Influencing mechanism of the intellectual capability of big data analytics on the operational performance of enterprises

Abstract

In the era of big data, data processing capability is key to gaining a competitive advantage for businesses. With appropriate technical and organizational resources in place, enterprises can extract considerable value from the vast amount of available data, thereby increasing their competitive advantage. Therefore, to utilize big data resources effectively, enterprises should focus on improving the intellectual abilities of big data analysts. Big data analytics intellectual capability (BDAIC) refers to the specialized skills and knowledge that employees of the enterprise possess, including technical, technical management, business, and relational knowledge, that would enable them to use analytics tools to accomplish organizational tasks and shape the core competitiveness of an enterprise. This study constructs a theoretical model that focuses on the mediating role of person-tool fit and examines the mechanisms by which BDAIC affects an enterprise's operational performance. The results show that BDAIC, which contains four basic categories of knowledge, positively influences an enterprise's operational efficiency. Additionally, person-tool matching mediates BDAIC's effect on an enterprise's operational performance. These findings explore the latest avenues of exploration in the research paradigm of big data analytics. Furthermore, this study has important implications for practitioners trying to use big data to improve business performance.

Keywords

• big data analytics intellectual capability
• Person-tool fit
• operational performance
• Resource-based view
• technical management knowledge