dc.contributor.author | Dugarte-Peña, German-Lenin | |
dc.contributor.author | Sánchez-Segura, María-Isabel | |
dc.contributor.author | Medina-Domínguez, Fuensanta | |
dc.contributor.author | De Amescua, Antonio | |
dc.contributor.author | González, Cleotilde | |
dc.date.accessioned | 2022-12-22T14:00:21Z | |
dc.date.available | 2022-12-22T14:00:21Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 1477-8238 | spa |
dc.identifier.uri | https://hdl.handle.net/10641/3211 | |
dc.description.abstract | Software 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.iso | eng | spa |
dc.publisher | Knowledge Management Research & Practice1477-8238 | spa |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Digital business management | spa |
dc.subject | Decision-making | spa |
dc.subject | Knowledge assets management | spa |
dc.subject | Decisions from experience | spa |
dc.subject | Digital business evolution | spa |
dc.title | An instance-based-learning simulation model to predict knowledge assets evolution involved in potential digital transformation projects. | spa |
dc.type | journal article | spa |
dc.type.hasVersion | SMUR | spa |
dc.rights.accessRights | open access | spa |
dc.description.extent | 1537 KB | spa |
dc.identifier.doi | 10.1080/14778238.2022.2064348 | spa |
dc.relation.publisherversion | https://www.tandfonline.com/doi/full/10.1080/14778238.2022.2064348?cookieSet=1 | spa |