisHernández Fernández, Lissette2018-11-26T14:01:24Z2018-11-26T14:01:24Z2018978-331993802-803029743https://hdl.handle.net/11323/1831http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-319-93803-5_48.Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This study presents a comparative analysis of the management of innovation among family and non-family companies of the Graphic Communication Industry in Colombia. For which a questionnaire was applied in order to know the divergences in the innovation process carried out by these two types of organizations. From this, the methodology of Generalized Linear Models (MLG) was used and the Bayesian inference was used on the parameters of the model, analyzing the effect of the family business, the products that commercialize on the management of innovation in goods observed as a product tangible Obtaining in this way, the identification of some characteristics of innovation management and divergences with non-family companies, among them: a tendency towards the type of preferred innovation, the different sources and objectives to innovate, and the factors that hinder its process of innovation.Gaitan, Mercedes-8b432aaf-b7fb-47b1-b4c8-9aa272a542fd-0Santander Abril, Jairo Enrique-a24b07d2-90d8-4e0e-8add-27c4f36d9045-0Viloria Silva, Amelec Jesus-0000-0003-2673-6350-600MOJICA HERAZO, JULIO CESAR-will be generated-orcid-0000-0003-2078-7843-600Hernandez Malpica, Pedro Emilio-a95f04a3-5130-497e-8101-015cd5dc12d8-0Martinez Ventura, Jairo Luis-9a4be2dc-8755-4837-af92-9f919e838237-0Hernández Fernández, Lissette-bc802d72-814d-407b-b73c-ec0640a444a1-0engLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Atribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bayesian inferenceFamily and non-family businessInnovation managementLogistic modelMCMCCompany family, innovation and colombian graphic industry: a bayesian estimation of a logistical modelDocumento de Conferenciahttp://purl.org/coar/resource_type/c_f744http://purl.org/coar/resource_type/c_c94fTextinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/redcol/resource_type/ECinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALCompany family, innovation and colombian.pdfCompany family, innovation and colombian.pdfapplication/pdf179884https://repositorio.cuc.edu.co/bitstreams/9eeec93a-083d-481f-90b7-fd039c06f47a/download95910ebbd81e17c5c15e49344ca35682MD51LICENSElicense.txtlicense.txttext/plain; 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