Our study explores the critical role played by organizational ambidexterity capabilities in the link between big data analytics, strategic innovation capabilities, and innovation performance. We developed a conceptual framework based on resource-based and dynamic capability views to examine the direct and indirect relationships among main variables. We used a quantitative approach to collect data from 172 Saudi IT and Telecom firms. We then employed structural equation modeling through Smart-PLS to test the study hypotheses. Our findings revealed that big data analytics and strategic innovation capabilities have a significant impact on organizational ambidexterity and then on innovation performance. Ambidexterity capability mediates between big data analytics capabilities and innovation performance and between strategic innovation capabilities and innovation performance. Our study contributes to the literature on big data and innovation. It offers valuable insights into the potential impacts of big data analytics, strategic innovation, and ambidexterity capabilities on innovation performance. It demonstrates how significantly boosting a firm’s capabilities for improved firms’ innovation performance can potentially enhance performance outcomes (e.g., competitiveness and sustainability). These findings provide managers with meaningful implications regarding the innovation performance that can be achieved by leveraging these important resources and capabilities.

This study explores the intricate relationships between entrepreneurial orientation (EO), digital orientation (DO), strategic agility (SA), and competitive advantage (CA) in Saudi firms. We investigate whether strategic agility serves as the missing link in these relationships. We examine direct and indirect effects among these constructs using structural equation modeling on data collected from 307 Saudi firms. The results confirm the direct positive links between EO and SA with CA. However, the findings also reveal a surprising result—there is no direct link between DO and CA. At the same time, both DO and EO exerted significant positive influences on SA. This study emphasizes the mediating role of SA. We find that SA partially mediates the relationship between EO and CA, underlining the importance of EO while pointing out the additional value given to this relationship by SA. Additionally, SA fully mediates the relationship between DO and CA, suggesting that firms must develop SA to translate their digital capabilities into CA. These results contribute to the strategic management literature by explaining the interplay between strategic orientations, agility, and competitive advantage in the Saudi context. They also provide practical implications for managers seeking to enhance the competitiveness of their firms during this digital age by showing that investing in SA in conjunction with entrepreneurial and digital resources is of paramount importance.

The current study combines the institutional theory and the TOE framework to examine the effects of environmental factors along with the mediating role of top managements support on the decision to adopt big data analytics (BDA) in supply chain management (SCM) in Saudi firms as context of a developing economy in transition. The statistical analyses of the PLSPM performed with Smartpls showed that the environment factors could affect directly and indirectly the intention to adopt BDA in SCM. From a theoretical perspective, combining TOE framework with an institutional theory perspective provided us with a research model that highlight the importance of TMS as a mediating factor that can help to assimilate the effects of environment factors when dealing with BDA adoption. From a managerial perspective, this research should be useful for practitioners involved in the SCM and interested by the use of BDA by showing them the critical roles that can be played by environment factors in the decision to adopt BDA in SCM.