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

Adilson Carlos Yoshikuni, Rajeev Dwivedi, Arnaldo Rabello de Aguiar Vallim Filho and Samuel Fosso Wamba

Technological Forecasting and Social Change, 2025, vol. 210, issue C

Abstract: 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.

Keywords: Big data analytics; Corporate performance; Dynamic capabilities; Environmental uncertainty; Innovation ambidexterity; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:210:y:2025:i:c:s0040162524006498

DOI: 10.1016/j.techfore.2024.123851

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