Big data analytics-enabled dynamic capabilities for corporate performance mediated through innovation ambidexterity: Findings from machine learning with cross-country analysis

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dc.contributor.authorYoshikuni A.C.
dc.contributor.authorDwivedi R.
dc.contributor.authorFilho A.R.D.A.V.
dc.contributor.authorWamba S.F.
dc.date.accessioned2024-12-01T06:10:11Z
dc.date.available2024-12-01T06:10:11Z
dc.date.issued2025
dc.description.abstract© 2024 Elsevier Inc.Practitioners and academics question how big data analytics (BDA) generates business value across diverse economic contexts. Early research on BDA capabilities has often been geographically concentrated, typically focusing on individual countries without comparing emerging and advanced economies. This study addresses this issue by exploring how BDA enables dynamic capabilities to influence innovation ambidexterity, drive corporate performance, and navigate environmental uncertainties in developing and developed economies. Therefore, the research model has been tested using 313 samples from Brazil, India, and the United States of America. Results suggest that BDA enabled dynamic capabilities to play an essential role in corporate performance mediated through innovation ambidexterity with different path effects for countries. A post hoc analysis was conducted to investigate the insignificant moderating effects of environmental uncertainties on the relationship between BDA-enabled dynamic capabilities and innovation ambidexterity and address this unexpected result. ML techniques demonstrated that high, medium, and low levels of innovation ambidexterity can be predicted by big data analytics-enabled dynamic capabilities under environmental uncertainty in 79 %. Higher innovation ambidexterity is concentrated in Brazilian prospector firms, mature-aged and in large sizes under higher environmental uncertainty. American and Indian firms are predominant in achieving a medium level of innovation ambidexterity by the analyzer and defender orientation strategy and are young-age and middle-size firms under higher environmental uncertainty.
dc.description.volume210
dc.identifier.doi10.1016/j.techfore.2024.123851
dc.identifier.issnNone
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/39785
dc.relation.ispartofTechnological Forecasting and Social Change
dc.rightsAcesso Restrito
dc.subject.otherlanguageBig data analytics
dc.subject.otherlanguageCorporate performance
dc.subject.otherlanguageDynamic capabilities
dc.subject.otherlanguageEnvironmental uncertainty
dc.subject.otherlanguageInnovation ambidexterity
dc.subject.otherlanguageMachine learning
dc.titleBig data analytics-enabled dynamic capabilities for corporate performance mediated through innovation ambidexterity: Findings from machine learning with cross-country analysis
dc.typeArtigo
local.scopus.citations2
local.scopus.eid2-s2.0-85208196769
local.scopus.subjectBig data analytic
local.scopus.subjectBusiness value
local.scopus.subjectCorporate performance
local.scopus.subjectCross-country analysis
local.scopus.subjectData analytics
local.scopus.subjectDynamics capability
local.scopus.subjectEnvironmental uncertainty
local.scopus.subjectInnovation ambidexterity
local.scopus.subjectLevel of innovation
local.scopus.subjectMachine-learning
local.scopus.updated2025-07-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85208196769&origin=inward
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