Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model

Riccardo Rialti, Lamberto Zollo, Alberto Ferraris and Ilan Alon

Technological Forecasting and Social Change, 2019, vol. 149, issue C

Abstract: Big data analytics (BDA) have the power to revolutionize traditional ways of doing business. Nevertheless, the impact of BDA capabilities on a firm's performance is still not fully understood. These capabilities relate to the flexibility of the BDA infrastructure and the skills of the management and the firm's personnel. Most scholars explored the phenomenon from either a theoretical standpoint or neglected intermediate factors, such as organizational traits. This article builds on the dynamic capabilities view to propose and empirically test a model exploring whether organizational ambidexterity and agility mediate the relationship between BDA capabilities and organizational performance. Using data from surveys of 259 managers of large European organizations, we tested a proposed model using bootstrapped moderated mediation analysis. We found that organizational BDA capabilities affect a firm's ambidexterity and agility, which, in turn, affect its performance. These results establish ambidexterity and agility as positive mediators in the relationship between organizational BDA capabilities and a firm's performance. Furthermore, the organizational resistance to the implementation of information management systems and the fit between the organization and these systems also moderated this relationship. Practical implications for managers are also discussed.

Keywords: Agility; Ambidexterity; Big data; Big Data Analytics (BDA); Management information systems; Organizational performance (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (84)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:149:y:2019:i:c:s0040162519311242

DOI: 10.1016/j.techfore.2019.119781

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