From Big Data Analytics to Organizational Agility: What Is the Mechanism?

Osama Musa Ali Al-Darras and Cem Tanova

SAGE Open, 2022, vol. 12, issue 2, 21582440221106170

Abstract: In the age of digitalization, big data analytics capabilities are considered one of the most critical organizational resources. Many organizations make considerable investments in these resources with an intention to improve their agility. However, the mechanism to reap agility from big data analytics still requires extensive empirical research and analysis. This study extends the big data analytics model by examining the mediating effects of entrepreneurial orientation between big data analytics capabilities and organizational agility. Partial least squares-structured equation modeling (PLS-SEM) was used to analyze the responses collected from 104 firms in Jordan. Results demonstrate that entrepreneurial orientation explains the relationship between big data analytics capabilities and agility. This finding contributes to the management literature by showing that big data analytics capabilities may enhance firm entrepreneurial orientation. While much of the prior research conceptualized the entrepreneurial orientation of the firm as a static characteristic, the current study argues that big data analytic capabilities play a key role in developing organizational agility through its role in improving entrepreneurial orientation, which subsequently creates value for firms, their customers, and the other stakeholders. Finally, challenges and future scope pertaining to this study are discussed. Recommendations for future studies on this promising topic include the use of longitudinal designs and mixed methods (quantitative with qualitative) approaches to provide researchers with new insights.

Keywords: organizational agility; big data analytics capabilities; entrepreneurial orientation; PLS-SEM (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:12:y:2022:i:2:p:21582440221106170

DOI: 10.1177/21582440221106170

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