Assessing the drivers of machine learning business value
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/100122 |
Resumo: | Machine learning (ML) is expected to transform the business landscape in the near future completely. Hitherto, some successful ML case-stories have emerged. However, how organizations can derive business value (BV) from ML has not yet been substantiated. We assemble a conceptual model, grounded on the dynamic capabilities theory, to uncover key drivers of ML BV, in terms of financial and strategic performance. The proposed model was assessed by surveying 319 corporations. Our findings are that ML use, big data analytics maturity, platform maturity, top management support, and process complexity are, to some extent, drivers of ML BV. We also find that platform maturity has, to some degree, a moderator influence between ML use and ML BV, and between big data analytics maturity and ML BV. To the best of our knowledge, this is the first research to deliver such findings in the ML field. |
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Assessing the drivers of machine learning business valueBusiness valueCompetitive advantageDynamic capabilities theoryMachine learningMarketingSDG 8 - Decent Work and Economic GrowthMachine learning (ML) is expected to transform the business landscape in the near future completely. Hitherto, some successful ML case-stories have emerged. However, how organizations can derive business value (BV) from ML has not yet been substantiated. We assemble a conceptual model, grounded on the dynamic capabilities theory, to uncover key drivers of ML BV, in terms of financial and strategic performance. The proposed model was assessed by surveying 319 corporations. Our findings are that ML use, big data analytics maturity, platform maturity, top management support, and process complexity are, to some extent, drivers of ML BV. We also find that platform maturity has, to some degree, a moderator influence between ML use and ML BV, and between big data analytics maturity and ML BV. To the best of our knowledge, this is the first research to deliver such findings in the ML field.NOVA School of Business and Economics (NOVA SBE)NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNReis, CarolinaRuivo, PedroOliveira, TiagoFaroleiro, Paulo2023-06-10T00:33:02Z2020-092020-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttp://hdl.handle.net/10362/100122eng0148-2963PURE: 18642163https://doi.org/10.1016/j.jbusres.2020.05.053info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:46:40Zoai:run.unl.pt:10362/100122Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:18.036950Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Assessing the drivers of machine learning business value |
title |
Assessing the drivers of machine learning business value |
spellingShingle |
Assessing the drivers of machine learning business value Reis, Carolina Business value Competitive advantage Dynamic capabilities theory Machine learning Marketing SDG 8 - Decent Work and Economic Growth |
title_short |
Assessing the drivers of machine learning business value |
title_full |
Assessing the drivers of machine learning business value |
title_fullStr |
Assessing the drivers of machine learning business value |
title_full_unstemmed |
Assessing the drivers of machine learning business value |
title_sort |
Assessing the drivers of machine learning business value |
author |
Reis, Carolina |
author_facet |
Reis, Carolina Ruivo, Pedro Oliveira, Tiago Faroleiro, Paulo |
author_role |
author |
author2 |
Ruivo, Pedro Oliveira, Tiago Faroleiro, Paulo |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
NOVA School of Business and Economics (NOVA SBE) NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
dc.contributor.author.fl_str_mv |
Reis, Carolina Ruivo, Pedro Oliveira, Tiago Faroleiro, Paulo |
dc.subject.por.fl_str_mv |
Business value Competitive advantage Dynamic capabilities theory Machine learning Marketing SDG 8 - Decent Work and Economic Growth |
topic |
Business value Competitive advantage Dynamic capabilities theory Machine learning Marketing SDG 8 - Decent Work and Economic Growth |
description |
Machine learning (ML) is expected to transform the business landscape in the near future completely. Hitherto, some successful ML case-stories have emerged. However, how organizations can derive business value (BV) from ML has not yet been substantiated. We assemble a conceptual model, grounded on the dynamic capabilities theory, to uncover key drivers of ML BV, in terms of financial and strategic performance. The proposed model was assessed by surveying 319 corporations. Our findings are that ML use, big data analytics maturity, platform maturity, top management support, and process complexity are, to some extent, drivers of ML BV. We also find that platform maturity has, to some degree, a moderator influence between ML use and ML BV, and between big data analytics maturity and ML BV. To the best of our knowledge, this is the first research to deliver such findings in the ML field. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-09 2020-09-01T00:00:00Z 2023-06-10T00:33:02Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/100122 |
url |
http://hdl.handle.net/10362/100122 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0148-2963 PURE: 18642163 https://doi.org/10.1016/j.jbusres.2020.05.053 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
12 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138009148293120 |