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dc.contributor.authorDugarte-Peña, German-Lenin
dc.contributor.authorSánchez-Segura, María-Isabel
dc.contributor.authorMedina-Domínguez, Fuensanta
dc.contributor.authorDe Amescua, Antonio
dc.contributor.authorGonzález, Cleotilde
dc.date.accessioned2022-12-22T14:00:21Z
dc.date.available2022-12-22T14:00:21Z
dc.date.issued2022
dc.identifier.issn1477-8238spa
dc.identifier.urihttps://hdl.handle.net/10641/3211
dc.description.abstractSoftware engineering professionals must consider the appropriate technological solutions to meet their client’s needs and the organisational impact. The decision to implement a solution is not explicitly based on how it empowers the knowledge assets. Organisational knowledge assets are the foundation of the knowledge economy and a key element in evaluating the health of an organisation. This paper provides software engineers with a simulation model which illustrates the decision-making process for the implementation of technological solutions based on an evaluation of their client’s knowledge assets and how such assets impact and are impacted by the deployment of a solution. We use an agent-based approach and implement an instance-based learning model (a cognitive approach) to represent scenarios for experience-based decisions. 11 case studies were used to train the prediction engine and validate the usefulness of the model in generating scenarios and nurturing decision-making and user experiences.spa
dc.language.isoengspa
dc.publisherKnowledge Management Research & Practice1477-8238spa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectDigital business managementspa
dc.subjectDecision-makingspa
dc.subjectKnowledge assets managementspa
dc.subjectDecisions from experiencespa
dc.subjectDigital business evolutionspa
dc.titleAn instance-based-learning simulation model to predict knowledge assets evolution involved in potential digital transformation projects.spa
dc.typejournal articlespa
dc.type.hasVersionSMURspa
dc.rights.accessRightsopen accessspa
dc.description.extent1537 KBspa
dc.identifier.doi10.1080/14778238.2022.2064348spa
dc.relation.publisherversionhttps://www.tandfonline.com/doi/full/10.1080/14778238.2022.2064348?cookieSet=1spa


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