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dc.contributor.authorLuvizan, Simone da Silva
dc.contributor.authorNascimento, Paulo Tromboni de Souza
dc.contributor.authorYu, Abraham Sin Oih
dc.date.accessioned2018-10-25T18:24:23Z
dc.date.available2018-10-25T18:24:23Z
dc.date.issued2017
dc.identifierhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85016177041&doi=10.1109%2fPICMET.2016.7806738&partnerID=40&md5=f52726654814294ac71ec62b12f8da52
dc.identifier.isbn9781509035953
dc.identifier.urihttp://hdl.handle.net/10438/25586
dc.description.abstractDespite the high expectations about Big Data (BD) innovation potential, academy lacks studies exploring its implications and the process to achieve the announced benefits. This paper aims to help filling this gap, analyzing the case of an innovative credit assessment model, based on behavioral profiles generated over mobile network data. We ponder about the innovative potential of such huge data sets when applied to purposes differing from the ones they were generated for. To this case theoretical lens, we propose a framework where Innovation Ecosystem concepts are articulated by Contextualist elements. We explore this approach as an alternative to study the phenomenon different dimensions. It was useful to highlight that, despite the potential benefits of the solution as an enabling technology for financial inclusion and new business models in credit area, the ecosystem required for such innovation has critical dependencies delaying its progress. The study also revealed strategies used to break the inertia and create a minimum viable footprint (MVF), as a first step to chase its innovation full goals. It shows that the planning of an incremental path can be a good gimmick to deal with dependencies and enable radical change in complex ecosystems. © 2016 Portland International Conference on Management of Engineering and Technology, Inc.eng
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofseriesPICMET 2016 - Portland International Conference on Management of Engineering and Technology: Technology Management For Social Innovation, Proceedings
dc.sourceScopus
dc.subjectEcologyeng
dc.subjectEcosystemseng
dc.subjectInnovationeng
dc.subjectBehavioral profileseng
dc.subjectComplex ecosystemseng
dc.subjectCredit evaluationseng
dc.subjectEnabling technologieseng
dc.subjectFinancial inclusionseng
dc.subjectInnovation potentialeng
dc.subjectNew business modelseng
dc.subjectPotential benefitseng
dc.subjectBig dataeng
dc.titleBig Data for innovation: The case of credit evaluation using mobile data analyzed by innovation ecosystem lenseng
dc.typeConference Proceedingseng
dc.contributor.unidadefgvEscolas::EAESPpor
dc.subject.bibliodataBig datapor
dc.contributor.affiliationFGV
dc.identifier.doi10.1109/PICMET.2016.7806738
dc.rights.accessRightsrestrictedAccesseng
dc.identifier.scopus2-s2.0-85016177041


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