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How to Predict the Innovation to SMEs? Applying the Data Mining Process to the Spinner Innovation Model

2023-01-31Journal article Open access
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dc.contributor.authorFigueiredo, Ronnie
dc.contributor.authorMagalhães, Carla
dc.contributor.authorHuber, Claudia Maria
dc.date.accessioned2023-12-12T09:56:31Z
dc.date.available2023-12-12T09:56:31Z
dc.date.issued2023-01-31
dc.descriptionFunding: This paper is financed by National Funding awarded by the FCT—Portuguese Foundation for Science and Technology to the project «UIDB/04928/2020» and NECE-UBI, R&D unit funded by the FCT —Portuguese Foundation for the Development of Science and Technology, Ministry of Education and Science, University of Beira Interior, Management and Economics Department, Estrada do Sineiro, 6200-209 Covilhã, Portugal.pt_PT
dc.description.abstractDespite the importance of small and medium-sized enterprises (SMEs) for the growth and development of companies, the high failure rate of these companies persists, and this correspondingly demands the attention of managers. Thus, to boost the company success rate, we may deploy certain approaches, for example predictive models, specifically for the SME innovation. This study aims to examine the variables that positively shape and contribute towards innovation of SMEs. Based on the Spinner innovation model, we explore how to predict the innovation of SMEs by applying the variables, namely knowledge creation, knowledge transfer, public knowledge management, private knowledge management and innovation. This study applied the data mining technique according to the cross industry standard process for data mining (CRISP-DM) method while the Statistical Package for the Social Sciences (SPSS_Version28) served to analyze the data collected from 208 SME employees in Oporto, Portugal. The results demonstrate how the Spinner innovation model positively influences the contributions of the SMEs. This SME-dedicated model fosters the creation of knowledge between internal and external interactions and increases the capacity to predict the SME innovation by 56%.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFigueiredo, R., Magalhães, C., & Huber, C. (2023). How to Predict the Innovation to SMEs? Applying the Data Mining Process to the Spinner Innovation Model. Social Sciences, 12(2), 75. https://doi.org/10.3390/socsci12020075pt_PT
dc.identifier.doihttps://doi.org/10.3390/socsci12020075pt_PT
dc.identifier.eissn2076-0760
dc.identifier.urihttp://hdl.handle.net/10400.8/9075
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationCentre of Applied Research in Management and Economics
dc.relation.publisherversionhttps://www.mdpi.com/journal/socscipt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSpinner innovationpt_PT
dc.subjectData miningpt_PT
dc.subjectPredictivept_PT
dc.subjectModelpt_PT
dc.subjectOpen innovationpt_PT
dc.titleHow to Predict the Innovation to SMEs? Applying the Data Mining Process to the Spinner Innovation Modelpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre of Applied Research in Management and Economics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04928%2F2020/PT
oaire.citation.issue2pt_PT
oaire.citation.startPage75pt_PT
oaire.citation.titleSocial Sciencespt_PT
oaire.citation.volume12pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFigueiredo de Andrade
person.familyNameRebelo de Magalhães
person.familyNameHuber
person.givenNameRonnie Joshé
person.givenNameCarla Marisa
person.givenNameClaudia Maria
person.identifier.ciencia-idDD1A-4F90-0050
person.identifier.ciencia-id2D14-5B66-7656
person.identifier.orcid0000-0002-6587-6113
person.identifier.orcid0000-0002-0193-971X
person.identifier.orcid0000-0003-4544-7351
person.identifier.scopus-author-id56539515500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication333245f9-acf2-48d9-be4f-cea8d83bc464
relation.isAuthorOfPublication6f4a5f2e-786a-4da0-99ea-0cef41938f27
relation.isAuthorOfPublicationb96c9153-9bbf-4526-ab38-2c8b7bd3d7fa
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